Sustainable Utilization of Machine-Vision-Technique-Based Algorithm in Objective Evaluation of Confocal Microscope Images

Aws Anaz, Neamah Kadhim, Omar Sadoon, Ghazwan Alwan and Mustafa Adhab ()
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Aws Anaz: Mechatronics Engineering Department, Engineering College, University of Mosul, Mosul 00964, Iraq
Neamah Kadhim: College of Science for Women, University of Baghdad, Baghdad 10071, Iraq
Omar Sadoon: Information Technology Center, University of Technology, Baghdad 10066, Iraq
Ghazwan Alwan: Mechanical Engineering Department, Engineering College, Tikrit University, Tikrit 34001, Iraq
Mustafa Adhab: Plant Protection Department, University of Baghdad, Baghdad 10071, Iraq

Sustainability, 2023, vol. 15, issue 4, 1-20

Abstract: Confocal microscope imaging has become popular in biotechnology labs. Confocal imaging technology utilizes fluorescence optics, where laser light is focused onto a specific spot at a defined depth in the sample. A considerable number of images are produced regularly during the process of research. These images require methods of unbiased quantification to have meaningful analyses. Increasing efforts to tie reimbursement to outcomes will likely increase the need for objective data in analyzing confocal microscope images in the coming years. Utilizing visual quantification methods to quantify confocal images with naked human eyes is an essential but often underreported outcome measure due to the time required for manual counting and estimation. The current method (visual quantification methods) of image quantification is time-consuming and cumbersome, and manual measurement is imprecise because of the natural differences among human eyes’ abilities. Subsequently, objective outcome evaluation can obviate the drawbacks of the current methods and facilitate recording for documenting function and research purposes. To achieve a fast and valuable objective estimation of fluorescence in each image, an algorithm was designed based on machine vision techniques to extract the targeted objects in images that resulted from confocal images and then estimate the covered area to produce a percentage value similar to the outcome of the current method and is predicted to contribute to sustainable biotechnology image analyses by reducing time and labor consumption. The results show strong evidence that t-designed objective algorithm evaluations can replace the current method of manual and visual quantification methods to the extent that the Intraclass Correlation Coefficient (ICC) is 0.9.

Keywords: confocal microscope imaging; objective outcome evaluation; machine vision technique (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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