Analysis and classification of kidney ultrasound images using SIFT features with neural network classifier
by T. Mangayarkarasi; D. Najumnissa Jamal
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 35, No. 4, 2021

Abstract: A unique method to analyse ultrasound scan images of kidney to classify renal abnormalities using SIFT features and artificial neural network is presented in this paper. The ultrasound kidney images are classified into four classes normal, cyst, calculi, and tumour. Preprocessing and denoising techniques are applied for the removal of speckle noise by applying median and Wiener filter. Segmentation to obtain region of interest (ROI) is carried out by applying fuzzy C-means clustering technique. Region of interest specifies to the objects such as kidney, calculi/stones, cysts and tumour inside the kidney present in the ultrasound scan images. First order statistical features and GLCM features are computed. To overcome the operator dependency of ultrasound scanning procedure rotational variance SIFT algorithm is applied. SIFT descriptors as features are obtained. Abnormalities are classified using supervised learning neural network algorithm (ANN). Classifier performance metrics are higher when hidden neurons are fixed as 101.

Online publication date: Fri, 07-May-2021

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