Authors: P. Anishiya; M. Sasikala
Addresses: Department of Electronics and Communication Engineering, MNM Jain Engineering College, Chennai, India ' Department of Electronics and Communication Engineering, Anna University, Chennai, India
Abstract: Adenocarcinoma originates from the glands. It causes changes in the gland architecture. The detection of adenocarcinoma requires histopathological examination of tissue specimens. At present, diagnosis and grading of the cancer depends on the visual interpretation of the biopsy samples by pathologist and thus, it may lead to a considerable amount of inter and intra-observer variability. To overcome this drawback and to reduce the reliance on the human interpretation and thereby reducing the workload of pathologists, many methods have been proposed. In this paper, a novel method to quantify a tissue for the purpose of automated stomach cancer diagnosis and grading is introduced. The stomach tissue images are preprocessed to compensate for colour variations. The Riesz wavelet transform is applied to the preprocessed stomach tissue images. From Riesz wavelet coefficients, 14 different statistical features were extracted. The wrapper-based feature selection is used. The reliability check on the final dataset is performed using ANOVA. In diagnosis, the tissue is classified into normal (non-malignant), well differentiated, moderately differentiated, poorly differentiated and tissue. The proposed system yielded a classification accuracy of 93.2% in diagnosing and 98.33% in grading.
Keywords: stomach adenocarcinoma; histopathological image analysis; colour normalisation; Riesz wavelet transform; cancer diagnosis; Hilbert transform; Simoncelli wavelet; ANOVA; support vector machine.
International Journal of Biomedical Engineering and Technology, 2020 Vol.34 No.3, pp.249 - 267
Received: 06 Apr 2017
Accepted: 30 Nov 2017
Published online: 30 Nov 2020 *