Classification of Squamous Cell Carcinoma Based On Color and Textural Features in Microscopic Images of Esophagus Tissues

This paper presents a method for feature extraction using color and texture from microscopic images of esophagus tissues obtained from the abnormal regions of human esophagus detected through endoscopy. This method is used for classification of Squamous Cell Carcinoma (SCC) of esophagus, namely, poorly differentiated SCC, moderately differentiated SCC, and well differentiated SCC. Three different color spaces, namely, HSV, YC_bC_r, and Lab, are used for color texture analysis to test the classification of SCC of esophagus. The texture features are extracted from the luminance channel and the color features are extracted from the chrominance channels. The color and textural features are fused to characterize texture properties of image. The experimental results show that the classification accuracy of 100% is obtained using YC_bC_r color space. Also, the proposed method is robust enough to yield 100% classification rate even with small training/ testing sample in case of poorly differentiated SCC in all the three color spaces. This is a significant result, since the number of training images is small in most cases and also the number of testing images of a patient may be small.