Table of Contents Author Guidelines Submit a Manuscript
Computational and Mathematical Methods in Medicine
Volume 2015, Article ID 710326, 14 pages
http://dx.doi.org/10.1155/2015/710326
Research Article

Segmentation of Regions of Interest Using Active Contours with SPF Function

1Department of Computer Engineering and Mathematics, Rovira i Virgili University, 43007 Tarragona, Spain
2Department of Computer Science & Engineering, Chung-Ang University, Seoul 156-756, Republic of Korea
3Korea Institute of Science & Technology Information, Daejeon 305-806, Republic of Korea

Received 16 October 2014; Revised 10 January 2015; Accepted 31 January 2015

Academic Editor: Tianye Niu

Copyright © 2015 Farhan Akram et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Segmentation of regions of interest is a well-known problem in image segmentation. This paper presents a region-based image segmentation technique using active contours with signed pressure force (SPF) function. The proposed algorithm contemporaneously traces high intensity or dense regions in an image by evolving the contour inwards. In medical image modalities these high intensity or dense regions refer to tumor, masses, or dense tissues. The proposed method partitions an image into an arbitrary number of subregions and tracks down salient regions step by step. It is implemented by enforcing a new region-based SPF function in a traditional edge-based level set model. It partitions an image into subregions and then discards outer subregion and partitions inner region into two more subregions; this continues iteratively until a stopping condition is fulfilled. A Gaussian kernel is used to regularize the level set function, which not only regularizes it but also removes the need of computationally expensive reinitialization. The proposed segmentation algorithm has been applied to different images in order to demonstrate the accuracy, effectiveness, and robustness of the algorithm.