Influence of MRI acquisition protocols and image intensity normalization methods on texture classification
Influence of MRI acquisition protocols and image intensity normalization methods on texture classification
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DOI：
10.1016/j.mri.2003.09.001
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发表时间：
2004-01-01
影响因子：
2.5
通讯作者：
Mariette, F
中科院分区：
文献类型：
--
作者：
Collewet, G;Strzelecki, M;Mariette, F
纹理分析方法量化了图像内灰度值的空间变化，从而可以提供关于所观察到的结构的有用信息。然而，由于使用不同的协议，它们对采集条件敏感，并且在MRI的情况下对扫描仪内和扫描仪间的变化敏感。两个协议和四个不同的条件下的灰度归一化的纹理分析方法应用于软奶酪的鉴别能力的影响进行了研究。选择了32个样品的软干酪在两个不同的成熟期（16个年轻和16个老样品），以获得两种不同的蛋白质凝胶的显微结构。在0.2T扫描仪上使用自旋回波序列获得质子密度和T2加权MR图像。灰度级根据四种方法标准化：原始灰度级、所有图像的相同最大值、所有图像的相同平均值以及动态限制为mu +/-3sigma。自动定义感兴趣区域，然后计算共生矩阵、游程矩阵的纹理描述符。梯度矩阵、自回归模型和小波变换。选择具有最低错误概率和平均相关系数的特征，并使用1-最近邻（1-NN）分类器进行分类。当使用mu +/-3 σ的动力学限制时，获得了最佳结果，这增强了两类之间的差异。结果表明的归一化方法和采集协议的分类的有效性，也对分类选择的参数的影响。这些结果表明，需要评估敏感性MR采集协议和灰度归一化方法时，纹理分析是必需的。(C)2004年爱思唯尔公司All rights reserved.
Texture analysis methods quantify the spatial variations in gray level values within an image and thus can provide useful information on the structures observed. However, they are sensitive to acquisition conditions due to the use of different protocols and to intra- and interscanner variations in the case of MRI. The influence was studied of two protocols and four different conditions of normalization of gray levels on the discrimination power of texture analysis methods applied to soft cheeses. Thirty-two samples of soft cheese were chosen at two different ripening periods (16 young and 16 old samples) in order to obtain two different microscopic structures of the protein gel. Proton density and T-2-weighted MR images were acquired using a spin echo sequence on a 0.2 T scanner. Gray levels were normalized according to four methods: original gray levels, same maximum for all images, same mean for all images, and dynamics limited to mu +/- 3sigma. Regions of interest were automatically defined, and texture descriptors were then computed for the co-occurrence matrix, run length matrix. gradient matrix, autoregressive model, and wavelet transform. The features with the lowest probability of error and average correlation coefficient were selected and used for classification with 1-nearest neighbor (1-NN) classifier. The best results were obtained when using the limitation of dynamics to mu +/- 3sigma, which enhanced the differences between the two classes. The results demonstrated the influence of the normalization method and of the acquisition protocol on the effectiveness of the classification and also on the parameters selected for classification. These results indicate the need to evaluate sensitivity to MR acquisition protocols and to gray level normalization methods when texture analysis is required. (C) 2004 Elsevier Inc. All rights reserved.