Effective Image Segmentation using Composite Energy Metric in Levelset Based Curve Evolution

  • Muhammad Moazzam Jawaid Department of Computer Systems Engineering, Mehran University of Engineering and Technology, Jamshoro, Pakistan
  • Bushra Naz Soomro Department of Computer Systems Engineering, Mehran University of Engineering and Technology, Jamshoro, Pakistan
  • Sajjad Ali Memon Department of Telecommunication Engineering, Mehran University of Engineering and Technology , jamshoro , Pakistan.
  • Noor-ur-Zaman Leghari Department of Computer Systems Engineering, Mehran University of Engineering and Technology, Jamshoro, Pakistan

Abstract

Accurate segmentation of anatomical organs in medical images is a complex task due to wide interpatient variability and several acquisition dependent artefacts. Moreover, image noise, low contrast and intensity inhomogeneity in medical data further amplifies the challeng. In this work, we propose an effective yet simple algorithm based on composite energy metric for precise detection of object boundaries. A number of methods have been proposed in literature for image segmentation; however, these methods employ individual characteristics of image including gradient, regional intensity or texture map. Segmentation based on individual featres often fail for complex images, especially for medical imagery. Accordingly, we propose that the segmentation quality can be improved by integrating local and global image features in the curve evolution. This work employs the classic snake model aka active contour model; however, the curve evolution force has been updated. In contast to the conventional image-based regional intensity statistics, the proposed snake model evolves using composite image energy. Hence, the proposed method offers a greater resistance to the local optima problem as well as initialization perturbations. Experimental results for both synthetic and 2D (Two Dimensional) real clinal images are presented in this work to validate the performance of the proposed method. The performance of the proposed model is evaluated with respect to expert-based manual ground truth. Accordingly, the proposed model achieves higher accuracy in comparison to the state-of-the-art region based segmentation methods of Lankton and Yin as reported in results section.

Published
Jan 1, 2019
How to Cite
JAWAID, Muhammad Moazzam et al. Effective Image Segmentation using Composite Energy Metric in Levelset Based Curve Evolution. Mehran University Research Journal of Engineering and Technology, [S.l.], v. 38, n. 1, p. 175-184, jan. 2019. ISSN 2413-7219. Available at: <http://publications.muet.edu.pk/index.php/muetrj/article/view/753>. Date accessed: 16 jan. 2019. doi: http://dx.doi.org/10.22581/muet1982.1901.14.
This is an open Access Article published by Mehran University of Engineering and Technolgy, Jamshoro under CCBY 4.0 International License