Bibliographies thématiques / Medical image sequences

Littérature scientifique sur le sujet « Medical image sequences »

Auteur : Grafiati
Publié le 25 mai 2024

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Articles de revues sur le sujet "Medical image sequences"

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Sagerer, G. « Automatic interpretation of medical image sequences ». Pattern Recognition Letters 8, no 2 (septembre 1988) : 87–102. http://dx.doi.org/10.1016/0167-8655(88)90050-5.

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Wang, Xiaowei, Shoulin Yin, Muhammad Shafiq, Asif Ali Laghari, Shahid Karim, Omar Cheikhrouhou, Wajdi Alhakami et Habib Hamam. « A New V-Net Convolutional Neural Network Based on Four-Dimensional Hyperchaotic System for Medical Image Encryption ». Security and Communication Networks 2022 (14 février 2022) : 1–14. http://dx.doi.org/10.1155/2022/4260804.

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In the transmission of medical images, if the image is not processed, it is very likely to leak data and personal privacy, resulting in unpredictable consequences. Traditional encryption algorithms have limited ability to deal with complex data. The chaotic system is characterized by randomness and ergodicity, which has advantages over traditional encryption algorithms in image encryption processing. A novel V-net convolutional neural network (CNN) based on four-dimensional hyperchaotic system for medical image encryption is presented in this study. Firstly, the plaintext medical images are processed into 4D hyperchaotic sequence images, including image segmentation, chaotic system processing, and pseudorandom sequence generation. Then, V-net CNN is used to train chaotic sequences to eliminate the periodicity of chaotic sequences. Finally, the chaotic sequence image is diffused to change the raw image pixel to realize the encryption processing. Simulation test analysis demonstrates that the proposed algorithm has better effect, robustness, and plaintext sensitivity.
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Ding, Wei Li, Feng Jiang et Jia Qing Yan. « Automatic Segmentation of the Skull in MRI Sequences Using Level Set Method ». Applied Mechanics and Materials 58-60 (juin 2011) : 2370–75. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.2370.

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Magnetic Resonance Imaging (MRI) has been widely used in clinical diagnose. Segmentation of these images obtained by MRI is a necessary procedure in medical image processing. In this paper, an improved level set algorithm was proposed to optimize the segmentation of MRI image sequences based on article [1]. Firstly, we add an area term and the edge indicator function to the total energy function for single image segmentation. Secondly, we presented a new method which uses the circumscribed polygon of the previous segmentation result as the initial contour of the next image to achieve automatic segmentation of image sequences. The algorithm was tested on MRI image sequences provided by Chuiyanliu Hospital, Chaoyang District of Beijing; the results have indicated that the proposed algorithm can effectively enhance the segmentation speed and quality of MRI sequences.
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Malawski, Filip, et Lukasz Czekierda. « COMPRESSION OF IMAGE SEQUENCES IN INTERACTIVE MEDICAL TELECONSULTATIONS ». Computer Science 18, no 1 (2017) : 95. http://dx.doi.org/10.7494/csci.2017.18.1.95.

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Yi, Fan, et Peihua Qiu. « Edge-Preserving Denoising of Image Sequences ». Entropy 23, no 10 (12 octobre 2021) : 1332. http://dx.doi.org/10.3390/e23101332.

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To monitor the Earth’s surface, the satellite of the NASA Landsat program provides us image sequences of any region on the Earth constantly over time. These image sequences give us a unique resource to study the Earth’s surface, changes of the Earth resource over time, and their implications in agriculture, geology, forestry, and more. Besides natural sciences, image sequences are also commonly used in functional magnetic resonance imaging (fMRI) of medical studies for understanding the functioning of brains and other organs. In practice, observed images almost always contain noise and other contaminations. For a reliable subsequent image analysis, it is important to remove such contaminations in advance. This paper focuses on image sequence denoising, which has not been well-discussed in the literature yet. To this end, an edge-preserving image denoising procedure is suggested. The suggested method is based on a jump-preserving local smoothing procedure, in which the bandwidths are chosen such that the possible spatio-temporal correlations in the observed image intensities are accommodated properly. Both theoretical arguments and numerical studies show that this method works well in the various cases considered.
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An, Dezhi, Jun Lu, Shengcai Zhang, Yan Li et António M. Lopes. « A Novel Selective Encryption Method Based on Skin Lesion Detection ». Mathematical Problems in Engineering 2020 (28 septembre 2020) : 1–13. http://dx.doi.org/10.1155/2020/7982192.

