Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
On the mean accuracy of statistical pattern recognizers
IEEE Transactions on Information Theory
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In this paper we introduce a new methodology to segment and detect colorectal polyps in endoscopic images obtained by a wireless capsule endoscopic device. The cornerstone of our approach is the fact that polyps are protrusions emerging from colonic walls. Thus, they can be segmented by simple curvature descriptors. Curvature is based on derivatives, thus very sensitive to noise and image artifacts. Furthermore, the acquired images are sampled on a grid which further complicates the computation of derivatives. To cope with these degradation mechanisms, we use use Local Polynomial Approximation, which, simultaneously, denoise the observed images and provides a continuous representation suitable to compute derivatives. On the top of the image segmentation, we built a support vector machine to classify the segmented regions as polyps or non-polyps. The features used in the classifier are selected with a wrapper selection algorithm (greedy forward feature selection algorithm with support vector machines). The proposed segmentation and detection methodology is tested in several scenarios presenting very good results both using the same video sequences as training data and testing data (cross-feature validation) and different video sequences as training and testing data.