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曾偉明 and Wai-ming Peter Tsang. "Computer aided ultrasonic flaw detection and characterization." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1987. http://hub.hku.hk/bib/B31231007.
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Tsang, Wai-ming Peter. "Computer aided ultrasonic flaw detection and characterization /." [Hong Kong : University of Hong Kong], 1987. http://sunzi.lib.hku.hk/hkuto/record.jsp?B12344928.
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Donnelley, Martin, and martin donnelley@gmail com. "Computer Aided Long-Bone Segmentation and Fracture Detection." Flinders University. Engineering, 2008. http://catalogue.flinders.edu.au./local/adt/public/adt-SFU20080115.222927.
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Medical imaging has advanced at a tremendous rate since x-rays were discovered in 1895. Today, x-ray machines produce extremely high-quality images for radiologists to interpret. However, the methods of interpretation have only recently begun to be augmented by advances in computer technology. Computer aided diagnosis (CAD) systems that guide healthcare professionals to making the correct diagnosis are slowly becoming more prevalent throughout the medical field.
Bone fractures are a relatively common occurrence. In most developed countries the number of fractures associated with age-related bone loss is increasing rapidly. Regardless of the treating physician's level of experience, accurate detection and evaluation of musculoskeletal trauma is often problematic. Each year, the presence of many fractures is missed during x-ray diagnosis. For a trauma patient, a mis-diagnosis can lead to ineffective patient management, increased dissatisfaction, and expensive litigation. As a result, detection of long-bone fractures is an important orthopaedic and radiologic problem, and it is proposed that a novel CAD system could help lower the miss rate. This thesis examines the development of such a system, for the detection of long-bone fractures.
A number of image processing software algorithms useful for automating the fracture detection process have been created. The first algorithm is a non-linear scale-space smoothing technique that allows edge information to be extracted from the x-ray image. The degree of smoothing is controlled by the scale parameter, and allows the amount of image detail that should be retained to be adjusted for each stage of the analysis. The result is demonstrated to be superior to the Canny edge detection algorithm. The second utilises the edge information to determine a set of parameters that approximate the shaft of the long-bone. This is achieved using a modified Hough Transform, and specially designed peak and line endpoint detectors.
The third stage uses the shaft approximation data to locate the bone centre-lines and then perform diaphysis segmentation to separate the diaphysis from the epiphyses. Two segmentation algorithms are presented and one is shown to not only produce better results, but also be suitable for application to all long-bone images. The final stage applies a gradient based fracture detection algorithm to the segmented regions. This algorithm utilises a tool called the gradient composite measure to identify abnormal regions, including fractures, within the image. These regions are then identified and highlighted if they are deemed to be part of a fracture.
A database of fracture images from trauma patients was collected from the emergency department at the Flinders Medical Centre. From this complete set of images, a development set and test set were created. Experiments on the test set show that diaphysis segmentation and fracture detection are both performed with an accuracy of 83%. Therefore these tools can consistently identify the boundaries between the bone segments, and then accurately highlight midshaft long-bone fractures within the marked diaphysis.
Two of the algorithms---the non-linear smoothing and Hough Transform---are relatively slow to compute. Methods of decreasing the diagnosis time were investigated, and a set of parallelised algorithms were designed. These algorithms significantly reduced the total calculation time, making use of the algorithm much more feasible.
The thesis concludes with an outline of future research and proposed techniques that---along with the methods and results presented---will improve CAD systems for fracture detection, resulting in more accurate diagnosis of fractures, and a reduction of the fracture miss rate.
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Bornefalk, Hans. "Computer-aided detection and novel mammography imaging techniques." Doctoral thesis, Stockholm, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3861.
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Kihlberg, Johan, and Simon Tegelid. "Map Aided Indoor Positioning." Thesis, Linköpings universitet, Reglerteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-77766.
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The popularity of wireless sensor networks is constantly increasing, both for use instatic machine to machine environments as well as dynamic environments wherethe sensor nodes are carried by humans. Higher demands are put on real-timetracking algorithms of the sensor nodes, both in terms of accuracy and speed. This thesis addresses the issue of tracking persons wearing small sensor nodeswithin a radio network. Focus lies on fusing sensor data in an efficient way withconsideration to the computationally constrained sensor nodes. Different sensorsare stochastically modelled, evaluated, and fused to form an estimate of the person’sposition. The central approach to solve the problem is to use a dead reckoning methodby detecting steps taken by the wearer combined with an Inertial MeasurementUnit to calculate the heading of the person wearing the sensor node. To decreasethe unavoidable drift which is associated with a dead reckoning algorithm, a mapis successfully fused with the dead reckoning algorithm. The information from themap can to a large extent remove drift. The developed system can successfully track a person wearing a sensor nodein an office environment across multiple floors. This is done with only minorknowledge about the initial conditions for the user. The system can recover fromdivergence situations which increases the long term reliability.Intresset för trådlösa sensornätverk ökar konstant, såväl för statiska maskintill-maskintillämpningar som för dynamiska miljöer där sensornoderna är burnaav människor. Allt högre krav ställs på positioneringsalgoritmer för sensornätverken,där både hög precision och låg beräkningstid ofta är krav. Denna rapport behandlar problemet med att bestämma positionen av personburnasensornoder. Rapportens fokus är att effektivt kombinera sensordatamed hänsyn till sensornodernas begränsade beräkningskapacitet. Olika sensorermodelleras stokastiskt, utvärderas och kombineras för att forma en skattning avsensornodens position. Den huvudsakliga metoden för att lösa problemet är att dödräkna sensornodbärarenssteg kombinerat med kompass och tröghetssensorer för att skattastegets riktning. En karta över byggnaden används för att reducera den annarsoundvikliga drift som härrör från dödräkning. Informationen från kartan visarsig i stor utsträckning kunna reducera den här driften. Det utvecklade systemet kan följa en person genom en kontorsmiljö somsträcker sig över flera våningsplan. Detta med enbart lite information om personensinitiala position. Systemet kan även återhämta sig från situationer däralgoritmen divergerar vilket ökar systemets pålitlighet på lång sikt.
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Pons, Rodríguez Gerard. "Computer-aided lesion detection and segmentation on breast ultrasound." Doctoral thesis, Universitat de Girona, 2014. http://hdl.handle.net/10803/129453.
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This thesis deals with the detection, segmentation and classification of lesions on sonography. The contribution of the thesis is the development of a new Computer-Aided Diagnosis (CAD) framework capable of detecting, segmenting, and classifying breast abnormalities on sonography automatically. Firstly, an adaption of a generic object detection method, Deformable Part Models (DPM), to detect lesions in sonography is proposed. The method uses a machine learning technique to learn a model based on Histogram of Oriented Gradients (HOG). This method is also used to detect cancer lesions directly, simplifying the traditional cancer detection pipeline. Secondly, different initialization proposals by means of reducing the human interaction in a lesion segmentation algorithm based on Markov Random Field (MRF)-Maximum A Posteriori (MAP) framework is presented. Furthermore, an analysis of the influence of lesion type in the segmentation results is performed. Finally, the inclusion of elastography information in this segmentation framework is proposed, by means of modifying the algorithm to incorporate a bivariant formulation. The proposed methods in the different stages of the CAD framework are assessed using different datasets, and comparing the results with the most relevant methods in the state-of-the-artAquesta tesi es centra en la detecció, segmentació i classificació de lesions en imatges d'ecografia. La contribució d'aquesta tesi és el desenvolupament d'una nova eina de Diagnòstic Assistit per Ordinador (DAO) capaç de detectar, segmentar i classificar automàticament lesions en imatges d'ecografia de mama. Inicialment, s'ha proposat l'adaptació del mètode genèric de detecció d'objectes Deformable Part Models (DPM) per detectar lesions en imatges d'ecografia. Aquest mètode utilitza tècniques d'aprenentatge automàtic per generar un model basat en l'Histograma de Gradients Orientats. Aquest mètode també és utilitzat per detectar lesions malignes directament, simplificant així l'estratègia tradicional. A continuació, s'han realitzat diferents propostes d'inicialització en un mètode de segmentació basat en Markov Random Field (MRF)-Maximum A Posteriori (MAP) per tal de reduir la interacció amb l'usuari. Per avaluar aquesta proposta, s'ha realitzat un estudi sobre la influència del tipus de lesió en els resultats aconseguits. Finalment, s'ha proposat la inclusió d'elastografia en aquesta estratègia de segmentació. Els mètodes proposats per a cada etapa de l'eina DAO han estat avaluats fent servir bases de dades diferents, comparant els resultats obtinguts amb els resultats dels mètodes més importants de l'estat de l'art
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Rabbani, Seyedeh Parisa. "Effect of image variation on computer aided detection systems." Thesis, KTH, Skolan för teknik och hälsa (STH), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-123546.
