Abstract
Digital medical image usage is common in health services and clinics. These data have a vital importance for diagnosis and treatment; therefore, preservation, protection, and archiving of these data are a challenge. Rapidly growing file sizes differentiated data formats and increasing number of files constitute big data, which traditional systems do not have the capability to process and store these data. This study investigates an efficient middle layer platform based on Hadoop and MongoDB architecture using the state-of-the-art technologies in the literature. We have developed this system to improve the medical image compression method that we have developed before to create a middle layer platform that performs data compression and archiving operations. With this study, a platform using MapReduce programming model on Hadoop has been developed that can be scalable. MongoDB, a NoSQL database, has been used to satisfy performance requirements of the platform. A four-node Hadoop cluster has been built to evaluate the developed platform and execute distributed MapReduce algorithms. The actual patient medical images have been used to validate the performance of the platform. The processing of test images takes 15,599 seconds on a single node, but on the developed platform, this takes 8,153 seconds. Moreover, due to the medical imaging processing package used in the proposed method, the compression ratio values produced for the non-ROI image are between 92.12% and 97.84%. In conclusion, the proposed platform provides a cloud-based integrated solution to the medical image archiving problem.
Highlights
Picture archiving and communication systems (PACS) and hospital information management systems (HIMS) have provided great improvements in the field of health services
Turkey is among the developing countries in the healthcare and modern health services, which are becoming widespread. e average rate of magnetic resonance imaging (MRI) scan for the Organization for Economic Co-operation and Development (OECD) countries is 52 per thousand people per year
The active contour model is preferred due to its successful properties such as region growth, threshold, and edge detection [9, 10]. e region-based active contour method is suitable for this model that is used for the separation of the region of interest (ROI) and the remaining part in the medical image [11, 12]
Summary
Picture archiving and communication systems (PACS) and hospital information management systems (HIMS) have provided great improvements in the field of health services. These systems have brought along some problems as well as the advantages of development. E region-based active contour method is suitable for this model that is used for the separation of the region of interest (ROI) and the remaining part (non-ROI) in the medical image [11, 12]. E OCR method is applied to the non-ROI region of the medical image, and the metadata and header information in the picture are obtained. The GridFS function is used because it provides an easy way to store and retrieve large les in MongoDB [7]
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