Image Analysis and Enhancement: General Methods and Biomedical Applications

• Published in: Pattern recognition and image analysis Vol. 33; no. 4; pp. 1493 - 1514
• Main Authors: Krylov, A. S., Nasonov, A. V., Sorokin, D. V., Khvostikov, A. V., Pavelyeva, E. A., Pchelintsev, Ya. A.
• Format: Journal Article
• Language: English
• Published: Moscow Pleiades Publishing 01.12.2023; Springer Nature B.V
• Subjects: Algorithms; Alzheimer's disease; Biomedical materials; Computational mathematics; Computed tomography; Computer Science; Cybernetics; Deep learning; Dermatology; Hermite-Gaussian modes; Image analysis; Image enhancement; Image processing; Image Processing and Computer Vision; Image processing systems; Image quality; Image resolution; Image segmentation; Laguerre functions; Mathematical analysis; Mathematical models; Medical imaging; Pattern Recognition; Resampling; Scientific Schools of the Lomonosov Moscow State University (Msu,) Moscow; the Russian Federation; image processing; medical images; image enhancement; mathematical methods; cell microscopy images; convolutional neural networks; hybrid methods; image analysis
• ISSN: 1054-6618; 1555-6212
• DOI: 10.1134/S1054661823040235

General methods of image processing, analysis and enhancement and their biomedical applications developed by the scientific school of the Laboratory of Mathematical Methods of Image Processing of the Faculty of Computational Mathematics and Cybernetics of Lomonosov Moscow State University are reviewed. The suggested general methods and algorithms of image quality enhancement for image resampling and super-resolution, ringing artifact reduction, image sharpening, image denoising, and image registration are described. Image analysis methods based on Hermite projection method, Gauss-Laguerre functions and the use of phase information are presented. We describe and review the developed methods for medical imaging tasks solution, including problems in histology, color Doppler flow mapping, ultrasound liver fibrosis diagnostics, CT brain perfusion, Alzheimer’s disease diagnostics, dermatology, chest X-ray image analysis, live cell image registration, tracking, segmentation and synthesis. The paper illustrates the basic research idea of the effectiveness of the hybrid approach when we jointly use classical mathematical methods and deep learning approaches.