Classification and recognition of medical images based on the SGTM neuroparadigm

Viktor Khavalko, Ivan Tsmots, Anastasija Kostyniuk, Christine Strauss

Veröffentlichungen: Beitrag in FachzeitschriftArtikelPeer Reviewed


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.

Seiten (von - bis)234-245
FachzeitschriftCEUR Workshop Proceedings
PublikationsstatusVeröffentlicht - Nov. 2019
Veranstaltung2nd International Workshop on Informatics and Data-Driven Medicine, IDDM 2019 - Lviv, Ukraine
Dauer: 11 Nov. 201913 Nov. 2019

ÖFOS 2012

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