Tesis sobre el tema "Unsupervised image segmentation"
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Horne, Caspar. "Unsupervised image segmentation /". Lausanne : EPFL, 1991. http://library.epfl.ch/theses/?nr=905.
Texto completoBaumann, Oliver Nicholas. "Connected operators for unsupervised image segmentation". Thesis, University of Southampton, 2004. https://eprints.soton.ac.uk/66319/.
Texto completoBarker, S. A. "Unsupervised image segmentation using Markov Random Field models". Thesis, University of Cambridge, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.596368.
Texto completoKam, Alvin Harvey Siew Wah. "A general multiscale scheme for unsupervised image segmentation". Thesis, University of Cambridge, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.621969.
Texto completoIslam, Mofakharul University of Ballarat. "Unsupervised Color Image Segmentation Using Markov Random Fields Model". University of Ballarat, 2008. http://archimedes.ballarat.edu.au:8080/vital/access/HandleResolver/1959.17/12827.
Texto completoMaster of Computing
Liu, Dongnan. "Supervised and Unsupervised Deep Learning-based Biomedical Image Segmentation". Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/24744.
Texto completoZhang, Xinwen. "Multi-modality Medical Image Segmentation with Unsupervised Domain Adaptation". Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/29776.
Texto completoIslam, Mofakharul. "Unsupervised color image segmentation using Markov Random Fields Model". Thesis, University of Ballarat, 2008. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/53709.
Texto completoMaster of Computing
Islam, Mofakharul. "Unsupervised color image segmentation using Markov Random Fields Model". University of Ballarat, 2008. http://archimedes.ballarat.edu.au:8080/vital/access/HandleResolver/1959.17/15694.
Texto completoMaster of Computing
Zheng, Hongwei. "Bayesian learning and regularization for unsupervised image restoration and segmentation". [S.l.] : [s.n.], 2007. http://opus.kobv.de/tuberlin/volltexte/2007/1623.
Texto completoStanford, Derek C. "Fast automatic unsupervised image segmentation and curve detection in spatial point patterns /". Thesis, Connect to this title online; UW restricted, 1999. http://hdl.handle.net/1773/8976.
Texto completoHasnat, Md Abul. "Unsupervised 3D image clustering and extension to joint color and depth segmentation". Thesis, Saint-Etienne, 2014. http://www.theses.fr/2014STET4013/document.
Texto completoAccess to the 3D images at a reasonable frame rate is widespread now, thanks to the recent advances in low cost depth sensors as well as the efficient methods to compute 3D from 2D images. As a consequence, it is highly demanding to enhance the capability of existing computer vision applications by incorporating 3D information. Indeed, it has been demonstrated in numerous researches that the accuracy of different tasks increases by including 3D information as an additional feature. However, for the task of indoor scene analysis and segmentation, it remains several important issues, such as: (a) how the 3D information itself can be exploited? and (b) what is the best way to fuse color and 3D in an unsupervised manner? In this thesis, we address these issues and propose novel unsupervised methods for 3D image clustering and joint color and depth image segmentation. To this aim, we consider image normals as the prominent feature from 3D image and cluster them with methods based on finite statistical mixture models. We consider Bregman Soft Clustering method to ensure computationally efficient clustering. Moreover, we exploit several probability distributions from directional statistics, such as the von Mises-Fisher distribution and the Watson distribution. By combining these, we propose novel Model Based Clustering methods. We empirically validate these methods using synthetic data and then demonstrate their application for 3D/depth image analysis. Afterward, we extend these methods to segment synchronized 3D and color image, also called RGB-D image. To this aim, first we propose a statistical image generation model for RGB-D image. Then, we propose novel RGB-D segmentation method using a joint color-spatial-axial clustering and a statistical planar region merging method. Results show that, the proposed method is comparable with the state of the art methods and requires less computation time. Moreover, it opens interesting perspectives to fuse color and geometry in an unsupervised manner. We believe that the methods proposed in this thesis are equally applicable and extendable for clustering different types of data, such as speech, gene expressions, etc. Moreover, they can be used for complex tasks, such as joint image-speech data analysis
Vantaram, Sreenath Rao. "Fast unsupervised multiresolution color image segmentation using adaptive gradient thresholding and progressive region growing /". Online version of thesis, 2009. http://hdl.handle.net/1850/9016.
Texto completoShen, Ruobing [Verfasser] y Gerhard [Akademischer Betreuer] Reinelt. "MILP Formulations for Unsupervised and Interactive Image Segmentation and Denoising / Ruobing Shen ; Betreuer: Gerhard Reinelt". Heidelberg : Universitätsbibliothek Heidelberg, 2018. http://d-nb.info/1177252724/34.
Texto completoWilhelm, Thorsten [Verfasser], Christian [Akademischer Betreuer] Wöhler y Franz [Gutachter] Kummert. "Uncertainty-based image segmentation with unsupervised mixture models / Thorsten Wilhelm ; Gutachter: Franz Kummert ; Betreuer: Christian Wöhler". Dortmund : Universitätsbibliothek Dortmund, 2019. http://d-nb.info/1213520568/34.
Texto completoSublime, Jérémie. "Contributions au clustering collaboratif et à ses potentielles applications en imagerie à très haute résolution". Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLA005/document.
Texto completoThis thesis presents several algorithms developed in the context of the ANR COCLICO project and contains two main axis: The first axis is concerned with introducing Markov Random Fields (MRF) based models to provide a semantic rich and suited algorithm applicable to images that are already segmented. This method is based on the Iterated Conditional Modes Algorithm (ICM algorithm) and can be applied to the segments of very high resolution (VHR) satellite pictures. Our proposed method can cope with highly irregular neighborhood dependencies and provides some low level semantic information on the clusters and their relationship within the image. The second axis deals with collaborative clustering methods developed with the goal of being applicable to as many clustering algorithms as possible, including the algorithms used in the first axis of this work. A key feature of the methods proposed in this thesis is that they can deal with either of the following two cases: 1) several clustering algorithms working together on the same data represented in different feature spaces, 2) several clustering algorithms looking for similar clusters in different data sets having similar distributions. Clustering algorithms to which these methods are applicable include the ICM algorithm, the K-Means algorithm, density based algorithms such as DB-scan, all Expectation-Maximization (EM) based algorithms such as the Self-Organizing Maps (SOM) and the Generative Topographic Mapping (GTM) algorithms. Unlike previously introduced methods, our models have no restrictions in term of types of algorithms that can collaborate together, do not require that all methods be looking for the same number of clusters, and are provided with solid mathematical foundations
Chahine, Chaza. "Fusion d'informations par la théorie de l'évidence pour la segmentation d'images". Thesis, Paris Est, 2016. http://www.theses.fr/2016PESC1030/document.
Texto completoInformation fusion has been widely studied in the field of artificial intelligence. Information is generally considered imperfect. Therefore, the combination of several sources of information (possibly heterogeneous) can lead to a more comprehensive and complete information. In the field of fusion are generally distinguished probabilistic approaches and non-probabilistic ones which include the theory of evidence, developed in the 70s. This method represents both the uncertainty and imprecision of the information, by assigning masses not only to a hypothesis (which is the most common case for probabilistic methods) but to a set of hypothesis. The work presented in this thesis concerns the fusion of information for image segmentation.To develop this method we start with the algorithm of Watershed which is one of the most used methods for edge detection. Intuitively the principle of the Watershed is to consider the image as a landscape relief where heights of the different points are associated with grey levels. Assuming that the local minima are pierced with holes and the landscape is immersed in a lake, the water filled up from these minima generate the catchment basins, whereas watershed lines are the dams built to prevent mixing waters coming from different basins.The watershed is practically applied to the gradient magnitude, and a region is associated with each minimum. Therefore the fluctuations in the gradient image and the great number of local minima generate a large set of small regions yielding an over segmented result which can hardly be useful. Meyer and Beucher proposed seeded watershed or marked-controlled watershed to surmount this oversegmentation problem. The essential idea of the method is to specify a set of markers (or seeds) to be considered as the only minima to be flooded by water. The number of detected objects is therefore equal to the number of seeds and the result is then markers dependent. The automatic extraction of markers from the images does not lead to a satisfying result especially in the case of complex images. Several methods have been proposed for automatically determining these markers.We are particularly interested in the stochastic approach of Angulo and Jeulin who calculate a probability density function (pdf) of contours after M simulations of segmentation using conventional watershed with N markers randomly selected for each simulation. Therefore, a high pdf value is assigned to strong contour points that are more detected through the process. But the decision that a point belong to the "contour class" remains dependent on a threshold value. A single result cannot be obtained.To increase the robustness of this method and the uniqueness of its response, we propose to combine information with the theory of evidence.The watershed is generally calculated on the gradient image, first order derivative, which gives comprehensive information on the contours in the image.While the Hessian matrix, matrix of second order derivatives, gives more local information on the contours. Our goal is to combine these two complementary information using the theory of evidence. The method is tested on real images from the Berkeley database. The results are compared with five manual segmentation provided as ground truth, with this database. The quality of the segmentation obtained by our methods is tested with different measures: uniformity, precision, recall, specificity, sensitivity and the Hausdorff metric distance
Yahiaoui, Meriem. "Modèles statistiques avancés pour la segmentation non supervisée des images dégradées de l'iris". Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLL006/document.
Texto completoIris is considered as one of the most robust and efficient modalities in biometrics because of its low error rates. These performances were observed in controlled situations, which impose constraints during the acquisition in order to have good quality images. The renouncement of these constraints, at least partially, implies degradations in the quality of the acquired images and it is therefore a degradation of these systems’ performances. One of the main proposed solutions in the literature to take into account these limits is to propose a robust approach for iris segmentation. The main objective of this thesis is to propose original methods for the segmentation of degraded images of the iris. Markov chains have been well solicited to solve image segmentation problems. In this context, a feasibility study of unsupervised segmentation into regions of degraded iris images by Markov chains was performed. Different image transformations and different segmentation methods for parameters initialization have been studied and compared. Optimal modeling has been inserted in iris recognition system (with grayscale images) to produce a comparison with the existing methods. Finally, an extension of the modeling based on the hidden Markov chains has been developed in order to realize an unsupervised segmentation of the iris images acquired in visible light
Fernandes, Clément. "Chaînes de Markov triplets et segmentation non supervisée d'images". Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAS019.
Texto completoHidden Markov chains (HMC) are widely used in unsupervised Bayesian hidden discrete data restoration. They are very robust and, in spite of their simplicity, they are sufficiently efficient in many situations. In particular for image segmentation, despite their mono-dimensional nature, they are able, through a transformation of the bi-dimensional images into mono-dimensional sequences with Peano scan (PS), to give satisfying results. However, sometimes, more complex models such as hidden Markov fields (HMF) may be preferred in spite of their increased time complexity, for their better results. Moreover, hidden Markov models (the chains as well as the fields) have been extended to pairwise and triplet Markov models, which can be of interest in more complex situations. For example, when sojourn time in hidden states is not geometrical, hidden semi-Markov (HSMC) chains tend to perform better than HMC, and such is also the case for hidden evidential Markov chains (HEMC) when data are non-stationary. In this thesis, we first propose a new triplet Markov chain (TMC), which simultaneously extends HSMC and HEMC. Based on hidden triplet Markov chains (HTMC), the new hidden evidential semi-Markov chain (HESMC) model can be used in unsupervised framework, parameters being estimated with Expectation-Maximization (EM) algorithm. We validate its interest through some experiments on synthetic data. Then we address the problem of mono-dimensionality of the HMC with PS model in image segmentation by introducing the “contextual” Peano scan (CPS). It consists in associating to each index in the HMC obtained from PS, two observations on pixels which are neighbors of the pixel considered in the image, but are not its neighbors in the HMC. This gives three observations on each point of the Peano scan, which leads to a new conditional Markov chain (CMC) with a more complex structure, but whose posterior law is still Markovian. Therefore, we can apply the usual parameter estimation method: Stochastic Expectation-Maximization (SEM), as well as study unsupervised segmentation Marginal Posterior Mode (MPM) so obtained. The CMC with CPS based supervised and unsupervised MPM are compared to the classic scan based HMC-PS and the HMF through experiments on artificial images. They improve notably the former, and can even compete with the latter. Finally, we extend the CMC-CPS to Pairwise Conditional Markov (CPMC) chains and two particular triplet conditional Markov chain: evidential conditional Markov chains (CEMC) and conditional semi-Markov chains (CSMC). For each of these extensions, we show through experiments on artificial images that these models can improve notably their non conditional counterpart, as well as the CMC with CPS, and can even compete with the HMF. Beside they allow the generality of markovian triplets to better play its part in image segmentation, while avoiding the substantial time complexity of triplet Markov fields
Mohammed, Abdulmalik. "Obstacle detection and emergency exit sign recognition for autonomous navigation using camera phone". Thesis, University of Manchester, 2017. https://www.research.manchester.ac.uk/portal/en/theses/obstacle-detection-and-emergency-exit-sign-recognition-for-autonomous-navigation-using-camera-phone(e0224d89-e743-47a4-8c68-52f718457098).html.
Texto completoYahiaoui, Meriem. "Modèles statistiques avancés pour la segmentation non supervisée des images dégradées de l'iris". Electronic Thesis or Diss., Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLL006.
Texto completoIris is considered as one of the most robust and efficient modalities in biometrics because of its low error rates. These performances were observed in controlled situations, which impose constraints during the acquisition in order to have good quality images. The renouncement of these constraints, at least partially, implies degradations in the quality of the acquired images and it is therefore a degradation of these systems’ performances. One of the main proposed solutions in the literature to take into account these limits is to propose a robust approach for iris segmentation. The main objective of this thesis is to propose original methods for the segmentation of degraded images of the iris. Markov chains have been well solicited to solve image segmentation problems. In this context, a feasibility study of unsupervised segmentation into regions of degraded iris images by Markov chains was performed. Different image transformations and different segmentation methods for parameters initialization have been studied and compared. Optimal modeling has been inserted in iris recognition system (with grayscale images) to produce a comparison with the existing methods. Finally, an extension of the modeling based on the hidden Markov chains has been developed in order to realize an unsupervised segmentation of the iris images acquired in visible light
Martínez, Usó Adolfo. "Unsupervised Band Selection and Segmentation in Hyper/Multispectral Images". Doctoral thesis, Universitat Jaume I, 2008. http://hdl.handle.net/10803/10483.
Texto completoSecondly, the problem of segmentation strictly speaking is still a challenging question whatever the input image would be.
