Image clustering using Zernike moments and self-organizing maps for gastrointestinal tract

Typically, the image features are compared to find the similarity among the images in a content-based image clustering system. However, images with high feature similarity may be different from each other in terms of semantics. Hence, this paper proposes a novel algorithm based on unsupervised neural classifier systems for in-vivo image clustering to address the semantic gap issue. The visual features are represented using Wavelet transform and Zernike moments, and a selforganizing map is utilized for the clustering of images. The algorithm-based prototype system is trained for categorizing gastral images in the respective clusters as per the similarity. The system can be used to segment images with automatic noise reduction and rotation invariances for given images. Experiments are performed on the real gastrointestinal images obtained from a known gastroenterologist, and the results using Daubechies Wavelet Transform + Zernike Moments on LUV color scheme yield 88.3% accuracy.