The high mortality rate associated with coronary heart disease (CHD) has driven intensive research in cardiac image analysis. The advent of computed tomography angiography (CTA) has turned non-invasive diagnosis of cardiovascular anomalies into reality as calcified coronary plaques can be easily identified due to high intensity values. However, detection and quantification of the non-calcified plaques in CTA is still a challenging problem because of their lower intensity values, which are often similar to the nearby blood and muscle tissues. In this work, we propose Bayesian posterior based model for precise quantification of the non-calcified plaques in CTA imagery. The only indicator of non-calcified plaques in CTA is relatively lower intensity. Hence, we exploited intensity variations to discriminate voxels into lumen and plaque classes. Based on the normal coronary segments, we computed the vessel-wall thickness in first step. In the subsequent step, we removed vessel wall from the segmented tree and employed Gaussian Mixture Model to compute optimal distribution parameters. In the final step, distribution parameters were employed in Bayesian posterior model to classify voxels into lumen or plaque. A total of 18 CTA volumes were analyzed in this work using two different approaches. According to the experimental results, mean Jaccard overlap is around 88% with respect to the manual expert. In terms of sensitivity, specificity and accuracy, the proposed method achieves 84.13% ,79.15% and 82.02% success, respectively. Conclusion: According to the experimental results, it is shown that the proposed plaque quantification method achieves accuracy equivalent to human experts.
With the continuous advancement in technology, web technologies have reached to a new height. Enterprise applications are meant to be the basic need of today’s world which aims to produce results that are highly reliable, portable and adaptable. With these enormous features, they needed the storage mechanism which could handle and store the data effectively. The storage system thus required was a database management system but again technical knowledge was required to make things work appropriately. However, this approach was the traditional one which requires data to be stored in tabular format whereas Object oriented architecture has taken the programming language to a whole new technical phase for which the traditional RDBMS will not efficiently accomplish the expected job. To fulfill this gap in the literature, Object Relational Mapping is emerged as a solution to provide which provide comparative technical features effortlessly. These characteristics simplify and make the mapping of objects in object-oriented programming languages more flexible, efficient and easy to use.Therefore, we propose in this paper that Object relational Modeling (ORM) relates each object of object oriented languages to corresponding rows in the table.
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