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Due to the semitrusted cloud, privacy protection of medical images in medical imaging clouds has become a precondition. For the privacy of patients and the security of medical images in the cloud, this paper proposes a selective encryption based on DNA sequence and chaotic maps for skin lesion image. Initially, we design a transition region-based level set evolution functional which is merged into a variational level set expression with two extra energy functionals, to segment skin lesion image. Once skin lesion detection has been performed, the detected skin lesion pixels are encrypted by employing chaotic systems and DNA sequences. We apply 2D-LASM and 1D-LSS to produce the pseudorandom sequences and use the hash function of the plaintext image to calculate the secret keys of the encryption system. Results demonstrate that the proposed segmentation method is particularly suitable for the detection of skin lesion images with strong noise and complex background. Meanwhile, security analysis also reveals that this selective encryption has a large security key space and high sensitivity to the plaintext image and the secret key.
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Ma, Bin, Bing Li, Xiao-Yu Wang, Chun-Peng Wang, Jian Li et Yun-Qing Shi. « Code Division Multiplexing and Machine Learning Based Reversible Data Hiding Scheme for Medical Image ». Security and Communication Networks 2019 (17 janvier 2019) : 1–9. http://dx.doi.org/10.1155/2019/4732632.

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In this paper, a new reversible data hiding (RDH) scheme based on Code Division Multiplexing (CDM) and machine learning algorithms for medical image is proposed. The original medical image is firstly converted into frequency domain with integer-to-integer wavelet transform (IWT) algorithm, and then the secret data are embedded into the medium frequency subbands of medical image robustly with CDM and machine learning algorithms. According to the orthogonality of different spreading sequences employed in CDM algorithm, the secret data are embedded repeatedly, most of the elements of spreading sequences are mutually canceled, and the proposed method obtained high data embedding capacity at low image distortion. Simultaneously, the to-be-embedded secret data are represented by different spreading sequences, and only the receiver who has the spreading sequences the same as the sender can extract the secret data and original image completely, by which the security of the RDH is improved effectively. Experimental results show the feasibility of the proposed scheme for data embedding in medical image comparing with other state-of-the-art methods.
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Wang, Yi Gang, Gang Yi Jiang et Mei Yu. « Study on Medical Micro-Image Mosaic with SIFT Features ». Advanced Materials Research 121-122 (juin 2010) : 476–81. http://dx.doi.org/10.4028/www.scientific.net/amr.121-122.476.

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In order to obtain panoramic view of medical micro-image in remote medical diagnosis system, we should mosaic micro-image sequence accurately in remote node. SIFT features are invariant to image scaling, rotation and translation. The aim of this study is to mosaic micro-image sequence by extracting SIFT features. Firstly, search coordinate of potential features through Gaussian pyramid (with 7 octaves × 6 scales) and then filter pseudo-keypoints. Secondly, describe these features with 128-D vector. The next step is to calculate matching keypoint pairs between two time-successive images according to minimum Euclidean distance. In this step, standard deviation comparing is proposed to eliminate wrong matching pairs and appropriate threshold(8000) through experiment is used to insure matching pairs. Then, construct image motion equation considering rotation and translation and compute motion parameter by solving equation according matching pairs. Finally mosaic all images. These steps are applied in 5 micro-image sequences such as slices of lung tissue, spleen, kidney, frog blood cell and sunflower tissue. The experiment results show that the gap between two images is vanishing and the proposed method can satisfy with medical micro-images sequence mosaic.
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Jiang, Huiyan, Hanqing Tan et Benqiang Yang. « A Priori Knowledge and Probability Density Based Segmentation Method for Medical CT Image Sequences ». BioMed Research International 2014 (2014) : 1–11. http://dx.doi.org/10.1155/2014/769751.

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This paper briefly introduces a novel segmentation strategy for CT images sequences. As first step of our strategy, we extract a priori intensity statistical information from object region which is manually segmented by radiologists. Then we define a search scope for object and calculate probability density for each pixel in the scope using a voting mechanism. Moreover, we generate an optimal initial level set contour based on a priori shape of object of previous slice. Finally the modified distance regularity level set method utilizes boundaries feature and probability density to conform final object. The main contributions of this paper are as follows: a priori knowledge is effectively used to guide the determination of objects and a modified distance regularization level set method can accurately extract actual contour of object in a short time. The proposed method is compared to other seven state-of-the-art medical image segmentation methods on abdominal CT image sequences datasets. The evaluated results demonstrate our method performs better and has the potential for segmentation in CT image sequences.
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Huang, Tongyuan, Jia Xu, Yuling Yang et Baoru Han. « Robust Zero-Watermarking Algorithm for Medical Images Using Double-Tree Complex Wavelet Transform and Hessenberg Decomposition ». Mathematics 10, no 7 (2 avril 2022) : 1154. http://dx.doi.org/10.3390/math10071154.

