Reconstruction of High Resolution Medical Image Using General Regression Neural Network (GRNN)
DOI:
https://doi.org/10.26740/jpfa.v10n2.p137-145Keywords:
General Reconstruction Neural Network, Resolution, Interval, Medical image, ReconstructionAbstract
Low image resolution has deficiencies in the diagnostic process, this will affect the quality of the image in describing an object in certain tissues or organs, especially in the process of examining patients by doctors or physicians based on the results of imaging medical devices such as CT-scans, MRIs and X-rays. Therefore, this study had developed a General Regression Neural Network (GRNN) type artificial neural network system to reconstruct a medical image so that the image has a significant resolution for the analysis process. The GRNN input layer uses grayscale intensity values with variations in the image position coordinates to produce an optimal resolution. There are four layers in this method, the first is input layer, the second is hidden layer, the third is summation, and the last layer is output. We examined the two parameters with different interval values of 0.2 and of 0.5. The result shows that the interval value of 0.2 is the optimal value to produce an output image that is identical to the input image. This is also supported by the results of the intensity curve of the RGB pattern matched between target and output.
References
Sprawls P. Physical Principles of Medical Imaging. Radiology, 1995; 200(2): 504. DOI: https://doi.org/10.1148/radiology.200.2.504.
Johns HE, Cunningham JR. The Physics of Radiology. 4th ed. Springfield, IL: Charles C Thomas Publisher; 1983.
Hohara S and Nohtomi A. Neutron Radiography. Osaka: Atomic Energy Research Institute, Kinki University; 1895.
Eldin MTA. Neutron Radiography. Alexandria: Alexandria University; 2011.
Dowsett D, Kenny PA, and Johnston RE. The Physics of Diagnostic Imaging. Florida: CRC Press; 2006.
Curry TS, Dowdey JE, and Murry RC. Cristensens physics of diagnostic radiology. 4th edition. Philadelphia: Lippincott Williams & Wilkins; 1990.
Rakhshan V. Image Resolution in the Digital Era: Notion and Clinical Implications. Journal of Dentistry Shiraz University of Medical Sciences. 2014; 15(4): 153-155. Available from: https://dentjods.sums.ac.ir/article_41607.html.
Kondo Y, Han XH, and Chen YW. Two-step Learning Based Super Resolution and Its Application to 3D Medical Volumes. Proceedings of IEEE 4th Global Conference on Consumer Electronics (GCCE). Osaka. 2015; 326-327. DOI: https://doi.org/10.1109/GCCE.2015.7398738.
Hong W, Chen W, and Zhang R. The Application of Neural Network in the Technology of Image Processing. Proceedings of the International MultiConference of Engineers and Computer Scientists. Hongkong. 2009; 195-199. Available from: http://www.iaeng.org/publication/IMECS2009/IMECS2009_pp195-199.pdf.
Tang X, Han C, and Liu X. Super-Resolution X-Ray Luminescence Optical Tomography Imaging. Proceeding of SPIE 11190, Optics in HealthCare and Biomedical Optics IX. Hangzhou, China. 2019; 111902M. DOI: https://doi.org/10.1117/12.2537540.
Mandal S, Greenblatt AB, and An J. Imaging Intelligence: AI Is Transforming Medical Imaging Across the Imaging Spectrum. IEEE Pulse. 2018; 9(5): 16-24. DOI: https://doi.org/10.1109/MPUL.2018.2857226.
Reddy KSN, Vikram BR, Rao LK, and Reddy BS. Image Compression and Reconstruction Using a New Approach by Artificial Neural Network. International Journal of Image Processing (IJIP). 2012; 6(2): 68-85. Available from: http://www.cscjournals.org/library/manuscriptinfo.php?mc=IJIP-517.
Isaac N. Handbook of Medical Image Processing and Analysis. 2nd Edition. Amsterdam: Elsevier; 2009.
Baucher S and Meyer F. The Morphological Approach to Segmentation the Watershed Transformation. France: Centre de Morphologie Mathématique Ecole des Mines de Paris. 1993; 433-481. Available from: http://www.cmm.mines-paristech.fr/~beucher/publi/SB_watershed.pdf.
Talele K, Shirsat A, Uplenchwar T, and Tuckley K. Facial expression recognition using general regression neural networks. Proceedings of IEEE Bombay Section Symposium (IBSS). Baramati. 2016; 1-6. DOI: https://doi.org/10.1109/IBSS.2016.7940203.
Umehara K, Ota J, Ishimaru N, Ohno S, Okamoto K, Suzuki T, Shirai N, and Ishida T. Super-resolution convolutional neural network for the improvement of the image quality of magnified images in chest radiographs. Proceeding of SPIE 10133, Medical Imaging. Orlando, Florida. 2017; 101331P. DOI: https://doi.org/10.1117/12.2249969.
