A robust method based on ICA and mixture sparsity for edge detection in medical images
A robust method based on ICA and mixture sparsity for edge detection in medical images
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DOI：
10.1007/s11760-009-0140-5
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发表时间：
2011-03
期刊：
影响因子：
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通讯作者：
X. Han;Yenwei Chen
中科院分区：
文献类型：
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作者：
X. Han;Yenwei Chen
提出了一种基于独立分量分析（ICA）的鲁棒边缘检测方法。已知从图像中提取的ICA基函数大多数是稀疏的，类似于定位和定向的感受野。本文首先利用p范数估计ICA基函数的稀疏性，然后选择稀疏基函数来表示图像的边缘信息。该方法首先对测试图像进行ICA基函数变换，然后利用所选稀疏基函数的分量提取测试图像的高频信息。此外，通过在ICA域中应用收缩算法滤除噪声分量，我们可以很容易地获得无噪声图像的稀疏分量，即使对于SN比很低的噪声图像，也可以实现一种鲁棒的边缘检测。通过对一些医学图像的实验，验证了该方法的有效性。
In this paper, a robust edge detection method based on independent component analysis (ICA) was proposed. It is known that most of the ICA basis functions extracted from images are sparse and similar to localized and oriented receptive fields. In this paper, theLpnorm is used to estimate sparseness of the ICA basis functions, and then, the sparser basis functions were selected for representing the edge information of an image. In the proposed method, a test image is first transformed by ICA basis functions, and then, the high-frequency information can be extracted with the components of the selected sparse basis functions. Furthermore, by applying a shrinkage algorithm to filter out the components of noise in the ICA domain, we can readily obtain the sparse components of the noise-free image, resulting in a kind of robust edge detection even for a noisy image with a very low SN ratio. The efficiency of the proposed method for edge detection is demonstrated by experiments with some medical images.