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.
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)
. Lesion Segmentation and Identification of Breast Tumor on Dynamic Contrast-Enhanced Magnetic Resonance Imaging[J]. Journal of Shanghai Jiaotong University(Science), 2014
, 19(5)
: 630
-635
.
DOI: 10.1007/s12204-014-1552-0
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