The aim of the thesis was familiarity of segmentation methods for automatic segmentation of MR images, using multiparametrical display. The theoretical part focuses on the description of methods of segmentation techniques. In the practical part are implemented K-means and level-set method. The methods are tested on the images of the brain obtained by different sequences (T1, T1c, T2, FLAIR). Segmentation methods are implemented in the program MATLAB. Implemented segmentation accuracy is demonstrated on data which there are reports reference results. Evaluation methods is performed using different classifiers decision. The K-means method is tested different metrics and different combinations of the input image. Finally, both methods are compared with one another and visually evaluated against the reference image.

A patient-specific model of the liver can supply accurate volume measurements for oncologists and lesion locations and liver visualisation for surgeons. Our work seeks to enable an automatic computational tool for liver quantification. To create this model, the liver shape must be segmented from 3D CT images. In doing so, we can quantify liver volume and restrict the region of interest to ease the task of tumour and vascular segmentation. The main objective of liver segmentation developed into a mission to fluently describe liver shape a priori in level-set methods. This thesis looks at the utility of an implicit shape representation based on the Poisson equation to describe highly variable shapes, with application to image segmentation. Our first contribution is analyses on four implicit shape representations based on the heat equation, the signed distance function, Poisson’s equation, and the logarithm of odds. For four separate shape case studies, we summarise the class of shapes through their shape representation using Principal Component Analysis (PCA). Each shape class is highly variable across a population, but have a characteristic structure. We quantitatively compare the implicit shape representations, within each class, by evaluating its compactness, and in the last case, also completeness. To the best of our knowledge, this study is novel in comparing several shape representations through a single dimension reduction method. Our second contribution is a hybrid region-based level set segmentation that simultaneously infers liver shape given the image data, integrates the Poisson-based shape function prior into the segmentation, and evolves the level set according to the image data. We test our algorithm on exemplary 2D liver axial slices. We compare results for each image to results from (a) level-set segmentation without a shape prior and (b) level-set segmentation with a shape prior based on the Signed Distance Transform (SDT). In both priors, shapes are projected from shape space through the sample population mean and its modes of variation (the minimum number of principal components to comprise at least 95% of the cumulative variance). We compare results on four individual cases using the Dice coefficient and the Hausdorff distance. This thesis introduces an implicit shape representation based on Poisson’s equation in the field of medical image segmentation, showing its influence on shape space summary and projection. We analyse the shape space for compactness, showing that it is more compact in each of our case studies by at least two-fold and as much as three-fold. For 3D liver shapes, we show that it is more complete than the other three implicit shape representations. We utilise its description efficiency for use in 2D liver image segmentation, implementing the first shape function prior based on the Poisson equation. We show a qualitative and quantitative improvement over segmentation results without any shape prior and comparable results to segmentation with a SDT shape prior.

Quantifying tissue volumes in pediatric brains from magnetic resonance (MR) images can provide insight into etiology and onset of neurological disease. Unbiased volumetric analysis can be applied to large population studies when automated image processing is possible. Standard segmentation strategies using adult atlases fail to account for varying tissue contrasts and types associated with the rapid growth and maturational changes seen in early neurodevelopment. The goal of this project was to develop an automated pipeline and two age-specific atlases capable of providing accurate tissue classification despite these challenges. The automated pipeline consisted of a stepwise initial atlas-to-subject registration, expectation maximization (EM) atlas based segmentation, and a post-processing level set segmentation for improved white/gray matter separation. This level set segmentation is a 3D and multiphase adaptation of a 2D method intended for use on images with the types of intensity Inhomogeneities found in MR images. The initial tissue maps required to determine spatial priors for the one-year-old atlas were created by manually cleaning the results of an adult atlas and the automated pipeline. Additional tissue maps were incrementally added until the spatial priors were sufficiently representative. The neonate atlas was similarly created, starting with the one-year-old atlas.

