Fully-Automated Fibroglandular Tissue Segmentation in Breast MRI
作者： Shandong Wu , Susan Weinstein , Brad M. Keller , Emily F. Conant , Despina Kontos
DOI: 10.1007/978-3-642-31271-7_32
关键词: Fully automated 、 Cluster analysis 、 Artificial intelligence 、 Computer vision 、 Segmentation 、 Similarity (geometry) 、 Medicine 、 Nonparametric statistics 、 Breast imaging 、 Breast MRI 、 Fibroglandular Tissue
摘要: We propose an automated segmentation method for estimating the fibroglandular (i.e., dense) tissue in breast MRI. The first step of our is to segment as organ from other imaged parts through integrated edge extraction and voting algorithm. Then, we apply nonparametric non-uniform intensity normalization (N3) algorithm segmented correct bias field which common After that, fuzzy C-means clustering performed categorize into two clusters, i.e., fat. results are compared manual segmentations, verified by experienced imaging radiologist, assess accuracy algorithm, where Dice's Similarity Coefficient (DSC) shows a 0.73 agreement experiments. benefit correction also shown comparison with obtained excluding step.
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