An Evaluation of Algorithms Applied to the Lattice Boltzmann Methods Technique for Segmentation of Medical Images

A Review

Authors

DOI:

https://doi.org/10.48112/jestt.v1i1.409

Abstract

Abstract Views: 36

This paper provides an overview of medical image separation techniques and statistical mechanics that rely primarily on the unconditional technique known as the lattice Boltzmann method LBM. The beauty of LBM is located in the increased computing frequency according to the medical image separation method with accuracy and specificity of more than ninety-five % compared to traditional strategies. Since clinical physics does not contain many facts about LBM, it is designed to provide an overview of the development of LBM studies. Since there is no overview paper on the progress of the studies in the LB approach, this paper provides an assessment to introduce some concepts related to the unique separation of medical images and the new LB method to investigate the hobby of fate and explore the fragmentation of clinical images. This work presents a brief evaluation of the scientific image separation techniques based mainly on the threshold, based on total neighbourhood, assembly, fragment detection, model-based, and radical technique approach Lattice Boltzmann LBM. This study had mentioned some separation techniques applied to scientific images, and emphasize that none of these problem areas have been fixed to acceptance, and significant improvements must be made to all decorated algorithms. Since LBM has speed and adaptability benefits to modelling to ensure incredible exceptional image processing with a reasonable amount of computer assets, expect this approach to become a hotspot for new studies in image processing.

Keywords:

Segmentation, Medical Physics, Radiation Therapy, Computed Tomography, Magnetic Resonance Imaging, Radiotherapy Planning Systems, Lattice Boltzmann Methods

References

Ahmed, M. N., Yamany, S. M., Mohamed, N., Farag, A. A., & Moriarty, T. (2002). A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE transactions on medical imaging, 21(3), 193-199.‏

Alahmar, D. (2019). Random Task Scheduler Algorithms as a Comparison and Access to the Best to Use in Real Time. International Journal of Scientific & Engineering Research, 1(5), 529-522.

Aneja, D., & Rawat, T. K. (2013). Fuzzy clustering algorithms for effective medical image segmentation. International Journal of Intelligent Systems and Applications, 5(11), 55-61.

Arabnia, H. R. (Ed.). (2010). Advances in computational biology (Vol. 680). Springer Science & Business Media.‏

Barot, V., Kapadia, V., & Pandya, S. (2020). QoS enabled IoT based low cost air quality monitoring system with power consumption optimization. Cybernetics and Information Technologies, 20(2), 122-140. https://doi.org/10.2478/cait-2020-0021.

Belaid, L. J., & Mourou, W. (2009). Image segmentation: a watershed transformation algorithm. Image Analysis & Stereology, 28(2), 93-102.‏

Benson, C. C., Lajish, V. L., & Rajamani, K. (2015, August). Brain tumor extraction from MRI brain images using marker based watershed algorithm. In 2015 International Conference on advances in computing, communications and informatics (ICACCI) (pp. 318-323). IEEE.‏

Boykov, Y. Y., & Jolly, M. P. (2001, July). Interactive graph cuts for optimal boundary & region segmentation of objects in ND images. In Proceedings eighth IEEE international conference on computer vision. ICCV 2001 (Vol. 1, pp. 105-112). IEEE. https://doi.org/10.1109/ICCV.2001.937505

Boykov, Y., Veksler, O., & Zabih, R. (2001). Fast approximate energy minimization via graph cuts. IEEE Transactions on pattern analysis and machine intelligence, 23(11), 1222-1239. https://doi.org/10.1109/34.969114

Cai, W., Chen, S., & Zhang, D. (2007). Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern recognition, 40(3), 825-838. https://doi.org/10.1016/j.patcog.2006.07.011

Candemir, S., Jaeger, S., Antani, S., Bagci, U., Folio, L. R., Xu, Z., & Thoma, G. (2016). Atlas-based rib-bone detection in chest X-rays. Computerized Medical Imaging and Graphics, 51, 32-39. https://doi.org/10.1016/j.compmedimag.2016.04.002

Chen, Y. (2010, October). A lattice-Boltzmann method for image inpainting. In 2010 3rd International Congress on Image and Signal Processing (Vol. 3, pp. 1222-1225). IEEE. https://doi.org/10.1109/CISP.2010.5647241

Chen, Y., Yan, Z., & Shi, J. (2007, August). Application of lattice Boltzmann method to image segmentation. In 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 6561-6564). IEEE. https://doi.org/10.1109/IEMBS.2007.4353863

