## 2 Related Work  
A considerable amount of work had been published on the use of HPC or cloud computing for healthcare big data processing in health. Most of the research focuses on images/data analysis acquired through traditional imaging modalities. This section highlights the most recent work on the use of HPC, cloud computing, and big data frameworks for medical image/data processing. An overview of the recent tools and techniques proposed for large scale image data processing using cloud and big data frameworks can be found in Table 1.  
A cloud-based system to process and analyze MRI Scans was proposed in Reference [28]. The tool was integrated with a threshold-based brain segmentation method to analyze regions of interest. It enabled users to upload their data on a simplified web page. The proposed tool has several benefits, such as better speed due to scalability and automatic up-gradation of the system without bothering the end-users. Experimental data was used to test the performances and functionality of the system. However, no parallel computing techniques were taken into consideration. A smart cloud system is proposed to address the problems related to big data storage by providing infrastructure support to small hospitals with limited resources [29]. The confidence connected segmentation method was used to detect the tumor object. It allowed users to upload MRI or CT scan images for detecting the tumor in real-time. A special issue on the use of cloud and parallel computing have been published in the Journal of X-Ray Science and Technology to highlight the state-of-the-art advances in image-guided diagnosis and therapy [22]. For instance, the use of parallel computing is investigated for modeling blood flow using computational fluid dynamics and smooth particle hydrodynamics in Reference [34]. An imaging application for neurological diseases and neuroimaging is presented in Reference [35]. An approach called \"cloud-based medical image processing- as-a-service\", utilizing the Apache Hadoop framework and HBase is proposed for efficient processing and retrieval of medical images [36]. Similarly, a cloud-based medical imaging system was designed and developed to reconstruct images for cancer patients using Proton Computing Tomography [37]. The proposed service uses Amazon Web Service (AWS) to process multi-client requests simultaneously. \begin{table}
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A generalized flow for medical image classification was proposed based on Azure cloud by [PERSON] et al. [38]. Similarly, [PERSON] et al. [39] stated that big data paradigms fit very well for the workflow needed for image reconstruction. The paper focuses on improving the image quality of CT scan through the iterative reconstruction algorithm. The work also evaluated the costs associated with different execution policies on a single node and distributed configurations. A processing pipeline for processing functional magnetic resonance imaging (fMRI) based upon PySpark was proposed in [30]. The proposed pipeline was evaluated on a single node, and the results revealed an improvement of fourfold. However, the efficiency was not evaluated on a computing cluster. An ultra-fast, scalable, and reliable reconstruction method for four-dimensional cone-beam CT was developed using Map-reduce [40]. The work revealed the potential use of Map-reduce to solve large-scale imaging problems using cloud computing. A tool called \"Java Image Science Toolkit (JIST)\" was developed and integrated with AWS [41]. Similarly, a Spark-based image segmentation algorithm was proposed to accelerate the processing of agriculture image big data [31].The input images are converted into pixel data and then distributed to the computing platform. In order to improve the efficiency of iterative computing, membership degrees of a pixel are calculated and updated in parallel. The experimental results indicated that the proposed Spark-based fuzzy C-means algorithm can perform 12.54 times faster on ten computing nodes.