Computer-aided tumor detection in endoscopic video using color wavelet features.
We present an approach to the detection of tumors in colonoscopic video. It is based on a new color feature extraction scheme to represent the different regions in the frame sequence. This scheme is built on the wavelet decomposition. The features named as color wavelet covariance (CWC) are based on the covariances of second-order textural measures and an optimum subset of them is proposed after the application of a selection algorithm. The proposed approach is supported by a linear discriminant analysis (LDA) procedure for the characterization of the image regions along the video frames. The whole methodology has been applied on real data sets of color colonoscopic videos. The performance in the detection of abnormal colonic regions corresponding to adenomatous polyps has been estimated high, reaching 97% specificity and 90% sensitivity.
D018256:Adenomatous Polyps / Q000145:classification / Q000175:diagnosis* / Q000473:pathology; D003111:Colonic Polyps / Q000145:classification / Q000175:diagnosis* / Q000473:pathology; D003113:Colonoscopy / Q000379:methods*; D003116:Color; D015179:Colorectal Neoplasms / Q000175:diagnosis / Q000473:pathology; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D015588:Observer Variation; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D014741:Video Recording / Q000379:methods*