Region Information-Based ROI Extraction by Multi-Initial Fast Marching Algorithm
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Region Information-Based ROI Extraction by Multi-Initial Fast Marching Algorithm Zhang Hongmei School of Life Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China Email: [email protected]
Bian Zhengzhong School of Life Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China Email: [email protected]
Guo Youmin First Affiliated Hospital, Xi’an Jiaotong University, Xi’an 710049, China Email: [email protected]
Ye Min Institute of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710064, China Email: [email protected]
Miao Yalin School of Life Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China Email: [email protected] Received 23 March 2003; Revised 10 January 2004; Recommended for Publication by Kyoung Mu Lee Region of interest (ROI) plays an important role in medical image analysis. In this paper, a new approach to ROI extraction based on the curve evolution is proposed. Different from the existent method, the proposed approach is efficient both in segmentation results and computational cost. The deforming curve is modeled as a monotonically marching front under a positive speed field, where a region speed function is derived by minimizing the new defined ROI energy, and integrated with the edge-based speed function. The curve evolution model integrating the ROI information has a large propagation range and could even drive the front in low-contrast and narrow thin areas. Moreover, a multi-initial fast marching algorithm, which permits the user to plant several seed curves as the initial front and evolves them simultaneously, is developed to fast implement the numerical solution. Selective planting seed curves could help the local growth and thus may further improve the segmentation results and reduce the computational cost. Experiments by our approach are presented and compared with that of the other methods, which show that the proposed approach could fast extract low-contrast and narrow thin ROI precisely. Keywords and phrases: ROI extraction, curve evolution, multi-initial fast marching algorithm, front, segmentation.
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INTRODUCTION
Region of interest (ROI) plays an important role in medical image analysis. Quantitative analysis of the shape and the properties of ROI could provide reliable data for diagnosing disease and the follow-up treatment planning [1]. As a result, to exploit accurate and fast ROI extraction method is in great need. In recent years, ROI extraction based on the curve evolution approaches that deform an initial curve towards the desired boundary have been extensively exploited. Snakes
or active contours first proposed by Kass et al. are energyminimizing curves that deform to fit the boundary of ROI [2]. The snakes are guided by the internal forces coming from the curve itself and external forces computed from the image data. Snakes and their variations are widely used in image segmentation. To overcome some drawbacks of classical snakes, region-based information are introduced to the model. Chakraborty et al. pro
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