title: New region-scalable discriminant and fitting energy functional for driving geometric active contours in medical image segmentation.
abstract: We propose a novel region-based geometric active contour model that uses region-scalable discriminant and fitting energy functional for handling the intensity inhomogeneity and weak boundary problems in medical image segmentation. The region-scalable discriminant and fitting energy functional is defined to capture the image intensity characteristics in local and global regions for driving the evolution of active contour. The discriminant term in the model aims at separating background and foreground in scalable regions while the fitting term tends to fit the intensity in these regions. This model is then transformed into a variational level set formulation with a level set regularization term for accurate computation. The new model utilizes intensity information in the local and global regions as much as possible; so it not only handles better intensity inhomogeneity, but also allows more robustness to noise and more flexible initialization in comparison to the original global region and regional-scalable based models. Experimental results for synthetic and real medical image segmentation show the advantages of the proposed method in terms of accuracy and robustness.
authors.affiliation: Key Laboratory of Optoelectronic Technology and Systems of Ministry of Education, College of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China.
authors.forename: Xuchu
authors.initials: X
authors.lastname: Wang
authors.affiliation: College of Computer and Information Science, Chongqing Normal University, Chongqing 400050, China.
authors.forename: Yanmin
authors.initials: Y
authors.lastname: Niu
authors.affiliation: Department of Anatomy, Third Military Medical University, Chongqing 400038, China.
authors.forename: Liwen
authors.initials: L
authors.lastname: Tan
authors.affiliation: Department of Anatomy, Third Military Medical University, Chongqing 400038, China.
authors.forename: Shao-Xiang
authors.initials: SX
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000000981:diagnostic imaging* / Q000473:pathology*; D001931:Brain Mapping / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted; D015203:Reproducibility of Results; D012984:Software; D014965:X-Rays
references.citation: IEEE Trans Image Process. 2010 Dec;19(12):3243-54
references.pmid: 20801742
references.citation: IEEE Trans Image Process. 2005 Oct;14(10):1512-23
references.pmid: 16238057
references.citation: IEEE Trans Med Imaging. 2007 Mar;26(3):405-21
references.pmid: 17354645
references.citation: IEEE Trans Image Process. 2008 Nov;17(11):2029-39
references.pmid: 18854247
references.citation: IEEE Trans Image Process. 2013 Jan;22(1):258-71
references.pmid: 22910114
references.citation: Comput Med Imaging Graph. 2009 Oct;33(7):520-31
references.pmid: 19482457
references.citation: IEEE Trans Image Process. 2008 Oct;17(10):1940-9
references.pmid: 18784040
references.citation: Comput Math Methods Med. 2013;2013:570635
references.pmid: 24302974
references.citation: IEEE Trans Image Process. 2006 Sep;15(9):2843-52
references.pmid: 16948327
references.citation: IEEE Trans Image Process. 2001;10(2):266-77
references.pmid: 18249617
references.citation: IEEE Trans Med Imaging. 1999 Nov;18(11):1085-97
references.pmid: 10661326