This thesis is focused on solving the whole process by means of building an image processing method that analyses and optimises the information acquired by a multispectral device. After that, it detects the main regions that are present in the scene in an image segmentation procedure. Therefore, this work will be divided into two parts. In the first part, an approach for selecting the most relevant subset of input bands will be presented. In the second part, this reduced representation of the initial bands will be the input data of a segmentation method.
Finally, the main contributions of this PhD work could be briefly summarised as follows. On the one hand, we have proposed a pre-processing stage with an unsupervised band selection approach based on information measures that reduces considerably the amount of data. This approach has been successfully compared with well-known algorithms of the literature, showing its good performance with regard to pixel image classification tasks. On the other hand, after the band selection stage, two unsupervised segmentation procedures for detecting the main parts in multispectral images have been also developed. Regarding to this segmentation part, we have mainly contributed with two measures of similarity among regions. An objective functional for selecting an optimal (or close to optimal) partition of the image is another relevant contribution too.
Văcar, Cornelia Paula. "Inversion for textured images : unsupervised myopic deconvolution, model selection, deconvolution-segmentation". Thesis, Bordeaux, 2014. http://www.theses.fr/2014BORD0131/document.
Texto completoThis thesis is addressing a series of inverse problems of major importance in the fieldof image processing (image segmentation, model choice, parameter estimation, deconvolution)in the context of textured images. In all of the aforementioned problems theobservations are indirect, i.e., the textured images are affected by a blur and by noise. Thecontributions of this work belong to three main classes: modeling, methodological andalgorithmic. From the modeling standpoint, the contribution consists in the development of a newnon-Gaussian model for textures. The Fourier coefficients of the textured images are modeledby a Scale Mixture of Gaussians Random Field. The Power Spectral Density of thetexture has a parametric form, driven by a set of parameters that encode the texture characteristics.The methodological contribution is threefold and consists in solving three image processingproblems that have not been tackled so far in the context of indirect observationsof textured images. All the proposed methods are Bayesian and are based on the exploitingthe information encoded in the a posteriori law. The first method that is proposed is devotedto the myopic deconvolution of a textured image and the estimation of its parameters.The second method achieves joint model selection and model parameters estimation froman indirect observation of a textured image. Finally, the third method addresses the problemof joint deconvolution and segmentation of an image composed of several texturedregions, while estimating at the same time the parameters of each constituent texture.Last, but not least, the algorithmic contribution is represented by the development ofa new efficient version of the Metropolis Hastings algorithm, with a directional componentof the proposal function based on the”Newton direction” and the Fisher informationmatrix. This particular directional component allows for an efficient exploration of theparameter space and, consequently, increases the convergence speed of the algorithm.To summarize, this work presents a series of methods to solve three image processingproblems in the context of blurry and noisy textured images. Moreover, we present twoconnected contributions, one regarding the texture models andone meant to enhance theperformances of the samplers employed for all of the three methods
Meléndez, Rodríguez Jaime Christian. "Supervised and unsupervised segmentation of textured images by efficient multi-level pattern classification". Doctoral thesis, Universitat Rovira i Virgili, 2010. http://hdl.handle.net/10803/8487.
Texto completoEsta tesis propone metodologías nuevas y eficientes para segmentar imágenes a partir de información de textura en entornos supervisados y no supervisados. Para el caso supervisado, se propone una técnica basada en una estrategia de clasificación de píxeles multinivel que refina la segmentación resultante de forma iterativa. Dicha estrategia utiliza métodos de reconocimiento de patrones basados en prototipos (determinados mediante algoritmos de agrupamiento) y máquinas de vectores de soporte. Con el objetivo de obtener el mejor rendimiento, se incluyen además un algoritmo para selección automática de parámetros y métodos para reducir el coste computacional asociado al proceso de segmentación. Para el caso no supervisado, se propone una adaptación de la metodología anterior mediante una etapa inicial de descubrimiento de patrones que permite transformar el problema no supervisado en supervisado. Las técnicas desarrolladas en esta tesis se validan mediante diversos experimentos considerando una gran variedad de imágenes.
Gordillo, Castillo Nelly. "Contributions to Automatic and Unsupervised MRI Brain Tumor Segmentation: A New Fuzzy Approach". Doctoral thesis, Universitat Politècnica de Catalunya, 2010. http://hdl.handle.net/10803/6210.
Texto completoIncreasingly, magnetic resonance imaging (MRI) scan is being used for suspected brain tumors, because in addition tooutline the normal brain structures in great detail, has a high sensitivity for detecting the presence of, or changes within, a tumor.Currently most of the process related to brain tumors such as diagnosis, therapy, and surgery planning are based on its previoussegmentation from MRI. Brain tumor segmentation from MRI is a challenging task that involves various disciplines. The tumors to besegmented are anatomical structures, which are often non-rigid and complex in shape, vary greatly in size and position, and exhibitconsiderable variability from patient to patient. Moreover, the task of labeling brain tumors in MRI is highly time consuming and thereexists significant variation between the labels produced by different experts.
The challenges associated with automated brain tumor segmentation have given rise to many different segmentationapproaches. Although the reported accuracy of the proposed methods is promising, these approaches have not gained wide acceptance among the neuroscientists for every day clinical practice. Two of the principal reasons are the lack of standardizedprocedures, and the deficiency of the existing methods to assist medical decision following a technician way of work.
For a brain tumor segmentation system has acceptance among neuroscientists in clinical practice, it should supportmedical decision in a transparent and interpretable way emulating the role of a technician, considering his experience and knowledge. This includes knowledge of the expected appearance, location, variability of normal anatomy, bilateral symmetry, andknowledge about the expected intensities of different tissues. The image related problems and the variability in tissue distribution among individuals in the human population makes that some degree of uncertainty must be considered together with segmentationresults.
A possible solution for designing complex systems, in which it is required to incorporate the experience of an expert, or the related concepts appear uncertain, is the use of soft computing techniques such as fuzzy systems. An important advantage of fuzzysystems is their ability for handling vague information.
In this work, it is proposed the development of a method to assist the specialists in the process of segmenting braintumors. The main objective is to develop a system that can follow a technician way of work, considering his experience andknowledge. More concretely, it is presented a fully automatic and unsupervised segmentation method, which considers humanknowledge. The method successfully manages the ambiguity of MR image features being capable of describing knowledge about thetumors in vague terms. The method was developed making use of the powerful tools provided by fuzzy set theory.
This thesis presents a step-by-step methodology for the automatic MRI brain tumor segmentation. For achieving the fullyautomatic and unsupervised segmentation, objective measures are delineated by means of adaptive histogram thresholds for defining the non-tumor and tumor populations. For defining the tumor population a symmetry analysis is conducted.
The proposed approach introduces a new way to automatically define the membership functions from the histogram. The proposed membership functions are designed to adapt well to the MRI data and efficiently separate the populations. Since any post-processing is needed, and the unique pre-processing operation is the skull stripping, the proposed segmentation technique reduces the computational times. The proposed approach is quantitatively comparable to the most accurate existing methods, even thoughthe segmentation is done in 2D.
Nait-Chabane, Ahmed. "Segmentation invariante en rasance des images sonar latéral par une approche neuronale compétitive". Phd thesis, Université de Bretagne occidentale - Brest, 2013. http://tel.archives-ouvertes.fr/tel-00968199.
