title: Novel structural descriptors for automated colon cancer detection and grading.
abstract: The histopathological examination of tissue specimens is necessary for the diagnosis and grading of colon cancer. However, the process is subjective and leads to significant inter/intra observer variation in diagnosis as it mainly relies on the visual assessment of histopathologists. Therefore, a reliable computer-aided technique, which can automatically classify normal and malignant colon samples, and determine grades of malignant samples, is required. In this paper, we propose a novel colon cancer diagnostic (CCD) system, which initially classifies colon biopsy images into normal and malignant classes, and then automatically determines the grades of colon cancer for malignant images. To this end, various novel structural descriptors, which mathematically model and quantify the variation among the structure of normal colon tissues and malignant tissues of various cancer grades, have been employed. Radial basis function (RBF) kernel of support vector machines (SVM) has been employed as classifier in order to classify/grade colon samples based on these descriptors. The proposed system has been tested on 92 malignant and 82 normal colon biopsy images. The classification performance has been measured in terms of various performance measures, and quite promising performance has been observed. Compared with previous techniques, the proposed system has demonstrated better cancer detection (classification accuracy=95.40%) and grading (classification accuracy=93.47%) capability. Therefore, the proposed CCD system can provide a reliable second opinion to the histopathologists.
authors.affiliation: DCIS, Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan; DCS&IT, University of Azad Jammu and Kashmir, Muzaffarabad, Azad Kashmir, Pakistan. Electronic address: saimarathore_2k6@yahoo.com.
authors.forename: Saima
authors.initials: S
authors.lastname: Rathore
authors.affiliation: DCIS, Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan.
authors.forename: Mutawarra
authors.initials: M
authors.lastname: Hussain
authors.affiliation: DCIS, Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan; Comsats Institute of Information Technology, Lahore, Pakistan.
authors.forename: Muhammad
authors.initials: M
authors.lastname: Aksam Iftikhar
authors.affiliation: DCIS, Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan.
authors.forename: Abdul
authors.initials: A
authors.lastname: Jalil
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D003110:Colonic Neoplasms / Q000145:classification / Q000473:pathology*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D008853:Microscopy / Q000379:methods*; D008875:Middle Aged; D060787:Neoplasm Grading; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D060388:Support Vector Machine
keywords: Colon cancer; Colon cancer detection; Colon cancer grading; Colon classification