Mohd Khuzi A, Besar R, Wan Zaki Wmd, Ahmad Nn
Biomed Imaging Interv J. 2009 Jul;5(3):e17. doi: 10.2349/biij.5.3.e17. Epub 2009 Jul 1.
Digital mammogram has become the most effective technique for early breast cancer detection modality. Digital mammogram takes an electronic image of the breast and stores it directly in a computer. The aim of this study is to develop an automated system for assisting the analysis of digital mammograms. Computer image processing techniques will be applied to enhance images and this is followed by segmentation of the region of interest (ROI). Subsequently, the textural features will be extracted from the ROI. The texture features will be used to classify the ROIs as either masses or non-masses. In this study normal breast images and breast image with masses used as the standard input to the proposed system are taken from Mammographic Image Analysis Society (MIAS) digital mammogram database. In MIAS database, masses are grouped into either spiculated, circumscribed or ill-defined. Additional information includes location of masses centres and radius of masses. The extraction of the textural features of ROIs is done by using gray level co-occurrence matrices (GLCM) which is constructed at four different directions for each ROI. The results show that the GLCM at 0º, 45º, 90º and 135º with a block size of 8X8 give significant texture information to identify between masses and non-masses tissues. Analysis of GLCM properties i.e. contrast, energy and homogeneity resulted in receiver operating characteristics (ROC) curve area of Az = 0.84 for Otsu's method, 0.82 for thresholding method and Az = 0.7 for K-mean clustering. ROC curve area of 0.8-0.9 is rated as good results. The authors' proposed method contains no complicated algorithm. The detection is based on a decision tree with five criterions to be analysed. This simplicity leads to less computational time. Thus, this approach is suitable for automated real-time breast cancer diagnosis system.
数字化乳腺钼靶摄影已成为早期乳腺癌检测最有效的技术手段。数字化乳腺钼靶摄影获取乳房的电子图像并直接存储在计算机中。本研究的目的是开发一个辅助分析数字化乳腺钼靶图像的自动化系统。将应用计算机图像处理技术来增强图像，随后对感兴趣区域（ROI）进行分割。接着，从感兴趣区域提取纹理特征。这些纹理特征将用于将感兴趣区域分类为肿块或非肿块。在本研究中，作为所提出系统标准输入的正常乳房图像和有肿块的乳房图像取自乳腺影像分析协会（MIAS）数字化乳腺钼靶数据库。在MIAS数据库中，肿块分为有毛刺的、边界清晰的或边界不清的。其他信息包括肿块中心的位置和肿块的半径。通过使用灰度共生矩阵（GLCM）来提取感兴趣区域的纹理特征，每个感兴趣区域在四个不同方向构建灰度共生矩阵。结果表明，对于大小为8×8的块，在0°、45°、90°和135°方向的灰度共生矩阵能提供显著的纹理信息以区分肿块和非肿块组织。对灰度共生矩阵属性（即对比度、能量和均匀性）的分析得出，大津法的受试者工作特征（ROC）曲线面积Az = 0.84，阈值法的Az = 0.82，K均值聚类的Az = 0.7。ROC曲线面积在0.8 - 0.9被认为是良好的结果。作者提出的方法不包含复杂算法。检测基于一个有五个标准需要分析的决策树。这种简单性导致计算时间减少。因此，这种方法适用于自动化实时乳腺癌诊断系统。
