利用改进的模糊c均值聚类算法对多色荧光原位杂交(M-FISH)图像进行分割,同时结合了空间和光谱信息

Jingyao Li, D. Lin, Yu-ping Wang
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引用次数: 6

摘要

多色荧光原位杂交(M-FISH)是一种快速检测染色体异常的成像技术,其中染色体的分割一直是一个挑战。在分割算法中可以利用M-FISH图像的多通道信息,利用通道间的相关信息进行更好的图像分割。此外,相邻像素具有相似的特征,因此可以进一步利用这些空间信息来提高算法对噪声的鲁棒性。基于此,本文提出了一种改进的模糊c均值(FCM)聚类算法,通过融合空间和光谱信息来克服传统FCM算法对噪声的敏感性等问题。在模拟和真实图像上的实验结果表明,与传统FCM和我们最近提出的改进自适应FCM (IAFCM)相比,我们提出的方法具有更高的分割精度和更低的假率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Segmentation of Multicolor Fluorescence In-Situ Hybridization (M-FISH) image using an improved Fuzzy C-means clustering algorithm while incorporating both spatial and spectral information
Multicolor Fluorescence In-Situ Hybridization (M-FISH) is an imaging technique for rapid detection of chromosomal abnormalities, where the segmentation of chromosomes has been a challenge. Multi-channel information of M-FISH images can be used in a segmentation algorithm to exploit the correlated information across channels for better image segmentation. In addition, the neighboring pixels share similar characteristics, so this spatial information can be further utilized to improve the robustness of the algorithm to the noise. Motivated by this fact, in this paper we proposed an improved Fuzzy C-means (FCM) clustering algorithm to overcome the problems of conventional FCM such as the sensitivity to noise by incorporating both spatial and spectral information. The experimental results on both simulated and real M-FISH images have shown that our proposed method can result in higher segmentation accuracy and lower false ratio than both conventional FCM and the improved adaptive FCM (IAFCM) we recently proposed.
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