Vo Kiet T, Sowmya Arcot
The School of Computer Science and Engineering, UNSW, Australia.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:3085-8. doi: 10.1109/IEMBS.2010.5626113.
A novel algorithm is presented for classification of four patterns of diffuse lung disease: normal, emphysema, honeycombing and ground glass opacity, on the basis of textural analysis of high resolution computed tomography (HRCT) lung images. The algorithm incorporates scale-space features based on Gaussian derivative filters and multi-dimensional multi-scale features based on wavelet and contourlet transforms of the original images. The mean, standard deviation, skewness and kurtosis along with generalized Gaussian density are used to model the output of filters and transforms, and construct feature vectors. Multi-class multiple kernel learning (m-MKL) classifier is used to evaluate the performance of the feature extraction scheme. The method is tested on a collection of 89 slices from 38 patients, each slice of size 512×512, 16 bits/pixel in DICOM format. The dataset contains 70,000 ROIs from slices already marked by experienced radiologists. The average sensitivity and specificity achieved is 94.16% and 98.68%, respectively.
本文提出了一种基于高分辨率计算机断层扫描（HRCT）肺部图像纹理分析的新型算法，用于对四种弥漫性肺部疾病模式进行分类：正常、肺气肿、蜂窝状和磨玻璃影。该算法结合了基于高斯导数滤波器的尺度空间特征以及基于原始图像小波和轮廓波变换的多维多尺度特征。利用均值、标准差、偏度、峰度以及广义高斯密度对滤波器和变换的输出进行建模，并构建特征向量。使用多类多核学习（m-MKL）分类器来评估特征提取方案的性能。该方法在来自38名患者的89个切片的数据集上进行了测试，每个切片大小为512×512，DICOM格式，16位/像素。该数据集包含来自已由经验丰富的放射科医生标记的切片的70,000个感兴趣区域（ROI）。所实现的平均灵敏度和特异性分别为94.16%和98.68%。