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Computer Aided Detection (CAD) systems are expecting to gain significant importance in terms of reducing the work load of radiologists and enabling the large screening programs. A large share of CAD systems are based on learning from examples, to enables the decision making between the images with or without disease. Images are simplified to numerical descriptors (features vectors) and the system is trained with these features. The common practical problem with CAD systems is training the system with a data from a specific source and testing it on a data from a different source; the variations between sources usually affect the CAD system function. The possible solutions for this problem are (1) normalizing images to make them look more equal, (2) choosing less variation sensitive features and (3) modifying the classifier so that it classifies the data from different sources more accurately. In this project the effect of image variations on the developed CAD system on chest radio graphs for Tuberculosis is studied at Diagnostic Image Analysis Group. Tuberculosis is one of the major healthcare problems in some parts of the world (1.3 million deaths in 2007) [1]. Although the system has a great performance on the train and test data from the same source, using different sub dataset for training and testing the system does not lead to the same result. To limit the effect of image variation of the CAD systems three different approaches are applied for normalizing the images: (1) Simple normalization, (2) local normalization and (3) multi band local normalization. All three approaches enhance the performance of the system in case of various sub datasets for training and testing purposes. According to the improvement achieved by applying normalization it is suggested as a solution for the stated problem above. Although the outcome of this study has satisfactory result, there is always room for further investigations and studies; in specific testing different approaches for finding less variation sensitive features and modifying the classification procedure to a more variation tolerant process.
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Llaquet, Bayo Antai. "Computer aided renal calculi detection using Convolutional Neural Networks." Thesis, Örebro universitet, Institutionen för naturvetenskap och teknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-52254.
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In this thesis a novel approach is developed to detect urethral stones based on a computer-aided process. The input data is a CT scan from the patient, which is a high-resolution 3D grayscale image. The algorithm developed extracts the regions that might be stones, based on the intensity values of the pixels in the CT scan. This process includes a binarizing process of the image, finding the connected components of the resulting binary image and calculating the centroid of each of the components selected. The regions that are suspected to be stones are used as input of a CNN, a modified version of an ANN, so they can be classified as stone or non-stone. The parameters of the CNN have been chosen based on an exhaustive hyperparameter search with different configurations to select the one that gives the best performance. The results have been satisfactory, obtaining an accuracy of 98,3%, a sensitivity of 99,5% and a F1 score of 98,3%.
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Guo, Yanhui. "Computer-Aided Detection of Breast Cancer Using Ultrasound Images." DigitalCommons@USU, 2010. https://digitalcommons.usu.edu/etd/635.
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Ultrasound imaging suffers from severe speckle noise. We propose a novel approach for speckle reduction using 2D homogeneity and directional average filters to remove speckle noise. We transform speckle noise into additive noise using a logarithm transformation. Texture information is employed to describe the speckle characteristics of the image. The homogeneity value is defined using texture information value, and the ultrasound image is transformed into a homogeneity domain from the gray domain. If the homogeneity value is high, the region is homogenous and has less speckle noise. Otherwise, the region is nonhomogenous, and speckle noise occurs. The threshold value is employed to distinguish homogenous regions from regions with speckle noise obtained from a 2D homogeneity histogram according to the maximal entropy principle. A new directional filtering is convoluted to remove noise from pixels in a nonhomogenous region. The filtering processing iterates until the breast ultrasound image is homogenous enough. Experiments show the proposed method improves denoising and edge-preserving capability. We present a novel enhancement algorithm based on fuzzy logic to enhance the fine details of ultrasound image features, while avoiding noise amplification and over-enhancement. We take into account both the fuzzy nature of an ultrasound and feature regions on images, which are significant in diagnosis. The maximal entropy principle utilizes the gray-level information to map the image into fuzzy domain. Edge and textural information is extracted in fuzzy domain to describe the features of lesions. The contrast ratio is computed and modified by the local information. Finally, the defuzzification operation transforms the enhanced ultrasound images back to the spatial domain. Experimental results confirm a high enhancement performance including fine details of lesions, without over- or under-enhancement. Identifying object boundaries in ultrasound images is a difficult task. We present a novel automatic segmentation algorithm based on characteristics of breast tissue and eliminating particle swarm optimization (EPSO) clustering analysis, thus transforming the segmentation problem into clustering analysis. Mammary gland characteristics in ultrasound images are utilized, and a step-down threshold technique is employed to locate the mammary gland area. Experimental results demonstrate that the proposed approach increases clustering speed and segments the mass from tissue background with high accuracy.
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Agarwal, Richa. "Computer aided detection for breast lesion in ultrasound and mammography." Doctoral thesis, Universitat de Girona, 2019. http://hdl.handle.net/10803/670295.
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In the field of breast cancer imaging, traditional Computer Aided Detection (CAD) systems were designed using limited computing resources and used scanned films (poor image quality), resulting in less robust application process. Currently, with the advancements in technologies, it is possible to perform 3D imaging and also acquire high quality Full-Field Digital Mammogram (FFDM).
Automated Breast Ultrasound (ABUS) has been proposed to produce a full 3D scan of the breast automatically with reduced operator dependency. When using ABUS, lesion segmentation and tracking changes over time are challenging tasks, as the 3D nature of the images make the analysis difficult and tedious for radiologists. One of the goals of this thesis is to develop a framework for breast lesion segmentation in ABUS volumes. The 3D lesion volume in combination with texture and contour analysis, could provide valuable information to assist radiologists in the diagnosis.
Although ABUS volumes are of great interest, x-ray mammography is still the gold standard imaging modality used for breast cancer screening due to its fast acquisition and cost-effectiveness. Moreover, with the advent of deep learning methods based on Convolutional Neural Network (CNN), the modern CAD Systems are able to learn automatically which imaging features are more relevant to perform a diagnosis, boosting the usefulness of these systems. One of the limitations of CNNs is that they require large training datasets, which are very limited in the field of medical imaging.
In this thesis, the issue of limited amount of dataset is addressed using two strategies: (i) by using image patches as inputs rather than full sized image, and (ii) use the concept of transfer learning, in which the knowledge obtained by training for one task is used for another related task (also known as domain adaptation). In this regard, firstly the CNN trained on a very large dataset of natural images is adapted to classify between mass and non-mass image patches in the Screen-Film Mammogram (SFM), and secondly the newly trained CNN model is adapted to detect masses in FFDM. The prospects of using transfer learning between natural images and FFDM is also investigated. Two public datasets CBIS-DDSM and INbreast have been used for the purpose. In the final phase of research, a fully automatic mass detection framework is proposed which uses the whole mammogram as the input (instead of image patches) and provides the localisation of the lesion within this mammogram as the output. For this purpose, OPTIMAM Mammography Image Database (OMI-DB) is used.
