Gastroesophageal Reflux Disease Diagnosis Using Hierarchical Heterogeneous Descriptor Fusion Support Vector Machine

Chun Rong Huang, Yan Ting Chen, Wei-Yiing Chen, Hsiu-Chi Cheng, Bor-Shyang Sheu

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

A new computer-aided diagnosis method is proposed to diagnose the gastroesophageal reflux disease (GERD) from endoscopic images of the esophageal-gastric junction. To avoid the interferences of different endoscope devices and automatic camera white balance adjustment, heterogeneous descriptors computed from heterogeneous color models are used to represent endoscopic images. Instead of concatenating these descriptors to a super vector, a hierarchical heterogeneous descriptor fusion support vector machine (HHDF-SVM) framework is proposed to simultaneously apply heterogeneous descriptors for GERD diagnosis and overcome the curse of dimensionality problem. During validation, heterogeneous descriptors are extracted from test endoscopic images at first. The classification result is obtained by using HHDF-SVM with heterogeneous descriptors. As shown in the experiments, our method can automatically diagnose GERD without any manual selection of region of interest and achieve better accuracy compared to states-of-the-art methods.

Original languageEnglish
Article number7182761
Pages (from-to)588-599
Number of pages12
JournalIEEE Transactions on Biomedical Engineering
Volume63
Issue number3
DOIs
Publication statusPublished - 2016 Mar 1

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering

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