Literature DB >> 16344127

Cell-competition algorithm: a new segmentation algorithm for multiple objects with irregular boundaries in ultrasound images.

Chung-Ming Chen1, Yi-Hong Chou, Curtis S K Chen, Jie-Zhi Cheng, Yen-Fu Ou, Fang-Cheng Yeh, Kuei-Wu Chen.   

Abstract

Segmentation of multiple objects with irregular contours and surrounding sporadic spots is a common practice in ultrasound image analysis. A new region-based approach, called cell-competition algorithm, is proposed for simultaneous segmentation of multiple objects in a sonogram. The algorithm is composed of two essential ideas. One is simultaneous cell-based deformation of regions and the other is cell competition. The cells are generated by two-pass watershed transformations. The cell-competition algorithm has been validated with 13 synthetic images of different contrast-to-noise ratios and 71 breast sonograms. Three assessments have been carried out and the results show that the boundaries derived by the cell-competition algorithm are reasonably comparable to those delineated manually. Moreover, the cell-competition algorithm is robust to the variation of regions-of-interest and a range of thresholds required for the second-pass watershed transformation. The proposed algorithm is also shown to be superior to the region-competition algorithm for both types of images.

Mesh:

Year:  2005        PMID: 16344127     DOI: 10.1016/j.ultrasmedbio.2005.09.011

Source DB:  PubMed          Journal:  Ultrasound Med Biol        ISSN: 0301-5629            Impact factor:   2.998


  6 in total

1.  An effective approach of lesion segmentation within the breast ultrasound image based on the cellular automata principle.

Authors:  Yan Liu; H D Cheng; Jianhua Huang; Yingtao Zhang; Xianglong Tang
Journal:  J Digit Imaging       Date:  2012-10       Impact factor: 4.056

2.  Automated image analysis of lung branching morphogenesis from microscopic images of fetal rat explants.

Authors:  Pedro L Rodrigues; Nuno F Rodrigues; Duarte Duque; Sara Granja; Jorge Correia-Pinto; João L Vilaça
Journal:  Comput Math Methods Med       Date:  2014-08-28       Impact factor: 2.238

3.  A Novel Segmentation Approach Combining Region- and Edge-Based Information for Ultrasound Images.

Authors:  Yaozhong Luo; Longzhong Liu; Qinghua Huang; Xuelong Li
Journal:  Biomed Res Int       Date:  2017-04-27       Impact factor: 3.411

4.  A Multi-Task Learning Framework for Automated Segmentation and Classification of Breast Tumors From Ultrasound Images.

Authors:  Jignesh Chowdary; Pratheepan Yogarajah; Priyanka Chaurasia; Velmathi Guruviah
Journal:  Ultrason Imaging       Date:  2022-02-07       Impact factor: 1.578

Review 5.  BUSIS: A Benchmark for Breast Ultrasound Image Segmentation.

Authors:  Yingtao Zhang; Min Xian; Heng-Da Cheng; Bryar Shareef; Jianrui Ding; Fei Xu; Kuan Huang; Boyu Zhang; Chunping Ning; Ying Wang
Journal:  Healthcare (Basel)       Date:  2022-04-14

6.  Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans.

Authors:  Jie-Zhi Cheng; Dong Ni; Yi-Hong Chou; Jing Qin; Chui-Mei Tiu; Yeun-Chung Chang; Chiun-Sheng Huang; Dinggang Shen; Chung-Ming Chen
Journal:  Sci Rep       Date:  2016-04-15       Impact factor: 4.379

  6 in total

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