The results obtained as part of this thesis showed higher performances compared to state-of-the-art methods, indicating that the proposed methods and frameworks have the potential to be implemented within advanced CAD systems, which can be used by radiologists in the breast cancer screeningEn el camp de les imatges de càncer de mama, els sistemes tradicionals de detecció assistida per ordinador (de l’anglès CAD) es van dissenyar utilitzant recursos informàtics limitats i pel·lícules de mamografia escanejades (del angles SFM) de qualitat d’imatge deficient, fet que va resultar en aplicacions poc robustes. Actualment, amb els avanços de les tecnologies, és possible realitzar imatges mèdiques en 3D i adquirir mamografies digitals (de l’anglès FFDM) d’alta qualitat. L’ultrasò automàtic de la mama (de l’anglès ABUS) ha estat proposat per adquirir imatges 3D de la mama amb escassa dependència del operador. Quan s’utilitza ABUS, la segmentació i seguiment de les lesions en el temps s ́on tasques complicades ja que la naturalesa 3D de les imatges fa que l’anàlisi sigui difícil i feixuc per els radiòlegs. Un dels objectius d’aquesta tesi és desenvolupar un marc per la segmentació semi-automàtica de lesions mamàries en volums ABUS. El volum de lesió 3D, en combinació amb l’anàlisi de la textura i el contorn, podria proporcionar informació valuosa per realitzar el diagnòstic radiològic. Tot i que els volums de ABUS són de gran interès, la mamografia de raigs X continua essent la modalitat d’imatge estàndard utilitzada per la detecció precoç del càncer de mama, degut principalment a la seva ràpida adquisició i rendibilitat. A més, amb l’arribada dels mètodes d’aprenentatge profund basats en xarxes neuronals convolucionals (de l’anglès CNN), els sistemes CAD moderns poden aprendre automàticament quines característiques de la imatge són més rellevants per realitzar un diagnòstic, fet que augmenta la utilitat d’aquests sistemes. Una de les limitacions de les CNN és que requereixen de grans conjunts de dades per entrenar, els quals són molt limitats en el camp de la imatge mèdica. En aquesta tesi, el tema de la poca disponibilitat d’imatges mediques s’aborda mitjançant dues estratègies: (i) utilitzant regions de la imatge com a entrada en comptes de les imatges de mida original, i (ii) mitjançant tècniques d’aprenentatge per transferència, en el que el coneixement après per a una determinada tasca es transfereix a una altra tasca relacionada (també conegut com a adaptació de domini). En primer lloc, la CNN entrenada en un conjunt de dades molt gran d’imatges naturals és adaptada per classificar regions de la imatge en tumor i no tumor de SFM i, en segon lloc, la CNN entrenada és adaptada per detectar tumors en FFDM. També s’ha investigat l’aprenentatge per transferència entre imatges naturals i FFDM. S’han utilitzat dos conjunts de dades públiques (CBIS-DDSM i INbreast) per aquest propòsit. En la fase final de la investigació, es proposa un marc de detecció automàtica de tumors utilitzant la mamografia original com entrada (en lloc de regions de la imatge) i que proporciona la localització de la lesió dins d’aquesta mamografia com a sortida. Per aquest propòsit s’utilitza una altra base de dades (OMI-DB). Els resultats obtinguts com a part d’aquesta tesi mostren millors rendiments en comparació amb l’estat de l’art, el que indica que els mètodes i marcs proposats tenen el potencial de ser implementats dins de sistemes CAD avançats, que poden ser utilitzats per radiòlegs en el cribratge del càncer de mama
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Panahandeh, Ghazaleh, Nasser Mohammadiha, and Magnus Jansson. "Ground Plane Feature Detection in Mobile Vision-Aided Inertial Navigation." KTH, Signalbehandling, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-99448.
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In this paper, a method for determining ground plane features in a sequence of images captured by a mobile camera is presented. The hardware of the mobile system consists of a monocular camera that is mounted on an inertial measurement unit (IMU). An image processing procedure is proposed, first to extract image features and match them across consecutive image frames, and second to detect the ground plane features using a two-step algorithm. In the first step, the planar homography of the ground plane is constructed using an IMU-camera motion estimation approach. The obtained homography constraints are used to detect the most likely ground features in the sequence of images. To reject the remaining outliers, as the second step, a new plane normal vector computation approach is proposed. To obtain the normal vector of the ground plane, only three pairs of corresponding features are used for a general camera transformation. The normal-based computation approach generalizes the existing methods that are developed for specific camera transformations. Experimental results on real data validate the reliability of the proposed method.QC 20121107
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McLoughlin, Kirstin J. "Computer aided detection of microcalcification clusters in digital mammogram images." Thesis, University of Canterbury. Electrical and Computer Engineering, 2004. http://hdl.handle.net/10092/6536.
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Recent advancements in computer technology have ensured that early detection of breast cancer, via computer aided detection (CAD) schemes, has become a rapidly expanding field of research. There is a desire to improve the detection accuracy of breast cancer without increasing the number of falsely identified cancers. The CAD scheme considered here is intended to assist radiologists in the detection of micro calcification clusters, providing a real contribution to the mammography screening process.
Factors that affect the detection accuracy of micro calcifications in digital mammograms include the presence of high spatial frequency noise, and locally linear high intensity structures known as curvilinear structures (CLS). The two issues considered are how to compensate for the high frequency image noise and how to detect CLS thus removing their influence on micro calcification detection. First, an adaptive approach to modelling the image noise is adopted. This is derived directly from each mammogram and is adaptable to varying imaging conditions. It is found that compensating for the high frequency image noise significantly improves micro calcification detection accuracy. Second, due to the varying size and orientation of CLS in mammogram images, a shape parameter is designed for their detection using a multiresolution wavelet filter bank. The shape parameter leads to an efficient way of distinguishing curvilinear structures from faint micro calcifications. This improves micro calcification detection performance by reducing the number of false positive detections related to CLS.
The detection and segmentation of micro calcification clusters is achieved by the development of a stochastic model, which classifies individual pixels within a mammogram into separate classes based on Bayesian decision theory. Both the high frequency noise model and CLS shape parameters are used as input to this segmentation process. The CAD scheme is specifically designed to be independent of the modality used, simultaneously exploiting the image data and prior knowledge available for micro calcification detection.
A new hybrid clustering scheme enables the distinction between individual and clustered micro calcifications, where clustered micro calcifications are considered more clinically suspicious. The scheme utilises the observed properties of genuine clusters (such as a uniform distribution) providing a practical approach to the clustering process. The results obtained are encouraging with a high percentage of genuine clusters detected at the expense of very few false positive detections.
An extensive performance evaluation of the CAD scheme helps determine the accuracy of the system and hence the potential contribution to the mammography screening process. Comparing the CAD scheme developed with previously developed micro calcification detection schemes shows that the performance of this method is highly competitive. The best results presented here give a sensitivity of 91% at an average false positive detection rate of 0.8 false positives per image.
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Ebrahimdoost, Yousef. "Computer aided detection of pulmonary embolism (PE) in CTA images." Thesis, Kingston University, 2012. http://eprints.kingston.ac.uk/24027/.
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Pulmonary embolism (PE) is an obstruction within the pulmonary arterial tree and in the majority of cases arises from a thrombosis that has travelled to the lungs via the venous system. Pulmonary embolism (PE) is a fatal condition which affects all age groups and is the third most common cause of death in the US. Computed tomographic angiography (CTA) imaging has recently emerged as an accurate method in the diagnosis of pulmonary embolism. Each CTA scan contains hundreds of CT images, so the accuracy and efficiency of interpreting such a large image data set is complicated due to various PE look-alikes and human factors such as attention span and eye fatigue. Moreover, manual reading and interpreting a large number of slices is time consuming and it is difficult to find all the pulmonary embolisms (PE) in a data set. Consequently, it is highly desirable to have a computer aided detection (CAD) system to assist radiologists in detecting and characterizing emboli in an accurate, efficient and reproducible manner. A computer aided detection (CAD) system for detection of pulmonary embolism is proposed in CTA images. Our approach is performed in three stages: firstly the pulmonary artery tree is extracted in the region of the lung and heart in order to reduce the search area (PE occurs inside the pulmonary artery) and aims to reduce the false detection rate. The pulmonary artery is separated from the surrounding organs by analyzing the second derivative of the Hessian matrix and then a hybrid method based on region growing and a new customized level set is used to extract the pulmonary artery (PA). In the level set implementation algorithm, a new stopping criterion is applied, a consideration often neglected in many level set implementations. In the second stage, pulmonary embolism candidates are detected inside the segmented pulmonary artery, by an analysis of three dimensional features inside the segmented artery. PE detection in the pulmonary artery is implemented using five detectors. Each detector responds to different properties of PE. In the third stage, filtering is used to exclude false positive detections associated with the partial volume effect on the artery boundary, flow void, lymphoid tissue, noise and motion artifacts. Soft tissue between the bronchial wall and the pulmonary artery is a common cause of false positive detection in CAD systems. A new feature, based on location is used to reduce false positives caused by soft tissue. The method was tested on 55 data scans (20 training data scans and 35 additional data scans for evaluation containing a total of 195 emboli). The system provided a segmentation of the PA up to the 6th division, which includes the sub-segmental level. Resulting performance gave 94% detection sensitivity with an average 4.1 false positive detections per scan. We demonstrated that the proposed CAD system can improve the performance of a radiologist, detecting 19 (11 %) extra PE which were not annotated by the radiologist.