Texto completoNasser, Khalafallah Mahmoud Lamees. "A dictionary-based denoising method toward a robust segmentation of noisy and densely packed nuclei in 3D biological microscopy images". Electronic Thesis or Diss., Sorbonne université, 2019. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2019SORUS283.pdf.
Texto completoCells are the basic building blocks of all living organisms. All living organisms share life processes such as growth and development, movement, nutrition, excretion, reproduction, respiration and response to the environment. In cell biology research, understanding cells structure and function is essential for developing and testing new drugs. In addition, cell biology research provides a powerful tool to study embryo development. Furthermore, it helps the scientific research community to understand the effects of mutations and various diseases. Time-Lapse Fluorescence Microscopy (TLFM) is one of the most appreciated imaging techniques which can be used in live-cell imaging experiments to quantify various characteristics of cellular processes, i.e., cell survival, proliferation, migration, and differentiation. In TLFM imaging, not only spatial information is acquired, but also temporal information obtained by repeating imaging of a labeled sample at specific time points, as well as spectral information, that produces up to five-dimensional (X, Y, Z + Time + Channel) images. Typically, the generated datasets consist of several (hundreds or thousands) images, each containing hundreds to thousands of objects to be analyzed. To perform high-throughput quantification of cellular processes, nuclei segmentation and tracking should be performed in an automated manner. Nevertheless, nuclei segmentation and tracking are challenging tasks due to embedded noise, intensity inhomogeneity, shape variation as well as a weak boundary of nuclei. Although several nuclei segmentation approaches have been reported in the literature, dealing with embedded noise remains the most challenging part of any segmentation algorithm. We propose a novel 3D denoising algorithm, based on unsupervised dictionary learning and sparse representation, that can both enhance very faint and noisy nuclei, in addition, it simultaneously detects nuclei position accurately. Furthermore, our method is based on a limited number of parameters, with only one being critical, which is the approximate size of the objects of interest. The framework of the proposed method comprises image denoising, nuclei detection, and segmentation. In the denoising step, an initial dictionary is constructed by selecting random patches from the raw image then an iterative technique is implemented to update the dictionary and obtain the final one which is less noisy. Next, a detection map, based on the dictionary coefficients used to denoise the image, is used to detect marker points. Afterward, a thresholding-based approach is proposed to get the segmentation mask. Finally, a marker-controlled watershed approach is used to get the final nuclei segmentation result. We generate 3D synthetic images to study the effect of the few parameters of our method on cell nuclei detection and segmentation, and to understand the overall mechanism for selecting and tuning the significant parameters of the several datasets. These synthetic images have low contrast and low signal to noise ratio. Furthermore, they include touching spheres where these conditions simulate the same characteristics exist in the real datasets. The proposed framework shows that integrating our denoising method along with classical segmentation method works properly in the context of the most challenging cases. To evaluate the performance of the proposed method, two datasets from the cell tracking challenge are extensively tested. Across all datasets, the proposed method achieved very promising results with 96.96% recall for the C.elegans dataset. Besides, in the Drosophila dataset, our method achieved very high recall (99.3%)
Xie, Zong-Shuo y 謝宗碩. "Image Segmentation Using Unsupervised Classification". Thesis, 2008. http://ndltd.ncl.edu.tw/handle/23386312314407812179.
Texto completo國立成功大學
電機工程學系碩博士班
96
As digital audio and video databases increasing, how to effectively manage all the more important. CBIR (content based image retrieval) of video content-based image retrieval system. The images have to cut in CBIR system is also very important, all the Query image will implement this step. So my main goal is to make a picture from the background and prospects, than to make CBIR can improve efficiency and better results.
Jha, Nupur y Anupama Deo. "Development of Unsupervised methods for medical Image Segmentation". Thesis, 2012. http://ethesis.nitrkl.ac.in/3777/1/Thesis.pdf.
Texto completoWang, Chung Han y 王宗涵. "Unsupervised Image Segmentation using Multi-label Graph Cuts". Thesis, 2016. http://ndltd.ncl.edu.tw/handle/92616521540163396109.
Texto completo國立清華大學
資訊工程學系
104
Image segmentation is an important issue in image editing and computer vision. Due to the complexity of information in images, efficient extraction of a foreground object is a challenging problem. Recently, several approaches based on optimization by graph cuts have been developed which successfully combine the color feature with the edge information. A problem is that the segmentation results heavily depend on the seeds selection. However, it is difficult to obtaining reliable seeds automatically. To overcome this problem, we propose an automatic scheme for image segmentation. Compare to the classical binary-label graph cuts, the results by the multi-label graph cuts do not heavily depend on the seeds selection. Our method uses the multi-label graph cuts to separate an image into multiple segments, and then classify the segments into the object and the background. We introduce the standard deviation to adapt the importance between the properties in our method. Experiments show that the proposed method yields more accurate segmentation results than the previous automatic approach and is comparable to the interactive approach.
Chang, Yun Ling y 張芸菱. "Unsupervised Image Co-segmentation Based on Hierarchical Clustering". Thesis, 2013. http://ndltd.ncl.edu.tw/handle/34147949820314694152.
Texto completo國立清華大學
資訊工程學系
101
"Co-segmentation" can increase the accuracy of object recognition. The concept of co-segmentation is the problem of simultaneously dividing multiple images into common object, reference each other to segment similar region as an object. In recent years, the problem of image co-segmentation has been widely discussed. In our paper, we believe that each image pre-processing can be divided to many appropriate segments, and then co-segmentation will get the better results. At beginning, we segment each image into number of suerpixels, and extract their color histogram features. And we follow the concept of hierarchical clustering, we merge pair of superpixels which most similar with each other into one superpixel in each iteration until the appropriate threshold. This is not only ensure the superpixels which merge together have same material, but also effective in reducing the amount of computation. In addition, each superpixel records the maximum relative distance. The value can be used as a range to increase the accuracy of our co-matching method. Finally, we use GrowCut to get the final result. The results show that our method can not only achieve better results, but also we don’t need to add any setting, it is a good way for user that produces results automatically.
Lo, Chun-Kuei y 羅鈞魁. "Unsupervised Image Segmentation using Defocus Map and Superpixel Grouping". Thesis, 2015. http://ndltd.ncl.edu.tw/handle/82798630353284423628.
Texto completo國立清華大學
資訊工程學系
104
Image segmentation is an important and difficult issue in computer vision and image processing. It categorized into two categories, supervised image segmentation and unsupervised image segmentation. The supervised methods need some interactions of users. It makes those methods inconvenient. Recently, most of segmentation methods usually use similarity which is defined by color difference or histogram. Every similarity has its weak side. In this paper, we proposed an unsupervised method. It uses defocus map, edge and color as similarity of pixels or superpixels. We generate an edge strength map. Then, we construct a minimum spanning tree with the superpixels and the edge map to divide the image to foreground and background. In our experiment, out method doesn’t need user interaction and the performance is better than previous superpixels grouping method.
林柏辰. "Unsupervised Image Co-segmentation Based on Cooperative Game Theory". Thesis, 2012. http://ndltd.ncl.edu.tw/handle/59334501484723184113.
