An extensive analysis of various texture feature extractors to detect Diabetes Mellitus using facial specific regions. (1st April 2017)

• Record Type: Journal Article
• Authors: Shu, Ting; Zhang, Bob; Yan Tang, Yuan
• Is Part Of: Computers in biology and medicine. Volume 83(2017)
• Page Start: 69
• Page End: 83
• Publication Date: 2017-04-01
• Subjects: Texture feature analysis -- Facial key block analysis -- Diabetes Mellitus detection -- Image gray-scale histogram -- Medical biometrics; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2017.02.005
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Introduction : Researchers have recently discovered that Diabetes Mellitus can be detected through non-invasive computerized method. However, the focus has been on facial block color features. In this paper, we extensively study the effects of texture features extracted from facial specific regions at detecting Diabetes Mellitus using eight texture extractors. Materials and methods: The eight methods are from four texture feature families: (1) statistical texture feature family: Image Gray-scale Histogram, Gray-level Co-occurance Matrix, and Local Binary Pattern, (2) structural texture feature family: Voronoi Tessellation, (3) signal processing based texture feature family: Gaussian, Steerable, and Gabor filters, and (4) model based texture feature family: Markov Random Field. In order to determine the most appropriate extractor with optimal parameter(s), various parameter(s) of each extractor are experimented. For each extractor, the same dataset (284 Diabetes Mellitus and 231 Healthy samples), classifiers ( k -Nearest Neighbors and Support Vector Machines), and validation method (10-fold cross validation) are used. Results: According to the experiments, the first and third families achieved a better outcome at detecting Diabetes Mellitus than the other two. Conclusions: The best texture feature extractor for Diabetes Mellitus detection is the Image Gray-scale Histogram with bin number=256, obtaining an accuracy of 99.02%, a sensitivity of 99.64%, and a specificity of 98.26% by using SVM. Abstract : Highlights: Extensively analyzed various texture feature extractors for diabetes detection. Extracted the texture features from facial specific regions in a noninvasive manner. Experimented on a dataset consisting of 284 diabetes and 231 (new) healthy samples. Best extractor was Image Gray-scale Histogram with an accuracy of 99.02% via SVM.