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Chen, Hao. "Kalman Filter Aided Tracking Loop In GPS Signal Spoofing Detection." University of Cincinnati / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1418909647.
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Stelle, Alvaro Luiz. "Real-time computer aided analysis of the electroencephalogram : a two-dimensional approach." Thesis, City University London, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.278928.
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Williams, Edward James. "A neural network based approach to fault detection in industrial processes." Thesis, University of Plymouth, 1994. http://hdl.handle.net/10026.1/1743.
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The need for automated fault detection methods has increased in line with the complexity of processing plant technology and their control systems. Fast and accurate fault detection and isolation (FDI) is essential if a controller is to be effective in a supervisory role. This thesis is concerned with developing an FDI system based upon artificial neural network techniques. The artificial neural network (ANN) is a mechanism based upon the concepts of information processing within the brain, and consequently has the ability to self adjust, or learn about a given problem domain. It can thus be utilised in currently favoured model-based FDI systems with the advantage that it can learn process dynamics by being presented examples of process input-output pairs without the need for traditional mathematically complex models. Similarly, ANNs can be taught to classify characteristics in the residual (or plant-model difference) signal without the necessity of constructing the types of filter used in more classical solutions. Initially, a class of feedforward neural network called the multilayer perceptron (MLP) is used to model mathematically simulated linear and nonlinear plants in order to demonstrate their abilities in this field, as well as investigating the consequence of parameter variation on model effectiveness and how the model can be utilised in a model-based FDI system. A principle aim of this research is to demonstrate the ability of the system to work online and in real-time on genuine industrial processes, and the plant nominated as a test bed - the Unilever Automated Freezer (UAF) - is introduced. The UAF, being a time-varying system, requires a novel system identification approach which has resulted in a number of cascaded MLPs to model the various stages in the phased startup of the process. In order to reduce model mismatch to a minimum, it was necessary to develop an effective switching mechanism between one MLP in the cascade and the next. Attempts using a rule-based switching mechanism, a simple MLP switch and an error based switching mechanism were made, before a solution incorporating a genetic algorithm and an MLP network was developed which had the capability of learning the optimum switching points. After the successful development of the model, a series of MLPs were trained to recognise the characteristics of a number of faults within the residual signals. Problems involving false alarms between certain faults were reduced by the introduction of templates - or information pertaining to when a particular fault was most evident in the residuals. The final solution consisting of an MLP Cascade model and fault isolation MLPs is essentially generic for this class of time-varying system, and the results achieved on the UAF were far superior to those of the currently used FDI system without the need for any extra sensory information. The MLP Cascade and associated switching device together with the development of an online real-time FDI system for a time-varying piece of industrial machinery, are deemed to be original contributions to knowledge.
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Marr, J. G. D. "Computer-aided detection systems for HPLC : Development, assessment and application of digital techniques for peak purity validation in HPLC utilising photodiode array detection." Thesis, University of Bradford, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.384261.
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Chen, Lei [Verfasser]. "Computer-aided detection of Parkinson's Disease using transcranial sonography / Lei Chen." Lübeck : Zentrale Hochschulbibliothek Lübeck, 2014. http://d-nb.info/1046712691/34.
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Nikravanshalmani, Alireza. "Computer aided detection and segmentation of intracranial aneurysms in CT angiography." Thesis, Kingston University, 2012. http://eprints.kingston.ac.uk/22974/.
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Accurate detection and segmentation of intracranial aneurysms plays an important role in diagnosing and reducing the incidence of subarachnoid haemorrhage (SAH) which is associated with high rates of morbidity and mortality. This research proposes a computer aided detection (CAD) and segmentation (CAS) of intracranial aneurysm in computer tomography angiography (CTA). The efficiency of the CAD/CAS system is boosted by pre-processing the input image with non-linear diffusion to smooth the CTA data while preserving the edges. A 3D region growing-based approach is used to extract the cerebral arteries followed by entropy-based search space reduction to retain the volume of the circle of Willis (CoW) and the proximal cerebral arteries where nearly all intracranial aneurysms are located, whilst eliminating the extracranial and very distal intracranial circulation. Because cerebral aneurysms vary in size we regard the problem of cerebral aneurysm detection as an intrinsically multi-scale problem and employ a multi-scale approach to all detection analysis. Shape index analysis is employed to determine potential aneurysmal regions (PARs). Hessian analysis and gradient vector field analysis which reveal 3D local shape information are used to further characterise the initial PARs. False positive reduction is then performed based on the analysis of the shape characterisations of the PARs. A ranking score is defined based on the outcomes of the shape analysis to rank the likelihood of PARs. The system allows user to navigate through the ranked PARs and select a candidate aneurysm for further analysis (CAS). The boundary of the selected aneurysm and its parent artery is delineated by using a 3D conditional morphology-based region growing approach. The output is presented to the user to be assessed for the aneurysm orientation relative to the parent vessel. A semi-automatic process is applied to detach the aneurysm from its parent artery. To have a fine segmentation of aneurysm which can be used for characterization of the aneurysm, a 3D geodesic active contour implemented in a level set framework is applied. The volume of the separated aneurysm is quantified as a typical characterization ofthe aneurysm. The system has been validated on a clinical dataset of 62 CT A scans with average 274 slices per scan (involving 17,028 CT slices) containing 70 aneurysms. Sizes of aneurysms vary between 3-16mm. 42 CTA scans have been used as a training dataset for parameter selection and 20 CTA scans have been used as a test dataset. The sensitivity of the systems for the CAD component is 97% with the average false positive of 2.24 per dataset (0.008 per slice). CAS performance was evaluated by dual visual judgment of an expert neuroradiologist and neurosurgeon. The detection and segmentation performance indicate the approach has potential in clinical applications.
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Al-Hinnawi, Abdel-Razzak. "Computer aided detection of clustered micro-calcifications in the digitised mammogram." Thesis, University of Aberdeen, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.301076.
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The presence of distributed micro-calcifications can be an indicator of early breast cancer. On the mammogram, they appear as bright smooth particles superimposed on the normal breast image background. Radiologists determine the occurrence of this lesion by detecting the individual micro-calcifications and then examining their distribution within the breast tissue. Due to the visual complexity of the mammogram, the detection sensitivity is usually less than 100%. The digital environment has the potential to increase the radiologist's accuracy. We have developed a computer aided detection (CAD) scheme that can identify clinically indicative clusters of micro-calcifications. The CAD algorithm emulates some aspects of the radiologists' approach by using contrast texture energy segmentation and morphological distribution analysis. On a local database of 61 mammograms digitised at 100μm with 8 bits intensity resolution, the CAD returns: a) 85% sensitivity (91% for malignant lesions and 78% for those that are benign), b) 0.33 false positive clusters (FPC) per image and c) 92% specificity. Therefore, the output from the CAD is shown to compare favourably with the performance of an expert radiologist. It also compares favourably with other CAD techniques, exceeding many algorithms which employ a higher level of mathematical complexity. The scheme is tested on an international database provided by the Mammographic Image Analysis Society. In this case it returns a) 96.4% sensitivity (100% for malignant lesions and 92% for those that are benign) b) 2.35 FPC rate per image and c) 33% specificity. The higher FPC rate is attributed to the different acquisition and production of the digital mammograms. It is concluded that this can be reduced by employing a shape analysis procedure to the CAD's final output. It is shown that the image processing principles we have implemented are generally successful on databases which are produced at other centres under different technical conditions.
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Alamri, Osamah R. "Turbo detection of sphere packing modulation aided space-time coding schemes." Thesis, University of Southampton, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.435719.
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Clark, DessyeDee M. "Computer-aided hypoglycemia detection in adolescents with insulin-dependent diabetes mellitus /." Thesis, Connect to this title online; UW restricted, 2001. http://hdl.handle.net/1773/7368.
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Ho, Pui Shan. "Computer-aided detection and classification of microcalcifications in digital breast tomosynthesis." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:0d454bf3-056c-4b45-8443-b5ac2eb03065.
