Deconvolution for Enhancement of Biological Images Obtained by Fluorescence Microscopy
Reetoja Nag1, Raunak Kumar Das2
1Reetoja Nag, PhD student, Centre for Biomaterials, Cellular and Molecular Theranostics, Vellore Institute of Technology,Vellore,Tamil Nadu, India.
2Raunak Kumar Das, Assisitant Professor, Centre for Biomaterials, Cellular and Molecular Theranostics, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
Manuscript received on November 12, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on 30 November, 2019. | PP: 7910-7918 | Volume-8 Issue-4, November 2019. | Retrieval Number: B2851078219/2019©BEIESP | DOI: 10.35940/ijrte.B2851.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Intricate details of cells and tissues can be visualised by fluorescence microscopy and the images obtained can be then be quantitatively analysed. However, during image acquisition, distortions of the images occur by convolving the object with Point Spread Function. To remove this blurring, computational deconvolution methods are used in which the original image is restored with improved contrast. Our study analysed various fluorescence images, after the nuclei segmentation of the images, by both Deblurring (Blind Deconvolution, Lucy Richardson and Wiener filtering) and Restoration algorithms (Inverse filtering and Regularised filtering), which are the two main categories of deconvolution methods, in MATLAB 2016b. After statistical analysis (Mann Whitney U test) of area and homogeneity of the segmented nuclei of the various images for the different deconvolution methods, statistical significant difference was found in the case of area ((p=0.027) for Original vs. Inverse filter and (p=0.029) for Original vs. Regularised filter)) for restoration algorithms and for homogeneity, it was found for original vs. all the deconvolution methods, which shows that quantitative evaluation of the features can be used to further determine the better deconvolution method and in this case Restoration algorithms proves better than Deblurring algorithms.
Keywords: Deconvolution, Deblurring Algorithms, Fluorescence Microscopy, Point Spread Function, Restoration algorithms.
Scope of the Article: Bio-Science and Bio-Technology.