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International Journal of Engineering and Advanced Research Technology

Volume 3 Issue 12 (December 2017)

S.No. Title & Authors Page No View
1

Title : Image Mining of Endoscopic Images for Colon Examination Based on Random Set and Co-ocurrence Texture Descriptors

Authors : Petra Perner

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Abstract :

The aim of our research was to develop a method that allows us automatically to discover the decision rules for diagnosing medical images in normal tissue images and images showing a polyp. We used a data set of images that came from an endoscope video system used for colon examination.  The data set contains 283 normal tissue images and 61 polyp images. The 283 normal images consist of dark regions and reflection. One must decide if the image shows a polyp or not.  This is a two-class problem. The unequal number of the data in the two classes makes our problem to an unbalanced data set problem. The polyps in the images were identified and selected by a “well-trained” medical expert. Based on these medical images, we study the behaviour of two different statistical texture descriptors, the co-occurrence matrix-texture descriptor and our novel Random set texture descriptor. We review the theory of both texture descriptors and then we apply them to our medical data set. We used a decision-tree induction method to learn the classification rules based on our tool “Decision Master”. In both cases, for the full unequally distributed data set and for the balanced data set, we achieved the best error rate based the Random-set texture descriptor. The performance of the co-occurrence matrix-texture descriptor was worse. For statistical based texture descriptors large enough texture are necessary that cannot always guaranteed for medical objects. Since the co-occurrence matrix is based on higher order statistic that might be the reason for the worse performance. The results show that decision tree induction and image analysis based on our novel texture descriptor is an excellent method to mine medical images for the decision rules even when the data set is unbalanced, but not only that makes our Random-set based texture descriptor favourable. It also gives a flexible way to describe the appearance of the medical objects in symbolic terms, the computation time is less, and it can be set up as software module that can be flexible used in different systems. 

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