abstract: Thousands of high-resolution images are generated each day. Segmenting, classifying, and analyzing the contents of these images are the key steps in image understanding. This thesis focuses on image segmentation and classification and its applications in synthetic, texture, natural, biomedical, and industrial images. A robust level-set-based multi-region and texture image segmentation approach is proposed in this thesis to tackle most of the challenges in the existing multi-region segmentation methods, including computational complexity and sensitivity to initialization. Medical image analysis helps in understanding biological processes and disease pathologies. In this thesis, two cell evolution analysis schemes are proposed for cell cluster extraction in order to analyze cell migration, cell proliferation, and cell dispersion in different cancer cell images. The proposed schemes accurately segment both the cell cluster area and the individual cells inside and outside the cell cluster area. The method is currently used by different cell biology labs to study the behavior of cancer cells, which helps in drug discovery. Defects can cause failure to motherboards, processors, and semiconductor units. An automatic defect detection and classification methodology is very desirable in many industrial applications. This helps in producing consistent results, facilitating the processing, speeding up the processing time, and reducing the cost. In this thesis, three defect detection and classification schemes are proposed to automatically detect and classify different defects related to semiconductor unit images. The first proposed defect detection scheme is used to detect and classify the solder balls in the processor sockets as either defective (Non-Wet) or non-defective. The method produces a 96% classification rate and saves 89% of the time used by the operator. The second proposed defect detection scheme is used for detecting and measuring voids inside solder balls of different boards and products. The third proposed defect detection scheme is used to detect different defects in the die area of semiconductor unit images such as cracks, scratches, foreign materials, fingerprints, and stains. The three proposed defect detection schemes give high accuracy and are inexpensive to implement compared to the existing high cost state-of-the-art machines. / Dissertation/Thesis / Ph.D. Electrical Engineering 2010

This work is rooted in a need for dermatologists to explore skin characteristicsin depth. The inuence of skin disease such as acne in dermal tissues is stilla complex task to assess. Among the possibilities, high frequency ultrasoundimaging is a paradigm shift to probe and characterizes upper and deep dermis.For this purpose, a cohort of 58 high-frequency 3D images has been acquiredby the French laboratory Pierre Fabre in order to study acne vulgaris disease.This common skin disorder is a societal challenge and burden aecting late adolescentsacross the world. The medical protocol developed by Pierre Fabre wasto screen a lesion every day during 9 days for dierent patients with ultrasoundimaging. The provided data features skin epidermis and dermis structure witha fantastic resolution. The strategy we led to study these data can be explainedin three steps. First, epidermis surface is detected among artifacts and noisethanks to a robust level-set algorithm. Secondly, acne spots are located on theresulting height map and associated to each other among the data by computingand thresholding a local variance. And eventually potential inammatorydermal cavities related to each lesion are geometrically and statistically characterizedin order to assess the evolution of the disease. The results presentan automatic algorithm which permits dermatologists to screen acne vulgarislesions and to characterize them in a complete data set. It can hence be a powerfultoolbox to assess the eciency of a treatment. / Detta arbete är grundat i en dermatologs behov att undersöka hudens egenskaperpå djupet. Påverkan av hudsjukdomar så som acne på dermala vävanderär fortfarande svårt att bedöma. Bland möjligheterna är högfrekvent ultraljudsavbildningett paradigmskifte för undersökning och karakterisering av övre ochdjupa dermis. I detta syfte har en kohort av 58 högfrekventa 3D bilder förvärvatsav det Franska laboratoriet Pierre Fabre för att studera sjukdomen acne vulgaris.Denna vanliga hudsjukdom är en utmaning för samhället och en bördasom påverkar de i slutet av tonåren över hela världen. Protokollet utvecklatav Pierre Fabre innebar att undersöka en lesion varje dag över 9 dagar förolika patienter med ultraljudavbildning. Den insamlade datan visar hudens epidermisoch dermis struktur med en fantastiskt hög upplösning. Strategin vianvände för att studera denna data kan förklaras i tre steg. För det första,hittas epidermis yta bland artifakter och brus tack vare en robust level-set algoritm.För det andra, acne äckar hittas på höjdkartan och associeras tillvarandra bland mätdatan genom en tröskeljämförelse över lokala variationer.Även potentiellt inammatoriska dermala hålrum relaterade till varje lesion blirgeometriskt ochj statistiskt kännetecknade för att bedöma sjukdomens förlopp.Resultaten framför en automatisk algoritm som gör det möjligt för dermatologeratt undersöka acne vulgaris lesioner och utmärka de i ett dataset. Detta kandärmed vara en kraftfull verktygslåda för att undersöka inverkan av en behandlingtill denna sjukdom.