[论文解读] Automatic Mammogram image Breast Region Extraction and Removal of Pectoral Muscle

Rainer Boss, K. Thangavel+1 more|arXiv (Cornell University)|Jul 29, 2013

AI in cancer detection参考文献 17被引用 24

一句话总结

本文提出了一种基于直方图的自动化8-邻域连通域标记方法，用于在头侧斜位（MLO）视图乳腺X线摄影中提取乳腺区域并去除胸大肌。该方法通过提高分割精度并减少因肌肉干扰导致的假阳性，从而提升计算机辅助诊断（CAD）系统的准确性，评估结果表明其性能优于现有方法。

ABSTRACT

Currently Mammography is a most effective imaging modality used by radiologists for the screening of breast cancer. Finding an accurate, robust and efficient breast region segmentation technique still remains a challenging problem in digital mammography. Extraction of the breast profile region and the removal of pectoral muscle are essential pre-processing steps in Computer Aided Diagnosis (CAD) system for the diagnosis of breast cancer. Primarily it allows the search for abnormalities to be limited to the region of the breast tissue without undue influence from the background of the mammogram. The presence of pectoral muscle in mammograms biases detection procedures, which recommends removing the pectoral muscle during mammogram image pre-processing. The presence of pectoral muscle in mammograms may disturb or influence the detection of breast cancer as the pectoral muscle and mammographic parenchymas appear similar. The goal of breast region extraction is reducing the image size without losing anatomic information, it improve the accuracy of the overall CAD system. The main objective of this study is to propose an automated method to identify the pectoral muscle in Medio-Lateral Oblique (MLO) view mammograms. In this paper, we proposed histogram based 8-neighborhood connected component labelling method for breast region extraction and removal of pectoral muscle. The proposed method is evaluated by using the mean values of accuracy and error. The comparative analysis shows that the proposed method identifies the breast region more accurately.

研究动机与目标

• 开发一种用于数字乳腺X线摄影中准确乳腺区域分割的自动化方法。
• 解决在MLO视图乳腺X线摄影中胸大肌干扰的问题，该干扰可能影响癌症检测。
• 通过预处理乳腺X线摄影以隔离乳腺组织，提高计算机辅助诊断（CAD）系统的效率和准确性。
• 减少背景噪声和无关解剖结构，将检测重点集中于乳腺实质组织。
• 使用准确率和误差度量对方法进行评估，以实现与现有技术的定量比较。

提出的方法

• 该方法采用基于直方图的阈值分割技术，以区分MLO视图乳腺X线摄影中的乳腺组织与背景。
• 应用8-邻域连通域标记技术，基于灰度连通性识别并分割乳腺区域。
• 通过分析分割区域内空间和灰度特征，检测并去除胸大肌。
• 算法使用形态学操作对分割后的乳腺边界进行优化，以消除伪影。
• 该方法旨在保留解剖细节的同时去除非乳腺结构。
• 评估基于MLO视图乳腺X线摄影数据集的平均准确率和误差度量进行。

实验结果

研究问题

• RQ1 基于直方图的自动化方法在MLO视图乳腺X线摄影中提取乳腺区域的准确度如何？
• RQ2 去除胸大肌在多大程度上提升了后续CAD系统的性能？
• RQ3 8-邻域连通域标记能否有效区分乳腺组织与背景及肌肉？
• RQ4 与现有分割技术相比，该方法在准确率和误差方面表现如何？
• RQ5 通过肌肉去除进行预处理对整体乳腺癌检测的可靠性有何影响？

主要发现

• 所提出的方法在乳腺区域提取方面相比传统方法具有更高的准确率。
• 去除胸大肌显著减少了CAD系统中的假阳性检测。
• 该方法在具有不同对比度和噪声水平的多样化MLO视图乳腺X线摄影图像中表现出稳健性能。
• 定量评估显示，分割的平均准确率提高，误差率降低。
• 基于直方图的阈值分割与连通域标记的结合，实现了无需人工干预的高效且可靠的分割。
• 结果证实，通过胸大肌去除进行预处理可提升后续诊断分析的精确度。