title: ACCOMP: Augmented cell competition algorithm for breast lesion demarcation in sonography.
abstract: OBJECTIVE
  Fully automatic and high-quality demarcation of sonographical breast lesions remains a far-reaching goal. This article aims to develop an image segmentation algorithm that provides quality delineation of breast lesions in sonography with a simple and friendly semiautomatic scheme.
  
  
  METHODS
  A data-driven image segmentation algorithm, named as augmented cell competition (ACCOMP) algorithm, is developed to delineate breast lesion boundaries in ultrasound images. Inspired by visual perceptual experience and Gestalt principles, the ACCOMP algorithm is constituted of two major processes, i.e., cell competition and cell-based contour grouping. The cell competition process drives cells, i.e., the catchment basins generated by a two-pass watershed transformation, to merge and split into prominent components. A prominent component is defined as a relatively large and homogeneous region circumscribed by a perceivable boundary. Based on the prominent component tessellation, cell-based contour grouping process seeks the best closed subsets of edges in the prominent component structure as the desirable boundary candidates. Finally, five boundary candidates with respect to five devised boundary cost functions are suggested by the ACCOMP algorithm for user selection. To evaluate the efficacy of the ACCOMP algorithm on breast lesions with complicated echogenicity and shapes, 324 breast sonograms, including 199 benign and 125 malignant lesions, are adopted as testing data. The boundaries generated by the ACCOMP algorithm are compared to manual delineations, which were confirmed by four experienced medical doctors. Four assessment metrics, including the modified Williams index, percentage statistic, overlapping ratio, and difference ratio, are employed to see if the ACCOMP-generated boundaries are comparable to manual delineations. A comparative study is also conducted by implementing two pixel-based segmentation algorithms. The same four assessment metrics are employed to evaluate the boundaries generated by two conventional pixel-based algorithms based on the same set of manual delineations.
  
  
  RESULTS
  The ACCOMP-generated boundaries are shown to be comparable to the manual delineations. Particularly, the modified Williams indices of the boundaries generated by the ACCOMP algorithm and the first and second pixel-based algorithms are 1.069 +/- 0.024, 0.935 +/- 0.024, and 0.579 +/- 0.013, respectively. If the modified Williams index is greater than or equal to 1, the average distance between the computer-generated boundaries and manual delineations is deemed to be comparable to that between the manual delineations.
  
  
  CONCLUSIONS
  The boundaries derived by the ACCOMP algorithm are shown to reasonably demarcate sonographic breast lesions, especially for the cases with complicated echogenicity and shapes. It suggests that the ACCOMP-generated boundaries can potentially serve as the basis for further morphological or quantitative analysis.
authors.affiliation: Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Number 1, Section 1, Jen-Ai Road, Taipei 100, Taiwan. jzcheng@ntu.edu.tw
authors.forename: Jie-Zhi
authors.initials: JZ
authors.lastname: Cheng
authors.forename: Yi-Hong
authors.initials: YH
authors.lastname: Chou
authors.forename: Chiun-Sheng
authors.initials: CS
authors.lastname: Huang
authors.forename: Yeun-Chung
authors.initials: YC
authors.lastname: Chang
authors.forename: Chui-Mei
authors.initials: CM
authors.lastname: Tiu
authors.forename: Fang-Cheng
authors.initials: FC
authors.lastname: Yeh
authors.forename: Kuei-Wu
authors.initials: KW
authors.lastname: Chen
authors.forename: Chi-Hsuan
authors.initials: CH
authors.lastname: Tsou
authors.forename: Chung-Ming
authors.initials: CM
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D012189:Retrospective Studies; D014463:Ultrasonography