Texto completo國立清華大學
資訊工程學系
100
Co-segmentation is a new topic in computer vision, which has been discussed lively in many literatures. It is defined as the task of jointly segmenting the common objects in a given set of images. Due to there are some limitations in previous methods, this thesis presents a game theoretic unsupervised approach by using the concept of heat diffusion and saliency to solve co-segmentation problem without these limitations. Our method is divided into two stages. First, the common objects discovery task is modeled by a cooperative game. In this game, each image is treated as player. All players want to maximize the overall payoffs (i.e. the gain of heat) by putting the heat sources appropriately. Note that we must ensure that no one will be likely to uncooperative. So we define some collaborative strategies. For each input image, the game structure generates corresponding labeled image which identifies the common objects and background. Then we use cooperative cut to solve energy minimization problem in the second stage. Our method takes advantage of cooperative game theory, which enables us to discover the common objects automatically and accurately. Experimental results demonstrate that in many cases the proposed method can perform much better than state-of-the-art co-segmentation method.
Pradhan, Smita. "Development of Unsupervised Image Segmentation Schemes for Brain MRI using HMRF model". Thesis, 2010. http://ethesis.nitrkl.ac.in/2870/1/final.pdf.
Texto completoSu, Chieh-An y 蘇玠安. "Unsupervised Image Segmentation Using Sailency Map and Dark Channel Prior". Thesis, 2017. http://ndltd.ncl.edu.tw/handle/hh395n.
Texto completo國立清華大學
資訊系統與應用研究所
105
Image saliency detection is a process to pop out the most salient part in the image, and shows up with image saliency map. However, some image saliency maps are not accurate enough to separate foreground and background from images with low contrast; dark channel prior (DCP) can transform these image into a clear image. In this paper, we first apply DCP in image saliency detection to emphasize foreground from image with low contrast saliency. Moreover, we propose a simple cutting method on image saliency. We convert the saliency map into a histogram and use a first degree polynomial to smooth the histogram. The deepest and widest valley of the smoothed histogram is chosen as the cutting threshold. The part higher than threshold is identified as foreground, and the other is background. In our experiment, it proves that the proposed method successfully segments the foreground and background from the image.
Huang, De-Kai y 黃得凱. "Unsupervised Symmetrical Parts Detection for Image Objects Segmentation and Its Application". Thesis, 2014. http://ndltd.ncl.edu.tw/handle/66892171818399179694.
Texto completoWu, Yu-Shan y 吳玉善. "The Study of Unsupervised Anchorperson Image Detection for News Story Segmentation". Thesis, 2007. http://ndltd.ncl.edu.tw/handle/07733182729009383205.
Texto completo國立交通大學
資訊科學與工程研究所
95
Building an automatic system for news story segmentation is an important and challenging task. A news story is composed of an anchorperson shot and a news footage shot, we can segment a news video into several stories if we know when the anchorperson shows up. This paper presents a method for anchorperson detection based on face detection. First, detecting human faces region in every news frame. Then, extracting features by the face region, and clustering on all features. Suppose that the biggest cluster is presented for anchorperson。This method would not effected by the complex background because it focuses only on the face region. And because of its unsupervised nature, the algorithm does not need to adjust model for different anchorpersons. The efficacy of the proposed method is tested on 5 h of news programs. Moreover, we integrate the proposed method to an existed news video library system and segmenting on the ETT news programs successfully.
Hsu, Chih-Yu y 許芷瑜. "Incorporating Texture Information into Region-based Unsupervised Image Segmentation Using Superpixels". Thesis, 2013. http://ndltd.ncl.edu.tw/handle/55475958151383671949.
Texto completo國立交通大學
資訊科學與工程研究所
101
Recently, an unsupervised image segmentation framework, Segmentation by Aggregating Superpixels (SAS) [2] is proposed and shown to be very promising. However, the texture cues, which have been shown to be very effective in many researches [14-18], are absent in [2]. In this thesis, we propose an effective method for incorporating texture information into the SAS framework, using superpixels. To extract texture information, our algorithm first uses texture filtering and subsequent Gaussian Mixture Models (GMM) clustering which is modified from [16]. Then, we develop an edge-aware low-pass filtering to generate multiple-scale texture superpixels from GMM clustering results. Finally, by joining texture superpixels with the superpixel set originally used in [2], the incorporation of texture information is accomplished. Our method achieves superior performance on the well-known Berkeley Segmentation Dataset (BSDS) under multiple prevailing region-based evaluation criteria when compared to other benchmark algorithms.
Yeh, Hao-Wei y 葉浩瑋. "Unsupervised Hierarchical Image Segmentation Based on Bayesian Sequential Partitioning and Merging". Thesis, 2016. http://ndltd.ncl.edu.tw/handle/pz2rqy.
Texto completo國立交通大學
電子研究所
105
In this thesis, we present an unsupervised hierarchical clustering algorithm based on a split-and-merge scheme. Using image segmentation as an example of the applications, we propose an unsupervised image segmentation algorithm which outperforms the existing algorithms. In the split phase, we propose an efficient partition algorithm, named Just-Noticeable-Difference Bayesian Sequential Partitioning (JND-BSP), to partition image pixels into a few regions, within which the color variations are perceived to be smoothly changing without apparent color differences. In the merge phase, we proposed a Probability Based Sequential Merging algorithm to sequentially construct a hierarchical structure that represents the relative similarity among these partitioned regions. Instead of generating a segmentation result with a fixed number of segments, the new algorithm produces an entire hierarchical representation of the given image in a single run. This hierarchical representation is informative and can be very useful for subsequent processing, like object recognition and scene analysis. To demonstrate the effectiveness and efficiency of our method, we compare our new segmentation algorithm with several existing algorithms. Experiment results show that our new algorithm can not only offers a more flexible way to segment images but also provides segmented results close to human’s visual perception. The proposed algorithm can also be widely used on applications analyzing other types of data, and can be used to analyze Big Data with high dimension efficiently.
Brink, Anton David. "Image models and the definition of image entropy applied to the problem of unsupervised segmentation". Thesis, 2016. http://hdl.handle.net/10539/20956.
Texto completoRegion segmentation of digital imges by unsupervised thresholding is a common, conceptually simple and important branch of image processing and analysis. Its applications range from that of simple binarization to serving as a useful pre-processing stage for operations such as pattern recognition and image restoration. While many different algorithms have been proposed for the automatic selection of the "correct" threshold the results vary widely in their general usefulness. A class of selection schemes is based on the principle of maximum entropy. This formalism, While effective, is usually involed without reference to its origins or its relationship to images. This thesis attempts to clarify the definition of what is meant by the entropy of an image, to which end various image and Image segmentation models are discussed and proposed. Some apparent shortcomings related to the use of the Shannon entropy formula are addressed and the outcome of the research is applied to the problem of threshold selection. The results indicate a marked improvement in performance of methods using some form(s) of context-related information over those which simply apply the entropy formula without regard to its spatially insensitive nature. Evaluation of results and processes is usually based
Su, Chun-Rong y 蘇俊榮. "Unsupervised Image Segmentation by Dual Morphological Operations and Peer-to-Peer Content-Based Image Retrieval Applications". Thesis, 2014. http://ndltd.ncl.edu.tw/handle/526w75.
