Computer Vision and Pattern Recognition Laboratory, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran.
Comput Biol Med. 2010 Apr;40(4):373-83. doi: 10.1016/j.compbiomed.2009.12.006. Epub 2010 Feb 23.
This paper is concerned with the design and development of an automatic mass classification of mammograms. The proposed method consists of three stages. In the first stage, preprocessing is performed to remove the pectoral muscles and to segment regions of interest. In the next stage contourlet transform is employed as a feature extractor to obtain the contourlet coefficients. This stage is completed by feature selection based on the genetic algorithm, resulting in a more compact and discriminative texture feature set. This improves the accuracy and robustness of the subsequent classifiers. In the final stage, classification is performed based on successive enhancement learning (SEL) weighted SVM, support vector-based fuzzy neural network (SVFNN), and kernel SVM. The proposed approach is applied to the Mammograms Image Analysis Society dataset (MIAS) and classification accuracies of 96.6%, 91.5% and 82.1% are determined over an efficient computational time by SEL weighted SVM, SVFNN and kernel SVM, respectively. Experimental results illustrate that the contourlet-based feature extraction in conjunction with the state-of-art classifiers construct a powerful, efficient and practical approach for automatic mass classification of mammograms.
本文致力于设计和开发一种自动的乳房 X 光片肿块分类方法。所提出的方法包括三个阶段。在第一阶段，进行预处理以去除胸肌并分割感兴趣的区域。在下一阶段，轮廓波变换作为特征提取器被用于获取轮廓波系数。这个阶段通过基于遗传算法的特征选择来完成，从而得到更紧凑和有区别的纹理特征集。这提高了后续分类器的准确性和鲁棒性。在最后阶段，基于连续增强学习（SEL）加权支持向量机、基于支持向量的模糊神经网络（SVFNN）和核支持向量机进行分类。所提出的方法应用于乳房 X 光片图像分析协会数据集（MIAS），通过 SEL 加权 SVM、SVFNN 和核 SVM 分别确定了 96.6%、91.5%和 82.1%的分类准确率，同时具有高效的计算时间。实验结果表明，基于轮廓波的特征提取与最先进的分类器相结合，为自动的乳房 X 光片肿块分类构建了一种强大、高效和实用的方法。
