Medical Image Retrieval Using Vector Quantization and Fuzzy S-tree

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• 作者：Jana Nowaková ; Michal Prílepok ; Václav Snášel
• 关键词：Vector quantization ; Image comparison ; Image classification ; TF ; IDF ; Fuzzy S ; tree ; Medical image ; NCD
• 刊名：Journal of Medical Systems
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：41
• 期：2
• 全文大小：
• 刊物类别：Mathematics and Statistics
• 刊物主题：Health Informatics; Health Informatics; Statistics for Life Sciences, Medicine, Health Sciences;
• 出版者：Springer US
• ISSN：1573-689X
• 卷排序：41

文摘

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.