Automated Feature Extraction of Hepatic US Image of Dog Using Image Processing
Pazhunkaran Sneha Pauly, Bindhu K Rajan · 2019
Image processing has been one of the key factors in the area of Biomedical engineering especially in the detection of diseases. Incorporation of engineering methods in medical science has paved way for many optimistic outcomes that are much easier, cost effective and less time consumer when compared to conventional techniques. Image processing is one such technique. Here, the Ultra Sonography (US) images of the canine affected with hepatic and splenic carcinoma is the source, of which the features of cancer affected area is found out. Image preprocessing, image segmentation and feature extraction are the three stages in image processing that aids in the extraction of different features of canine inclined towards such cancers. Weiner filter is used for image denoising and Contrast Limited Adaptive Histogram Equalization (CLAHE) is the method for enhancement of the US images. These two processes comprise of image preprocessing. Image segmentation is achieved using adaptive threshold method. Finally, Gray Level Co-occurrence Matrix (GLCM) is used to extract the features of the segmented area.