Journal of Medical Systems

, 41:18 | Cite as

Medical Image Retrieval Using Vector Quantization and Fuzzy S-tree

  • Jana Nowaková
  • Michal Prílepok
  • Václav Snášel
Image & Signal Processing
Part of the following topical collections:
  1. New Technologies and Bio-inspired Approaches for Medical Data Analysis and Semantic Interpretation


The aim of the article is to present a novel method for fuzzy medical image retrieval (FMIR) using vector quantization (VQ) with fuzzy signatures in conjunction with fuzzy S-trees. In past times, a task of similar pictures searching was not based on searching for similar content (e.g. shapes, colour) of the pictures but on the picture name. There exist some methods for the same purpose, but there is still some space for development of more efficient methods. The proposed image retrieval system is used for finding similar images, in our case in the medical area – in mammography, in addition to the creation of the list of similar images – cases. The created list is used for assessing the nature of the finding – whether the medical finding is malignant or benign. The suggested method is compared to the method using Normalized Compression Distance (NCD) instead of fuzzy signatures and fuzzy S-tree. The method with NCD is useful for the creation of the list of similar cases for malignancy assessment, but it is not able to capture the area of interest in the image. The proposed method is going to be added to the complex decision support system to help to determine appropriate healthcare according to the experiences of similar, previous cases.


Vector quantization Image comparison Image classification TF-IDF Fuzzy S-tree Medical image NCD 


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© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Jana Nowaková
    • 1
  • Michal Prílepok
    • 1
  • Václav Snášel
    • 1
  1. 1.Faculty of Electrical Engineering and Computer Science, Department of Computer ScienceVŠB - Technical University of OstravaOstrava - PorubaCzech Republic

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