A new method of SC image processing for confluence estimation. (October 2017)

• Record Type: Journal Article
• Authors: Soleimani, Sajjad; Mirzaei, Mohsen; Toncu, Dana-Cristina
• Is Part Of: Micron. Volume 101(2017)
• Page Start: 206
• Page End: 212
• Publication Date: 2017-10
• Subjects: Image processing -- Uneven background -- Denoising -- Stem cells -- Confluency; Microscopy -- Periodicals; Electron Probe Microanalysis -- Periodicals; Microscopy -- Periodicals; Microscopie -- Périodiques; Microscopy; Periodicals; 502.82
• DOI: 10.1016/j.micron.2017.07.013
• Languages: English
• ISSNs: 0968-4328

Abstract

Highlights: Stem Cells are expanded and proliferated by culturing on petri dish. The level of stem cell confluency was estimated by image processing method. Coupling a novel image denoising method with an adaptive thresholding technique. Our method can process Cell images with various defects and uneven background. Abstract: Stem cells images are a strong instrument in the estimation of confluency during their culturing for therapeutic processes. Various laboratory conditions, such as lighting, cell container support and image acquisition equipment, effect on the image quality, subsequently on the estimation efficiency. This paper describes an efficient image processing method for cell pattern recognition and morphological analysis of images that were affected by uneven background. The proposed algorithm for enhancing the image is based on coupling a novel image denoising method through BM3D filter with an adaptive thresholding technique for improving the uneven background. This algorithm works well to provide a faster, easier, and more reliable method than manual measurement for the confluency assessment of stem cell cultures. The present scheme proves to be valid for the prediction of the confluency and growth of stem cells at early stages for tissue engineering in reparatory clinical surgery. The method used in this paper is capable of processing the image of the cells, which have already contained various defects due to either personnel mishandling or microscope limitations. Therefore, it provides proper information even out of the worst original images available.