Neighborhood Structural Similarity Mapping for the Classification of Masses in Mammograms
Neighborhood Structural Similarity Mapping for the Classification of Masses in Mammograms
复制标题
DOI：
10.1109/jbhi.2017.2715021
复制
发表时间：
2018-05-01
影响因子：
7.7
通讯作者：
Chakraborty, Jayasree
中科院分区：
文献类型：
--
作者：
Rabidas, Rinku;Midya, Abhishek;Chakraborty, Jayasree
在本文中，两个新的特征提取方法，使用邻域结构相似性（NSS），提出了表征乳腺肿块为良性或恶性。由于像素的灰度分布是不同的良性和恶性肿块，更规则和均匀的模式是可见的良性肿块相比，恶性肿块，所提出的方法利用相邻区域的肿块之间的相似性，通过设计两个新的功能，即，NSS-I和NSS-II，捕捉在不同尺度的全局相似性。作为这些全局特征的补充，计算出均匀的局部二进制模式，并与所提出的特征相结合，以提高分类效率。的性能的功能进行了评估，使用的图像从小型乳腺摄影图像分析协会（迷你MIAS）和数字数据库的乳腺摄影筛查（DDSM）数据库，其中的10倍交叉验证技术与Fisher线性判别分析，选择最佳的功能集后，使用逐步逻辑回归方法。mini-MIAS数据库的最佳受试者工作特征曲线下面积为0.98，准确率为94.57%，DDSM数据库的最佳受试者工作特征曲线下面积为0.93，准确率为85.42%。
In this paper, two novel feature extraction methods, using neighborhood structural similarity (NSS), are proposed for the characterization of mammographic masses as benign or malignant. Since gray-level distribution of pixels is different in benign and malignant masses, more regular and homogeneous patterns are visible in benign masses compared to malignant masses; the proposed method exploits the similarity between neighboring regions of masses by designing two newfeatures, namely, NSS-I and NSS-II, which capture global similarity at different scales. Complementary to these global features, uniform local binary patterns are computed to enhance the classification efficiency by combining with the proposed features. The performance of the features are evaluated using the images from the mini-mammographic image analysis society (mini-MIAS) and digital database for screening mammography (DDSM) databases, where a tenfold cross-validation technique is incorporated with Fisher linear discriminant analysis, after selecting the optimal set of features using stepwise logistic regression method. The best area under the receiver operating characteristic curve of 0.98 with an accuracy of 94.57% is achieved with the mini-MIAS database, while the same for the DDSM database is 0.93 with accuracy 85.42%.