title: Segmentation of Regions of Interest Using Active Contours with SPF Function.
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.
authors.affiliation: Department of Computer Engineering and Mathematics, Rovira i Virgili University, 43007 Tarragona, Spain ; Department of Computer Science & Engineering, Chung-Ang University, Seoul 156-756, Republic of Korea.
authors.forename: Farhan
authors.initials: F
authors.lastname: Akram
authors.affiliation: Department of Computer Science & Engineering, Chung-Ang University, Seoul 156-756, Republic of Korea ; Korea Institute of Science & Technology Information, Daejeon 305-806, Republic of Korea.
authors.forename: Jeong Heon
authors.initials: JH
authors.lastname: Kim
authors.affiliation: Department of Computer Science & Engineering, Chung-Ang University, Seoul 156-756, Republic of Korea.
authors.forename: Chan-Gun
authors.initials: CG
authors.lastname: Lee
authors.affiliation: Department of Computer Science & Engineering, Chung-Ang University, Seoul 156-756, Republic of Korea.
authors.forename: Kwang Nam
authors.identifier: 0000-0002-7420-9216
authors.initials: KN
authors.lastname: Choi
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008327:Mammography / Q000706:statistics & numerical data; D015233:Models, Statistical; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods