Abstract:
In the past two decades, automatic colon cancer detection has become an active research
area. Traditionally, colon cancer is diagnosed using microscopic analysis of pathological
tissue imagery. However, the process is subjective and leads to considerable inter/intra
observer variation in diagnosis. Therefore, reliable computer-aided colon cancer diagnostic
systems are in high demand. In this thesis, a computer-aided colon cancer diagnostic (CAD)
system has been proposed that comprises three main phases.
In the rst phase, an unsupervised colon biopsy image segmentation technique, which is
based on a few novel extensions in traditional object oriented texture analysis based segmentation
technique, has been developed. The second phase deals with classi cation of
colon image and gene based datasets into normal and malignant classes. For the colon
biopsy image based datasets, two classi cation techniques based on hybridization of various
features have been proposed. In these techniques, some traditional features such as
morphological and texture, variants of traditional features, and some novel features which
have especially been designed to capture the variation between normal and malignant colon
tissues have been used. Similarly, for the gene expression based dataset, a novel technique
that utilizes various feature selection strategies for solving the challenging problem of larger
dimensionality of gene based datasets, and a weighted majority voting based ensemble of
various SVM classi ers for performance improvement has been proposed.
In the third phase of this work, the structural variation in the shape of lumen among various
colon cancer grades has been quanti ed in terms of a few novel structural features.
These features are used for the classi cation of malignant colon biopsy images into various
cancer grades. Performance of the proposed diagnostic system has been validated on
various datasets, and superior qualitative and quantitative performance has been observed
compared to previously reported methods of colon cancer detection.