Texto completo國立臺灣科技大學
電機工程系
102
In this thesis, we proposed to perform content-based image retrieval (CBIR) on Internet scale databases connected through peer-to-peer (P2P) networks, abbreviated as P2P-CBIR, which utilizes an intelligent preprocessing to identify the object regions and provides scalable retrieval function. For preprocessing, we proposed a dual multiScalE Graylevel mOrphological open and close recoNstructions (SEGON) algorithm, and utilized edge coverage rate to segment foreground (FG) object regions in one image. To improve FG object segmentation accuracy, a background (BG) gray-level variation mesh is built. The SEGON was developed from a macroscopic perspective on image BG gray levels and implemented through regular procedures to deal with large-scale database images. To evaluate the segmentation accuracy, the probability of coherent segmentation labeling, i.e., the normalized probability random index (PRI), between a computer-segmented image and the hand-labeled one is computed for comparisons. Experiments showed that the proposed object segmentation method outperforms others in the PRI performance. The normalized correlation coefficient of features among query samples was computed to integrate the similarity ranks of different features in order to perform multi-instance queries with multiple features (MIMF). Retrieval precision–recall (PR) and rank performances, with and without SEGON, were compared. Performing SEGON-enabled CBIR on large-scale databases yields higher PR performance. For performing Internet scale CBIR, a P2P-CBIR system has been proposed, which helps to effectively explore the large-scale image database distributed over connected peers. The decentralized unstructured P2P network topology is adopted to compromise with the structured one, and informed-like instead of blind-like searching approach enables flexible routing control when peers join/leave or network fails. The P2P-CBIR adopts MIMF to reduce average network traffics while maintaining high retrieval accuracy on the query peer. In addition, scalable retrieval control can also be developed based on the P2P-CBIR framework, which can adapt the query scope and progressively refine the accuracy during the retrieval process. We also proposed to record instant local database characteristics of peers for the P2P-CBIR system to update peer linking information. By reconfiguring system at each regular interval time, we can effectively reduce trivial peer routing and retrieval operations due to imprecise configurations. We also proposed to optimally configure the P2P-CBIR system such that, under a certain number of online users, which would yield the highest recall rate. Experiments show that the average recall rate of the proposed P2P-CBIR method with reconfiguration is higher than that of the one without, and the latter outperforms previous methods, under the same retrieval scope, i.e., same time-to-live (TTL) settings. Furthermore, simulations demonstrate that, with the optimal configuration, recall rates can be improved while the network traffic of each peer is reduced, under the same number of on-line users.
Κωστόπουλος, Σπυρίδων. "Development of supervised and unsupervised pixel-based classification methods for medical image segmentation". Thesis, 2009. http://nemertes.lis.upatras.gr/jspui/handle/10889/1877.
Texto completoΣε σχέση με άλλες μορφές καρκίνου, ο καρκίνος του μαστού είναι μεταξύ των ευρέως μελετημένων τύπων καρκίνου, ωστόσο, υπάρχουν ακόμη σημαντικά ανοικτά ζητήματα προς διερεύνηση. Ένα από αυτά τα είναι ο προσδιορισμός της σπουδαιότητας ορισμένων βιολογικών παραγόντων, όπως ο βαθμός διαφοροποίησης της κακοήθειας (ΒΔΚ) του όγκου και το επίπεδο έκφρασης των Οιστρογονικών Υποδοχέων (ΟΥ), στην κλινική διαχείριση της νόσου. Μέχρι τώρα, η εκτίμηση του ΒΔΚ του όγκου και της έκφρασης των ΟΥ είναι βασισμένη στην οπτική αξιολόγηση ιστολογικών δειγμάτων, τα οποία λαμβάνονται από αντιπροσωπευτικές περιοχές του μαστού, στο μικροσκόπιο. Συγκεκριμένα, σύμφωνα με τις οδηγίες του Παγκόσμιου Οργανισμού Υγείας, ο ΒΔΚ του όγκου καθορίζεται από την οπτική εκτίμηση ορισμένων ιστολογικών χαρακτηριστικών γνωρισμάτων σε ιστολογικά δείγματα που έχουν υποστεί χρώση Αιματοξυλίνης - Ηωσίνης (Heamatoxylin & Eosin-Η&Ε), ενώ σύμφωνα με τις οδηγίες της Αμερικάνικης Εταιρείας Κλινικής Ογκολογίας, η έκφραση των ΟΥ πρέπει να εκτιμάται ως το εκατοστιαίο ποσοστό των εκφρασμένων πυρήνων σε δείγματα βαμμένα με ανοσοϊστοχημικές τεχνικές (Immunohistochemistry-IHC). Πρόσφατες μελέτες έχουν προσπαθήσει να εντοπίσουν εάν υπάρχει σύνδεση μεταξύ του ΒΔΚ του όγκου και της έκφρασης των ΟΥ στον όγκο, συσχετίζοντας τον ΒΔΚ από εικόνες με χρώση H&E με τον ποσοστό των εκφρασμένων ΟΥ σε δείγματα IHC. Αυτή η συσχέτιση φαίνεται να είναι σημαντική στις διάφορες ακολουθούμενες στρατηγικές για τη θεραπεία του καρκίνου του μαστού. Εντούτοις, ο προσδιορισμός της έκφρασης των ΟΥ παρουσιάζει ορισμένες αδυναμίες: α) υπάρχει σημαντική μεταβλητότητα μεταξύ των ειδικών σχετικά με το πρωτόκολλο που ακολουθείται για τον υπολογισμό της έκφρασης των ΟΥ, β) είναι δύσκολο να εκτιμηθεί με ακρίβεια η έκφραση των ΟΥ, δεδομένου ότι θα απαιτούσε τη μέτρηση του συνόλου των θετικά εκφρασμένων πυρήνων από τον ειδικό ιστοπαθολόγο. Στην κλινική πράξη, λαμβάνεται συνήθως μια χονδρική εκτίμηση από τον ιστοπαθολόγο, μέσω μικροσκοπίου, παρατηρώντας αντιπροσωπευτικές περιοχές των δειγμάτων όπου υπάρχει μεγάλη συγκέντρωση εκφρασμένων πυρήνων σε ΟΥ. Ως εκ τούτου, η αξιολόγηση της έκφρασης των ΟΥ, που έχει θεωρηθεί από προηγούμενες μελέτες ως βασική μέτρηση για τη συσχέτιση μεταξύ ΟΥ και του βαθμού διαφοροποίησης των όγκων, είναι επιρρεπής στην υποκειμενικότητα του ειδικού. Για τον λόγο αυτό απαιτούνται πιο αξιόπιστες μέθοδοι. Η παρούσα διατριβή πραγματοποιήθηκε σε αναζήτηση εναλλακτικών, πιο αξιόπιστων μεθόδων. Έτσι οι στόχοι της παρούσας διατριβής είναι: (i) η ανάπτυξη μιας αξιόπιστης μεθοδολογίας τμηματοποίησης ιστολογικών εικόνων μικροσκοπίας επεξεργασμένες με χρώση IHC για τον εντοπισμό των πυρήνων που εκφράζουν τους ΟΥ για την αντικειμενική ποσοτικοποίηση της έκφρασης των ΟΥ στον καρκίνο του μαστού, (ii) η διερεύνηση ενδεχόμενης σχέσης μεταξύ της έκφρασης των ΟΥ και του ΒΔΚ του όγκου, συνδυάζοντας την πληροφορία των ιστολογικών δειγμάτων, που προέρχονται από τον καρκινικό ιστό του ίδιου ασθενούς και έχουν υποστεί επεξεργασία με ανοσοϊστοχημική χρώση και με χρώση H&E, (iii) η διερεύνηση πιθανής συσχέτισης στις μεταβολές της υφής της χρωματίνης με τις μεταβολές στην υφή των πυρήνων που εκφράζουν τους ΟΥ, και (iv) η διερεύνηση της δυνατότητας της προτεινόμενης μεθοδολογίας σε άλλους τομείς επεξεργασίας και ανάλυσης ιατρικών εικόνων. Για την εκπλήρωση των ανωτέρω στόχων και σε αναζήτηση αξιόπιστων μεθόδων για την ποσοτικοποίηση της έκφρασης των ΟΥ και της σύνδεσή της με το ΒΔΚ του όγκου, σχεδιάστηκε, αναπτύχθηκε και εφαρμόστηκε μια νέα μεθοδολογία βασισμένη στην αναγνώριση προτύπων ημι-εποπτευόμενης μάθησης για την ανάλυση ιστοπαθολογικής εικόνας. Επιπλέον, η κατάλληλη τροποποίηση της προτεινόμενης μεθόδου μπορεί να οδηγήσει στη γενίκευση της μεθοδολογικής προσέγγισης της ταξινόμησης εικονοστοιχείων για την επεξεργασία και την ανάλυση ιατρικών εικόνων, πέρα αυτών της μικροσκοπίας, όπως εικόνες από Aγγειογραφία Υπολογιστικής Τομογραφίας.