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With the rapid development of smart medical care, copyright security for medical images is becoming increasingly important. To improve medical images storage and transmission safety, this paper proposes a robust zero-watermarking algorithm for medical images by fusing Dual-Tree Complex Wavelet Transform (DTCWT), Hessenberg decomposition, and Multi-level Discrete Cosine Transform (MDCT). First, the low-frequency sub-band of the medical image is obtained through the DTCWT and MDCT. Then Hessenberg decomposition is used to construct the visual feature vector. Meanwhile, the encryption of the watermarking image by combining cryptographic algorithms, third-party concepts, and chaotic sequences enhances the algorithm’s security. In the proposed algorithm, zero-watermarking technology is utilized to assure the medical images’ completeness. Compared with the existing algorithms, the proposed algorithm has good robustness and invisibility and can efficiently extract the watermarking image and resist different attacks.
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Thèses sur le sujet "Medical image sequences"

1

Fan, Li. « 3D reconstruction and deformation analysis from medical image sequences with applications in left ventricle and lung / ». free to MU campus, to others for purchase, 2000. http://wwwlib.umi.com/cr/mo/fullcit?p9999280.

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Forsberg, Anni. « Enhancement of X-ray Fluoroscopy Image Sequences using Temporal Recursive Filtering and Motion Compensation ». Thesis, Linköping University, Department of Biomedical Engineering, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-6494.

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This thesis consider enhancement of X-ray fluoroscopy image sequences. The purpose is to investigate the possibilities to improve the image enhancement in Biplanar 500, a fluoroscopy system developed by Swemac Medical Appliances, for use in orthopedic surgery.

An algorithm based on recursive filtering, for temporal noise suppression, and motion compensation, for avoidance of motion artifacts, is developed and tested on image sequences from the system. The motion compensation is done both globally, by using the theory of the shift theorem, and locally, by subtracting consecutive frames. Also a new type of contrast adjustment is presented, received with an unlinear mapping function.

The result is a noise reduced image sequence that shows no blurring effects upon motion. A brief study of the result shows, that both the image sequences with this algorithm applied and the contrast adjusted images are preferred by orthopedists compared to the present images in the system.

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Dietenbeck, Thomas. « Segmentation of 2D-echocardiographic sequences using level-set constrained with shape and motion priors ». Phd thesis, INSA de Lyon, 2012. http://tel.archives-ouvertes.fr/tel-00838767.

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The aim of this work is to propose an algorithm to segment and track the myocardium using the level-set formalism. The myocardium is first approximated by a geometric model (hyperquadrics) which allows to handle asymetric shapes such as the myocardium while avoiding a learning step. This representation is then embedded into the level-set formalism as a shape prior for the joint segmentation of the endocardial and epicardial borders. This shape prior term is coupled with a local data attachment term and a thickness term that prevents both contours from merging. The algorithm is validated on a dataset of 80 images at end diastolic and end systolic phase with manual references from 3 cardiologists. In a second step, we propose to segment whole sequences using motion information. To this end, we apply a level conservation constraint on the implicit function associated to the level-set and express this contraint as an energy term in a variational framework. This energy is then added to the previously described algorithm in order to constrain the temporal evolution of the contour. Finally the algorithm is validated on 20 echocardiographic sequences with manual references of 2 experts (corresponding to approximately 1200 images).
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Zhang, Heye. « An inverse framework for estimating cardiac electrophysiological activity from medical image sequence / ». View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?ECED%202007%20ZHANGHY.

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Sjölund, Jens. « MRI based radiotherapy planning and pulse sequence optimization ». Licentiate thesis, Linköpings universitet, Medicinsk informatik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-115796.