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Currently, mammography is the most common imaging technology used in breast screening. Low dose X-rays are passed through the breast to generate images called mammograms. One type of breast abnormality is a cluster of microcalcifications. Usually, in benign cases, microcalcifications result from the death of fat cells or are due to secretion by the lobules. However, in some cases, clusters of microcalcifications are indicative of early breast cancer, partly because of the secretions by cancer cells or the death of such cells. Due to the different attenuation characteristics of normal breast tissue and microcalcifications, the latter ideally appear as bright white spots and this allows detection and analysis for breast cancer classification. Microcalcification detection is one of the primary foci of screening and has led to the development of computer-aided detection (CAD) systems. However, a fundamental limitation of mammography is that it gives a 2D view of the tightly compressed 3D breast. The depths of entities within the breast are lost after this imaging process, even though the breast tissue is spread out as a result of the compression force applied to the breast. The superimposition of tissues can occlude cancers and this has led to the development of digital breast tomosynthesis (DBT). DBT is a three-dimensional imaging involving an X-ray tube moving in an arc around the breast, over a limited angular range, producing multiple images, which further undergo a reconstruction step to form a three-dimensional volume of breast. However, reconstruction remains the subject of research and small microcalcifications are "smeared" in depth by current algorithms, preventing detailed analysis of the geometry of a cluster. By using the geometry of the DBT acquisition system, we derive the "epipolar" trajectory of a microcalcification. As a first application of the epipolars, we develop a clustering algorithm after using the Hough transform to find corresponding points generated from a microcalcification. Noise points can also be isolated. In addition, we show how microcalcification projections can be detected adaptively. Epipolar analysis has also led to a novel detection algorithm for DBT using a Bayesian method, which estimates a maximum a posterior (MAP) labelling in each individual image and subsequently for all projections iteratively. Not only does this algorithm output the binary decision of whether a pixel is a microcalcification, it can predict the approximate depth of the microcalcification in the breast if it is. Based on the epipolar analysis, reconstruction of just a region of interest (ROI) e.g. microcalcification clusters is possible and it is more straightforward than any existing method using reconstruction slices. This potentially enables future classification of breast cancer when more clinical data becomes available.
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Zhang, Zhiyong. "Investigation of a computer-aided detection solution for breast focal asymmetry." Thesis, University of Huddersfield, 2011. http://eprints.hud.ac.uk/id/eprint/11302/.
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Breast focal asymmetry is an important sign of early breast cancer and might be a developing masses or other underlying breast cancer. However, breast focal asymmetry is very difficult to recognise, even for the specialists of radiologists because focal asymmetries lack the boundary and consistent shape of masses and are similar to normal fibroglandular tissues. The computer-aided detection (CADe) for focal asymmetry is a novel research topic in the field of computer-aided detection of breast cancer. The detection of focal asymmetry has the significant value to save women’s live in the early stage of breast cancer. At present, there is only one existing dedicated detection solution for focal asymmetry, which was proposed in 2008. The aim of this research is to develop a novel dedicated computer-aided detection solution for focal asymmetry and overcome the weakness of the existing approach. The objectives of this research are to propose a novel solution for detecting focal asymmetry, a novel breast segmentation approach, a novel pectoral muscle detection approach and a dedicated focal asymmetry detection approach. The literature review of previous researches for focal asymmetry, asymmetric breast findings, breast segmentation and pectoral muscle detection have been extensively carried out. Based on the literature review, a range of hypotheses have been proposed for breast segmentation, pectoral muscle detection and focal asymmetry detection. Furthermore, a novel framework and three novel approaches have been proposed and developed in this research. Firstly, the automatic breast segmentation (ABS) approach was proposed to segment the skin-line of breast from mammographic images and preserve the nipple at the same time, a task which very few breast segmentation methods claimed. The experimental results showed that the ABS approach can adequately segment the breast skin-line and preserve the nipple if it is in profile. The proposed ABS approach is one of three existing breast segmentation approaches that can segment breast skin-line and preserve the nipple at the same time. Secondly, a novel approach for detecting pectoral muscle has been proposed to detect and remove pectoral muscle from the segmented breast areas. The experimental results showed that the proposed maximum intensity change (MIC) approach can detect pectoral muscle with high quality. Thirdly, the novel approach for detecting focal asymmetry has been proposed in this research and various features have been extracted to classify a suspicious region into the category of focal asymmetry or non-focal asymmetry. The experimental results indicated that the proposed detection approach can detect focal asymmetry with the 81.8% sensitivity and the 0.333 false positive regions per image. This research has proposed a novel framework and three novel approaches to build the computer-aided detection solution for focal asymmetry. This research overcomes the weakness of current approach for detecting focal asymmetry. The proposed detection approach is the second dedicated solution for detecting breast focal asymmetry in existence.
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Asl, Babak Ghafary. "A Computer Aided Detection System for Cerebral Microbleeds in Brain MRI." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-6053.
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Advances in MR technology have improved the potential for visualization of small lesions in brain images. This has resulted in the opportunity to detect cerebral microbleeds (CMBs), small hemorrhages in the brain that are known to be associated with risk of ischemic stroke and intracerebral bleeding. Currently, no computerized method is available for fully- or semi-automated detection of CMBs. In this paper, we propose a CAD system for the detection of CMBs to speed up visual analysis in population-based studies. Our method consists of three steps: (i) skull-stripping (ii) initial candidate selection (iii) reduction of false-positives using a two layer classi cation and (iv) determining the anatomical location of CMBs. The training and test sets consist of 156 subjects (448 CMBs) and 81 subjects (183 CMBs), respectively. The geometrical, intensity-based and local image descriptor features were used in the classi cation steps. The training and test sets consist of 156 subjects (448 CMBs) and 81 subjects (183 CMBs), respectively. The sensitivity for CMB detection was 90% with, on average, 4 false-positives per subject.
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Stewart, James. "The application of artificial intelligence to fault detection in hydraulic cylinder drive systems." Thesis, Cardiff University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.284493.
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An expert system approach to fault diagnosis of fluid power circuits is considered with emphasis on leakage flow detection, and for valvecontrolled cylinders. Two test rigs were used, one being a solenoid-valve controlled cylinder operated directly and in an open-loop mode, the other being a servo-valve controlled actuator operated by microcomputer and in a closed-loop mode. Both systems incorporated the use of on-line dynamic data, and for the closed-loop case operation and fault diagnosis was integrated into an automated procedure. Flow leakage detection was considered a priority, and an alternative approach using displaced volumes was successfully implemented. The research work concentrated initially on the use of an expert system and the establishment of an appropriate knowledge base using a hybrid reasoning approach. This approach was found to be excellent for single-fault conditions but could not differentiate components of multiple-fault conditions, other than that they existed, due to the use of a minimum number of flow sensors. Additional techniques were then considered for the closed-loop control system utilising steady-state position error, time series analysis, and Artificial Neural Networks. It was found that the consideration of steady-state error gave information complementary to the existing knowledge base but could not give any additional information. The use of an artificial neural network was found to give more information with regards to multiple-fault conditions, resulting in a percentage probability for each fault combination.
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Kelsey, Matthew Douglas. "THE DEVELOPMENT AND EVALUATION OF TECHNIQUES FOR USE IN MAMMOGRAPHIC SCREENING COMPUTER AIDED DETECTION SYSTEMS." OpenSIUC, 2011. https://opensiuc.lib.siu.edu/dissertations/331.