Shih, Hsueh-Fu y 施學甫. "Segmentation of wound image and optimization based on genetic algorithm and unsupervised evaluation". Thesis, 2017. http://ndltd.ncl.edu.tw/handle/496kb4.
Texto completo國立臺灣大學
生醫電子與資訊學研究所
105
After the surgery being taken, the after care of the surgical wound has a great impact toward the patients’ prognosis. It’s often takes few days even few weeks for the wound to stabilize. It’s is a great cost of health care and nursing resources. The advance of image process and machine learning improves the accuracy of wound assessment and analysis and there are some recent works started on this field of wound analysis. In our tele-health scenario, we hope the user can use their mobile device to obtain an accurate result without using high-end camera. In this literature, we proposed an image segmentation algorithm based on edge detection and Hough transform. We further developed an optimization method based on unsupervised image segmentation evaluation and genetic algorithm. The result was evaluated by the image provided by NTUH, division of surgery. We also implemented an analysis system cooperate with NTUH telehealth center, which has been used on pacemaker implantation patient. The result of performing this segmentation algorithm on the data set provided by NTUH, Division of cardiovascular surgery, achieve the accuracy of 75.7%, after the optimization of genetic algorithm it achieves 94.3%.
Zheng, Hongwei [Verfasser]. "Bayesian learning and regularization for unsupervised image restoration and segmentation / vorgelegt von Hongwei Zheng". 2007. http://d-nb.info/985294930/34.
Texto completoLi, Kun-Lung y 李昆龍. "A Study of Unsupervised Image Segmentation of Cervical Cancer based on Self-organizing map". Thesis, 2012. http://ndltd.ncl.edu.tw/handle/41633817933735768332.
Texto completo朝陽科技大學
資訊管理系碩士班
100
The morbidity and mortality of cervical cancer can be reduced by the screening of the precancerous lesions. Pap smears, colposcopy and biopsy are the most common screening tools. Pap smear is the first-line tool because of its high specificity and low cost. But its false-positive rate is too high and must be confirmed by other tools. Biopsy is a deterministic examination for cervical neoplasia. However, it is not suitable for the high probability of false-positive. Digital colposcopy is a promising technology for the detection of cervical intraepithelial neoplasia. However, there are no quantitative criteria for the differential of precancerous lesions and it is subjected to the variation of inter-observer and intra-observer. Therefore, automated image analysis of colposcopic images is thus necessary for the improvement of diagnosis of colposcopy. The segmenetation of the lession from digital colposcopyic image is a key issue of this analysis. Our goal is to develop a segmentation policy that can separate images into regions which contain the lesion areas. These areas can be provided to the analysis system and help doctor to make the diagnosis.
Lee, Yong Jae 1984. "Visual object category discovery in images and videos". Thesis, 2012. http://hdl.handle.net/2152/ETD-UT-2012-05-5381.
Texto completotext
Mateen, Syed Abdul. "Sequential Extraction Thresholding Clustering for Segmentation of Coastal Upwelling on Sea Surface Temperature Images". Master's thesis, 2017. http://hdl.handle.net/10362/29116.
Texto completoOmran, Mahamed G. H. "Particle swarm optimization methods for pattern recognition and image processing". Thesis, 2005. http://hdl.handle.net/2263/29826.
Texto completoThesis (PhD)--University of Pretoria, 2006.
Computer Science
unrestricted
Δασκαλάκης, Αντώνιος. "Optimization of cDNA microarray image analysis methods". Thesis, 2009. http://nemertes.lis.upatras.gr/jspui/handle/10889/2957.
Texto completoΗ έκφραση της γενετικής πληροφορίας, σε όλους τους οργανισμούς, χαρακτηρίζεται από μια σταθερή κατάσταση «ροής» στην οποία όμως μόνο ένα μέρος του γονιδίου μέσα στο γονιδίωμα (genome) εκφράζεται ανά χρονική στιγμή. Το γονιδιακό μοτίβο έκφρασης (gene expression pattern or gene expression profile) θα μπορούσαμε να πούμε ότι αντανακλά την αντίδραση των κυττάρων στα διάφορα εξωτερικά ερεθίσματα. Για να μπορέσουν να απαντηθούν ερωτήματα σχετικά με τους μηχανισμούς που επηρεάζουν και μεταβάλλουν τη γονιδιακή έκφραση ανάλογα με το εξωτερικό ερέθισμα είναι απαραίτητη η μελέτη της γονιδιακής έκφρασης σε μεταγραφικό επίπεδο (transcription level) ή/και άλλα στάδια (παράγοντες) που ρυθμίζουν τη γονιδιακή έκφραση (gene regulation) σε επίπεδο mRNA. Με τη χρήση της τεχνολογίας των μικροσυστοιχιών, οι ερευνητές έχουν πλέον τη δυνατότητα να μελετήσουν ταυτόχρονα την γονιδιακή έκφραση δεκάδων ή και εκατοντάδων χιλιάδων γονιδίων σε ιστούς, κύτταρα όγκους κλπ με τη χρήση ενός και μόνο πειράματος. Κατά συνέπεια, και από τη στιγμή που τα γονιδιακά μοτίβα έκφρασης συσχετίζονται έντονα λειτουργικά (functionally correlated), η τεχνολογία των μικροσυστοιχιών παρέχει ανεκτίμητης αξίας πληροφορίες που μπορούν να δώσουν ώθηση τόσο στην ανάπτυξη της βασικής έρευνας π.χ. μελέτη των γονιδιακών προφίλ έκφρασης διαφορετικών ιστών όσο και στην ανάπτυξη της εφαρμοσμένης έρευνας π.