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Radiotherapy plays an increasingly important role in cancer treatment, and medical imaging plays an increasingly important role in radiotherapy. Magnetic resonance imaging (MRI) is poised to be a major component in the development towards more effective radiotherapy treatments with fewer side effects. This thesis attempts to contribute in realizing this potential. Radiotherapy planning requires simulation of radiation transport. The necessary physical properties are typically derived from CT images, but in some cases only MR images are available. In such a case, a crude but common approach is to approximate all tissue properties as equivalent to those of water. In this thesis we propose two methods to improve upon this approximation. The first uses a machine learning algorithm to automatically identify bone tissue in MR. The second, which we refer to as atlas-based regression, can be used to generate a realistic, patient-specific, pseudo-CT directly from anatomical MR images. Atlas-based regression uses deformable registration to estimate a pseudo-CT of a new patient based on a database of aligned MR and CT pairs. Cancerous tissue has a dierent structure from normal tissue. This affects molecular diusion, which can be measured using MRI. The prototypical diusion encoding sequence has recently been challenged with the introduction of more general  waveforms. To take full advantage of their capabilities it is, however, imperative to respect the constraints imposed by the hardware while at the same time maximizing the diffusion encoding strength. In this thesis we formulate this as a constrained optimization problem that is easily adaptable to various hardware constraints.
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Mhedhbi, Imen. « Compression en qualité diagnostic de séquences d’images médicales pour des plateformes embarquées ». Electronic Thesis or Diss., Paris 6, 2015. http://www.theses.fr/2015PA066745.

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Les hôpitaux et les centres médicaux produisent une énorme quantité d'images médicales numériques chaque jour, notamment sous la forme de séquences d'images. En raison de la grande capacité de stockage et de la bande passante de transmission limitée, une technique de compression efficace est nécessaire. Nous avons proposé un algorithme de compression de séquences d'images médicales MMWaaves. Il repose sur l'utilisation de modèles Markovien couplé avec le codeur Waaves de la société Cira qui est certifié en tant que dispositif médicale. Nous avons démontré que MMWaaves a apporté un gain de compression supérieur à 30% par rapport à JPEG2000 et Waaves tout en gardant la qualité nécessaire pour les diagnostics cliniques (SSIM>0.98). En outre, il a permis d'atteindre des taux de compression égaux à ceux obtenus par H.264 en améliorant la qualité. Ensuite, nous avons développé une nouvelle chaine de compression MLPWaaves à base de différence en DWT suivie d'un nouveau modèle de tri adaptatif LPEAM permettant l'optimisation de la stationnarité locale des coefficients. Nous avons obtenu un gain de compression allant à 80% par rapport à Waaves et JPEG2000 tout en assurant une qualité exceptionnelle pour le diagnostic médical. Finalement, afin de transmettre à distance les images médicales du centre de santé à l'appareil mobile du médecin, nous avons proposé un système de télé-radiologie pour le codage et le décodage basé sur nouveau paradigme multithreading. La validation de cette nouvelle solution a été réalisée sur deux plateformes différentes. Nous avons obtenu un facteur d'accélération égal à 5 sur un Intel Core i7-2600 et un facteur égal à 3 sur une tablette Samsung Galaxy
Hospitals and medical centers produce an enormous amount of digital medical images every day especially in the form of image sequences. Due to the large storage size and limited transmission and width, an efficient compression technique is necessary. We first proposed a compressor algorithm for medical images sequences MMWaaves. It is based on Markov fields coupled with the certified medical device Waaves of Cira company. We demonstrated that MMWaaves provided a compression gains greater than 30% compared to JPEG2000 and Waaves while ensuring outstanding image quality for medical diagnosis (SSIM> 0.98). In addition, it achieved compression rates equal to those obtained by H.264 while improving the image quality. Then we developed a new compression algorithm MLPWaaves based on DWT difference followed by a new adaptive scanning model LPEAM in order to optimize the local stationary of wavelet coefficients. We obtained a compression gain up to 80% compared to Waaves and JPEG2000 while ensuring exceptional quality for medical diagnosis. Finally, in order to transmit medical images for diagnostic from the health center to the mobile device of the doctor, we proposed client-server remote radiology system for encoding and decoding. It is based on a multithreading paradigm to accelerate treatment. The validation of this solution was performed on two different platforms. We achieved an acceleration factor of 5 on an Intel Core i7-2600 and a factor of 3 on Samsung Galaxy tablet
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Hernàndez, i. Sabaté Aura. « Exploring Arterial Dynamics and Structures in IntraVascular UltraSound Sequences ». Doctoral thesis, Universitat Autònoma de Barcelona, 2009. http://hdl.handle.net/10803/5792.

