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Composite fuzzy-wavelet-based active contour for medical image segmentation

Hiren Mewada (Department of Electrical Engineering, Prince Mohammad Bin Fahd University, Al Khobar, Kingdom of Saudi Arabia)
Amit V. Patel (CHARUSAT Space Research and Technology Centre, Charotar University of Science and Technology, Anand, India)
Jitendra Chaudhari (CHARUSAT Space Research and Technology Centre, Charotar University of Science and Technology, Anand, India)
Keyur Mahant (Department of Electronics and Communication, Charotar University of Science and Technology, Anand, India)
Alpesh Vala (CHARUSAT Space Research and Technology Centre, Charotar University of Science and Technology, Anand, India)

Engineering Computations

ISSN: 0264-4401

Article publication date: 5 June 2020

Issue publication date: 28 October 2020

114

Abstract

Purpose

In clinical analysis, medical image segmentation is an important step to study the anatomical structure. This helps to diagnose and classify abnormality in the image. The wide variations in the image modality and limitations in the acquisition process of instruments make this segmentation challenging. This paper aims to propose a semi-automatic model to tackle these challenges and to segment medical images.

Design/methodology/approach

The authors propose Legendre polynomial-based active contour to segment region of interest (ROI) from the noisy, low-resolution and inhomogeneous medical images using the soft computing and multi-resolution framework. In the first phase, initial segmentation (i.e. prior clustering) is obtained from low-resolution medical images using fuzzy C-mean (FCM) clustering and noise is suppressed using wavelet energy-based multi-resolution approach. In the second phase, resultant segmentation is obtained using the Legendre polynomial-based level set approach.

Findings

The proposed model is tested on different medical images such as x-ray images for brain tumor identification, magnetic resonance imaging (MRI), spine images, blood cells and blood vessels. The rigorous analysis of the model is carried out by calculating the improvement against noise, required processing time and accuracy of the segmentation. The comparative analysis concludes that the proposed model withstands the noise and succeeds to segment any type of medical modality achieving an average accuracy of 99.57%.

Originality/value

The proposed design is an improvement to the Legendre level set (L2S) model. The integration of FCM and wavelet transform in L2S makes model insensitive to noise and intensity inhomogeneity and hence it succeeds to segment ROI from a wide variety of medical images even for the images where L2S failed to segment them.

Keywords

Citation

Mewada, H., Patel, A.V., Chaudhari, J., Mahant, K. and Vala, A. (2020), "Composite fuzzy-wavelet-based active contour for medical image segmentation", Engineering Computations, Vol. 37 No. 9, pp. 3525-3541. https://doi.org/10.1108/EC-11-2019-0529

Publisher

:

Emerald Publishing Limited

Copyright © 2020, Emerald Publishing Limited

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