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Master's Dissertation
DOI
Document
Author
Full name
Antonio Alex Lopes
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
Ribeirão Preto, 2019
Supervisor
Committee
Felipe, Joaquim Cezar (President)
Murta Junior, Luiz Otavio
Ribeiro, Marcela Xavier
Soares, Edson Garcia
Title in Portuguese
Processamento e análise de imagens histológicas de pólipos para o auxílio ao diagnóstico de câncer colorretal
Keywords in Portuguese
Pólipo intestinal
Processamento de imagens médicas
Redes complexas
Segmentação de imagens
Abstract in Portuguese
Segundo o Instituto Nacional do Câncer (INCA), o câncer de colorretal é o terceiro tipo de câncer mais comum entre os homens e o segundo entre as mulheres. Atualmente a avaliação visual feita por um patologista é o principal método utilizado para o diagnóstico de doenças a partir de imagens microscópicas obtidas por meio de amostras em exames convencionais de biópsia. A utilização de técnicas de processamento computacional de imagens possibilita a identificação de elementos e a extração de características, o que contribui com o estudo da organização estrutural dos tecidos e de suas variações patológicas, levando a um aumento da precisão no processo de tomada de decisão. Os conceitos e técnicas envolvendo redes complexas são recursos valiosos para o desenvolvimento de métodos de análise estrutural de componentes em imagens médicas. Dentro dessa perspectiva, o objetivo geral deste trabalho foi o desenvolvimento de um método capaz de realizar o processamento e a análise de imagens obtidas em exames de biópsias de tecidos de pólipo de cólon para classificar o grau de atipia da amostra, que pode variar em: sem atipia, baixo grau, alto grau e câncer. Foram utilizadas técnicas de processamento, incluindo um conjunto de operadores morfológicos, para realizar a segmentação e a identificação de estruturas glandulares. A seguir, procedeu-se à análise estrutural baseada na identificação das glândulas, usando técnicas de redes complexas. As redes foram criadas transformado os núcleos das células que compõem as glândulas em vértices, realizando a ligação dos mesmos com 1 até 20 arestas e a extração de medidas de rede para a criação de um vetor de características. A fim de avaliar comparativamente o método proposto, foram utilizados extratores clássicos de características de imagens, a saber, Descritores de Haralick, Momentos de Hu, Transformada de Hough, e SampEn2D. Após a avaliação do método proposto em diferentes cenários de análise, o valor de acurácia geral obtida pelo mesmo foi de 82.0%, superando os métodos clássicos. Conclui-se que o método proposto para classificação de imagens histológicas de pólipos utilizando análise estrutural baseada em redes complexas mostra-se promissor no sentido de aumentar a acurácia do diagnóstico de câncer colorretal
Title in English
Processing and analysis of histological images of polyps to aid in the diagnosis of colorectal cancer
Keywords in English
Complex networks
Image segmentation
Intestinal polyp
Medical image processing
Abstract in English
According to the National Cancer Institute (INCA), colorectal cancer is the third most common cancer among men and the second most common cancer among women. Currently the main method used for the diagnosis of diseases from microscopic images obtained through samples in conventional biopsy tests are the visual evaluation made by a pathologist. The use of computational image processing techniques allows the identification of elements and the extraction of characteristics, which contributes to the study of the structural organization of tissues and their pathological variations, leading to an increase of precision in the decision making process. Concepts and techniques involving complex networks are valuable resources for the development of structural analysis methods of components in medical images. In this perspective, the general objective of this work was the development of a method capable of performing the image processing and analysis obtained in biopsies of colon polyp tissue to classify the degree of atypia of the sample, which may vary in: without atypia, low grade, high grade and cancer. Processing techniques including a set of morphological operators, were used to perform the segmentation and identification of glandular structures. Next, structural analysis was performed based on glands identification, using complex network techniques.The networks were created transforming the core of the cells that make up the glands in vertices, making the connection of the same with 1 to 20 edges and the extraction of network measurements to create a vector of characteristics. In order to comparatively evaluate the proposed method, classical image characteristic extractors were used, namely, Haralicks Descriptors, Hus Moments, Hough Transform, and SampEn2D. After the evaluation of the proposed method in different analysis scenarios, the overall accuracy value obtained by it was 82.0%, surpassing the classical methods. It is concluded that the proposed method for the classification of histological images of polyps using structural analysis based on complex networks is promising in order to increase the accuracy of the diagnosis of colorectal cancer
 
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Publishing Date
2019-07-02
 
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