Gletsos Miltiades, Mougiakakou Stavroula G, Matsopoulos George K, Nikita Konstantina S, Nikita Alexandra S, Kelekis Dimitrios
Laboratory of Biomedical Simulation and Imaging, Faculty of Electrical and Computer Engineering, National Technical University of Athens, 15773 Athens, Greece.
IEEE Trans Inf Technol Biomed. 2003 Sep;7(3):153-62. doi: 10.1109/titb.2003.813793.
In this paper, a computer-aided diagnostic (CAD) system for the classification of hepatic lesions from computed tomography (CT) images is presented. Regions of interest (ROIs) taken from nonenhanced CT images of normal liver, hepatic cysts, hemangiomas, and hepatocellular carcinomas have been used as input to the system. The proposed system consists of two modules: the feature extraction and the classification modules. The feature extraction module calculates the average gray level and 48 texture characteristics, which are derived from the spatial gray-level co-occurrence matrices, obtained from the ROIs. The classifier module consists of three sequentially placed feed-forward neural networks (NNs). The first NN classifies into normal or pathological liver regions. The pathological liver regions are characterized by the second NN as cyst or "other disease." The third NN classifies "other disease" into hemangioma or hepatocellular carcinoma. Three feature selection techniques have been applied to each individual NN: the sequential forward selection, the sequential floating forward selection, and a genetic algorithm for feature selection. The comparative study of the above dimensionality reduction methods shows that genetic algorithms result in lower dimension feature vectors and improved classification performance.
本文提出了一种用于从计算机断层扫描（CT）图像中对肝脏病变进行分类的计算机辅助诊断（CAD）系统。取自正常肝脏、肝囊肿、血管瘤和肝细胞癌的非增强CT图像的感兴趣区域（ROI）已被用作该系统的输入。所提出的系统由两个模块组成：特征提取模块和分类模块。特征提取模块计算平均灰度级和48个纹理特征，这些特征源自从感兴趣区域获得的空间灰度共生矩阵。分类器模块由三个顺序排列的前馈神经网络（NN）组成。第一个神经网络将肝脏区域分类为正常或病理区域。病理肝脏区域由第二个神经网络表征为囊肿或“其他疾病”。第三个神经网络将“其他疾病”分类为血管瘤或肝细胞癌。三种特征选择技术已应用于每个单独的神经网络：顺序前向选择、顺序浮动前向选择和用于特征选择的遗传算法。对上述降维方法的比较研究表明，遗传算法产生更低维度的特征向量并提高了分类性能。
