An extensive analysis of various texture feature extractors to detect Diabetes Mellitus using facial specific regions

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.

Source: Computers in Biology and Medicine - February 20, 2017 Category: Bioinformatics Authors: Ting Shu, Bob Zhang, Yuan Yan Tang Source Type: research