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An Effective Approach of Lesion Segmentation Within the Breast Ultrasound Image Based on the Cellular Automata Principle.

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 strat...

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Published in:Journal of Digital Imaging Vol. 25; no. 5; pp. 580 - 591
Main Authors: Liu, Yan, Cheng, H., Huang, Jianhua, Zhang, Yingtao, Tang, Xianglong
Format: diagnostic images research tables/charts Journal Article
Published: Springer Nature Oct2012
Online Access:View this record in EBSCOhost
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      dt: Oct2012
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      pub: Springer Nature
      place: New York, New York
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        atl: An Effective Approach of Lesion Segmentation Within the Breast Ultrasound Image Based on the Cellular Automata Principle.
      aug:
        au:
          Liu, Yan
          Cheng, H.
          Huang, Jianhua
          Zhang, Yingtao
          Tang, Xianglong
        affil: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, No. 92, Xidazhi Street Harbin 150001 People's Republic of China
      sug:
        subj:
          Breast Neoplasms Ultrasonography
          Radiographic Image Interpretation, Computer-Assisted
          Radiographic Image Enhancement
          Cells
          Breast Neoplasms Pathology
          Evaluation Research
          Human
          Funding Source
      ab: 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.
      pubtype: Academic Journal
      doctype:
        diagnostic images
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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