Performance Evaluation of Hardware Designs, Thinning, and Segmentation Algorithms in M-Health Environments

In this chapter, the authors have described the experimental analysis steps required for converting original veins images into thinned veins images by applying resample, segmentation, filtering, and thinning algorithms in the cloud IoT-based m-health environments. It is a little bit difficult to make a distinction between the vein pattern and the surroundings particularly in the cases of unclear and thin veins images. However, after applying the resample, segmentation, median filters, and thinning algorithms in the cloud IoT-based m-health environment, the superior quality veins image patterns of a single line are obtained.

The results of palm-dorsa-veins-based m-health systems in a cloud-computing environment are discussed and analyzed in a detailed way in this chapter of the book. The sample images S1, S2, S3, and S4 are being used for hardware designs and performance evaluation in the cases of re-sampling, segmentation, median filters, thinning and Top veins, which will be used for critically ill and general patients' identity verification in the cloud IoT-based m-health environments. The ModelSim-Altera hardware design language is used as a simulator tool to simulate the hardware design with sample veins images. Further, the ModelSim-Altera simulation outcomes are compared with MATLAB implementations for evaluating the performances of hardware designs of the described algorithms in the cloud IoT-based m-health environment. The outcomes are analyzed, and the details of these outcomes are discussed in this chapter.


IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 36210-36225 ◽  
Author(s):  
Yanan Li ◽  
Ziyun Huang ◽  
Zhiguo Cao ◽  
Hao Lu ◽  
Haihui Wang ◽  
...  

2019 ◽  
Vol 91 (1) ◽  
pp. 1601-1610
Author(s):  
Bernhard Stoeckl ◽  
Michael Preininger ◽  
Vanja Subotić ◽  
Hartmuth Schroettner ◽  
Peter Sommersacher ◽  
...  

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