Multiresolution analysis and classification of small bowel medical images.
This is the first reported work in the area of small bowel image classification and a novel analysis system was developed. Principles of human texture perception were used to design features which can discriminate between abnormal and normal images. The proposed method extracts statistical features from the wavelet domain, which describe the homogeneity of localized areas within the small bowel images. To ensure that robust features were extracted, a shift-invariant discrete wavelet transform (SIDWT) was explored. LDA classification was used with the leave one out method to improve classification under the small database scenario. A total of 75 abnormal and normal bowel images were used for experimentation resulting in high classification rates: 85% specificity and 85% sensitivity. The success of the system can be accounted to the discriminatory and robust feature set (translation, scale and semi-rotational invariant), which successfully classified various sizes and types of pathologies at multiple viewing angles.
Khademi
April
A
Dept. of Electrical and Computer Engineering, Ryerson University, Tor., ON, Canada. akhademi@ieee.org
Krishnan
Sridhar
S
D053704:Capsule Endoscopy / Q000379:methods*; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007421:Intestine, Small / Q000473:pathology*