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Les malalties cardiovasculars són una de les principals causes de mortalitat als països desenvolupats. La majoria d'elles són degudes a malalties arterials (especialment les coron ries), que vénen causades per l'acumulació de placa. Aquesta patologia estreny el flux sanguini (estenosi) i afecta les propietats elàstiques i bio-mecàniques (arteriosclerosi) de les artèries. En les últimes dècades, l'Ecografia Intra-Coronària (EIC) ha esdevingut una tècnica usual de diagnòstic per la imatge i seguiment de les malalties coronàries. L'EIC està basada en un cateterisme que mostra una seqüència d'imatges corresponents a seccions de l'artèria sota estudi. La inspecció visual de cadascuna d'aquestes imatges proporciona informació sobre el percentatge d'estenosi, mentre que la inspecció de les vistes longitudinals propociona informació sobre les propietats bio-mecàniques, que pot prevenir un desenllaç fatal de la malaltia cardiovascular. Per una banda, la dinàmica arterial (deguda al batec del cor, entre d'altres) és un dels principals artefactes per poder explorar les propietats biomecàniques. Al mateix temps, les mesures manuals d'estenosi requereixen un traçat manual de les vores del vas, tasca feixuga que consumeix molt de temps i que pot patir variabilitat entre observadors.
Aquesta tesi proposa vàries eines de processament d'imatge per explorar la dinàmica de les artèries i les seves estructures. Presentem un model físic per extreure, analitzar i corregir la dinàmica rígida transversal dels vasos i per recuperar la fase cardíaca. A més, introduïm un mètode estadístic-determinista per a la detecció automàtica de les vores del vas. En particular, l'enfoquem a segmentar l'adventícia. Un protocol de validació acurat per assegurar una aplicació clínica fiable dels mètodes és un pas crucial en qualsevol proposta d'algorisme. En aquesta tesi tenim especial cura de dissenyar protocols de validació per a cadascuna de les tècniques proposades i contribuïmm a la validació de la dinàmica in vivo amb un indicador objectiu i quantitatiu per mesurar la quantitat de moviment suprimida.
Cardiovascular diseases are a leading cause of death in developed countries. Most of them are caused by arterial (specially coronary) diseases, mainly caused by plaque accumulation. Such pathology narrows blood flow (stenosis) and affects artery bio-mechanical elastic properties (atherosclerosis). In the last decades, IntraVascular UltraSound (IVUS) has become a usual imaging technique for the diagnosis and follow up of arterial diseases. IVUS is a catheter-based imaging technique which shows a sequence of cross sections of the artery under study. Inspection of a single image gives information about the percentage of stenosis. Meanwhile, inspection of longitudinal views provides information about artery bio-mechanical properties, which can prevent a fatal outcome of the cardiovascular disease. On one hand, dynamics of arteries (due to heart pumping among others) is a major artifact for exploring tissue bio-mechanical properties. On the other one, manual stenosis measurements require a manual tracing of vessel borders, which is a time-consuming task and might suffer from inter-observer variations.
This PhD thesis proposes several image processing tools for exploring vessel dynamics and structures. We present a physics-based model to extract, analyze and correct vessel in-plane rigid dynamics and to retrieve cardiac phase. Furthermore, we introduce a deterministic-statistical method for automatic vessel borders detection. In particular, we address adventitia layer segmentation. An accurate validation protocol to ensure reliable clinical applicability of the methods is a crucial step in any proposal of an algorithm. In this thesis we take special care in designing a validation protocol for each approach proposed and we contribute to the in vivo dynamics validation with a quantitative and objective score to measure the amount of motion suppressed.
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Elbita, Abdulhakim M. « Efficient Processing of Corneal Confocal Microscopy Images. Development of a computer system for the pre-processing, feature extraction, classification, enhancement and registration of a sequence of corneal images ». Thesis, University of Bradford, 2013. http://hdl.handle.net/10454/6463.

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Corneal diseases are one of the major causes of visual impairment and blindness worldwide. Used for diagnoses, a laser confocal microscope provides a sequence of images, at incremental depths, of the various corneal layers and structures. From these, ophthalmologists can extract clinical information on the state of health of a patient’s cornea. However, many factors impede ophthalmologists in forming diagnoses starting with the large number and variable quality of the individual images (blurring, non-uniform illumination within images, variable illumination between images and noise), and there are also difficulties posed for automatic processing caused by eye movements in both lateral and axial directions during the scanning process. Aiding ophthalmologists working with long sequences of corneal image requires the development of new algorithms which enhance, correctly order and register the corneal images within a sequence. The novel algorithms devised for this purpose and presented in this thesis are divided into four main categories. The first is enhancement to reduce the problems within individual images. The second is automatic image classification to identify which part of the cornea each image belongs to, when they may not be in the correct sequence. The third is automatic reordering of the images to place the images in the right sequence. The fourth is automatic registration of the images with each other. A flexible application called CORNEASYS has been developed and implemented using MATLAB and the C language to provide and run all the algorithms and methods presented in this thesis. CORNEASYS offers users a collection of all the proposed approaches and algorithms in this thesis in one platform package. CORNEASYS also provides a facility to help the research team and Ophthalmologists, who are in discussions to determine future system requirements which meet clinicians’ needs.
The data and image files accompanying this thesis are not available online.
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Elbita, Abdulhakim Mehemed. « Efficient processing of corneal confocal microscopy images : development of a computer system for the pre-processing, feature extraction, classification, enhancement and registration of a sequence of corneal images ». Thesis, University of Bradford, 2013. http://hdl.handle.net/10454/6463.

