.png){kind=icon}
返回
Mammogram segmentation using multi-atlas deformable registration
.png){kind=icon}
OA
AI
DOI：10.1016/j.compbiomed.2019.06.001.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Accurate breast region segmentation is an important step in various automated algorithms involving detection of lesions like masses and microcalcifications, and efficient telemammography. While traditional segmentation algorithms underperform due to variations in image quality and shape of the breast region, newer methods from machine learning cannot be readily applied as they need a large training dataset with segmented images. In this paper, we propose to overcome these limitations by combining clustering with deformable image registration. Using clustering, we first identify a set of atlas images that best capture the variation in mammograms. This is done using a clustering algorithm where the number of clusters is determined using model selection on a low dimensional projection of the images. Then, we use these atlas images to transfer the segmentation to similar images using deformable image registration algorithm. Our technique also overcomes the limitation of very few landmarks for registration in breast images. We evaluated our method on the mini-MIAS and DDSM datasets against three existing state-of-the-art algorithms using two performance metrics, Jaccard Index and Hausdorff Distance. We demonstrate that the proposed approach is indeed capable of identifying different types of mammograms in the dataset and segmenting them accurately.
Keyword:
Mammograms
Breast region segmentation
Clustering
Atlas based image registration
Atlas selection
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.3
论文数:
8.3K
被引数:
3.3W
机构
引用论文
Robust automatic breast and pectoral muscle segmentation from scanned mammograms.png)
SIGNAL PROCESSING
SIGNAL PROCESSINGIF3.6
PREAI
分享收藏
分享收藏Image segmentation by histogram thresholding using hierarchical cluster analysis基于层次聚类分析的直方图阈值图像分割
PREAI
分享收藏
查看更多内容