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 and 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, and Alpesh Vala CHARUSAT Space Research and Technology Centre, Charotar University of Science and Technology, Anand, India 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 Wavelet transform, Image segmentation, Active contour, Fuzzy C-mean, Legendre polynomial Paper type Research paper Contour for medical image segmentation Received 24 November 2019 Revised 15 March 2020 17 April 2020 21 April 2020 Accepted 27 April 2020 Engineering Computations © Emerald Publishing Limited 0264-4401 DOI 10.1108/EC-11-2019-0529 The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/0264-4401.htm