Colour and Texture Based Gastrointestinal Tissue Discrimination

Wireless capsule endoscopy is a colour imaging technology that enables close examination of the interior of the entire small intestine. The wireless capsule endoscope (WCE) operates for ~ 8 hours and captures ~ 40,000 useful images. The images are viewed by a clinician as a video sequence, generally taking over an hour to analyse. In this paper we present a method of automatically discriminating stomach and intestine tissue which can significantly speed-up one key part of the video analysis time, namely the process of locating the Pylorus - the valve between the stomach and the intestine. We divide the WCE image into 28 sub-regions and process only those regions where tissue is clearly visible. We create a feature vector using colour and texture information. The colour features are derived from hue saturation chromaticity histograms of the useful regions, compressed using a hybrid transform, incorporating the discrete cosine transform (DCT) and principal component analysis (PCA). The texture features are derived by singular value decomposition of the same tissue regions. After training the support vector classifier, we apply a discriminator algorithm, which scans the video with an increasing step and builds up a classification result sequence. By minimizing the number of misclassifications within this sequence, we predict the most probable position of the Pylorus. We present experimental results that demonstrate the effectiveness of this method