Improved Information for Endoscopy Diseases Using K-Mean and Super-Pixel Segmentation in Wireless Endoscopy Dataset
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2016, Vol 4, Issue 4
Abstract
Wireless Capsule Endoscopy (WCE) needs computerized method to reduce the review time for its large image detain this paper, we propose an improved Bag Of Feature (BOF) method to assist classification of polyps in WCE images. Instead of utilizing a single Scale-Invariant Feature Transform (SIFT) feature in the traditional BoF method, we extract different textural features from the neighborhoods of the key points and integrate them together as synthetic descriptors to carry out classification tasks. Specifically, we study influence of the number of visual words, the patch size and different classification methods in terms of classification performance. Comprehensive experimental results reveal that the best classification performance is obtained with the integrated feature strategy using the SIFT and the Complete Local Binary Pattern (CLBP) feature, the visual words with a length of 120, the patch size of 8*8, and the Support Vector Machine (SVM). The achieved classification accuracy reaches 93.2%, confirming that the proposed scheme is promising for classification of polyps in WCE images To locate and identify next stages of ulcer and tuberculosis using Gaussian kernel algorithm, canny edge detection and k-mean algorithm for clustering .This can be implemented using dot net and c-sharp languages.
Authors and Affiliations
Vinitha M. K, Vishalini. S, Sushmitha. L, Lalitha. S. D
Keywords
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