21 July 2017 Fuzzy entropy thresholding and multi-scale morphological approach for microscopic image enhancement
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Proceedings Volume 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017); 104202K (2017) https://doi.org/10.1117/12.2282150
Event: Ninth International Conference on Digital Image Processing (ICDIP 2017), 2017, Hong Kong, China
Abstract
Microscopic images provide lots of useful information for modern diagnosis and biological research. However, due to the unstable lighting condition during image capturing, two main problems, i.e., high-level noises and low image contrast, occurred in the generated cell images. In this paper, a simple but efficient enhancement framework is proposed to address the problems. The framework removes image noises using a hybrid method based on wavelet transform and fuzzy-entropy, and enhances the image contrast with an adaptive morphological approach. Experiments on real cell dataset were made to assess the performance of proposed framework. The experimental results demonstrate that our proposed enhancement framework increases the cell tracking accuracy to an average of 74.49%, which outperforms the benchmark algorithm, i.e., 46.18%.
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Jiancan Zhou, Jiancan Zhou, Yuexiang Li, Yuexiang Li, Linlin Shen, Linlin Shen, } "Fuzzy entropy thresholding and multi-scale morphological approach for microscopic image enhancement", Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104202K (21 July 2017); doi: 10.1117/12.2282150; https://doi.org/10.1117/12.2282150
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