Sasikala M, Kumaravel N
Department of Instrumentation Engineering, Madras Institute of Technology, Anna University, Chennai, Tamil Nadu, India.
J Med Eng Technol. 2008 May-Jun;32(3):198-205. doi: 10.1080/03091900701455524.
In this work, the classification of brain tumours in magnetic resonance images is studied by using optimal texture features. These features are used to classify three sets of brain images - normal brain, benign tumour and malignant tumour. A wavelet-based texture feature set is derived from the region of interest. Each selected brain region of interest is characterized with both its energy and texture features extracted from the selected high frequency subband. An artificial neural network classifier is employed to evaluate the performance of these features. Feature selection is performed by a genetic algorithm. Principal component analysis and classical sequential methods are compared against the genetic approach in terms of the best recognition rate achieved and the optimal number of features. A classification performance of 98% is achieved in a genetic algorithm with only four of the available 29 features. Principal component analysis and classical sequential methods require a larger feature set to attain the similar classification accuracy of 98%. The optimal texture features such as range of angular second moment, range of sum variance, range of information measure of correlation II and energy selected by the genetic algorithm provide best classification performance with lower computational effort.
在这项工作中，通过使用最优纹理特征研究了磁共振图像中脑肿瘤的分类。这些特征用于对三组脑图像进行分类——正常脑、良性肿瘤和恶性肿瘤。基于小波的纹理特征集是从感兴趣区域导出的。每个选定的脑感兴趣区域通过其能量和从选定高频子带提取的纹理特征来表征。采用人工神经网络分类器来评估这些特征的性能。特征选择通过遗传算法进行。在获得的最佳识别率和最优特征数量方面，将主成分分析和经典顺序方法与遗传方法进行了比较。在仅使用29个可用特征中的4个特征的遗传算法中实现了98%的分类性能。主成分分析和经典顺序方法需要更大的特征集才能达到98%的相似分类准确率。遗传算法选择的最优纹理特征，如角二阶矩范围、和方差范围、相关信息测度II范围和能量，以较低的计算量提供了最佳分类性能。
