Automated colon cancer detection using hybrid of novel geometric features and some traditional features. (1st October 2015)

• Record Type: Journal Article
• Authors: Rathore, Saima; Hussain, Mutawarra; Khan, Asifullah
• Is Part Of: Computers in biology and medicine. Volume 65(2015)
• Page Start: 279
• Page End: 296
• Publication Date: 2015-10-01
• Subjects: Colon cancer -- Classification -- Elliptic objects -- Colon biopsy -- Elliptic Fourier descriptors -- SIFT -- Morphological, geometric, and texture features; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2015.03.004
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Automatic classification of colon into normal and malignant classes is complex due to numerous factors including similar colors in different biological constituents of histopathological imagery. Therefore, such techniques, which exploit the textural and geometric properties of constituents of colon tissues, are desired. In this paper, a novel feature extraction strategy that mathematically models the geometric characteristics of constituents of colon tissues is proposed. In this study, we also show that the hybrid feature space encompassing diverse knowledge about the tissues׳ characteristics is quite promising for classification of colon biopsy images. This paper thus presents a hybrid feature space based colon classification (HFS-CC) technique, which utilizes hybrid features for differentiating normal and malignant colon samples. The hybrid feature space is formed to provide the classifier different types of discriminative features such as features having rich information about geometric structure and image texture. Along with the proposed geometric features, a few conventional features such as morphological, texture, scale invariant feature transform (SIFT), and elliptic Fourier descriptors (EFDs) are also used to develop a hybrid feature set. The SIFT features are reduced using minimum redundancy and maximum relevancy (mRMR). Various kernels of support vector machines (SVM) are employed as classifiers, and their performance is analyzed on 174 colon biopsy images. The proposed geometric features have achieved an accuracy of 92.62%, thereby showing their effectiveness. Moreover, the proposed HFS-CC technique achieves 98.07% testing and 99.18% training accuracy. The better performance of HFS-CC is largely due to the discerning ability of the proposed geometric features and the developed hybrid feature space. Highlights: A novel colon cancer detection system has been proposed. The proposed system utilizes a hybrid of traditional and novel features for classification. Novel geometric features, which mathematically quantify the variation in the structure of normal and malignant colon tissues, have been proposed. The proposed geometric features show superior classification performance compared to traditional features. RBF kernel of SVM shows promising results for classification of colon cancer data.