Open Access

ARTICLE

A Weighted Spatially Constrained Finite Mixture Model for Image Segmentation

Mohammad Masroor Ahmed^1,*, Saleh Al Shehri², Jawad Usman Arshed³, Mahmood Ul Hassan⁴, Muzammil Hussain⁵, Mehtab Afzal⁶

1 Department of Computer Science, Capital University of Science & Technology, Islamabad, 45730, Pakistan
2 Department of Computer Science, Jubail University College, Jubail, 31961, Saudi Arabia
3 Department of Computer Science, University of Baltistan, Skardu, 16100, Pakistan
4 Department of Computer Skills, Preparatory Year Najran University, 1988, Saudi Arabia
5 Department of Computer Science, University of Management & Technology, Lahore, 54782, Pakistan
6 Department of Computer Science, University of Lahore, Lahore, 54500, Pakistan

* Corresponding Author: Mohammad Masroor Ahmed. Email:

Computers, Materials & Continua 2021, 67(1), 171-185. https://doi.org/10.32604/cmc.2021.014141

Received 01 September 2020; Accepted 15 October 2020; Issue published 12 January 2021

Abstract

Spatially Constrained Mixture Model (SCMM) is an image segmentation model that works over the framework of maximum a-posteriori and Markov Random Field (MAP-MRF). It developed its own maximization step to be used within this framework. This research has proposed an improvement in the SCMM’s maximization step for segmenting simulated brain Magnetic Resonance Images (MRIs). The improved model is named as the Weighted Spatially Constrained Finite Mixture Model (WSCFMM). To compare the performance of SCMM and WSCFMM, simulated T1-Weighted normal MRIs were segmented. A region of interest (ROI) was extracted from segmented images. The similarity level between the extracted ROI and the ground truth (GT) was found by using the Jaccard and Dice similarity measuring method. According to the Jaccard similarity measuring method, WSCFMM showed an overall improvement of 4.72%, whereas the Dice similarity measuring method provided an overall improvement of 2.65% against the SCMM. Besides, WSCFMM significantly stabilized and reduced the execution time by showing an improvement of 83.71%. The study concludes that WSCFMM is a stable model and performs better as compared to the SCMM in noisy and noise-free environments.

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