Mokhtari M, Daneshmand PG, and Rabbani H. Optical Coherence Tomography Image Reconstruction Using Morphological Component Analysis. Proceedings of 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Berlin, Germany. 2019; 5601-5604. DOI: https://doi.org/10.1109/EMBC.2019.8857782.
Yu H, Liu D, Shi H, Yu H, Wang Z, Cross B, Bramler M, and Huang TS. Computed Tomography Super-Resolution Using Convolutional Neural Networks. Proceedings of IEEE International Conference on Image Processing (ICIP). Beijing. 2017; 3944-3948. DOI: https://doi.org/10.1109/ICIP.2017.8297022.
Chen Y, Guan H, Hagen CK, Olivo A, Anastasio MA. A Joint-Reconstruction Approach for Single-Shot Edge Illumination X-Ray Phase-Contrast Tomography. Proceedings of SPIE 10132, Medical Imaging 2017: Physics of Medical Imaging. Florida. 2017; 1013217. DOI: https://doi.org/10.1117/12.2255545.
Schmidt TG, Barber RF, and Sidky EY. Spectral CT Metal Artifact Reduction with An Optimization-Based Reconstruction Algorithm. Proceedings of SPIE 10132, Medical Imaging: Physics of Medical Imaging. Florida. 2017; 101321B. DOI: https://doi.org/10.1117/12.2249079.
Rashmi, Kumar M, and Saxena R. Algorithm and Technique on Various Edge Detection: A Survey. Signal and Image Processing: An International Journal (SIPIJ). 2013; 4(3): 6575. DOI: http://dx.doi.org/10.5121/sipij.2013.4306.
Alilou VK and Yaghmaee F. Application of GRNN Neural Network in Non-Texture Image Inpainting and Restoration. Pattern Recognition Letters. 2015; 62: 24-31. DOI: https://doi.org/10.1016/j.patrec.2015.04.020.
Panda SS, Prasad MSRS, Prasad MNM, and Naidu SKVR. Image Compression Using Back Propagation Neural Network. International Journal of Engineering Science & Advanced Technology. 2012; 2(1): 74-78. Available from: https://www.ijesat.org/Volumes/2012_Vol_02_Iss_01/IJESAT_2012_02_01_12.pdf.
Al-Mahasneh AJ, Anavatti S, Garratt M, and Pratama M. Applications of General Regression Neural Networks in Dynamic Systems. IntechOpen; 2018. DOI: https://doi.org/10.5772/intechopen.80258.
Bej G, Akui A, Pal A, Dey T, Chauduri A, Alam S, Khandaii R, and Bhattacharyya N. X-Ray Imaging and General Regression Neural Network (GRNN) for Estimation of Silk Content in Cocoons. PerMIn '15: Proceedings of the 2nd International Conference on Perception and Machine Intelligence. Department of Science and Technology, Government of India. 2015; 7176. DOI: https://doi.org/10.1145/2708463.2709048.
Asad B, Zhi-jiang D, Li-ning S, Reza K, and Fereidoun MA. Fast 3D reconstruction of Ultrasonic Images Based on Generalized Regression Neural Network. World Congress on Medical Physics and Biomedical Engineering IFMBE Proceedings Vol 14. Berlin. 2006; 3117-3120. DOI: https://doi.org/10.1007/978-3-540-36841-0_787.
Kumar B, Sinha GR, and Thakur K. Quality Assessment of Compressed MR Medical Images using General Regression Neural Network. International Journal of Pure & Applied Sciences & Technology. 2011; 7(2): 158-169. Available from: https://www.researchgate.net/profile/Professor_G_Sinha/publication/302953762_Quality_assessment_of_compressed_MR_medical_images_using_general_regression_neural_network/links/58d4a593aca2727e5e9af371/Quality-assessment-of-compressed-MR-medical-images-using-general-regression-neural-network.pdf.
Navega D, Coelho JDO, Cunha E, and Curate F. DXAGE: A New Method for Age at Death Estimation Based on Femoral Bone Mineral Density and Artificial Neural Networks. Journal of Forensic Sciences. 2018; 63(2): 497-503. DOI: https://doi.org/10.1111/1556-4029.13582.
Meschino G, Moler EG, and Passoni LI. Semiautomated Segmentation of Bone Marrow Biopsies Images Based on Texture Features and Generalized Regression Neural Networks. X Congreso Argentino de Ciencias de la Computación. National University La Plata, Buinos Aires; 2004. Available from: http://sedici.unlp.edu.ar/handle/10915/22371.
