Shape and size adapted local fractal dimension for the classification of polyps in HD colonoscopy

This work proposes a new method for computing the local fractal dimension for the classification of colonic polyps. First an image is segmented by an algorithm based on the idea of the watershed transform. The resultant connected components (blobs) show the local mucosal structure at local minima and maxima in the image and model the pit pattern structure of the mucosa. The local fractal dimension is computed using two different filter masks, an anisotropic Gaussian filter mask and an elliptic binary filter mask, which are especially adapted to the shapes and sizes of the blobs. By specifically fitting shapes and sizes of the filter masks for each blob, our feature is scale, orientation and viewpoint invariant. The proposed method outperforms other methods commonly used for mucosal texture classification.