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Minimize the percentage of noise in biomedical images using neural networks.

Saudagar AK - ScientificWorldJournal (2014)

Bottom Line: This work is in continuity of an ongoing research project aimed at developing a system for efficient image compression approach for telemedicine in Saudi Arabia.We compare the efficiency of this technique against existing image compression techniques, namely, JPEG2000, in terms of compression ratio, peak signal to noise ratio (PSNR), and computation time.To our knowledge, the research is the primary in providing a comparative study with other techniques used in the compression of biomedical images.

View Article: PubMed Central - PubMed

Affiliation: Department of Information Systems, College of Computers and Information Sciences, Al Imam Mohammad Ibn Saud Islamic University (IMSIU), P.O. Box 5701, Riyadh 11432, Saudi Arabia.

ABSTRACT
The overall goal of the research is to improve the quality of biomedical image for telemedicine with minimum percentages of noise in the retrieved image and to take less computation time. The novelty of this technique lies in the implementation of spectral coding for biomedical images using neural networks in order to accomplish the above objectives. This work is in continuity of an ongoing research project aimed at developing a system for efficient image compression approach for telemedicine in Saudi Arabia. We compare the efficiency of this technique against existing image compression techniques, namely, JPEG2000, in terms of compression ratio, peak signal to noise ratio (PSNR), and computation time. To our knowledge, the research is the primary in providing a comparative study with other techniques used in the compression of biomedical images. This work explores and tests biomedical images such as X-rays, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET).

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Comparison of PSNR in dB when the three methods are applied.
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Related In: Results  -  Collection


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fig6: Comparison of PSNR in dB when the three methods are applied.

Mentions: For the evaluation of the suggested approach a simulation model is developed using Matlab and has been tested on various original gray-scale samples of medical images (Figure 2) such as human nerve cells and human body organs of different dimensions collected from hospitals in King Fahd Medical City, Riyadh, Saudi Arabia, with 500 dpi resolution. The training error plot for neural system developed is as shown in Figure 3. The Q1 processing sample was read with various specifications (Figure 4(a)). The output image using conventional JPEG2000 approach is as shown in Figure 4(b). The output image using improved BPNN is as shown in Figure 4(c) and the output image after applying the proposed approach is as shown in Figure 4(d). In [36] the authors compare the proposed approach with the existing approaches with respect to compression ratio and other factors were left for future work. The observations are as shown in Figures 5, 6, and 7.


Minimize the percentage of noise in biomedical images using neural networks.

Saudagar AK - ScientificWorldJournal (2014)

Comparison of PSNR in dB when the three methods are applied.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4127272&req=5

fig6: Comparison of PSNR in dB when the three methods are applied.
Mentions: For the evaluation of the suggested approach a simulation model is developed using Matlab and has been tested on various original gray-scale samples of medical images (Figure 2) such as human nerve cells and human body organs of different dimensions collected from hospitals in King Fahd Medical City, Riyadh, Saudi Arabia, with 500 dpi resolution. The training error plot for neural system developed is as shown in Figure 3. The Q1 processing sample was read with various specifications (Figure 4(a)). The output image using conventional JPEG2000 approach is as shown in Figure 4(b). The output image using improved BPNN is as shown in Figure 4(c) and the output image after applying the proposed approach is as shown in Figure 4(d). In [36] the authors compare the proposed approach with the existing approaches with respect to compression ratio and other factors were left for future work. The observations are as shown in Figures 5, 6, and 7.

Bottom Line: This work is in continuity of an ongoing research project aimed at developing a system for efficient image compression approach for telemedicine in Saudi Arabia.We compare the efficiency of this technique against existing image compression techniques, namely, JPEG2000, in terms of compression ratio, peak signal to noise ratio (PSNR), and computation time.To our knowledge, the research is the primary in providing a comparative study with other techniques used in the compression of biomedical images.

View Article: PubMed Central - PubMed

Affiliation: Department of Information Systems, College of Computers and Information Sciences, Al Imam Mohammad Ibn Saud Islamic University (IMSIU), P.O. Box 5701, Riyadh 11432, Saudi Arabia.

ABSTRACT
The overall goal of the research is to improve the quality of biomedical image for telemedicine with minimum percentages of noise in the retrieved image and to take less computation time. The novelty of this technique lies in the implementation of spectral coding for biomedical images using neural networks in order to accomplish the above objectives. This work is in continuity of an ongoing research project aimed at developing a system for efficient image compression approach for telemedicine in Saudi Arabia. We compare the efficiency of this technique against existing image compression techniques, namely, JPEG2000, in terms of compression ratio, peak signal to noise ratio (PSNR), and computation time. To our knowledge, the research is the primary in providing a comparative study with other techniques used in the compression of biomedical images. This work explores and tests biomedical images such as X-rays, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET).

Show MeSH