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Corneal diseases are one of the major causes of visual impairment and blindness worldwide. Used for diagnoses, a laser confocal microscope provides a sequence of images, at incremental depths, of the various corneal layers and structures. From these, ophthalmologists can extract clinical information on the state of health of a patient’s cornea. However, many factors impede ophthalmologists in forming diagnoses starting with the large number and variable quality of the individual images (blurring, non-uniform illumination within images, variable illumination between images and noise), and there are also difficulties posed for automatic processing caused by eye movements in both lateral and axial directions during the scanning process. Aiding ophthalmologists working with long sequences of corneal image requires the development of new algorithms which enhance, correctly order and register the corneal images within a sequence. The novel algorithms devised for this purpose and presented in this thesis are divided into four main categories. The first is enhancement to reduce the problems within individual images. The second is automatic image classification to identify which part of the cornea each image belongs to, when they may not be in the correct sequence. The third is automatic reordering of the images to place the images in the right sequence. The fourth is automatic registration of the images with each other. A flexible application called CORNEASYS has been developed and implemented using MATLAB and the C language to provide and run all the algorithms and methods presented in this thesis. CORNEASYS offers users a collection of all the proposed approaches and algorithms in this thesis in one platform package. CORNEASYS also provides a facility to help the research team and Ophthalmologists, who are in discussions to determine future system requirements which meet clinicians’ needs.
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Gérard, Olivier. « Modelisation de sequences par techniques adaptatives : prevision de decharges de batterie et extraction de contours dans des images medicales ». Paris 6, 1999. http://www.theses.fr/1999PA066565.

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L'objet de cette these est de proposer et de developper une methode generale et robuste permettant de traiter deux types de problematique de traitement de sequences. L'approche generale se fonde sur une modelisation pertinente du phenomene observe et sur l'inference statistique des parametres de cette modelisation et de leurs variations. Pour les deux problematiques, nous avons developpe des systemes hierarchiques et hybrides, tirant parti des capacites d'approximateurs universels des reseaux de neurones et integrant de la connaissance a priori sur le probleme. Le premier probleme traite s'inscrit dans le cadre general de la prevision du comportement d'un systeme dynamique evoluant selon le contexte. Plus precisement, nous avons propose un nouveau systeme hierarchique et evolutif pour prevoir la fin de decharge de batteries rechargeables alimentant un appareil portable. Le modele original propose utilise deux reseaux de neurone. Le premier est un simple modele d'une courbe de decharge alors que le second est responsable de l'adaptation aux donnees contextuelles et estime les parametres du premier reseau. La version incrementale proposee permet une adaptation en ligne aux variabilites comportementales des batteries. Les resultats obtenus sont bons avec une erreur moyenne de 6 minutes pour un evenement qui peut intervenir dans un intervalle de 10 heures. Le second probleme aborde consiste en l'extraction automatique d'un contour dans des images medicales : le contour du ventricule gauche dans des images angiocardiographiques. La delineation precise de cet objet sert de base a la mesure de quantites tres utiles au diagnostic de maladies cardio-vasculaires. Le systeme propose utilise le plus possible de l'information a priori de haut niveau afin de restreindre la recherche vers le contour le plus probable. Cette recherche se base sur un modele hybride reseau de neurones - chaine de markov cachee. Les resultats prometteurs obtenus demontrent l'interet de cette demarche.
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Livres sur le sujet "Medical image sequences"

1

L, Kamberova Gerda, et Shah Shishir Kirit 1971-, dir. DNA array image analysis : Nuts & bolts. Skippack, PA : DNA Press, 2002.

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Ibrahim, El-Sayed H. Heart Mechanics : Magnetic Resonance Imaging--Mathematical Modeling, Pulse Sequences, and Image Analysis. Taylor & Francis Group, 2017.

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Ibrahim, El-Sayed H. Heart Mechanics : Magnetic Resonance Imaging--Mathematical Modeling, Pulse Sequences, and Image Analysis. Taylor & Francis Group, 2017.

