Vol.67, No.1, 2021, pp.171-185, doi:10.32604/cmc.2021.014141
OPEN ACCESS
ARTICLE
A Weighted Spatially Constrained Finite Mixture Model for Image Segmentation
  • Mohammad Masroor Ahmed1,*, 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:
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.
Keywords
Finite mixture model; maximum aposteriori; Markov random field; image segmentation
Cite This Article
M. M. Ahmed, S. A. Shehri, J. U. Arshed, M. U. Hassan, M. Hussain et al., "A weighted spatially constrained finite mixture model for image segmentation," Computers, Materials & Continua, vol. 67, no.1, pp. 171–185, 2021.
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