VETERINARY BONE FRACTURE DETECTION AND FRACTURE CLASSIFICATION USING DIGITAL IMAGE PROCESSING
Keywords:
Segmentation, Thresholding, SVM, GLCM, Transverse, Oblique, Spiral, ComminutedAbstract
This paper presents abnormality detection from segmentation techniques for leg fracture segmentation from animal x-ray images. Bone fractures are common in animals. Treatment of fractures is important. If the correct intervention is not perfom1ed, there may be complications in the fracture improvement process. So, it is essential to make a correct and quick decision. In general, x-ray images are used in the diagnosis of fracture in veterinary medicine. But, the diagnosis of bone fracture by taking X-Ray image is risky for clinicians due to radiation exposure. Gaussian filtering is used to remove the noise from the x-ray images. fracture is segmented from x-ray image by performing thresholding segmentation operations. Experiments are performed on clinical data set to present the severity of the fracture in images for threshold segmentation methods studied. Extract the Features from segmented images using GLCM techniques. SVM algorithm is use for classify the given animal x-ray image is fractured or not. Using thresholding segmentation techniques, fractures are separated from x-ray images. Utilizing GLCM techniques, extract the features from segmented images. The SVM method is used to determine whether or not the provided animal x-ray image is broken and identify its type of fracture exist in animal x-ray images.
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