Investigating a Denoising Approach to an Improved Otsu Segmentation on Cell Images

Abstract:

Image denoising provides an opportunity to minimize unwanted signal from any image in order to improve its interpretation by either human or machine. In the medical context, image denoising serves as a critical element of image processing as it helps to improve the quality of data presented for manual or automatic diagnosis. While there exist a number of image denoising methods such as the median, diffusion and Gaussian filtering, selecting a suitable one for cell segmentation may be challenging as one is tasked with ensuring adopted denoising method preserves critical object structures, like boundaries, while at the same time minimizing noise. In this paper, we discuss two popular denoising methods (diffusion filtering and Gaussian filtering) and investigates their significance, in improving the accuracy of segmented cell images, both individually and by their combinations. Experiment carried out on public and private datasets of cell images indicates an improved segmentation accuracy when cell images are first denoised with the combination of diffusion and Gaussian filtering as against individual denoising methods.

• Periodical: Journal of Biomimetics, Biomaterials and Biomedical Engineering (Volume 33)
• Pages: 59-64
• DOI: https://doi.org/10.4028/www.scientific.net/JBBBE.33.59
• Online since: July 2017
• Authors: Kazeem Oyeyemi Oyebode*
• Keywords: Cell Segmentation, Diffusion Filtering, Gaussian Filtering, Image Denoising, Otsu Thresholding

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