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A Novel Feature Extraction Scheme for Medical X-Ray Images

By Prachi.G.Bhende and Dr.A.N.Cheeran


X-ray images are gray scale images with almost the same textural characteristic. Conventional texture or color\ud features cannot be used for appropriate categorization in medical x-ray image archives. This paper presents a\ud novel combination of methods like GLCM, LBP and HOG for extracting distinctive invariant features from Xray\ud images belonging to IRMA (Image Retrieval in Medical applications) database that can be used to perform\ud reliable matching between different views of an object or scene. GLCM represents the distributions of the\ud intensities and the information about relative positions of neighboring pixels of an image. The LBP features are\ud invariant to image scale and rotation, change in 3D viewpoint, addition of noise, and change in illumination A\ud HOG feature vector represents local shape of an object, having edge information at plural cells. These features\ud have been exploited in different algorithms for automatic classification of medical X-ray images. Excellent\ud experimental results obtained in true problems of rotation invariance, particular rotation angle, demonstrate that\ud good discrimination can be achieved with the occurrence statistics of simple rotation invariant local binary\ud patterns

Topics: : Gray level co-occurrence matrix (GLCM), Local Binary Pattern (LBP), Histogram of Oriented Gradients (HOG), Engineering (General). Civil engineering (General), TA1-2040, Technology, T
Publisher: International Journal of Engineering Research and Applications
Year: 2016
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