This paper critically surveys the aspect of digitalized image processing and segmentation with central focus on artificial intelligence. A digitalized image is composed of numerous elements that must be "analyzed," since better “phrasing”, and the "research" done on such elements might disclose a lot of strange data. This data may assist in solving a variety of business challenges, which is one of the numerous end objectives associated with image processing. Digital image analysis of image processing is a collection of methodologies applied to process computerized images. It integrates accomplishing basis assignments such noise minimization and more complex assignments such as image classification, fault diagnostics, emotional response, image fragmentation and image segmentation. Image enhancement applying neural networks has, over the past few decades, been applied widely in the clinical setting due to the advent of advanced computing ecosystems and algorithms. Medicine, industries, police departments, agriculture, defense, finance and security are some of the additional domains where the concept of image processing and segmentation can be applied.
R. Bergman and H. Nachlieli, "Perceptual Segmentation: Combining Image Segmentation With Object Tagging", IEEE Transactions on Image Processing, vol. 20, no. 6, pp. 1668-1681, 2011. Available: 10.1109/tip.2010.2088970.
K. Huang and T. Tan, "Vs-star: A visual interpretation system for visual surveillance", Pattern Recognition Letters, vol. 31, no. 14, pp. 2265-2285, 2010. Available: 10.1016/j.patrec.2010.05.029.
P. LECH, "Binarization of document images using the modified local-global Otsu and Kapur algorithms", PrzeglD Elektrotechniczny, vol. 1, no. 2, pp. 73-76, 2015. Available: 10.15199/48.2015.02.18.
D. Porembka, M. Behbehani, L. Wan and S. Sehlhorst, "Development Of Edge Detection Algorithms For The Identification Of Regional Wall Motion Abnormalities In A Canine Model", Anesthesiology, vol. 81, no., p. A546, 1994. Available: 10.1097/00000542-199409001-00545.
L. Zhengdong, "A Garment Image Segmentation Method Based on Salient Region and JSEG", Journal of Software, vol. 10, no. 11, pp. 1274-1282, 2015. Available: 10.17706//jsw.10.11.1274-1282.
J. Bonito, "A Bottom-Up Approach to Examining Group-Level Communication Patterns: A Multilevel Latent Profile Analysis of Functional Group Interaction", Human Communication Research, vol. 45, no. 2, pp. 202-225, 2019. Available: 10.1093/hcr/hqy020.
R. Feiz and M. Rezghi, "A splitting method for total least squares color image restoration problem", Journal of Visual Communication and Image Representation, vol. 46, pp. 48-57, 2017. Available: 10.1016/j.jvcir.2017.03.001.
K. Bhuvaneswari and P. Geetha, "Semantic Segmentation and Categorization of Brain MRI Images for Glioma Grading", Journal of Medical Imaging and Health Informatics, vol. 4, no. 4, pp. 554-566, 2014. Available: 10.1166/jmihi.2014.1286.
M. Lopez de Prado and M. Lewis, "Detection of False Investment Strategies Using Unsupervised Learning Methods", SSRN Electronic Journal, 2018. Available: 10.2139/ssrn.3167017.
L. Belaid and W. Mourou, "Image Segmentation: A Watershed Transformation Algorithm", Image Analysis & Stereology, vol. 28, no. 2, p. 93, 2011. Available: 10.5566/ias.v28.p93-102.
S. Yan, S. Sayad and S. Balke, "Image quality in image classification: Adaptive image quality modification with adaptive classification", Computers & Chemical Engineering, vol. 33, no. 2, pp. 429-435, 2009. Available: 10.1016/j.compchemeng.2008.10.017.
Haldorai and A. Ramu, “An Intelligent-Based Wavelet Classifier for Accurate Prediction of Breast Cancer,” Intelligent Multidimensional Data and Image Processing, pp. 306–319.
Acknowledgements
Author(s) thanks to Tongji University for research lab and equipment support.
Funding
No funding was received to assist with the preparation of this manuscript.
Ethics declarations
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Availability of data and materials
No data available for above study.
Author information
Contributions
All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.
Corresponding author
Mei Kurokawa
Mei Kurokawa
School of Applied Chemistry, Tongji University, Shanghai, China.
Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
Cite this article
Mei Kurokawa, “Survey on Artificial Intelligence based Image Processing and Image Segmentation analysis”, Journal of Biomedical and Sustainable Healthcare Applications, vol.1, no.2, pp. 096-104, July 2021. doi: 10.53759/0088/JBSHA202101012.
