It also paves the way for future innovation around the use of mobile devices, which will further increase efficiency, speeding up processes and further reducing costs. While we considered opportunities with other products, ultimately we came right back to OpenText.
With OpenText EnCase Endpoint Investigator, we partnered with an organization whose product had a proven track record. Our clinical providers are thrilled with the seamless access to patient data, which they have as a result of Clinical Archiving, a unique approach to this challenge—which many organizations in the industry face. In the medical industry, the pressure to reduce costs while increasing the quality of care is relentless. Efficient and secure communication of PHI and medical records is an important requirement for healthcare institutions as they strive to attain these difficult goals.
However, data access remains an important bottleneck that limits computing bandwidth. In order to get a high computing capacity, the proposed architecture is designed to separate data access and computing, in this way, we can achieve the computation directly on incoming video stream without needing an external frame buffer. A Future for Integrated Diagnostic Helping 15 3. Our study established an approximation of the required computing capacity of about 50 GOPs for an average power consumption of less than a half Watt, and a maximum silicon area of 15 mm dedicated to computations.
As shown previously, algorithms can easily be divided in elementary stages and pipelined. One of the most efficient architecture models consists in splitting a whole multiprocessor architecture into elementary computing tiles as shown in Figure 3. Each of them acts as an autonomous SIMD computer that can execute a process. Figure 3. Each computing tiles is connected using a bus, allowing the execution of different kind of processes.
For example, video processing are chained as shown in the first section can be mapped onto each computing tile. A P processors computing tile. Output pixel stream Health Management Different Approaches and Solutions 16 Different instances of computing tiles are characterized in terms of computing capacity, power consumption, silicon area in function of their number of processor and memory resources. An example characterization of the architecture is shown on Figure 3.
Reaching a computing capacity of GOPs that would be required for image processing in diagnostic helping device would require 4mm of silicon area and mW of power consumption. Each computing tile can be generated with a set of parameters that are given by the designer.
For example the data-path width, usually 8 to 32 bits and its operators, memory maps, that is distributed in each processor or that is shared with all processor of a same computing tile. Sizing the whole architecture depends on the total required computing capacity, but also on the computing capacity that the designer need for each task that will be ported on each computing tile.
Designer may uses results of the characterization, as the example shown on Figure 3. He can generate computing tiles and connect them to the communication bus. Final synthesizes and simulations are required to check the designed architecture. Finally, the eISP can be integrated into a complete System on Chip or to a Lab on Chip that include control and communication components. Characterization of the power consumption of a single computing tile eISP architecture versus number of processors. We can add up to two frame buffer for HD p require that would require 4 mm for each frame.
Thus, allow high level processing such as video compression and labelling that requires up to several dozen GOPs and a frame memory depending on the selected implementation. Conclusion This chapter has presented the algorithms that could be used for digital image processing in handled diagnostic devices, and more precisely in the case of endoscopy.
As research in consumer devices imaging is intense, a comparisons of the algorithms that are used in that domain is done in this chapter. This work shows similarities between the approaches. These similarities can be exploited in order to transfer the hardware processors initially designed for consumers market such as cell phone or gaming to integrated medical domain. The case of the endoscopic video capsule is used due to its highly constrained integrability, as well in terms of silicon area or power consumption and computational capacity. A state of the art of the architectures that could match these constraints is described.
It shows that the existent architectures do not to perfectly cope with computational requirement, silicon area or power consumption. A computing architecture derived from the eISP, an image signal processor designed for low level image enhancement is proposed. With less than 5 mm and 0. Due to its programmability, it can be used not only as image enhancement architecture, but also as a high-level diagnostic helping processor by executing processes like form recognition, 3D-reconstruction, shape detector etc.
The use of such signal processing architecture in conjunction with complete robotized diagnostic helping platforms as Valdastri, may allows the conception of an autonomous lab-on-chip that would be able to execute simple tasks like free move and biopsy. References A. Abbo, R. Kleihorst, V. Choudhary, L.
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Health Management - Different Approaches and Solutions. Edited by: Krzysztof Śmigórski. ISBN , Published Health Management Different Approaches Solutions - Free ebook download as PDF File .pdf), Text File .txt) or read book online for free.
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