The article Medical Image Retrieval Using Vector Quantization and Fuzzy S-tree, written by Jana Nowaková, Michal Prílepok and Václav Snášel, was originally published electronically on the publisher's internet portal (currently SpringerLink) on December 15, 2016 without open access.

• Publikační typ
• tisková chyba MeSH

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.

• Klíčová slova
• Fuzzy S-tree, Image classification, Image comparison, Medical image, NCD, TF-IDF, Vector quantization,
• MeSH
• algoritmy MeSH
• fuzzy logika * MeSH
• komprese dat MeSH
• lidé MeSH
• mamografie metody   MeSH
• rozpoznávání automatizované metody   MeSH
• systémy pro podporu klinického rozhodování organizace a řízení   MeSH
• ukládání a vyhledávání informací metody   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH