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Abstract — In this paper we show how Grid computing can be used to improve the operation of a medical image search system. The paper introduces the basic principles of a content-based image retrieval (CBIR) system and identifies the computationally challenging tasks in the system. For the computationally challenging tasks an efficient design is proposed that uses distributed Grid computing to carry out the image processing in a distributed and efficient way. The algorithms of the search system are executed by using a real medical image collection as input and a Grid computing infrastructure to provide the needed computing power. Finally, the results show how the image processing task that required tens of hours to complete can be processed by using only a fraction of the originally required computing time. I.

A general purpose medical image retrieval framework has been proposed with two subsystems namely enrollment and the query subsystem. As an attempt to design a new content based image retrieval methodology following the above framework, MAXI-MIN approach is implemented for the ultra sound kidney images for the retrieval process. Around hundred ultrasound kidney images have been collected from the clinical laboratory and fourteen features have been extracted from the existing literature for database creation. The difference between the feature of query image and features of each image in the database has been calculated. The image which is more similar to the query image has been retrieved as the resultant image based on the maximum number of occurrences of features for the minimum difference. If the query image does not match with the stored database image, the query image is added as a new image in the database. The process is highly automated and the system is capable of working effectively across different issues without human interference.

The last detailed review of Content-based Image Retrieval Systems (CBIRS) is that from Veltkamp and Tanase [VT02], updated in 2002. Since then, many new systems emerged, other systems were improved, but many systems are no longer supported. This paper reconsiders the systems described by Veltkamp and Tanase and proposes in addition for a selection of existing CBIRS a quantitative comparison. For this purpose we developed a benchmarking system for CBIRS based on how accurate they could match ideal (ground truth) results. As measure we introduced the ˜ RankWRN, which we developed based on the Normalized Avarage Rank. Our measure allows fair and accurate comparisons (due to multiple similarity values) of different CBIRS with respect to different queries. None of the other benchmarks allow this comparison. The choice for a system to be compared quantitatively was motivated by availability of the source or runtime code and the liveness (i.e., active development on the CBIRS). The benchmarked systems were SIMBA, SIMPLIcity, PictureFinder, ImageFinder, Caliph&Emir, VIPER/GIFT and Oracle Intermedia. Results show that VIPER/GIFT performs best in the settings of our benchmark, Caliph&Emir is second and SIMBA third. The default parameter settings of the systems tend to show the best results. MPEG-7 descriptors, which are used in Caliph&Emir, show a good performance in the benchmarking. 1