title: An effective approach of lesion segmentation within the breast ultrasound image based on the cellular automata principle.
abstract: In this paper, a novel lesion segmentation within breast ultrasound (BUS) image based on the cellular automata principle is proposed. Its energy transition function is formulated based on global image information difference and local image information difference using different energy transfer strategies. First, an energy decrease strategy is used for modeling the spatial relation information of pixels. For modeling global image information difference, a seed information comparison function is developed using an energy preserve strategy. Then, a texture information comparison function is proposed for considering local image difference in different regions, which is helpful for handling blurry boundaries. Moreover, two neighborhood systems (von Neumann and Moore neighborhood systems) are integrated as the evolution environment, and a similarity-based criterion is used for suppressing noise and reducing computation complexity. The proposed method was applied to 205 clinical BUS images for studying its characteristic and functionality, and several overlapping area error metrics and statistical evaluation methods are utilized for evaluating its performance. The experimental results demonstrate that the proposed method can handle BUS images with blurry boundaries and low contrast well and can segment breast lesions accurately and effectively.
authors.affiliation: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, No. 92, Xidazhi Street, Harbin, 150001, People's Republic of China.
authors.forename: Yan
authors.initials: Y
authors.lastname: Liu
authors.forename: H D
authors.initials: HD
authors.lastname: Cheng
authors.forename: Jianhua
authors.initials: J
authors.lastname: Huang
authors.forename: Yingtao
authors.initials: Y
authors.lastname: Zhang
authors.forename: Xianglong
authors.initials: X
authors.lastname: Tang
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology*; D016022:Case-Control Studies; D016208:Databases, Factual; D005188:False Negative Reactions; D005189:False Positive Reactions; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D013624:Taiwan; D016217:Ultrasonography, Mammary / Q000379:methods*