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 reflec...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.展开更多
基金the National Basic Research Program(973) of China(No.2010CB732506)the National Science & Technology Pillar Program(No.2012BAI15B07)+1 种基金the National Natural Science Foundation of China(Nos.61104041 and 61201397)the Science Foundation of Fujian Province(No.2012J01261)
文摘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.