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The material presented in this dissertation details techniques developed to aid in the detection of a specific type of cancerous lesion visible on screening mammography images. These spiculated lesions most often appear as centrally bright objects with semi-defined borders. Furthermore, lesion margins are composed of indicative spiculations or fine tendrils projecting outward from the mass center. The techniques developed here to identify these characteristics and detect these objects are intended to operate as a processing pipeline. The first group of these processing stages is responsible for converting raw mammogram pixel data into localized and described objects. A second group of processing stages categorizes these objects by manipulating their descriptors and evaluating their meaning. At the conclusion of this processing pipeline, it is intended that image pixels which designate a cancerous mass will be highlighted and presented to a human operator as an aid in the early detection of breast cancers. The initial problem of object localization is addressed with breast tissue region extraction followed by a specialized spot detection algorithm. Tissue region extraction is accomplished using specific dataset image domain knowledge along with a simple threshold segmentation algorithm. Once this image area of interest is specified, contained objects of interest are identified using Iterative Disjoint Region Detection (IDRD). This specialized procedure utilizes iterative threshold segmentation to produce a three dimensional map of each image's pixel space. In this map, two dimensions directly correspond to the spatial dimension of the original image while the third corresponds to the normalized gray level of individual pixels. Traversing this map from the brightest pixel values to the darkest yields object "peaks", which are taken to be seeds of visible objects. Seeds are further processed at each successive threshold iteration by considering the effects of combining adjacent designations. This seeding process effectively detected all objects of interest with at least one seed. Because it was designed as a general purpose spot detection algorithm, many non-cancerous locally bright objects were detected as well. These other detections accounted for a wide majority of the seeds noted in each mammogram with approximately thirty to sixty seeds identified in most dataset images. A complementary task to object localization is the identification of each object's visible border and pixel area. This process is accomplished by a customized general purpose region growing routine, commonly known as pixel aggregation. During this procedure, spatially attached pixels are considered for inclusion with a prototype region defined by the region's corresponding seed object. Candidate pixels must meet a gray tone similarity criteria with our inclusion interval computed using the template region's average gray value. This process is supplemented by a leakage detection mechanism which serves to detect and recover from over segmentation of non-target objects in the image space. Leakage detection operates by tracking pixel aggregation rates for each iteration of the region growing process. A leakage is said to occur if the aggregation rate profile exhibits telltale characteristics of object border crossing followed by segmentation of an adjacent object. Once objects have been localized and their member pixels identified through the proceeding procedures it is the purpose of the next system stage to describe these objects using various measured features. The extraction of these measurements is the final step in transforming objects from image based visual depictions to abstract numerical representations. This new representation facilitates the forthcoming statistical treatment of these objects. Feature extraction is accomplished using a number of general use as well as special purpose measurements which quantify characteristics such as object shape, texture, and parent seed evolution. A total of forty-one feature measurements are extracted in order to insure full representation of detected objects and to facilitate accurate object class membership. In the next section of work, we seek to categorize these objects which have just been detected, segmented and described using feature measurements. The roll of a statistical classifier in accomplishing this is presented along with specifics as to the type of classifier used here. The use of a Bayes classifier is discussed and rationalized along with the development of the parametric Gaussian model for class conditional density estimation. Along with classifier development, a treatment of system performance evaluation is given. The Free-response Receiver Operating Characteristic (FROC) is described as an appropriate method by which to evaluate observer studies. This method suits the described CAD system, as a certain number of false positive detections are seen as acceptable and the system goal is to maximize mass sensitivity within these bounds. Our CAD system supplements the traditional classifier components by considering the effects of advanced feature vector manipulation. In total, five distinct models are developed including various iterations of feature selection and feature vector transformation. The Select model is presented as a benchmark and consists of a cumulative performance based feature selection step. The PCT Select and the DCT Select models are used to generate new feature vectors from the original measured set as linear combinations of its elements. PCT and DCT indicate the vector transformation model, Principle Components Transform and Discrete Cosine Transform respectively. Once transformed, the resultant feature vectors are processed with the same Select feature selection routine as in the benchmark model. The goal with both Transform-Select feature manipulation models is to generate a compact feature set which retains all of the necessary discriminatory information from measured features while rejecting measured characteristics which do not support accurate object classification. Two related models are also considered which measure the impact of implementing feature pre-selection on the PCT Select and the DCT Select models. The aptly named Select PCT Select and the Select DCT Select models seek to remove measured features which contain no discriminatory information from the pool of transformed data. System performance results for the five selection models are then compared to discern the contribution of each in the detection of cancerous masses. A complete analysis of the feature selection and transformation models show that while the benchmark Select model performs reasonably, considerable performance improvements are possible using feature vector manipulation methods. Performance metrics are generated with the use of a Free-response Receiver Operating Characteristic (FROC) plot. This method compares the mass detection sensitivity possible to the number of false positive detections per mammogram evaluated. Feature selection and classifier training is performed to maximized this sensitivity at a particular operating point, 4 FPpI. This point is taken as within the range of acceptable false indications in a typical clinical setting. Overall, the best system performance is seen with the use of the Select DCT Select feature model (84.51% sensitivity at 4 FPpI). This corresponds to a net increase of eighteen additional mass detections with the same amount of false positive indications and an increased mass sensitivity of 84.51% from 71.53% using the benchmark Select model. The other selection model using a pre-selection stage, Select PCT Select, reports similar performance results. This model is used to detect 118 true positive masses, sixteen more then the Select model and just two less then the Select DCT Select model. Both of the other system configurations, PCT Select and DCT Select, were able to detect 109 true masses in the data set. This corresponds to a 76.76% mass sensitivity at 4 FPpI. Although not as impressive as results generated with the pre-selection models, this is still a 5.23% improvement in mass sensitivity in comparison to the benchmark.
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Jack, James. "Computer aided analysis of inflammatory muscle disease using magnetic resonance imaging." Thesis, Loughborough University, 2015. https://dspace.lboro.ac.uk/2134/19579.
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Inflammatory muscle disease (myositis) is characterised by inflammation and a gradual increase in muscle weakness. Diagnosis typically requires a range of clinical tests, including magnetic resonance imaging of the thigh muscles to assess the disease severity. In the past, this has been measured by manually counting the number of muscles affected. In this work, a computer-aided analysis of inflammatory muscle disease is presented to help doctors diagnose and monitor the disease. Methods to quantify the level of oedema and fat infiltration from magnetic resonance scans are proposed and the disease quantities determined are shown to have positive correlation against expert medical opinion. The methods have been designed and tested on a database of clinically acquired T1 and STIR sequences, and are proven to be robust despite suboptimal image quality. General background information is first introduced, giving an overview of the medical, technical, and theoretical topics necessary to understand the problem domain. Next, a detailed introduction to the physics of magnetic resonance imaging is given. A review of important literature from similar and related domains is presented, with valuable insights that are utilised at a later stage. Scans are carefully pre-processed to bring all slices in to a common frame of reference and the methods to quantify the level of oedema and fat infiltration are defined and shown to have good positive correlation with expert medical opinion. A number of validation tests are performed with re-scanned subjects to indicate the level of repeatability. The disease quantities, together with statistical features from the T1-STIR joint histogram, are used for automatic classification of the disease severity. Automatic classification is shown to be successful on out of sample data for both the oedema and fat infiltration problems.
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Lonkar, Gajanan M. "Computer aided detection of defects in FRP bridge decks using infrared thermography." Morgantown, W. Va. : [West Virginia University Libraries], 2005. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=4368.
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Thesis (M.S.)--West Virginia University, 2005.Title from document title page. Document formatted into pages; contains xii, 129 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 120-123).
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Owino, Erisa. "Computer-aided spectrophotometric detection in HPLC for the examination of drug stability." Thesis, University of Bradford, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.305662.
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Alias, Mohamad Yusoff. "Minimum bit error rate multiuser detection for multiple antenna aided uplink OFDM." Thesis, University of Southampton, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.432466.
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Ion, Adina Izabela. "Computer aided detection and measurement of peripheral arterial diseases from CTA images." Thesis, Kingston University, 2013. http://eprints.kingston.ac.uk/26273/.
