Abstract
ABSTRACTUtilization of online medical images was limited due to the lack of effective search methods, and text-based searches have been a dominating approach for the medical database management. Medical images of various modalities cannot be effectively indexed or organized with traditional text-based retrieval techniques. This has led to the use of image content for the processing and organizing the database, constituting the so-called content-based image retrieval systems. This paper presents two proposed methods, namely fuzzy connectedness image segmentation with geometric moments (FCISGMs), and localized entropy-based medical image retrieval (LEBIR) for retrieval of Digital Imaging and Communications in Medicine images. FCISGM exploits shape features for precise image retrieval by using fuzzy connectedness image segmentation. LEBIR uses localized entropy for minimizing number of computation which results in efficient image retrieval. Experimental evaluation reveals that the proposed methods outperform the existing methods in terms of precision and recall.
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