Chowdhury, N., Toth, R., Chappelow, J., Kim, S., Motwani, S., Punekar, S., ... & Madabhushi, A. (2012). Concurrent segmentation of the prostate on MRI and CT via linked statistical shape models for radiotherapy planning. Medical Physics, 39(4), 2214-2228.‏

Cuadra, M. B., Pollo, C., Bardera, A., Cuisenaire, O., Villemure, J. G., & Thiran, J. P. (2004). Atlas-based segmentation of pathological MR brain images using a model of lesion growth. IEEE transactions on medical imaging, 23(10), 1301-1314. https://doi.org/10.1109/TMI.2004.834618

D Datta, S Mishra, SS Rajest, (2020) Quantification of tolerance limits of engineering system using uncertainty modeling for sustainable energy. International Journal of Intelligent Networks, Vol.1, 2020, pp.1-8, https://doi.org/10.1016/j.ijin.2020.05.006

Egmont-Petersen, M., de Ridder, D., & Handels, H. (2002). Image processing with neural networks—a review. Pattern recognition, 35(10), 2279-2301. https://doi.org/10.1016/S0031-3203(01)00178-9

Elazab, A., Wang, C., Jia, F., Wu, J., Li, G., & Hu, Q. (2015). Segmentation of brain tissues from magnetic resonance images using adaptively regularized kernel-based fuzzy-means clustering. Computational and mathematical methods in medicine, 2015. https://doi.org/10.1155/2015/485495

Girimaji, S. (2013). Lattice Boltzmann Method: Fundamentals and Engineering Applications with Computer Codes AA Mohammed, Springer, New York, 2011, LVIII, 238 pp., $129. AIAA Journal, 51(1), 278-279. https://doi.org/10.2514/1.J051744

Goubalan, S. R., Goussard, Y., & Maaref, H. (2016, September). Unsupervised malignant mammographic breast mass segmentation algorithm based on pickard Markov random field. In 2016 IEEE International Conference on Image Processing (ICIP) (pp. 2653-2657). IEEE. https://doi.org/10.1109/ICIP.2016.7532840

Hanbury, A. (2007). Image segmentation by region based and watershed algorithms. Wiley Encyclopedia of Computer Science and Engineering, 1543-1552.‏

Haralick, R. M., & Shapiro, L. G. (1985). Image segmentation techniques. Computer vision, graphics, and image processing, 29(1), 100-132.‏

Held, K., Kops, E. R., Krause, B. J., Wells, W. M., Kikinis, R., & Muller-Gartner, H. W. (1997). Markov random field segmentation of brain MR images. IEEE transactions on medical imaging, 16(6), 878-886. https://doi.org/10.1109/42.650883

Huang, Y. L., & Chen, D. R. (2004). Watershed segmentation for breast tumor in 2-D sonography. Ultrasound in medicine & biology, 30(5), 625-632.‏

Kang, W. X., Yang, Q. Q., & Liang, R. P. (2009, March). The comparative research on image segmentation algorithms. In 2009 First international workshop on education technology and computer science (Vol. 2, pp. 703-707). IEEE.‏

Li, Y., & Chi, Z. (2005). MR Brain image segmentation based on self-organizing map network. International Journal of Information Technology, 11(8), 45-53.

López-Mir, F., Naranjo, V., Angulo, J., Alcañiz, M., & Luna, L. (2014). Liver segmentation in MRI: A fully automatic method based on stochastic partitions. Computer methods and programs in biomedicine, 114(1), 11-28.‏

Lopez-Molina, C., De Baets, B., Bustince, H., Sanz, J., & Barrenechea, E. (2013). Multiscale edge detection based on Gaussian smoothing and edge tracking. Knowledge-Based Systems, 44, 101-111. https://doi.org/10.1016/j.knosys.2013.01.026

Mahde, A. H. T. (2019). Speech Recognition by Improving the Performance of Algorithms Used in Discrimination. International Journal of Computer Science & Information Technology (IJCSIT) Vol, 11.

Mahmood, N., Shah, A. S. A. D. U. L. L. A. H., Waqas, A., Abubakar, A. D. A. M. U., Kamran, S. H. A. F. I. A., & Zaidi, S. B. (2015). Image segmentation methods and edge detection: An application to knee joint articular cartilage edge detection. Journal of Theoretical and Applied Information Technology, 71(1), 87-96.

