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CT imaging is widely used for a variety of diagnostic and therapeutic purposes. It is a best effective and efficient tool in kidney stones diagnostic and treatment strategies whenever contactless measurements of quality are necessary to examine different types of stones. Accurate segmentation is a key process in CT diagnostic imaging. But, there are some challenges to distinguish among stones, stents or nephrostomy tubes, calcification and bone fragments in stone diagnostic CT imaging. This study proposed and experimentally investigated a scheme to reduce false positive for stone detection and to extract meaningful structural information for diagnosis of kidney stones. Firstly, the interested object (stone) is segmented by Otsu's thresholding method and morphological operation. Secondly, the parameters based on 3D morphological features are measured for stone detection. In false positive reduction, the unwanted disturbances (bone fragments, image noise, calcification and stents) are eliminated using novel volume-ratio based thresholding and SAV ratio based thresholding. Finally, it provided an output that identifies meaningful structural information (Surface-area-to-volume ratio, volume, location and density of kidney stone) for kidney stone diagnosis. Digitized transverse abdomen CT images from 35 patients with kidney stone cases were statistically analyzed and validated. The estimation of coordinate points in the stone region was measured independently by the expert radiologists to get the validated data for the analysis. The proposed algorithm was significantly reduced the false positives with 70% of overall accuracy. Moreover, it can present the meaningful information of the kidney stone. This may lead to more accurate stone management.

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With relations growing larger, distributed, and queries becoming more complex, parallel query processing is an increasingly attractive option for improving the performance of database systems. Distributed and parallel query processing has been widely used in data intensive applications where data of relevance to users are stored at multiple locations. In this paper, we propose a three-tier middleware system for optimizing and processing of distributed queries in parallel on Cluster Grid architecture. The main contribution of this paper is providing transparent and integrated access to distributed heterogeneous data resources, getting performance improvements of implicit parallelism by extending technologies from parallel databases. We also proposed the dynamic programming algorithm for query optimization and site selection algorithm for resource balancing. An example query for employee databases is used throughout the paper to show the benefits of the system.