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A review of imaging techniques for systems biology

The purpose of this book is to provide a broad sampling of the rapidly expanding field of advanced imaging in medicine and biology. The book is aimed toward radiologists, medical physicists, scientists in the pharmacologic and biologic industries, and computer software and hardware specialists in this field. Some of the material presented would benefit dermatologists, ophthalmologists, pathologists, cardiologists, veterinarians, geneticists, and virtual reality developers.

Trainees in all these specialties would also benefit from this book. Part I consists of 10 chapters that describe the various imaging modalities: micro-CT, experimental MRI, 3-dimensional ultrasound, laser scanning, optical coherence tomography, mass spectroscopy—based tissue imaging, optical projection tomography, ultrastructure imaging, and an introduction to medical imaging modalities.

One chapter is dedicated to imaging and evaluation of live tissues at the microscopic level. Part II is dedicated to software and includes 5 chapters: volume visualization using virtual reality, surface rendering, CAVEman an object-oriented model of the human body , image-based finite element analysis, and geometric morphometrics. Five chapters constitute Part III. These chapters describe applications in audiology, molecular MRI, genomic data visualization, anatomic imaging, and postgenomic biology and functional measures of therapy.

The chapters are well written, easy to follow, and provide comprehensive references. Some of these chapters may overwhelm certain readers but stimulate those readers to further pursue these topics. Two chapters that stand out are those describing optical projection imaging and imaging evaluating live tissues at the microscopic level. 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. These complex imaging applications have to follow anatomical and pathological structures while performing image acquisition, which demands efficient high-performance imaging hardware.

The processing cores perform floating point operations.

MIS2017: Genomics, Advanced Imaging, And The Future Of Medicine

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. 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.


Nathan Cross, MD

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.

Imaging applications for clinical methods and pathology study need high performance and efficiency.

Original Research ARTICLE

Several image processing environments and processing architectures exist for the medical imaging application, but to the best of our information, a programmable and high-performance scalable processing system is required for medical imaging applications. 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, application specific hardware cores, and vector processors.

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. The HPMIS processing architecture uses multi-processor core architecture, each core using bit RISC instruction set architecture ISA is integrated with the design for programmability, average performance, and low-cost systems. The authors showed the value of GPU for the tremendous compute capacity that reproduces the CT images and presents them on screen. The proposed system decreases the size of the accelerators lookup tables.

Systems biology

The system takes two-dimensional images to process applications. The ViPS gives a high performance by using advanced hardware architectural support such as registration system, memory system, and processing system. Imaging has become an essential tool in modern medicine science.

General Info

Numerous powerful platforms to register, store, analyze, and process medical imaging applications have appeared in recent years. However, these systems are developed for a specific imaging application problem [ 19 , 20 , 21 ] and do not support generalized problems [ 22 ], which includes an indigenous system that could be customized according to the local needs. Moreover, the cost of such systems is on higher side that contributes to the increase in expenses of each scan.

This results in discouraging patients, who could not afford these high costs and therefore compromise on their health. In this chapter, a high-performance medical imaging system HPMIS is proposed that registers stores and processes complex and multi-dimensional medical imaging application in real-time. The high-performance medical imaging system provides a user-friendly programming environment and high-performance architecture to perform imaging data acquisition, registration, storage, analysis, and performs segmentation, filtering, and recognition for complex real-time complex and multidimensional medical images or videos.

The proposed system is highly reliable concerning cost, performance, and power. The proposed high-performance medical imaging system HPMIS has five sub systems: registration system, memory system, processing system, programming toolkit, and test applications. The registration system RS deals with a number of medical imaging devices with various interfaces.


The RS supports multidimensional and scattered graphics data. The RS manages X-ray radiography, ultrasonic images, etc.

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The RISC core is employed to obtain medical images having a complex structure, whereas the FPGA core is used to gather data having fixed data flow patterns. The registration system aligns images reasonably isotropic resolution and do not distort or deform the anatomical and pathological structures of images. The system supports bit pixel resolution and is directly linked with memory system. The HPMIS memory system uses three types of memories: the program memory, the specialized medical memory, and the main memory.

The descriptors provide the medical imaging programmer to choose a processing core for image processing and explain the complicated image structure. The high-performance specialized imaging memory holds the complex medical image data pattern to speed up the data transfer accesses.