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Peripheral Arterial Disease (pAD) afflicts more than 2.7 million people in the U.K. per year, and it is projected to increase rapidly within the current decade. PAD is a product of obstruction (stenosis or occlusion) of vessels feeding the body's extremities, and it is most often encountered in the lower extremities. Treatment of the disease is dependent on the specific anatomic segments afflicted, the degree of stenosis and its length. A common technique for imaging PAD is Computed Tomography-Angiography (CTA). The acquired CTA images are then investigated by a radiologist for disease assessment. However due to the large size of the PAD CTA datasets (1000-2000 slices) the radiologist's examination is time consuming and laborious. This project brings a contribution to the investigation of PAD in CTA datasets by the development of a tool for the radiologist, a fully automatic system for the detection and measurement of PAD, as currently there are no such systems efficacious for the disease. The proposed system is comprised of two components: a Computer Aided Detection (CAD) component and a Computer Aided Measurement (CAM) component. The CAD component is designed for artery segmentation and stenosis detection. The stage of artery segmentation is accomplished by using a 3D region growing method and an innovative 3D fast morphology operation. CAD methodologies commonly employ morphological operations as a tool in the segmentation process, along with extended series of CTA images. This large dataset requires careful attention to be paid towards optimizing the computational process in terms of time efficiency. In order to meet this goal, an optimized morphology algorithm is presented, which reduces the computation time by a factor of 10. A skeletonization based centreline technique is applied on the detected artery, and it then provides the basis for the measurement stage. Orthogonal planes to the centreline are used in order to obtain cross sectional images. The artery profile is then built based on vessel areas measured in the cross sectional images and an automated process of stenosis detection is performed. The CAM component of the system accurately measures and quantifies the stenosis and overcomes the challenge brought by the partial volume effect. In this respect, a hybrid method for partial volume correction is employed locally, on the candidate areas of stenosis detected by the CAD component, based on Maximum a Posterior (MAP) and Markov Random Field (MRF) expectation maximization method. The CAD-CAM system has been successfully implemented and applied on phantom and patient data (twenty data sets from The University Hospital of Lausanne (CHUV)) and the evaluation was carried out through the visual judgment of two experienced radiologists. Within the CAD component, the artery segmentation was evaluated and a total of 15 peripheral arterial trees were correctly extracted. The proposed stenosis detection method was evaluated on 525 arterial segments (each dataset was partitioned into 35 segments) from which 132 exhibited stenosis caused by soft plaque. The system achieved a sensitivity of 88% and a specificity of 96%. The CAM component has been evaluated using phantom data, and the average error of the diameter measurement was 8%.
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Tembey, Mugdha. "Computer-Aided Diagnosis for Mammographic Microcalcification Clusters." [Tampa, Fla.] : University of South Florida, 2003. http://purl.fcla.edu/fcla/etd/SFE0000168.
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Sowter, Christopher. "Objective grading of malignant neoplasms of bladder by computer aided image analysis of nuclear pleomorphism." Thesis, Open University, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.237822.
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Hosseini, Rahil. "Fuzzy based approach for modelling uncertainty in classification for a computer aided detection." Thesis, Kingston University, 2012. http://eprints.kingston.ac.uk/24621/.
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A computerized image analysis technology suffers from imperfection, imprecision and vagueness of the input data and its propagation to all individual components of the technology including image enhancement, segmentation and object classification. Furthermore, a computerized medical image analysis system (CMIAS) deals with another source of ambiguity that is inherent in the image-based practice of medicine and intuitive knowledge of experts. Therefore, a CMIAS such as computer aided detection (CAD) technologies implicitly suffer from uncertainty and vagueness both from image analysis techniques and medical diagnosis. Although several technology-oriented studies have been reported for CAD, no attempt has been made to address, model and overcome these types of uncertainty in the design of the CAD. However, uncertainty issues directly affect the accuracy of the system. This study addresses the main sources of the uncertainty in a CAD system. While uncertainty outcomes are latent in the input of a classifier, the aim is to model them in the classification for a CAD application. For this, this research takes advantages of type-2 fuzzy logic (T2FL). Integrating a T2FL model for object classification in CAD architecture allows us to model uncertainty issues. For this, an automatic approach models uncertainty in training dataset using membership function of a type-2 fuzzy set. This approach was applied to the candidate nodule classification problem in a lung CAD application. The ROC (receiver operating characteristic) analysis of the classifier results (with an average accuracy 95% (area under the ROC curve) for nodule classification) reveals that the T2FL is more capable of capturing the uncertainty in the model and achieving better performance results compared to type-l fuzzy logic counterpart. Furthermore, the research introduces the idea of uncertain rule-based pattern classification in environments which exhibit a lack of expert knowledge and with an imperfect training dataset. An automatic approach for rule extraction is presented which takes advantages of genetic algorithm for learning rule set of an T2FL system from training samples. The proposed approach was applied to the popular Wisconsin breast cancer diagnosis (WBCD) database. Analysis of the performance results reveals that this approach is competitive with, the best results of other proposed fuzzy classification methods to date in terms of trade-off between accuracy and interpretability, with an average accuracy of 96.6 % for the breast cancer diagnosis problem. This study introduces the concept of uncertainty in a CAD application. This is a first attempt toward modelling uncertainty issues in classification component for a CAD. The main contribution is automatically modelling uncertainties using membership functions and a rule set of a type-2 fuzzy logic. The performance evaluation on two different CAD classification problems (1) nodule classification in a lung CAD and (2) the WBCD diagnosis problem using Mammography CAD reveals the superiority of the T2FLS classifier for managing high levels of uncertainty compared to the T1FLS counterpart and providing classification that is more accurate. This approach is significant from two major aspects (l) clinical view: by producing more accurate results for diagnosis problems which can save more human lives, (2) technical view: modelling uncertainties in the design of a classifier using automatically presented approach for IT2FLS membership and rules generation. This is critical for multi-dimensional classification problems with large number of inputs and lack of expert knowledge as is the case for most of medical diagnosis problems.
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He, Qinfen. "Computer-aided instrument system for the detection and analysis of partial discharge activity." Thesis, Glasgow Caledonian University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.282747.
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Zhang, Yuqi. "Lattice Reduction Aided Multiple Input Multiple Output Detection Algorithm Design For 5G Communication." Thesis, KTH, Teknisk informationsvetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209559.
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In recent years, multiple input multiple output system has raised intensive researchinterests for its huge potentials to achieve higher spectral efficiency anddata rate. However, transmitting multiple data streams through the same timeand frequency resources simultaneously leads to severe interference betweentransmitted signals. In order to solve this problem, lattice reduction is proposedas a suboptimal maximum likelihood algorithm. It aims at finding aset of better basis vectors for the channel matrix and results in satisfactoryperformance improvements compared to conventional linear detectors. But theaverage complexity of lattice reduction algorithm is too high for practical uses.This thesis studies conventional linear detection algorithms and lattice reductionaided detection algorithms for uplink receiver design. Further an iterativelattice reduction algorithm is proposed by exploiting the frequency coherence inan orthogonal frequency division multiplexing system to achieve low complexity.In this thesis, algorithm performances are verified in various scenarios throughsimulations. Results show promising performance improvements for presentedlattice reduction detection algorithms, at the cost of an acceptable complexityincrease.Under de senaste åren har MIMO system fått ökat intensiva forskningsintressenför sina stora möjligheter att uppnå högre spektral effektivitet och datahastighet.Överföring av era dataströmmar i samma tid och frekvensresurser lederemellertid samtidigt till allvarlig störning mellan sända signaler. För att lösadetta problem föreslås lattice reduction som en suboptimal maximal likelihoodalgorithm. Det syftar till att hitta en uppsättning bättre basviktorer för kanalmatrisenoch resultera i tillfredsställande prestandaförbättringar jämfört medkonventionella linjära detektorer. Men den genomsnittliga komplexiteten hoslattice reduction är för hög för praktiska användningsområden. Denna avhandlingstuderar konventionella linjära detekteringsalgoritmer och lattice reductiondetekteringsalgoritmer för uplink-mottagare. Vidare föreslås en iterativ latticereduction algoritm genom utnyttjande av frekvenssamhäftningen i ett OFDMsystem.I denna avhandling verifieras utförandet av olika algoritmer i olika scenariergenom simulering. Resultat visar lovande prestandaförbättringar för presenteradecc reduction detekteringsalgoritmer, till kostnaden för en acceptabelkomplexitetsökning.
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Xu, Chong. "Reduced-complexity near-optimal Ant-Colony-aided multi-user detection for CDMA systems." Thesis, University of Southampton, 2009. https://eprints.soton.ac.uk/206015/.
