Segmentation Of Region Of Interest And Extraction Of Significant Features For Hep-2 Images

• Main Author: Khaw, Wil Bond
• Format: Thesis
• Language: English
• Published: 2017
• Subjects: TK1-9971 Electrical engineering. Electronics. Nuclear engineering
• Online Access: http://eprints.usm.my/39407/1/Khaw_Wil_Bond_24_Pages.pdf; http://eprints.usm.my/39407/
• Institution: Universiti Sains Malaysia

Human Epithelial type 2 (HEp-2) images are important in detecting the antinuclear autoantibody (ANA) in diagnosis of autoimmune disease in human body. Generally, HEp-2 cells can be classified into six main patterns, namely Centromere, Nucleolar, Homogeneous, Cytoplasmic, Fine Speckled and Coarse Speckled. However, in current technology, HEp-2 images can only be analysed manually by indirect immunofluorescence (IIF) test. The result of IIF test has very high variability and very dependent on the experience of physicists. Therefore, digitalize the IIF test becomes the new interest to researchers as well as in this research, where segmentation and features extraction of HEp-2 images will be focused. In segmentation of HEp-2 images, the current state-of-the-art techniques failed to provide a satisfied segmented result. Therefore, a combination of two conventional methods (i.e. Fuzzy C-Means (FCM) clustering and thresholding) has been proposed in this study. From the result, the segmented images are smoother, more consistent and with lesser noises compared to other state-of-the-art methods. In feature extraction stage, this study proposes to extract five features, which are Contrast, Energy, Correlation, Homogeneity, and Entropy. Based on the results obtained, the five proposed features can successfully differentiate the staining patterns of HEp-2 cells. In short, the proposed methods in this research have high capability to be introduced in hospital for detection of HEp-2 images for xix autoimmune disease. The proposed method has been proven with higher accuracy which can reduce the shortcoming of the existing IIF test.