Medical Image Processing and Analysis (M.I.P.A.) Group, Laboratory of Medical Physics, School of Medical Science, University of Patras, 26 500 Rion - Patras, Greece.
Comput Methods Programs Biomed. 2011 Dec;104(3):307-15. doi: 10.1016/j.cmpb.2011.03.007. Epub 2011 Apr 30.
In the present study, an adaptation of the Markov Random Field (MRF) segmentation model, by means of the stationary wavelet transform (SWT), applied to complementary DNA (cDNA) microarray images is proposed (WMRF). A 3-level decomposition scheme of the initial microarray image was performed, followed by a soft thresholding filtering technique. With the inverse process, a Denoised image was created. In addition, by using the Amplitudes of the filtered wavelet Horizontal and Vertical images at each level, three different Magnitudes were formed. These images were combined with the Denoised one to create the proposed SMRF segmentation model. For numerical evaluation of the segmentation accuracy, the segmentation matching factor (SMF), the Coefficient of Determination (r(2)), and the concordance correlation (p(c)) were calculated on the simulated images. In addition, the SMRF performance was contrasted to the Fuzzy C Means (FCM), Gaussian Mixture Models (GMM), Fuzzy GMM (FGMM), and the conventional MRF techniques. Indirect accuracy performances were also tested on the experimental images by means of the Mean Absolute Error (MAE) and the Coefficient of Variation (CV). In the latter case, SPOT and SCANALYZE software results were also tested. In the former case, SMRF attained the best SMF, r(2), and p(c) (92.66%, 0.923, and 0.88, respectively) scores, whereas, in the latter case scored MAE and CV, 497 and 0.88, respectively. The results and support the performance superiority of the SMRF algorithm in segmenting cDNA images.
在本研究中，提出了一种通过平稳小波变换（SWT）对 cDNA 微阵列图像进行马尔可夫随机场（MRF）分割模型的改进（WMRF）。对初始微阵列图像进行了 3 级分解方案，然后进行软阈值滤波技术。通过逆过程，创建了去噪图像。此外，通过使用每个级别滤波的水平和垂直小波的振幅，形成了三个不同的幅度。这些图像与去噪图像结合，创建了所提出的 SMRF 分割模型。为了对分割准确性进行数值评估，在模拟图像上计算了分割匹配因子（SMF）、决定系数（r²）和一致性相关系数（p(c)）。此外，还将 SMRF 性能与模糊 C 均值（FCM）、高斯混合模型（GMM）、模糊 GMM（FGMM）和传统 MRF 技术进行了对比。通过平均绝对误差（MAE）和变异系数（CV）还在实验图像上测试了间接准确性性能。在后一种情况下，还测试了 SPOT 和 SCANALYZE 软件的结果。在前一种情况下，SMRF 获得了最佳的 SMF、r²和 p(c)（分别为 92.66%、0.923 和 0.88）得分，而在后一种情况下，MAE 和 CV 得分分别为 497 和 0.88。这些结果支持 SMRF 算法在分割 cDNA 图像方面的性能优势。