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Reduced-complexity near-maximum-likelihood Ant-Colony Optimization (ACO) assisted Multi-User Detectors (MUDs) are proposed and investigated. The exhaustive search complexity of the optimal detection algorithm may be deemed excessive for practical applications. For example, a Space-Time Block Coded (STBC) two transmit assisted K = 32-user system has to search through the candidate-space for finding the final detection output during 264 times per symbol duration by invoking the Euclidean-distance-calculation of a 64-element complex-valued vector. Hence, a nearoptimal or near-ML MUDs are required in order to provide a near-optimal BER performance at a significantly reduced complexity. Specifically, the ACO assisted MUD algorithms proposed are investigated in the context of a Multi-Carrier DS-CDMA (MC DS-CDMA) system, in a Multi-Functional Antenna Array (MFAA) assisted MC DS-CDMA system and in a STBC aided DS-CDMA system. The ACO assisted MUD algorithm is shown to allow a fully loaded MU system to achieve a near-single user performance, which is similar to that of the classic Minimum Mean Square Error (MMSE) detection algorithm. More quantitatively, when the STBC assisted system support K = 32 users, the complexity imposed by the ACO based MUD algorithm is a fraction of 1 × 10−18 of that of the full search-based optimum MUD. In addition to the hard decision based ACO aided MUD a soft-output MUD was also developed,which was investigated in the context of an STBC assisted DS-CDMA system using a three-stage concatenated, iterative detection aided system. It was demonstrated that the soft-output system is capable of achieving the optimal performance of the Bayesian detection algorithm.
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Tao, Yimo. "Multi-Level Learning Approaches for Medical Image Understanding and Computer-aided Detection and Diagnosis." Thesis, Virginia Tech, 2010. http://hdl.handle.net/10919/42292.
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With the rapid development of computer and information technologies, medical imaging has become one of the major sources of information for therapy and research in medicine, biology and other fields. Along with the advancement of medical imaging techniques, computer-aided detection and diagnosis (CAD/CADx) has recently emerged to become one of the major research subjects within the area of diagnostic radiology and medical image analysis. This thesis presents two multi-level learning-based approaches for medical image understanding with applications of CAD/CADx. The so-called "multi-level learning strategy" relies on that supervised and unsupervised statistical learning techniques are utilized to hierarchically model and analyze the medical image content in a "bottom up" way.
As the first approach, a learning-based algorithm for automatic medical image classification based on sparse aggregation of learned local appearance cues is proposed. The algorithm starts with a number of landmark detectors to collect local appearance cues throughout the image, which are subsequently verified by a group of learned sparse spatial configuration models. In most cases, a decision could already be made at this stage by simply aggregating the verified detections. For the remaining cases, an additional global appearance filtering step is employed to provide complementary information to make the final decision. This approach is evaluated on a large-scale chest radiograph view identification task and a multi-class radiograph annotation task, demonstrating its improved performance in comparison with other state-of-the-art algorithms. It also achieves high accuracy and robustness against images with severe diseases, imaging artifacts, occlusion, or missing data.
As the second approach, a learning-based approach for automatic segmentation of ill-defined and spiculated mammographic masses is presented. The algorithm starts with statistical modeling of exemplar-based image patches. Then, the segmentation problem is regarded as a pixel-wise labeling problem on the produced mass class-conditional probability image, where mass candidates and clutters are extracted. A multi-scale steerable ridge detection algorithm is further employed to detect spiculations. Finally, a graph-cuts technique is employed to unify all outputs from previous steps to generate the final segmentation mask. The proposed method specifically tackles the challenge of inclusion of mass margin and associated extension for segmentation, which is considered to be a very difficult task for many conventional methods.Master of Science
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Mazinani, Mahdi. "Computer aided detection and measurement of coronary artery disease from computed tomography angiography images." Thesis, Kingston University, 2012. http://eprints.kingston.ac.uk/24527/.
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Coronary artery disease is one of the most pernicious diseases around the world and early identification of vascular disease can help to reduce morbidity and mortality. Assessment of the degree of vascular obstruction, or stenosis, is critical for classifying the risks of the future vascular events. Automatic detection and quantification of stenosis are important in assessing coronary artery disease from medical imagery, especially for disease progression. Important factors affecting the reproducability and robustness of accuarate quantification arise from the partial volume effect and other noise sources. The main goal of this study is to present a fully automatic approach for detection and quantification of the stenosis in the coronary arteries. The proposed approach begins by building a 3D reconstruction of the coronary arterial system and then making accurate measurement of the vessel diameter from a robust estimate of the vessel cross-section. The proposed algorithm models the partial volume effect using a Markovian fuzzy clustering method in the process of accurate quantification of the degree of stenosis. To evaluate the accuracy and reproducibility of the measurement, the method was applied to a vascular phantom that was scanned using different protocols. The algorithm was applied to 20 CTA patient datasets containing a total of 85 stenoses, which were all successfully detected, with an average false positive rate of 0.7 per scan.
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Qi, Xuguang. "AUTOMATED MIDLINE SHIFT DETECTION ON BRAIN CT IMAGES FOR COMPUTER-AIDED CLINICAL DECISION SUPPORT." VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/504.
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Midline shift (MLS), the amount of displacement of the brain’s midline from its normal symmetric position due to illness or injury, is an important index for clinicians to assess the severity of traumatic brain injury (TBI). In this dissertation, an automated computer-aided midline shift estimation system is proposed. First, a CT slice selection algorithm (SSA) is designed to automatically select a subset of appropriate CT slices from a large number of raw images for MLS detection. Next, ideal midline detection is implemented based on skull bone anatomical features and global rotation assumptions. For the actual midline detection algorithm, a window selection algorithm (WSA) is applied first to confine the region of interest, then the variational level set method is used to segment the image and extract the ventricle contours. With a ventricle identification algorithm (VIA), the position of actual midline is detected based on the identified right and left lateral ventricle contours. Finally, the brain midline shift is calculated using the positions of detected ideal midline and actual midline. One of the important applications of midline shift in clinical medical decision making is to estimate the intracranial pressure (ICP). ICP monitoring is a standard procedure in the care of severe traumatic brain injury (TBI) patients. An automated ICP level prediction model based on machine learning method is proposed in this work. Multiple features, including midline shift, intracranial air cavities, ventricle size, texture patterns, and blood amount, are used in the ICP level prediction. Finally, the results are evaluated to assess the effectiveness of the proposed method in ICP level prediction.
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Sprague, Matthew J. "A Genetic Algorithm Approach to Feature Selection for Computer Aided Detection of Lung Nodules." University of Dayton / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1480465837455442.
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Bornefalk, Hermansson Anna. "Resampling Evaluation of Signal Detection and Classification : With Special Reference to Breast Cancer, Computer-Aided Detection and the Free-Response Approach." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Univ.-bibl [distributör], 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-7452.
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Jeon, Woojay. "Pitch detection of polyphonic music using constrained optimization." Thesis, Georgia Institute of Technology, 2002. http://hdl.handle.net/1853/15802.
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Monteiro, Joao Paulo da Silva Ferreira. "Computer aided detection in mammography." Dissertação, 2011. http://hdl.handle.net/10216/61463.
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Monteiro, Joao Paulo da Silva Ferreira. "Computer aided detection in mammography." Master's thesis, 2011. http://hdl.handle.net/10216/61463.
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Rosado, Luís Filipe Caeiro Margalho Guerra. "Computer-aided Detection of Malaria Parasites." Tese, 2018. https://repositorio-aberto.up.pt/handle/10216/116876.
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Rosado, Luís Filipe Caeiro Margalho Guerra. "Computer-aided Detection of Malaria Parasites." Doctoral thesis, 2018. https://repositorio-aberto.up.pt/handle/10216/116876.
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It, Goh See, and 吳詩逸. "Computer-aided Interference Detection of Geometrical Objects." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/39273900729513403217.
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碩士國立中正大學
機械工程學系暨研究所
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The purpose of this research is to establish wide range computer simulation techniques to detect interference through a user-friendly computer aided tools. This study uses geometrical object includes: cylinder, pyramid, sphere, and cuboids which commonly use in graphics design as test objects. The interference detection between geometrical models is divided into three steps. First, we use uniform grid to create spatial partitioning to reduce the computation consumption between objects. Next we use bounding sphere to rough compute objects distances. After the objects contact with each other, at last we use Separating Axis Theorem to perform detail computation. Using this three-step computation method we can reduce unnecessary computation. We just need calculate object with near distance, whether if it has interference with each other. This study use coordinate transformation matrix to simulate motion of geometrical objects. Finally, we use Visual Basic 6.0 programming language combine with OpenGL to create the simulation.
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Prabhu, Vinay Uday. "Network Aided Classification and Detection of Data." Tese, 2015. https://hdl.handle.net/10216/107378.
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