Title: Analysis and classification of kidney ultrasound images using SIFT features with neural network classifier

Authors: T. Mangayarkarasi; D. Najumnissa Jamal

Addresses: Sri Sai Ram Engineering College, Chennai, Tamil Nadu, India; B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, Tamil Nadu, India ' B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, Tamil Nadu, India

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.

Keywords: ultrasound scan images; speckle noise; median filter; Wiener filter; SIFT features; GLCM; fuzzy C-means segmentation; artificial neural network; SIFT descriptors; clustering technique.

DOI: 10.1504/IJBET.2021.114813

International Journal of Biomedical Engineering and Technology, 2021 Vol.35 No.4, pp.340 - 361

Received: 01 Dec 2017
Accepted: 27 Feb 2018
Published online: 07 May 2021 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article