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.

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Computer-aided tumor detection in endoscopic video using color wavelet features

Classification with color and texture: jointly or separately?

RGB, a nonuniform color space, is almost universally accepted by the image processing community as the means for representing color. On the other hand, perceptually uniform spaces, such as L*a*b*, as well as approximately-uniform color spaces, such as HSV, exist, in which measured color differences are proportional to the human perception of such differences. This paper compares RGB with L*a*b* and HSV in terms of their effectiveness in color texture analysis. There has been a limited but increasing amount of work on the color aspects of textured images. The results have shown that incorporating color into a texture analysis and recognition scheme can be very important and beneficial. The presented methodology uses a family of Gabor filters specially tuned to measure specific orientations and sizes within each color texture. Effectiveness is measured by the classification performance of each color space, as well as by classifier-independent measures. Experimental results are obtained with a variety of color texture Images. Perceptually uniform spaces are shown to outperform RGB in many cases.

Perceptually uniform color spaces for color texture analysis: an empirical evaluation

Experiments in colour texture analysis

http://machinevision.iem.yzu.edu.tw/english/tech/Automated%20surface%20inspection%20for%20statistical%20textures.pdf

George Paschos

Papers

Fast color texture recognition using chromaticity moments

Histogram ratio features for color texture classification

Chromatic correlation features for texture recognition