Mercado-Aguirre, I. M., Patiño-Vanegas, A., & Contreras-Ortiz, S. H. (2017, March). Region growing segmentation of ultrasound images using gradients and local statistics. In Medical Imaging 2017: Ultrasonic Imaging and Tomography (Vol. 10139, pp. 338-343). SPIE.‏

Park, H., Bland, P. H., & Meyer, C. R. (2003). Construction of an abdominal probabilistic atlas and its application in segmentation. IEEE Transactions on medical imaging, 22(4), 483-492. https://doi.org/10.1109/TMI.2003.809139

Pavlidis, T. (2012). Algorithms for graphics and image processing. Springer Science & Business Media.‏

Pham, D. L., Xu, C., & Prince, J. L. (2000). Current methods in medical image segmentation. Annual review of biomedical engineering, 2(1), 315-337.‏

Posada-Gómez, R., Sandoval-González, O. O., Sibaja, A. M., Portillo-Rodríguez, O., & Alor-Hernández, G. (2011). Digital image processing using LabVIEW. Practical Applications and Solutions Using LabVIEW Software, InTech, 297-316.‏

Ramesh, K. K. D., Kumar, G. K., Swapna, K., Datta, D., & Rajest, S. S. (2021). A review of medical image segmentation algorithms. EAI Endorsed Transactions on Pervasive Health and Technology, 7(27), e6-e6.‏

Ramesh, K. K. D., Kumar, G. K., Swapna, K., Datta, D., & Rajest, S. S. (2021). A review of medical image segmentation algorithms. EAI Endorsed Transactions on Pervasive Health and Technology, 7(27), e6-e6. https://doi.org/10.4108/eai.12-4-2021.169184

Salah, M. B., Mitiche, A., & Ayed, I. B. (2010). Multiregion image segmentation by parametric kernel graph cuts. IEEE Transactions on Image Processing, 20(2), 545-557. https://doi.org/10.1109/TIP.2010.2066982

Sharma, D. K., & Hooda, D. S. (2010). Some Generalized Information Measures: Their characterization and Applications. LAP Lambert Academic Pub..

Sharp, G., Fritscher, K. D., Pekar, V., Peroni, M., Shusharina, N., Veeraraghavan, H., & Yang, J. (2014). Vision 20/20: perspectives on automated image segmentation for radiotherapy. Medical physics, 41(5), 050902.‏

Song, Y., & Yan, H. (2017, December). Image segmentation techniques overview. In 2017 Asia Modelling Symposium (AMS) (pp. 103-107). IEEE.

Suganya, M., & Anandakumar, H. (2013, December). Handover based spectrum allocation in cognitive radio networks. In 2013 International Conference on Green Computing, Communication and Conservation of Energy (ICGCE) (pp. 215-219). IEEE. https://doi.org/10.1109/ICGCE.2013.6823431

Sur, A., Sah, R. P., & Pandya, S. (2020). Milk storage system for remote areas using solar thermal energy and adsorption cooling. Materials Today: Proceedings, 28, 1764-1770. https://doi.org/10.1016/j.matpr.2020.05.170.

Wongchomphu, P., & Eiamkanitchat, N. (2014, March). Enhance neuro-fuzzy system for classification using dynamic clustering. In The 4th Joint International Conference on Information and Communication Technology, Electronic and Electrical Engineering (JICTEE) (pp. 1-6). IEEE. https://doi.org/10.1109/JICTEE.2014.6804071

Zaitoun, N. M., & Aqel, M. J. (2015). Survey on image segmentation techniques. Procedia Computer Science, 65, 797-806.‏

Zhang, D. Q., & Chen, S. C. (2004). A novel kernelized fuzzy c-means algorithm with application in medical image segmentation. Artificial intelligence in medicine, 32(1), 37-50. https://doi.org/10.1016/j.artmed.2004.01.012

Zhang, W., & Shi, B. (2012). Application of lattice Boltzmann method to image filtering. Journal of Mathematical Imaging and Vision, 43, 135-142. https://doi.org/10.1007/s10851-011-0295-x

Zhang, Y. J. (2006). An overview of image and video segmentation in the last 40 years. Advances in Image and Video Segmentation, 1-16.

Zhang, Y., Brady, M., & Smith, S. (2001). Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE transactions on medical imaging, 20(1), 45-57. https://doi.org/10.1109/42.906424

An Evaluation of Algorithms Applied to the Lattice Boltzmann Methods Technique for Segmentation of Medical Images: A Review

Published

2023-02-28

How to Cite

Alahmar, H. T. M. (2023). An Evaluation of Algorithms Applied to the Lattice Boltzmann Methods Technique for Segmentation of Medical Images: A Review. Journal of Engineering, Science and Technological Trends, 1(1), 22–32. https://doi.org/10.48112/jestt.v1i1.409