基于全局阈值和弱边界逼近的乳房x线图像胸肌自动分割

Syeda Iffat Naz, Monika Shah, M. Bhuiyan
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引用次数: 3

摘要

切除胸肌是乳房x光自动检测肿瘤的主要挑战。胸肌的强度值与乳房区域的肿瘤(如果有的话)相同。它会干扰乳腺肿瘤的检测,并使假阳性误差最大化。本文介绍了一种从乳房x光检查中去除胸肌的有效方法。通过使用凸壳分割高强度区域来去除不需要的标签。中值滤波用于去除椒盐噪声。然后采用全局阈值法,通过对胸肌与乳腺病变的连接进行像素计数,去除阈值过程中与胸肌区域一起出现的乳腺组织。如果一个小区域的像素数小于胸肌边界上的预设值,则认为该区域不是胸肌的一部分。使用这个近似,胸肌外的区域被移除。最小连接阵列也用于分割与胸肌相连的部分,而不是胸肌的一部分。使用著名的mini-MIAS数据库322张乳房x光片来研究所提出方法的性能。92.86%的图像分割良好;4.97%的图像也被分割到一个可接受的水平。这种性能明显优于几种现有技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic Segmentation of Pectoral Muscle in Mammogram Images Using Global Thresholding and Weak Boundary Approximation
Removal of pectoral muscle is a major challenge in automatic detection of tumors breast mammograms. The intensity value for pectoral muscle is same as tumor presented (if any) in the breast region. It interferes in the detection of breast tumor and maximizes false positive error. In this paper, an efficient approach is introduced to remove the pectoral muscle from breast mammograms. Unwanted labels are removed by segmenting high intensity areas using convex-hull. Median filter is used to remove salt and pepper noise. Then global thresholding is used for the removal of breast tissues which appear along with pectoral muscle region during thresholding by pixel count on the connection of pectoral muscle and breast lesions. If the number of pixels in a small area are less than preset value on the boundary of pectoral muscle than it is considered not to be the part of pectoral muscle. Using this approximation regions outside the pectoral muscle are removed. Also minimal connectivity array is used to segment the portion connected to pectoral muscle which is not a part of it. The well-known mini-MIAS database with 322 mammograms is used to study the performance of the proposed method. 92.86% images are well segmented; 4.97% images are also segmented to an acceptable level. This performance is significantly better than that of several existing techniques.
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