Texture Feature Analysis for Oral Cancer Detection

  • 張 銘軒

Student thesis: Master's Thesis

Abstract

In recent years oral cancer screening techniques based on optical characteristics examination have been developed which allows a much more feasible solution for preliminary diagnosis In earlier research an oral cavity imaging system has been designed and applied to clinical test and data collection which provides two different excitation light sources for oral fluorescence imaging meanwhile the captured fluorescence images are analyzed with a recognition algorithm to detect oral cancer Based on the fact that neoplasia causes fluorescent and morphological changes in lesion the algorithm computes intensity and standard deviations of ROI (Region of interest) as features for classification In order to achieve higher classification accuracy we proposed feature extraction methods based on texture analysis to extract more effective and reliable image features In this study spatial frequency transformation methods are implemented including wavelet-based texture analysis and 2D-HHT (Hilbert Huang Transform) texture feature extraction Both approaches investigate texture information by decomposing original image into different frequency channels from which statistical and co-occurrence features are extracted For comparison different types of texture analysis including fractal analysis and co-occurrence matrix methods are also implemented on the same dataset with Fisher’s Discriminant Ratio as a criterion for evaluate discriminability of features Compared to simplest statistical property used in former work texture analysis successfully brings improvement in characterizing oral cancer lesions; where spatial-frequency transform methods show the best discriminability which can be further applied to construction more reliable classifier
Date of Award2016 Aug 18
Original languageEnglish
SupervisorPau-Choo Chung (Supervisor)

Cite this

Texture Feature Analysis for Oral Cancer Detection
銘軒, 張. (Author). 2016 Aug 18

Student thesis: Master's Thesis