In 2012, more than 1.6 million new cases of breast cancer were diagnosed and about half a million women died of breast cancer. The incidence has increased in the developing world. The mortality, however, has decreased. This is thought to partly be the result of advances in diagnosis and treatment. Studying tissue samples from biopsies through a microscope is an important part of diagnosing breast cancer. Recent techniques include camera-equipped microscopes and whole slide scanning systems that allow for digital high-throughput scanning of tissue samples. The introduction of digital pathology has simplified parts of the analysis, but manual interpretation of tissue slides is still labor intensive and costly, and involves the risk for human errors and inconsistency. Digital image analysis has been proposed as an alternative approach that can assist the pathologist in making an accurate diagnosis by providing additional automatic, fast and reproducible analyses. This thesis addresses the automation of conventional analyses of tissue, stained for biomarkers specific for the diagnosis of breast cancer, with the purpose of complementing the role of the pathologist. In order to quantify biomarker expression, extraction and classification of sub-cellular structures are needed. This thesis presents a method that allows for robust and fast segmentation of cell nuclei meeting the need for methods that are accurate despite large biological variations and variations in staining. The method is inspired by sparse coding and is based on dictionaries of local image patches. It is implemented in a tool for quantifying biomarker expression of various sub-cellular structures in whole slide images. Also presented are two methods for classifying the sub-cellular localization of staining patterns, in an attempt to automate the validation of antibody specificity, an important task within the process of antibody generation.  In addition, this thesis explores methods for evaluation of multimodal data. Algorithms for registering consecutive tissue sections stained for different biomarkers are evaluated, both in terms of registration accuracy and deformation of local structures. A novel region-growing segmentation method for multimodal data is also presented. In conclusion, this thesis presents computerized image analysis methods and tools of potential value for digital pathology applications.

Automated tissue image analysis aims to develop algorithms for a variety of histological applications. This has important implications in the diagnostic grading of cancer such as in breast and prostate tissue, as well as in the quantification of prognostic and predictive biomarkers that may help assess the risk of recurrence and the responsiveness of tumors to endocrine therapy. In this thesis, we use pattern recognition and image analysis techniques to solve several problems relating to histopathology and immunohistochemistry applications. In particular, we present a new method for the detection and localization of tissue microarray cores in an automated manner and compare it against conventional approaches. We also present an unsupervised method for color decomposition based on modeling the image formation process while taking into account acquisition noise. The method is unsupervised and is able to overcome the limitation of specifying absorption spectra for the stains that require separation. This is done by estimating reference colors through fitting a Gaussian mixture model trained using expectation-maximization. Another important factor in histopathology is the choice of stain, though it often goes unnoticed. Stain color combinations determine the extent of overlap between chromaticity clusters in color space, and this intrinsic overlap sets a main limitation on the performance of classification methods, regardless of their nature or complexity. In this thesis, we present a framework for optimizing the selection of histological stains in a manner that is aligned with the final objective of automation, rather than visual analysis. Immunohistochemistry can facilitate the quantification of biomarkers such as estrogen, progesterone, and the human epidermal growth factor 2 receptors, in addition to Ki-67 proteins that are associated with cell growth and proliferation. As an application, we propose a method for the identification of paired antibodies based on correlating probability maps of immunostaining patterns across adjacent tissue sections. Finally, we present a new feature descriptor for characterizing glandular structure and tissue architecture, which form an important component of Gleason and tubule-based Elston grading. The method is based on defining shape-preserving, neighborhood annuli around lumen regions and gathering quantitative and spatial data concerning the various tissue-types.

Submitted by Ricardo Cedraz Duque Moliterno (ricardo.moliterno@uefs.br) on 2016-09-13T21:44:53Z No. of bitstreams: 1 Disserta??o_George.pdf: 4996097 bytes, checksum: ece2301b72ccb1d9d33a2e2837531079 (MD5) / Made available in DSpace on 2016-09-13T21:44:53Z (GMT). No. of bitstreams: 1 Disserta??o_George.pdf: 4996097 bytes, checksum: ece2301b72ccb1d9d33a2e2837531079 (MD5) Previous issue date: 2016-02-29 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior - CAPES / The realization of an accurate diagnosis from histological images requires pathologists with practical experience because the characteristics of these images lead to a subjective analysis, which often hamper the accuracy of diagnosis. Systems that help to achieve better diagnoses can minimize doubts and improve the quality of diagnosis, influencing on increasing the effectiveness of medical treatments. This paper describes the research and development of PathoSpotter, a computer system to aid in the identification of diseases from histological images. The PathoSpotter proposes to reduce the lack of support work to histopathological diagnosis of renal diseases since much has been done in the area of cancer, but there is few published material in relation to the Digital Pathology applied to nephrology and hepatology. Our goal in this study was to apply the PathoSpotter the classification of proliferative glomerulopathy, which is a family of primary diseases affecting the kidneys. The work was based on a data set consisting of 811 histological pictures glomeruli and classical techniques of processing digital images and histopathology were used. The PathoSpotter presented a performance of 88.4% accuracy, which was similar to other Digital Pathology jobs that can be found in the literature. / A realiza??o do diagn?stico preciso a partir de imagens histol?gicas requer m?dicos patologistas com vasta experi?ncia pr?tica, pois as caracter?sticas dessas imagens conduzem a uma an?lise subjetiva que muitas vezes dificultam a exatid?o do diagn?stico. Sistemas que auxiliam a obten??o de melhores diagn?sticos podem minimizar d?vidas e melhorar a qualidade dos diagn?sticos, influenciando no aumento da efic?cia dos tratamentos m?dicos. Este trabalho descreve a pesquisa e o desenvolvimento do PathoSpotter, um sistema computacional para aux?lio na identifica??o de patologias a partir de imagens histol?gicas. O PathoSpotter se prop?e a reduzir a car?ncia de trabalhos de apoio ao diagn?stico histopatol?gico das doen?as renais, j? que muito tem sido feito na ?rea de neoplasias, mas h? pouco material publicado em rela??o ? Patologia Digital aplicada ? nefrologia ou hepatologia. Nosso objetivo neste trabalho foi aplicar o PathoSpotter na classifica??o das glomerulopatias proliferativas, que ? uma fam?lia de doen?as prim?rias que afetam os rins. O trabalho se baseou em um conjunto de dados composto por 811 imagens histol?gicas de glom?rulos, e foram utilizadas t?cnicas cl?ssicas de processamento de imagens e histopatologia digital. O PathoSpotter apresentou um desempenho de 88,4% de acur?cia, resultado similar ao de outros trabalhos de Patologia Digital que podem ser encontrados na literatura especializada.