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This paper presents abnormality detection from segmentation techniques for leg fracture segmentation from animal X-ray images. 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 dataset to present the severity of the fracture in images for threshold segmentation methods studied and extract the features from segmented images using GLCM techniques. SVM algorithm is used for classify the given animal X-ray image which is fractured or not. Using thresholding segmentation techniques, fractures are separated from X-ray images. Utilizing GLCM techniques extracts the features from segmented images. The SVM method is used to determine whether or not the provided animal X-ray image is broken.

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A Study on Abnormality Detection and Classification of Diseases on X-Ray Images of Animals

  • 会议论文

  • 发表日期：

    2026年

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  • G. G. Rajput,
  • Sumitra M. Mudda

摘要

This paper presents abnormality detection from segmentation techniques for leg fracture segmentation from animal X-ray images. 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 dataset to present the severity of the fracture in images for threshold segmentation methods studied and extract the features from segmented images using GLCM techniques. SVM algorithm is used for classify the given animal X-ray image which is fractured or not. Using thresholding segmentation techniques, fractures are separated from X-ray images. Utilizing GLCM techniques extracts the features from segmented images. The SVM method is used to determine whether or not the provided animal X-ray image is broken.