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CT Image Feature Extraction Using GLCM for Xinjiang Local Liver Hydatid |
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Abstract: The feature extraction is the key of the interpretation and analysis of an image. For extracting CT imaging features of Xinjiang local Liver hydatid, an approach is proposed, which can extract liver and hydatid lesion features at the same time, by using the gray level co-occurrence matrix. First, the liver slice CT images are normalized, while removing the noise by using the median filter and enhancing the contrast of the liver and the lesion area by using histogram equalization, to obtain a clear gray image; then, its gray-scale is reduced, gray-based Symbiosis Matrix texture feature extraction methods are used to extract texture features embodied in the mean and the standard deviation of ASM, ENT, CON, IDM and CORRLN of CT images of Xinjiang local mono-hydatid cyst and multiple daughter hydatid cyst and healthy liver. After statistical analysis, marked differences are found between mono-hydatid cyst and multiple daughter hydatid cyst CT images in ASM and ENT and IDM, as statistically significant, and finally, Bayes identification and classification are carried out, with classification accuracy rate of 93.33%. The results show the effectiveness of our method to describe liver hydatid CT images characteristics, which would help to classify and retrieve liver hydatid CT images to some extent.
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Received: 18 June 2010
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