Sankara Rao P and Kumar RK. An Improved Neural Network to Estimate Effort of Medical Imaging Software Development. Journal of Medical Imaging and Health Informatics. 2016; 6(8): 1977-1982. DOI: https://doi.org/10.1166/jmihi.2016.1960.
Johns HE, Cunningham JR. The Physics of Radiology. 4th ed. Springfield, IL: Charles C Thomas Publisher; 1983.
Hohara S and Nohtomi A. Neutron Radiography. Osaka: Atomic Energy Research Institute, Kinki University; 1895.
Eldin MTA. Neutron Radiography. Alexandria: Alexandria University; 2011.
Dowsett D, Kenny PA, and Johnston RE. The Physics of Diagnostic Imaging. Florida: CRC Press; 2006.
Curry TS, Dowdey JE, and Murry RC. Cristensens physics of diagnostic radiology. 4th edition. Philadelphia: Lippincott Williams & Wilkins; 1990.
Rakhshan V. Image Resolution in the Digital Era: Notion and Clinical Implications. Journal of Dentistry Shiraz University of Medical Sciences. 2014; 15(4): 153-155. Available from: https://dentjods.sums.ac.ir/article_41607.html.
Kondo Y, Han XH, and Chen YW. Two-step Learning Based Super Resolution and Its Application to 3D Medical Volumes. Proceedings of IEEE 4th Global Conference on Consumer Electronics (GCCE). Osaka. 2015; 326-327. DOI: https://doi.org/10.1109/GCCE.2015.7398738.
Hong W, Chen W, and Zhang R. The Application of Neural Network in the Technology of Image Processing. Proceedings of the International MultiConference of Engineers and Computer Scientists. Hongkong. 2009; 195-199. Available from: http://www.iaeng.org/publication/IMECS2009/IMECS2009_pp195-199.pdf.
Tang X, Han C, and Liu X. Super-Resolution X-Ray Luminescence Optical Tomography Imaging. Proceeding of SPIE 11190, Optics in HealthCare and Biomedical Optics IX. Hangzhou, China. 2019; 111902M. DOI: https://doi.org/10.1117/12.2537540.
Mandal S, Greenblatt AB, and An J. Imaging Intelligence: AI Is Transforming Medical Imaging Across the Imaging Spectrum. IEEE Pulse. 2018; 9(5): 16-24. DOI: https://doi.org/10.1109/MPUL.2018.2857226.
Reddy KSN, Vikram BR, Rao LK, and Reddy BS. Image Compression and Reconstruction Using a New Approach by Artificial Neural Network. International Journal of Image Processing (IJIP). 2012; 6(2): 68-85. Available from: http://www.cscjournals.org/library/manuscriptinfo.php?mc=IJIP-517.
Isaac N. Handbook of Medical Image Processing and Analysis. 2nd Edition. Amsterdam: Elsevier; 2009.
Baucher S and Meyer F. The Morphological Approach to Segmentation the Watershed Transformation. France: Centre de Morphologie Mathématique Ecole des Mines de Paris. 1993; 433-481. Available from: http://www.cmm.mines-paristech.fr/~beucher/publi/SB_watershed.pdf.
Talele K, Shirsat A, Uplenchwar T, and Tuckley K. Facial expression recognition using general regression neural networks. Proceedings of IEEE Bombay Section Symposium (IBSS). Baramati. 2016; 1-6. DOI: https://doi.org/10.1109/IBSS.2016.7940203.
Umehara K, Ota J, Ishimaru N, Ohno S, Okamoto K, Suzuki T, Shirai N, and Ishida T. Super-resolution convolutional neural network for the improvement of the image quality of magnified images in chest radiographs. Proceeding of SPIE 10133, Medical Imaging. Orlando, Florida. 2017; 101331P. DOI: https://doi.org/10.1117/12.2249969.
Mokhtari M, Daneshmand PG, and Rabbani H. Optical Coherence Tomography Image Reconstruction Using Morphological Component Analysis. Proceedings of 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Berlin, Germany. 2019; 5601-5604. DOI: https://doi.org/10.1109/EMBC.2019.8857782.
Yu H, Liu D, Shi H, Yu H, Wang Z, Cross B, Bramler M, and Huang TS. Computed Tomography Super-Resolution Using Convolutional Neural Networks. Proceedings of IEEE International Conference on Image Processing (ICIP). Beijing. 2017; 3944-3948. DOI: https://doi.org/10.1109/ICIP.2017.8297022.
Chen Y, Guan H, Hagen CK, Olivo A, Anastasio MA. A Joint-Reconstruction Approach for Single-Shot Edge Illumination X-Ray Phase-Contrast Tomography. Proceedings of SPIE 10132, Medical Imaging 2017: Physics of Medical Imaging. Florida. 2017; 1013217. DOI: https://doi.org/10.1117/12.2255545.
