Classification of multicolor fluorescence in situ hybridization (M-FISH) images with sparse representation.
Classification of multicolor fluorescence in situ hybridization (M-FISH) images with sparse representation.
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DOI：
10.1109/tnb.2012.2189414
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发表时间：
2012-06
影响因子：
3.9
通讯作者：
Wang YP
中科院分区：
文献类型：
--
作者：
Cao H;Deng HW;Li M;Wang YP
近年来，稀疏表示和压缩感知在应用数学和信号处理中引起了相当大的兴趣，但在医学图像处理中的成功有限。在本文中，我们开发了一种基于稀疏表示的分类（SRC）算法的基础上L1范数最小化的染色体分类荧光原位杂交（M-FISH）图像。该算法已经过测试，我们建立了一个全面的M-FISH数据库，表现出更好的分类性能。当与其他像素级的M-FISH图像分类器，如模糊C-均值（FCM）聚类算法和自适应模糊C-均值（AFCM）聚类算法，我们提出了较早的目前的方法给出了最低的分类错误。为了评估不同SRC用于M-FISH成像分析的性能，测试和比较了三种不同的稀疏表示方法，即同伦方法、正交匹配追踪（OMP）和最小角度回归（LARS）。统计分析的结果表明，同伦方法明显优于其他两种方法。我们的工作表明，基于稀疏表示的分类器与适当的模型可以优于许多现有的分类器M-FISH分类，包括那些我们之前提出的，这可以显着提高染色体分析在癌症和遗传性疾病诊断的MRI成像系统。
There has been a considerable interest in sparse representation and compressive sensing in applied mathematics and signal processing in recent years but with limited success to medical image processing. In this paper we developed a sparse representation-based classification (SRC) algorithm based on L1-norm minimization for classifying chromosomes from multicolor fluorescence in situ hybridization (M-FISH) images. The algorithm has been tested on a comprehensive M-FISH database that we established, demonstrating improved performance in classification. When compared with other pixel-wise M-FISH image classifiers such as fuzzy c-means (FCM) clustering algorithms and adaptive fuzzy c-means (AFCM) clustering algorithms that we proposed earlier the current method gave the lowest classification error. In order to evaluate the performance of different SRC for M-FISH imaging analysis, three different sparse representation methods, namely, Homotopy method, Orthogonal Matching Pursuit (OMP), and Least Angle Regression (LARS), were tested and compared. Results from our statistical analysis have shown that Homotopy based method is significantly better than the other two methods. Our work indicates that sparse representations based classifiers with proper models can outperform many existing classifiers for M-FISH classification including those that we proposed before, which can significantly improve the multicolor imaging system for chromosome analysis in cancer and genetic disease diagnosis.