Color-based template selection for detection of gastric abnormalities in video endoscopy

Hussam Ali, Muhammad Sharif, Mussarat Yasmin, Mubashir Husain Rehmani

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Computer-aided diagnosis of gastric diseases from endoscopy frames is an important task. It facilitates both the patient and gastroenterologist in terms of time, money and most important health. Colors are the basic visual features of endoscopic images and also provide clues about abnormal regions in endoscopy frames. A variety of color spaces available for representation of color frames. However, we are not certain about which color space is more suitable for representing color features of gastric images. This paper presents a comparison of color features in different color spaces for detection of abnormal areas in chromoendoscopy (CH) frames. In addition, the CH images are segmented by using an existing color-difference based segmentation method Delta E (ΔE). A framework for automatic segmentation is presented for endoscopy images by selecting a template image in ΔE by using trained models. For classification, colors features are also merged with texture descriptors. The support vector machine (SVM) classifier is trained on color features and also the hybrid color combined texture characteristics. Then the trained classifier is used to group CH frames into abnormal and normal classes. ΔE with manual template selection has achieved 57.44% accuracy and 56.88% accuracy with the automated process. Moreover, the suggested method achieves 86.6% accuracy and 0.91 area under the curve for the classification of gastric lesions.

Original languageEnglish
Article number101668
JournalBiomedical Signal Processing and Control
Volume56
DOIs
Publication statusPublished - Feb 2020
Externally publishedYes

Keywords

  • Abnormality detection
  • Color histogram
  • Computer aided diagnosis (CAD)
  • Endoscopy
  • Segmentation
  • Support vector machine (SVM)

Fingerprint

Dive into the research topics of 'Color-based template selection for detection of gastric abnormalities in video endoscopy'. Together they form a unique fingerprint.

Cite this