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Ibrahim, El-Sayed H. Heart Mechanics : Magnetic Resonance Imaging--Mathematical Modeling, Pulse Sequences, and Image Analysis. Taylor & Francis Group, 2017.

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Ibrahim, El-Sayed H. Heart Mechanics : Magnetic Resonance Imaging--Mathematical Modeling, Pulse Sequences, and Image Analysis. Taylor & Francis Group, 2017.

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Ibrahim, El-Sayed H. Heart Mechanics : Magnetic Resonance Imaging - Mathematical Modeling, Pulse Sequences and Image Analysis. Taylor & Francis Group, 2017.

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Webb, Heather. Dante, Artist of Gesture. Oxford University PressOxford, 2022. http://dx.doi.org/10.1093/oso/9780192866998.001.0001.

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Abstract Dante, Artist of Gesture proposes a visual technique for reading Dante’s Commedia, as if the striking gestural images that it imprints on the reader’s mind were arranged in an architectural space. Art historians have shown how series of discrete images or scenes in medieval places of worship, such as the programme of mosaics in the Baptistery of San Giovanni in Florence or the programme of frescoes in the Scrovegni Chapel in Padua, establish not only narrative sequences but also typological parallelisms between registers, forging links between those registers by the use of colour and gestural forms. This book takes up those techniques to show that the Commedia likewise invites the reader to make visual links between disparate, non-sequential moments in the text. In other words, Dante’s poem asks readers to view its verbally articulated sequences of images with a set of observational tools that could be acquired from the practice of engaging with and meditating on the bodily depictions of vice and virtue in fresco cycles or programmes of mosaics in places of worship. One of the most inherently visible aspects of the Commedia is the representation of signature gestures of the characters described in each of the realms. The tracing of described gestures and bodily signs across the canticles of the poem provides a key for identifying affective and devotional itineraries within the text.
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Kamberova, Gerda, et Shishir Shah. DNA Array Image Analysis : Nuts & Bolts (Nuts & Bolts series). DNA Press, 2002.

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Mitchell, Scott A. Buddhism in America. Bloomsbury Publishing Plc, 2016. http://dx.doi.org/10.5040/9781474204064.

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Buddhism in America provides the most comprehensive and up to date survey of the diverse landscape of US Buddhist traditions, their history and development, and current methodological trends in the study of Buddhism in the West, located within the translocal flow of global Buddhist culture. Divided into three parts (Histories; Traditions; Frames), this introduction traces Buddhism's history and encounter with North American culture, charts the landscape of US Buddhist communities, and engages current methodological and theoretical developments in the field. The volume includes: - A short introduction to Buddhism - A historical survey from the 19th century to the present - Coverage of contemporary US Buddhist communities, including Theravada, Mahayana, and Vajrayana Theoretical and methodological issues and debates covered include: - Social, political and environmental engagement - Race, feminist, and queer theories of Buddhism - Secular Buddhism, digital Buddhism, and modernity - Popular culture, media, and the arts Pedagogical tools include chapter summaries, discussion questions, images and maps, a glossary, and case studies. The book's website provides recommended further resources including websites, books and films, organized by chapter. With individual chapters which can stand on their own and be assigned out of sequence, Buddhism in America is the ideal resource for courses on Buddhism in America, American Religious History, and Introduction to Buddhism.
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Chapitres de livres sur le sujet "Medical image sequences"

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Koprowski, Robert. « Analysis of Image Sequences ». Dans Processing Medical Thermal Images, 83–119. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61340-6_6.

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Roos, Paul, et Max A. Viergever. « Reversible Data Compression of Angiographic Image Sequences ». Dans Medical Images : Formation, Handling and Evaluation, 595–605. Berlin, Heidelberg : Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-77888-9_31.

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Vicar, Tomas, Roman Jakubicek, Jiri Chmelik et Radim Kolar. « Registration of Medical Image Sequences Using Auto-differentiation ». Dans Medical Imaging and Computer-Aided Diagnosis, 169–78. Singapore : Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-16-6775-6_15.

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Kim, Wonjin, Wonkyeong Lee, Sun-Young Jeon, Nayeon Kang, Geonhui Jo et Jang-Hwan Choi. « Deep Denoising Network for X-Ray Fluoroscopic Image Sequences of Moving Objects ». Dans Machine Learning for Medical Image Reconstruction, 95–104. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17247-2_10.

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Aroukatos, Nikolaos G., Kostas Manes et Stelios Zimeras. « Social Networks Medical Image Steganography Using Sub-Fibonacci Sequences ». Dans Annals of Information Systems, 171–85. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23341-3_13.

