Rangayyan R M, El-Faramawy N M, Desautels J E, Alim O A
Department of Electrical and Computer Engineering, The University of Calgary, Alta., Canada.
IEEE Trans Med Imaging. 1997 Dec;16(6):799-810. doi: 10.1109/42.650876.
Most benign breast tumors possess well-defined, sharp boundaries that delineate them from surrounding tissues, as opposed to malignant tumors. Computer techniques proposed to date for tumor analysis have concentrated on shape factors of tumor regions and texture measures. While shape measures based on contours of tumor regions can indicate differences in shape complexities between circumscribed and spiculated tumors, they are not designed to characterize the density variations across the boundary of a tumor. In this paper we propose a region-based measure of image edge profile acutance which characterizes the transition in density of a region of interest (ROI) along normals to the ROI at every boundary pixel. We investigate the potential of acutance in quantifying the sharpness of the boundaries of tumors, and propose its application to discriminate between benign and malignant mammographic tumors. In addition, we study the complementary use of various shape factors based upon the shape of the ROI, such as compactness, Fourier descriptors, moments, and chord-length statistics to distinguish between circumscribed and spiculated tumors. Thirty-nine images from the Mammographic Image Analysis Society (MIAS) database and an additional set of 15 local cases were selected for this study. The cases included 16 circumscribed benign, seven circumscribed malignant, 12 spiculated benign, and 19 spiculated malignant lesions. All diagnoses were proven by pathologic examinations of resected tissue. The contours of the lesions were first marked by an expert radiologist using X-Paint and X-Windows on a SUN-SPARCstation 2 Workstation. For computation of acutance, the ROI boundaries were iteratively approximated using a split/merge and end-point adjustment technique to obtain the best-fitting polygonal approximation. The jackknife method using the Mahalanobis distance measure in the BMDP (Biomedical Programs) package was used for classification of the lesions using acutance and the shape factors as features in various combinations. Acutance alone resulted in a benign/malignant classification accuracy of 95% the MIAS cases. Compactness alone gave a circumscribed/spiculated classification rate of 92.3% with the MIAS cases. Acutance in combination with a moment-based shape measure and a Fourier descriptor-based measure gave four-group classification rate of 95% with the MIAS cases. The results indicate the importance of including lesion edge definition with shape information for classification of tumors, and that the proposed measure of acutance fills this need.
与恶性肿瘤不同，大多数良性乳腺肿瘤具有清晰明确的边界，可将其与周围组织区分开来。迄今为止提出的用于肿瘤分析的计算机技术主要集中在肿瘤区域的形状因素和纹理测量上。虽然基于肿瘤区域轮廓的形状测量可以显示出边界清晰的肿瘤和有毛刺的肿瘤在形状复杂性上的差异，但它们并非旨在表征肿瘤边界处的密度变化。在本文中，我们提出了一种基于区域的图像边缘轮廓锐度测量方法，该方法可表征感兴趣区域（ROI）在每个边界像素处沿垂直于ROI方向的密度变化。我们研究了锐度在量化肿瘤边界清晰度方面的潜力，并提出将其应用于鉴别乳腺钼靶图像中的良性和恶性肿瘤。此外，我们还研究了基于ROI形状的各种形状因素（如紧凑度、傅里叶描述符、矩和弦长统计量）的互补使用，以区分边界清晰的肿瘤和有毛刺的肿瘤。本研究从乳腺钼靶图像分析协会（MIAS）数据库中选取了39幅图像，并另外收集了15例本地病例。这些病例包括16例边界清晰的良性病变、7例边界清晰的恶性病变、12例有毛刺的良性病变和19例有毛刺的恶性病变。所有诊断均通过对切除组织的病理检查得到证实。病变的轮廓首先由一位专家放射科医生在SUN-SPARCstation 2工作站上使用X-Paint和X-Windows进行标记。为了计算锐度，使用分割/合并和端点调整技术对ROI边界进行迭代逼近，以获得最佳拟合多边形逼近。在BMDP（生物医学程序）软件包中，使用马氏距离度量的留一法，将锐度和形状因素以各种组合作为特征对病变进行分类。仅使用锐度，MIAS病例的良性/恶性分类准确率为95%。仅使用紧凑度，MIAS病例的边界清晰/有毛刺分类率为92.3%。将锐度与基于矩的形状测量和基于傅里叶描述符的测量相结合，MIAS病例的四组分类率为95%。结果表明，在肿瘤分类中纳入病变边缘定义和形状信息很重要，并且所提出的锐度测量方法满足了这一需求。
