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 Rechnungswesen, Innovation und Strategie

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

102020 Medizinische Informatik, 102003 Bildverarbeitung, 305901 Computerunterstützte Diagnose und Therapie

Schlagwörter

ASJC Scopus Sachgebiete

Computer Science(all)

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