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Azzabou, Noura, et Nikos Paragios. « Spatio-temporal Speckle Reduction in Ultrasound Sequences ». Dans Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008, 951–58. Berlin, Heidelberg : Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85988-8_113.

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Li, Wanyue, Yi He, Wen Kong, Jing Wang, Guohua Deng, Yiwei Chen et Guohua Shi. « SequenceGAN : Generating Fundus Fluorescence Angiography Sequences from Structure Fundus Image ». Dans Simulation and Synthesis in Medical Imaging, 110–20. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87592-3_11.

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Alberti, Marina, Simone Balocco, Xavier Carrillo, Josepa Mauri et Petia Radeva. « Automatic Non-rigid Temporal Alignment of IVUS Sequences ». Dans Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012, 642–50. Berlin, Heidelberg : Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33415-3_79.

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Sundar, Hari, Ali Khamene, Liron Yatziv et Chenyang Xu. « Automatic Image-Based Cardiac and Respiratory Cycle Synchronization and Gating of Image Sequences ». Dans Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009, 381–88. Berlin, Heidelberg : Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04271-3_47.

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Song, Xubo B., Andriy Myronenko, Stephen R. Plank et James T. Rosenbaum. « Registration of Microscopic Iris Image Sequences Using Probabilistic Mesh ». Dans Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006, 553–60. Berlin, Heidelberg : Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11866763_68.

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Actes de conférences sur le sujet "Medical image sequences"

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Richens, Dominic, Morris Goldberg et Brian Morton. « Multimodality workstation for cardiac image sequences ». Dans Medical Imaging 1993, sous la direction de Yongmin Kim. SPIE, 1993. http://dx.doi.org/10.1117/12.146993.

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Skrinjar, Oskar, Yi-Yu Chou et Hemant Tagare. « Transitive nonrigid image registration : application to cardiac MR image sequences ». Dans Medical Imaging 2004, sous la direction de J. Michael Fitzpatrick et Milan Sonka. SPIE, 2004. http://dx.doi.org/10.1117/12.536230.

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Danudibroto, Adriyana, Jørn Bersvendsen, Oana Mirea, Olivier Gerard, Jan D'hooge et Eigil Samset. « Image-based temporal alignment of echocardiographic sequences ». Dans SPIE Medical Imaging, sous la direction de Neb Duric et Brecht Heyde. SPIE, 2016. http://dx.doi.org/10.1117/12.2216192.

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Hartmann, M., M. A. Simons, B. Yih et R. A. Kruger. « Blood Flow Determination From Fluoroscopic Image Sequences ». Dans Medical Imaging II, sous la direction de Roger H. Schneider et Samuel J. Dwyer III. SPIE, 1988. http://dx.doi.org/10.1117/12.968658.

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Farison, James B., Yong-gab Park, Qun Yu et Hong Lu. « KL transformation of spatially invariant image sequences ». Dans Medical Imaging 1997, sous la direction de Kenneth M. Hanson. SPIE, 1997. http://dx.doi.org/10.1117/12.274108.

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Higgins, William E., Andrien J. Wang et Joseph M. Reinhardt. « Semiautomatic 4D analysis of cardiac image sequences ». Dans Medical Imaging 1996, sous la direction de Eric A. Hoffman. SPIE, 1996. http://dx.doi.org/10.1117/12.237878.

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Ehrhardt, Jan, Dennis Säring et Heinz Handels. « Optical flow based interpolation of temporal image sequences ». Dans Medical Imaging, sous la direction de Joseph M. Reinhardt et Josien P. W. Pluim. SPIE, 2006. http://dx.doi.org/10.1117/12.652559.

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Kennedy, Jonathon M., Michael Simms, Emma Kearney, Anita Dowling, Andrew Fagan et Neil J. O'Hare. « High-speed lossless compression for angiography image sequences ». Dans Medical Imaging 2001, sous la direction de Seong K. Mun. SPIE, 2001. http://dx.doi.org/10.1117/12.428103.

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Close, Robert A., James S. Whiting, Xiaolin Da et Neal L. Eigler. « Stabilized display of coronary x-ray image sequences ». Dans Medical Imaging 2004, sous la direction de Robert L. Galloway, Jr. SPIE, 2004. http://dx.doi.org/10.1117/12.535854.

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Roos, Paul, et Max A. Viergever. « Registration And Reversible Compression Of Angiographic Image Sequences ». Dans 1989 Medical Imaging, sous la direction de Samuel J. Dwyer III, R. Gilbert Jost et Roger H. Schneider. SPIE, 1989. http://dx.doi.org/10.1117/12.953279.

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