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Proceedings Paper

Fuzzy entropy thresholding and multi-scale morphological approach for microscopic image enhancement
Author(s): Jiancan Zhou; Yuexiang Li; Linlin Shen
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Paper 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%.

Paper Details

Date Published: 21 July 2017
PDF: 6 pages
Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104202K (21 July 2017); doi: 10.1117/12.2282150
Show Author Affiliations
Jiancan Zhou, Shenzhen Univ. (China)
Yuexiang Li, Shenzhen Univ. (China)
Linlin Shen, Shenzhen Univ. (China)

Published in SPIE Proceedings Vol. 10420:
Ninth International Conference on Digital Image Processing (ICDIP 2017)
Charles M. Falco; Xudong Jiang, Editor(s)

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