Title: Adaptive multi-threshold based de-noising filter for medical image applications

Authors: A. Ramya; D. Murugan; G. Murugeswari; Nisha Joseph

Addresses: Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tamil Nadu, India ' Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tamil Nadu, India ' Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tamil Nadu, India ' Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tamil Nadu, India

Abstract: Medical image processing is the emerging research area and many researchers contributed to medical image processing by proposing new techniques for medical image enhancement and abnormality detection. Interpretation of medical images is a challenging problem because of the unavoidable noise produced by the medical imaging devices and interference. In this work, a new framework is proposed for noise detection and reduction. This framework comprises two phases. First phase is the noise detection phase which is performed using the newly proposed adaptive multi-threshold scheme (AMT). In second phase, modification of noisy pixel is done using edge preserving median filter (EPM), which conserves the edge component and controls the blurring effect with preservation of fine details of interior region. The proposed work is tested with benchmark images and few medical images. It produces promising result and the results are compared with existing two-stage noise reduction techniques. Popular performance metrics such PSNR and SSIM are used for evaluation. Quantitative analysis and experimental results demonstrate that the proposed method is more efficient and suitable for medical image pre-processing.

Keywords: noise removal; noise detection; impulse noise; multi-threshold; edge preserving.

DOI: 10.1504/IJCVR.2019.10020654

International Journal of Computational Vision and Robotics, 2019 Vol.9 No.3, pp.272 - 292

Received: 30 Sep 2017
Accepted: 03 May 2018

Published online: 02 May 2019 *

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