Open AccessMulti‐scale analysis of ulcer disease detection from WCE images
Author(s) -
Souaidi Meryem,
Ansari Mohamed El
Publication year - 2019
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2019.0415
Subject(s) - artificial intelligence , pattern recognition (psychology) , computer science , capsule endoscopy , robustness (evolution) , support vector machine , feature extraction , computer vision , medicine , radiology , biochemistry , chemistry , gene
Wireless capsule endoscopy (WCE) proves its robustness as a great technology to examine the entire digestive tract or the small intestine. An automatic computer‐aided design method is proposed in this study, in a manner to differentiate between ulcer disease and normal WCE images. A multi‐scale analysis‐based grey‐level co‐occurrence matrix (GLCM) is conducted here. The main step, the co‐occurrence matrix (GLCM), is computed from each sub‐band Laplacian pyramid decomposition, so as to extract the common Haralick features. Moreover, the p ‐value and area under the curve are used to select the relevant characteristics from the feature descriptor. This proposed approach was separately applied to the components of CIELab colour space. Ulcer detection was performed using the support vector machine. The findings demonstrate an encouraging detection rate performance of 95.38% for accuracy and 97.42% for sensitivity based on the first dataset and an average accuracy of 99.25 and 98.51% of sensitivities for the second dataset.