PRACA POGLĄDOWA
Advanced image analysis for medical diagnostics: a system for segmentation and classification using level set methods and AI algorithms
 
Więcej
Ukryj
1
WSEI University
 
2
Institute of Philosophy and Sociology of the Polish Academy of Sciences
 
 
Data nadesłania: 28-06-2024
 
 
Data akceptacji: 20-07-2024
 
 
Data publikacji: 20-08-2024
 
 
JoMS 2024;57(Numer specjalny 3):757-771
 
SŁOWA KLUCZOWE
DZIEDZINY
_Inne
 
STRESZCZENIE
This work aims to implement and utilize an advanced computer system for image analysis and processing through artificial intelligence. The system will evaluate images from multiple sources. As a result, a comprehensive e-Medicus system will be developed to capture and analyze X-ray data and classify cancer cells. This innovative tool, with its unique features tailored for medical facilities, will assist them in capturing and analyzing X-ray images and CT scan results. The e-Medicus system offers several benefits for medical facilities, including efficient automation of photo analysis, tracking changes in a patient's condition over time, and facilitating the identification of medical changes and data classification. A multimedia presentation of the change process will use the contour set function, allowing for topological changes in solution properties. The system integrates novel procedures and algorithms from theoretical computer science and numerical mathematics, leveraging neural networks, genetic algorithms, semantic networks, image ontology, rough set theory, contour set methods, and hybrid algorithms. These developed algorithms enhance methods and concepts in image segmentation, gathering, transmitting, storing, extracting, and effectively presenting information.
Licencja
REFERENCJE (12)
1.
Argenziano, G., Soyer, P.H., De Giorgi, V., Piccolo, D. (2000) Interactive atlas of dermatoscopy, EDRA.
 
2.
Balla-Arabe, S., Gao, X. (2013). A Fast and Robust Level Set Method for Image Segmentation Using Fuzzy Clustering and Lattice Boltzmann Method, IEEE Trans Cybern., vol. 43, 3.
 
3.
Braun. R.P., Rabinovitz. H.S. (2005). Dermoscopy of pigmented skin lesions, J. Am. Acad. Dermatol., vol. 52, 109–121.
 
4.
Gdula, A., Rymarczyk, T. (2015). Application Computational Algorithms for Analysis of Dental Image, in Proc. of WD.
 
5.
Johr, R.H. (202). Dermoscopy: Alternative melanocytic algorithms-the ABCD rule of dermatoscopy, Menzies scoring method, and 7-point checklist, Clin Dermatol., vol. 20, 3, 240–247.
 
6.
Li, C., Kao, C., Gore, J. C., Ding, Z. (2008). Minimization of Region-Scalable Fitting Energy for Image Segmentation, IEEE Trans. Image Processing, vol. 17, 10, 1940–1949.
 
7.
Mumford, D., Shah, J. (1989). Optimal approximation by piecewise smooth functions and associated variational problems, Commun. Pure Appl. Math., vol. 42, 5, 577–685, [Online]. Available: https://doi.org/10.1002/cpa.31....
 
8.
Osher, S., Fedkiw, R. (2003). Level Set Methods and Dynamic Implicit Surfaces, Springer, New York.
 
9.
Osher, S., Sethian, J.A. (1988). Fronts Propagating with Curvature Dependent Speed: Algorithms Based on Hamilton-Jacobi Formulations, J. Comput. Phys., vol. 79, 12–49.
 
10.
Rymarczyk, T. (2012). Characterization of the shape of unknown objects by inverse numerical methods, Przegląd Elektrotechniczny, 7b, 2012, 138–140.
 
11.
Rymarczyk, T., Osior, K. (2013). E-Medicus System for Analysis and Images Segmentation, in Proc. of. IIPhWD.
 
12.
Rymarczyk, T., Filipowicz, S.F., Sikora, J., Polakowski, K. (2009). A piecewise-constant minimal partition problem in the image reconstruction, Przegląd Elektrotechniczny, 12, 141–143.
 
eISSN:2391-789X
ISSN:1734-2031
Journals System - logo
Scroll to top