χ. μελέτη ασθενειών, δράση φαρμάκων και ορμονών κλπ. Παρά τη δυνατότητα που παρέχει η τεχνολογία των μικροσυστοιχιών για την ταυτόχρονη μέτρηση των επιπέδων έκφρασης χιλιάδων γονιδίων, η ποσοτικοποίηση της γονιδιακής έκφρασης (δηλ. η εξαγωγή των επιπέδων έκφρασης των γονιδίων), επηρεάζεται από τους διάφορους τύπους θορύβου που υπεισέρχονται τόσο κατά την πειραματική διαδικασία κατασκευής των μικροσυστοιχιών (π.χ. προετοιμασία δειγμάτων) όσο και από τα πιθανοκρατικά χαρακτηριστικά που διέπουν τη διαδικασία ανίχνευσης (microarray scanning procedure) των μικροσυστοιχιών (π.χ. λάθη ανίχνευσης). Η «θορυβώδης» φύση των γονιδίων και κατά συνέπεια των μετρούμενων γονιδιακών εκφράσεων «κρύβει» (obscure) μερικά από τα πιο σημαντικά χαρακτηριστικά των βιολογικών διαδικασιών ενδιαφέροντος και καθιστά δύσκολη την εξαγωγή χρήσιμων βιολογικών συμπερασμάτων. Από τα παραπάνω διαφαίνεται ότι η μείωση του θορύβου είναι μια πολύ σημαντική διαδικασία η οποία θα πρέπει να ενσωματωθεί στην αλγοριθμική μεθοδολογία που μέχρι στιγμής χρησιμοποιείται για την εξαγωγή των γονιδιακών εκφράσεων από τις εικόνες μικροσυστοιχιών. Με αυτό τον τρόπο θα ελαχιστοποιηθούν τα πιθανά «λάθη» τα οποία μεταφέρονται (propagate) κατά τη διαδικασία εξαγωγής των εντάσεων (μέσω της χρησιμοποιούμενης αλγοριθμικής μεθοδολογίας) και τελικά επηρεάζουν την «ακριβή» εξαγωγή των γονιδιακών εκφράσεων. ‘Ως πιθανή λύση για την αντιμετώπιση του θορύβου στις εικόνες μικροσυστοιχιών, έχει προταθεί στη διεθνή βιβλιογραφία η χρήση τεχνικών αναβάθμισης εικόνας. Τα αποτελέσματα αυτών των επιστημονικών εργασιών συμπεραίνουν ότι με τη χρήση τεχνικών αναβάθμισης η ποιότητα των επεξεργασμένων εικόνων είναι σαφώς καλύτερη. Ωστόσο, καμία από αυτές τις εργασίες δεν μελετάει εάν οι τεχνικές αναβάθμισης οδηγούν στον ακριβέστερο προσδιορισμό των παρυφών των κουκίδων (spot) από τις οποίες εξάγονται οι γονιδιακές εκφράσεις ή εάν βοηθάνε στη μείωση της μεταβλητότητας (variability) των εξαγόμενων γονιδιακών εκφράσεων. Επιπρόσθετα, όπως έχει ήδη προαναφερθεί, ο θόρυβος παρεμποδίζει την εξαγωγή χρήσιμων βιολογικών συμπερασμάτων. Παρά το μεγάλο πλήθος εξελιγμένων μεθόδων που έχουν προταθεί στη διεθνή βιβλιογραφία για την αποτροπή της ομαδοποίησης γονιδίων που χαρακτηρίζονται ως «θορυβώδη», δεν έχει καθοριστεί ακόμα (από τους ειδικούς) μια ενιαία μέθοδος που να βρίσκει και να ομαδοποιεί γονίδια τα οποία θα παρέχουν βιολογικά χρήσιμες πληροφορίες. Αποτέλεσμα αυτής της «ασυμφωνίας» μεταξύ των ειδικών αποτελεί η εξαγωγή διαφορετικών βιολογικών συμπερασμάτων ανάλογα α) με τον αριθμό των δημιουργούμενων γονιδιακών ομάδων (που εξαρτάται άμεσα από τη διαφορετική μέθοδο ομαδοποίησης (clustering)) και β) με τις διαφοροποιήσεις που μπορεί να έχουμε στις παραμέτρους των διαφόρων μεθόδων ομαδοποίησης. H παρούσα διατριβή στοχεύει στη δημιουργία ενός ολοκληρωμένου πλαισίου για την επεξεργασία και ανάλυση εικόνων μικροσυστοιχιών με σκοπό την βελτιστοποίηση της εξαγωγής και κατά συνέπεια της ποσοτικοποίησης των γονιδιακών εντάσεων από εικόνες μικροσυστοιχιών κουκίδων (spotted cDNA microarray images). Οι στόχοι της παρούσας διατριβής συνοψίζονται ως εξής: α) μοντελοποίηση και περιορισμός των επιδράσεων του θορύβου σε εικόνες μικροσυστοιχιών κουκίδων κατά τέτοιο τρόπο ώστε να αυξηθεί η ακρίβεια των εξαγόμενων γονιδιακών εκφράσεων, β) μελέτη της επίδρασης του θορύβου και βελτιστοποίηση των μεθόδων ανάλυσης των γονιδιακών εκφράσεων με σκοπό τη διευκόλυνση των βιολόγων στην εξαγωγής βιολογικών συμπερασμάτων και την καλύτερη κατανόηση της βιολογικής διεργασίας που μελετάται, γ) εισαγωγή ενός ημιεποπτευόμενου (semi-supervised) κριτηρίου που στηριζόμενο σε βιολογικές πληροφορίες θα αποσκοπεί στην ανεύρεση βιολογικά σημαντικών ομάδων γονιδίων τα οποία ταυτόχρονα θα απαντούν σε συγκεκριμένα βιολογικά ερωτήματα ,δ) μελέτη της επίδρασης και της απόδοσης διαφόρων τεχνικών κατάτμησης εικόνων μικροσυστοιχιών κουκίδων, τόσο ανωτάτου επιπέδου (state-of-art) όσο και νέων, στην ποσοτικοποίηση γονιδιακών εκφράσεων. Για την πραγματοποίηση των παραπάνω στόχων σχεδιάστηκε και κατασκευάστηκε μια πλήρως δομημένη μεθοδολογία (a complete and robust framework) που περιελάμβανε αλγοριθμους επεξεργασίας και ανάλυσης εικόνας κουκίδων μικροσυστοιχιών Η προτεινόμενη μεθοδολογία ενσωμάτωσε στην ήδη υπάρχουσα αλγοριθμική μεθοδολογία (microarray analysis pipeline) έναν πρωτότυπο συνδυασμό τεχνικών επεξεργασίας και ανάλυσης εικόνας βασισμένο στην εις βάθος ποσοτική έρευνα της επίδρασης του θορύβου στην κατάτμηση κουκίδων (spot segmentation), στην εξαγωγή εντάσεων και στην εξόρυξη δεδομένων (data mining). Επιπρόσθετα, κατά την παρούσα διατριβή προτάθηκαν, κατασκευάστηκαν και αξιολογήθηκαν νέες τεχνικές κατάτμησης εικόνας από μικροσυστοιχές κουκίδων. Η χρησιμότητα των προτεινόμενων μεθοδολογιών αξιολογήθηκε τόσο σε εικονικές (simulated) όσο και σε πραγματικές εικόνες μικροσυστοιχιών κουκίδων.
Nandaia, Morna. "Os Sistemas de Informação Geográfica e Detecção Remota na Determinação das Regiões de Risco por Malária na Guiné-Bissau". Master's thesis, 2015. http://hdl.handle.net/10362/15892.
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