J Shanghai Jiaotong Univ Sci

Journal of shanghai Jiaotong University (Science) ›› 2014, Vol. 19 ›› Issue (5): 630-635.doi: 10.1007/s12204-014-1552-0

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Lesion Segmentation and Identification of Breast Tumor on Dynamic Contrast-Enhanced Magnetic Resonance Imaging

Lesion Segmentation and Identification of Breast Tumor on Dynamic Contrast-Enhanced Magnetic Resonance Imaging

MA Wen-jun1 (马文军), HONG Rong-rong2 (洪荣荣), YE Shao-zhen2 (叶少珍), YANG Yue3 (杨月),LI Yue-hua3 (李跃华), CHEN Li4, ZHANG Su1* (张素)   

  1. (1. School of Biomedical Engineering, Shanghai Jiaotong University, Shanghai 200030, China; 2. College of Math and Computer Science, Fuzhou University, Fuzhou 350116, China; 3. Shanghai Jiaotong University Affiliated Sixth People’s Hospital, Shanghai 200233, China; 4. Pediatric Oncology Branch, National Cancer Institute National Institutes of Health, Gaithersburg 20878, USA)
  2. (1. School of Biomedical Engineering, Shanghai Jiaotong University, Shanghai 200030, China; 2. College of Math and Computer Science, Fuzhou University, Fuzhou 350116, China; 3. Shanghai Jiaotong University Affiliated Sixth People’s Hospital, Shanghai 200233, China; 4. Pediatric Oncology Branch, National Cancer Institute National Institutes of Health, Gaithersburg 20878, USA)
  • Online:2014-10-31 Published:2014-11-12
  • Contact: ZHANG Su (张素) E-mail:suzhang@sjtu.edu.cn

Abstract: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can show subtle lesion morphology, improve the display of lesion definitions, and objectively reflect the blood supply of breast tumors; it can also reflect different strengthening patterns of normal tissues and lesion areas after medical tracer injection. DCE-MRI has become an important basis for the clinical diagnosis of breast cancer. To DCE-MRI data acquired from several hospitals across multiple provinces, a series of in-silico computational methods were applied for lesion segmentation and identification of breast tumor in this paper. The image segmentation methods include Otsu segmentation of subtraction images, signal-interference-ratio segmentation method and an improved variational level set method, each has its own application scope. After that, the distribution of benign and malignant in lesion region is identified based on three-time-point theory. From the experiment, the analysis of DCE-MRI data of breast tumor can show the distribution of benign and malignant in lesion region, provide a great help for clinicians to diagnose breast cancer more expediently and lay a basis for medical diagnosis and treatment planning.

Key words: breast tumor|lesion segmentation| identification of benign and malignant

摘要： Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can show subtle lesion morphology, improve the display of lesion definitions, and objectively reflect the blood supply of breast tumors; it can also reflect different strengthening patterns of normal tissues and lesion areas after medical tracer injection. DCE-MRI has become an important basis for the clinical diagnosis of breast cancer. To DCE-MRI data acquired from several hospitals across multiple provinces, a series of in-silico computational methods were applied for lesion segmentation and identification of breast tumor in this paper. The image segmentation methods include Otsu segmentation of subtraction images, signal-interference-ratio segmentation method and an improved variational level set method, each has its own application scope. After that, the distribution of benign and malignant in lesion region is identified based on three-time-point theory. From the experiment, the analysis of DCE-MRI data of breast tumor can show the distribution of benign and malignant in lesion region, provide a great help for clinicians to diagnose breast cancer more expediently and lay a basis for medical diagnosis and treatment planning.

关键词: breast tumor|lesion segmentation| identification of benign and malignant

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