Matrix Depiction Based Cyst Detection in Pediatric Aged MRI/ ULTRASONIC and Ultrasonic Images
AUTHORS
Kai Zhang,Dept. of Information Science and Engineering, University of Jinan, China
N.Thirupathi Rao,Dept. of Computer Science & Engineering, Vignan’s Institute of Information Technology, India
Debnath Bhattacharyya,Dept. of Computer Science & Engineering, Vignan’s Institute of Information Technology, India
ABSTRACT
Human brain is one of the most important organs in human body and it plays a vital role in the functioning of almost all parts of a body. The successful functioning of the human brain always leads to the human beings performing well in almost all types of works being performed by the human being. If the brain problems in children are a big problem not only to the children but also to the parents too. It may affect the actual growth of the children too. The good condition of this part is always a good sign of good health and good attitude of any human being. Colloid cyst are some of the problems that may occur at various locations of the human brain and if the cyst was identified earlier in the human brain, it can be removed through surgery and the life of a human being can be saved. If the cyst had not identified earlier, it may leads to the death of the human being in some special cases. Hence, identification of colloid cyst in human brain is one of the most important task and consideration for the doctors and lab technicians to identify it in the early stages of its growth. Hence, in the current article an attempt has been made to identify the cyst in pediatric aged children brain in the early stages also. In the current model, a new technique known as the identification was done from a monochrome image with matrix depiction method. The cyst was identified by using non-dependent threshold method from the matrix depicted method. The identification process was carried out by an algorithm and the proposed method was verified with various set of input images and the outputs are analyzed. The results are displayed in the results and discussions section in detail.
KEYWORDS
Pediatric, Fixed threshold method, Neuroepithelial cyst, Magnetic resonance images (MRI/ULTRASONIC), Matrix depiction, Ultrasonic images, Monochrome images, Cyst
REFERENCES
[1] Q. Javed and A. Dutta, “Third ventricular colloid cyst and organic hypomania,” Progress in Neurology and Psychiatry, vol.18, no.6, pp.18-20, (2014) DOI: 10.1002/pnp.355(CrossRef)(Google Scholar)
[2] http://www.medicalnewstoday.com/articles/181727.php, (2018)
[3] http://www.abta.org/secure/resource-one-sheets/cysts.pdf, (2018)
[4] K. Sheikh, V. Sutar and S. Thigale, “Clustering based segmentation approach to detect brain tumor from MRI/ultrasonic scan,” International Journal of Computer Applications, vol.118, no.8, pp.36-39, (2015) DOI:10.5120/20768-3224(CrossRef)(Google Scholar)
[5] E. E. Mohd, A. Muhd, M. Mohd, H. Z. Z. Htike and S. L. Win, “Brain tumor convergence and services,” IJITCS, vol.4, no.1, pp.1-11, (2014)
[6] V. D. Dharmale and P. A. Tijare, “Segmentation and canny edge method in MRI/ULTRASONIC brain cyst detection,” International Journal of Advanced Computer Research, vol.3, no.4, pp.289-293, (2013)
[7] L. P. Bhaiya, S. Goswami and V. Pali, “Classification of MRI/ULTRASONIC brain images using neuro fuzzy model,” International Journal of Engineering Inventions, vol.1, no.4, pp.27-31, (2012)
[8] M. Tariq, A. Khawajah and M. Hussain, “Image processing with the specific focus on early tumor detection,” International Journal of Machine Learning and Computing, vol.3, no.5, pp.404-407, (2013)
[9] C. Mamourian, L. D. Cromwell and R. E. Harbaugh, “Colloid cyst of third ventricle: Sometimes more conspicuous on CT than MR,” AJNR Am J Neuroradiol, pp.875-878, (1998)
[10] Peter D Caie, Ying Zhou, Arran K Turnbull, Anca Oniscu, “Model histopathologic feature identified through image analysis augments stage II colorectal cancer clinical reporting,” Oncotarget, vol.7, no.28, pp.120-132, (2016)
[11] Debapriya Hazra, Debnath Bhattacharyya, and Hye-Jin Kim, “Detection of colloid cyst in brain through image processing techniques,” International Journal of Multimedia and Ubiquitous Engineering, vol.11, no.9, pp.343-354, (2016)
[12] Lal A, Tejero H and Charif S, “Spontaneous decrease in size and change in MRI/ULTRASONIC signal characteristics of a colloid cyst in a pediatric patient,” Journal of Clinical Case Reports, vol.1, no.1, pp.1-4, (2018)
[13] E. Adinarayana, “Edge detector for spectral image using new approach,” International Journal of Digital Contents and Applications for Smart Devices, vol.3. no.1, pp.9-18, (2016)
[14] Dae-Hoon Hwang, “A study on multi-tag method for advanced image search,” International Journal of Software Engineering for Smart Device, vol.4, no.1, pp.15-22, (2017)
[15] Youngkyung Lee, Chaeho Cho, and Yoojae Won, “A text-learning based method of detecting personal information,” International Journal of Reliable Information and Assurance, vol.4. no.2, pp.7-12, (2016)
[16] Nikhil Chaudhari, Akshat Sharma and Anisha M. Lal, “Crack detection using image processing for automobiles and aircrafts,” International Journal of Engineering & Technology for Automobile Security, vol.1, no.1, pp.17-28, (2017)
[17] Suvajit Dutta, Bonthala CS Manideep, Syed Muzamil Basha, Ronnie D. Caytiles and N. Ch. S. N. Iyengar, “Classification of diabetic retinopathy images by using deep learning models,” International Journal of Grid and Distributed Computing, vol.11, no.1, pp.99-106, (2018)
[18] Zhijun Gao, Wei Bu, Xiangqian Wu and Yalin Zheng, “Intra-retinal layer segmentation of macular OCT Images using edge superpixels and manifold ranking method,” International Journal of Future Generation Communication and Networking, vol.10, no.6, pp.81-98, (2017)
[19] Kiranpreet and Prince Verma, “Clustering amelioration and optimization with swarm intelligence for color image segmentation,” International Journal of Database Theory and Application, vol.8, no.5, pp.51-64, (2015)
[20] Yu Jun, Rui Yong, Tang Yuan Yan, Tao Dacheng, “High-order distance-based multiview stochastic learning in image classification,” IEEE Transactions on Cybernetics, vol.44, no.12, pp.2431-2442, (2014)
[21] Yu Jun, Zhang Baopeng, Kuang Zhengzhong, Lin Dan, and Fan Jianping, “iPrivacy: Image privacy protection by identifying sensitive objects via deep multi-task learning,” IEEE Transactions on Information Forensics And Security, vol.12, no.5, pp.1005-1016, (2017) DOI: 10.1109/TIFS.2016.2636090(CrossRef)(Google Scholar)