Classification and recognition of medical images based on the SGTM neuroparadigm

Autor(en)
Viktor Khavalko, Ivan Tsmots, Anastasija Kostyniuk, Christine Strauss
Abstrakt

The paper discusses methods and algorithms for medical images preprocessing, their classification and recognition, which are oriented to use in machine vision systems. The structure and description of a number of software subsystems of image processing have been developed. The paper considers and analyzes the effectiveness of using methods for improving the visual quality of images as a stage of images pre-processing before classification. It is shown that image pre-processing is an effective and has significant impact on the accuracy of the images classification. The simulation of methods for improving the images’ quality showed the correspondence of the practical results with the theoretical results, confirming to the reliability of the proposed approaches and full working capacity of the developed software product. For implementation of the subsystem of medical images classification, a neuroparadigm of successive geometric transformations model is adapted.

Organisation(en)
Institut für Betriebswirtschaftslehre
Externe Organisation(en)
Lviv Polytechnic National University, Stepan Gzhytskyi National University of Veterinary Medicine and Biotechnologies Lviv
Journal
CEUR Workshop Proceedings
Band
2488
Seiten
234-245
Anzahl der Seiten
12
ISSN
1613-0073
Publikationsdatum
11-2019
Peer-reviewed
Ja
ÖFOS 2012
Medizinische Informatik, Bildverarbeitung, Computerunterstützte Diagnose und Therapie
Schlagwörter
ASJC Scopus Sachgebiete
Link zum Portal
https://ucris.univie.ac.at/portal/de/publications/classification-and-recognition-of-medical-images-based-on-the-sgtm-neuroparadigm(9749dd47-7ef8-4152-a60b-450637570d29).html