**1. Introduction**

In medical imaging, high-performance graphics systems are being used for early diagnosing, planning treatment, and observing the problems. The medical imaging system plays

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

an important role in disease diagnostic time and cost. Therefore, to manage the industry demands, the medical health-care industry is looking for high-performance imaging system that can predict and identify the disease in an early stage without the support of an expert. As the performance of these devices grows, application-specific and high-performance hardware are required to run complicated/complex medical applications.

standalone heterogeneous system, which can perform image registration, storage, and processing in real-time environment. A software programming model is also proposed, which facilitates the medical scientists to write their imaging application without going into details of hardware. The proposed system is efficient in terms of performance and consumes low

A High-Performance System Architecture for Medical Imaging

http://dx.doi.org/10.5772/intechopen.83581

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Imaging applications for clinical methods and pathology study need high performance and efficiency. Several image processing environments and processing architectures exist for the medical imaging application, but to the best of our information, a programmable and highperformance scalable processing system is required for medical imaging applications.

Ibanez et al. [5] designed an open-source medical imaging toolkit called the Insight Toolkit (ITK). The developed toolkit supports a number of platforms and gives programmers with an

Schroeder et al. [6] developed an open-source Visualization ToolKit (VTK) for three-dimensional processing and visualization. VTK toolkit assists C++ libraries and algorithms for different parallel processing technologies and provides an interface to integrate with different

Wolf et al. [7] introduced Medical Imaging Interaction Toolkit (MITK) that assists to program medical image-based clinical software. The MITK gives an application programming framework that links with the ITK and the VTK libraries. The proposed HPMIS provides a medical imaging programming model, which supports data registration, memory management, and processing in hardware, and is easy to program. The HPMIS programming model is scalable for different architectures having reduced instruction set computing (RISC) multi-processors,

Bakalash et al. [8] suggested the MediCube system for 3D medical imaging. The system gives the reconstruction and visualization of three-dimension complex medical images. The processing system of the MediCube uses a RISC processor that performs the parallel processing for real-time voxel representation, whereas the HPMIS handles 3D medical imaging using a specialized local memory system and uses reconfigurable processing cores to process the incoming data. Bluetechnix [9] Black camera boards use specialized DSP processors and provide excellent image processing abilities at the expense of power, price, and complexity. Lee

Jinghong et al. [11] proposed image processing system having heterogeneous (DSP and FPGA) processing cores. The HPMIS uses FPGA accelerators for high-speed data acquisition from single or multiple sensors. The HPMIS processing architecture uses multi-processor core architecture, each core using 32-bit RISC instruction set architecture (ISA) is integrated with

the design for programmability, average performance, and low-cost systems.

power due to the best utilization of hardware-software approach.

extended C++ framework of software tools for image understanding.

application specific hardware cores, and vector processors.

et al. [10] proposed a SONY DSP processing-based system.

**2. Related work**

databases.

Precisely managing the medical information from multiple imaging equipment, processing them, and then displaying the result using various visual approaches give more detailed knowledge of understanding a disease state. The visual presentation performs multiple alignments and registration techniques using the complex and multi-dimensional images. The arrangement and registration of complex medical images having sparse data and control flow is a hard process. A medical imaging machine (e.g. radiological imaging) consumes 75% of processing time while aligning and registering, whereas a CT scan aligns images having a sample space of three-dimension with the reasonable isotropic resolution. These complex imaging applications have to follow anatomical and pathological structures while performing image acquisition, which demands efficient high-performance imaging hardware.

Medical imaging system uses different processing hardware such as reduced instruction set computer (RISC), application-specific instruction set processor (ASIP), single instruction multiple data (SIMD) processor, graphical processing units (GPUs), and field programmable gate arrays (FPGAs) [1, 2]. GPUs architecture uses advance vector processor architecture with dedicated memory and multiple stream multiprocessor (SM) having SIMD. The processing cores perform floating point operations. This high-end computing capability allows medical imaging applications to render complex medical images. In the past years, the GPU programming tools are grown and become competent in solving complex medical algorithm. On the other side, the performance of GPUs processing cores also increased, which allows medical imaging applications to give better results while diagnosing a problem.

Medical imaging is consistently held to be one of the most important advances in the history of medicine and has become an integral part of the diagnosis and treatment of patients around the globe. The medical statistic [3, 4] confirms that the early stage disease prediction, for example, breast, colorectal and lung cancers, etc., can save lives. This demands an improvement in diagnosis of the disease and screening techniques that generate high class, multidimensional images. With the development of medical imaging technology, the complexity of images also increased. It needs a high-performance computing architecture for real environment application processing. Existing medical imaging processing architectures face different issues and limitations related to hardware and software. Therefore, an efficient, scalable, and easily programmable high-performance medical imaging hardware architecture is required to solve complex imaging problems.

In this chapter, we proposed a high-performance medical imaging system (HPMIS) for medical applications. The proposed system works as a standalone device that processes images taken from different medical imaging equipment in real time. The HPMIS architecture is a standalone heterogeneous system, which can perform image registration, storage, and processing in real-time environment. A software programming model is also proposed, which facilitates the medical scientists to write their imaging application without going into details of hardware. The proposed system is efficient in terms of performance and consumes low power due to the best utilization of hardware-software approach.