Schmidt TG, Barber RF, and Sidky EY. Spectral CT Metal Artifact Reduction with An Optimization-Based Reconstruction Algorithm. Proceedings of SPIE 10132, Medical Imaging: Physics of Medical Imaging. Florida. 2017; 101321B. DOI: https://doi.org/10.1117/12.2249079.
Rashmi, Kumar M, and Saxena R. Algorithm and Technique on Various Edge Detection: A Survey. Signal and Image Processing: An International Journal (SIPIJ). 2013; 4(3): 6575. DOI: http://dx.doi.org/10.5121/sipij.2013.4306.
Alilou VK and Yaghmaee F. Application of GRNN Neural Network in Non-Texture Image Inpainting and Restoration. Pattern Recognition Letters. 2015; 62: 24-31. DOI: https://doi.org/10.1016/j.patrec.2015.04.020.
Panda SS, Prasad MSRS, Prasad MNM, and Naidu SKVR. Image Compression Using Back Propagation Neural Network. International Journal of Engineering Science & Advanced Technology. 2012; 2(1): 74-78. Available from: https://www.ijesat.org/Volumes/2012_Vol_02_Iss_01/IJESAT_2012_02_01_12.pdf.
Al-Mahasneh AJ, Anavatti S, Garratt M, and Pratama M. Applications of General Regression Neural Networks in Dynamic Systems. IntechOpen; 2018. DOI: https://doi.org/10.5772/intechopen.80258.
Bej G, Akui A, Pal A, Dey T, Chauduri A, Alam S, Khandaii R, and Bhattacharyya N. X-Ray Imaging and General Regression Neural Network (GRNN) for Estimation of Silk Content in Cocoons. PerMIn '15: Proceedings of the 2nd International Conference on Perception and Machine Intelligence. Department of Science and Technology, Government of India. 2015; 7176. DOI: https://doi.org/10.1145/2708463.2709048.
Asad B, Zhi-jiang D, Li-ning S, Reza K, and Fereidoun MA. Fast 3D reconstruction of Ultrasonic Images Based on Generalized Regression Neural Network. World Congress on Medical Physics and Biomedical Engineering IFMBE Proceedings Vol 14. Berlin. 2006; 3117-3120. DOI: https://doi.org/10.1007/978-3-540-36841-0_787.
Kumar B, Sinha GR, and Thakur K. Quality Assessment of Compressed MR Medical Images using General Regression Neural Network. International Journal of Pure & Applied Sciences & Technology. 2011; 7(2): 158-169. Available from: https://www.researchgate.net/profile/Professor_G_Sinha/publication/302953762_Quality_assessment_of_compressed_MR_medical_images_using_general_regression_neural_network/links/58d4a593aca2727e5e9af371/Quality-assessment-of-compressed-MR-medical-images-using-general-regression-neural-network.pdf.
Navega D, Coelho JDO, Cunha E, and Curate F. DXAGE: A New Method for Age at Death Estimation Based on Femoral Bone Mineral Density and Artificial Neural Networks. Journal of Forensic Sciences. 2018; 63(2): 497-503. DOI: https://doi.org/10.1111/1556-4029.13582.
Meschino G, Moler EG, and Passoni LI. Semiautomated Segmentation of Bone Marrow Biopsies Images Based on Texture Features and Generalized Regression Neural Networks. X Congreso Argentino de Ciencias de la Computación. National University La Plata, Buinos Aires; 2004. Available from: http://sedici.unlp.edu.ar/handle/10915/22371.
Sankara Rao P and Kumar RK. An Improved Neural Network to Estimate Effort of Medical Imaging Software Development. Journal of Medical Imaging and Health Informatics. 2016; 6(8): 1977-1982. DOI: https://doi.org/10.1166/jmihi.2016.1960.
Downloads
Published
2020-12-31
How to Cite
Perkasa, Y. S., Syaja’ah, K. N., Ismadelani, L. and Agustina, R. D. (2020) “Reconstruction of High Resolution Medical Image Using General Regression Neural Network (GRNN)”, Jurnal Penelitian Fisika dan Aplikasinya (JPFA), 10(2), pp. 137–145. doi: 10.26740/jpfa.v10n2.p137-145.
Issue
Section
Articles
License
Author(s) who wish to publish with this journal should agree to the following terms:
- Author(s) retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-Non Commercial 4.0 License (CC BY-NC) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal for noncommercial purposes.
- Author(s) are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
The publisher publish and distribute the Article with the copyright notice to the JPFA with the article license CC-BY-NC 4.0.
Abstract views: 622
,
PDF Downloads: 348
