IJBTT

Investigation of the FMRI based Carotid Occlusion Disease diagnostic System

	International Journal of Engineering Trends and Technology (IJETT)
	© 2017 by IJETT Journal
	Volume-48 Number-6
	Year of Publication : 2017
	Authors : Shailesh V. Bhalerao
	DOI :  10.14445/22315381/IJETT-V48P254

Citation 

Shailesh V. Bhalerao "Investigation of the FMRI based Carotid Occlusion Disease diagnostic System", International Journal of Engineering Trends and Technology (IJETT), V48(6),305-308 June 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract
Implementation of carotid occlusion disease detection which is related to the carotid artery. The artery in which blood blockage are present in the neck region that type of artery are called carotid artery. There are many patients who are suffering from this problem that cannot diagnose it in earlier stage due to absence of prognosis techniques related to this disease. As a result,many patient suffer in severe stages as delayed medical diagnosis. By evaluating exiting techniques of carotid occlusion, many researchers performed different imaging modalities such as ultrasound imaging, arteriography, magnetic resonance angiography (MRA) and transcarotid to anlyze prametric analysis of this disease. In this paper, we have been worked on 7-T FMRI image in which using these raw 2D images, we can detect this diseases in initial stage to assist medical expert for further medication. In this, we consider the two carotid blockage artery type as one is Internal Carotid Artery (ICA) and other one is the external carotid artery (ECA).Our scope of research is internal carotid artery (ICA) in which specifically blockage is detected using functional magnetic resonance imaging (FMRI) modality. The detection algorithm (classifier) is implemented on FPGA based hardware where pre-processing and post-processing algorithmic stages are developed and optimized. In this implementation, it has specified algorithmic optimization along with powerful graphics processing unit (GPUs) which gives comparative better results for implementing carotid detection with specified parameters (accuracy, selectivity & specificity). For implementation of carotid detection, we have developed FCM (Fuzzy C-Means) clustering algorithm architecture on FPGA hardware which gives better accurate and efficient noise reduction based disease diagnosis along with less power consumption and less memory storage[LE].

 References

[1] Yinghua Lu, TinghuaiMa, Changhong Yin, Xiaoyu Xie, Wei Tian and ShuiMing Zhong, “Implementation of the Fuzzy C-Means Clustering Algorithm in Meteorological Data”, International Journal of Database Theory and Application 2014
[2] Subhagata Chattopadhyay, Dilip Kumar Pratihar, Sanjib Chandra De Sarkar, “ A Comparative study of fuzzy c-means algorithm and entropy-based fuzzy clustering algorithms”2014.
[3] Yeu-Jhy Chang, Shinn-Kuang Lin, Shan-Jin Ryu, and Yau-, Yau Wai, “Common Carotid Artery Occlusion: Evaluation with Duplex Sonography”2013
[4] Dibya Jyoti Bora Dr. Anil Kumar Gupta Department Of Computer Science And Applications, Barkatullah University, Bhopal, India, “A Comparative study Between Fuzzy Clustering Algorithm and Hard Clustering Algorithm” 2 Apr 2014
[5] Michael R Jaff1, Gregory V Goldmakher, Michael H Lev and Javier M Romero, “Imaging of the carotid arteries: the role of duplex Ultrasonography, magnetic resonance arteriography, and computerized tomographic arteriography”2012
[6] Ai-Hsien Li a, Yao-Hung Wang b, Hsiang-Fong Kao a, Lin-Hsue Yang c, Lung Chand, Shu-Hsun Chue, Hon-Man Liu b, “Aggressive revascularization of acute internal carotid artery occlusion in patients with NIHSSN20 and poor collateral circulation: Preliminary report”,2011
[7] Roman Herziga, Petr Hluštika, b, Karel Urbaneka, Miroslav Vaverkac, Stanislav Bu?valb, Josef Macha?c, Ivanka Vlachovaa, Bohdan K?upkaa, Andrea Bartkovaa, Daniel Šaaka, Jan Mareša, Petr Kaovskya “Can we identify patients with carotid occlusion who would benefit from ec/ic bypass? Review”, 2014
[8] Masahiro Tamaki, Keiji Kidoguchi, Takashi Mizobe, Junji Koyama, Takeshi Kondoh, Takashi Sakurai, Eiji Kohmura, Koichi Yokono, and Keiji Umetani, “Carotid Artery Occlusion and Collateral Circulation in C57Black/6J Mice Detected by Synchrotron Radiation Microangiography”,2010
[9] John VanMeter, Ph.D. Center for Functional and Molecular Imaging Georgetown University Medical Center, “BOLD fMRI”2014
[10] Nikos K. Logothetis, “What we can do and what we cannot do with fMRI”2014
[11] Lokesh Zambare, S.V.Bhalerao, “Design Augmented Embedded System for 2D-3D Visualization in Cardiac Surgery”4, April 2014.
[12] Kamila E Sip, Andreas Roepstorff, William McGregor and Chris D Frith, “Detecting deception: the scope and limits”,2012
[13] M. Lewandowskia, M. Walczaka, P. Karwata, B. Witeka, A. Nowickia and P.KarÃlowiczb, “Research & Medical Doppler platform”,2012

Keywords
Internal carotid artery (ICA), External carotid artery (ECA), Functional magnetic resonance imaging (FMRI), field-programmable gate array(FPGA), Graphics processing units (GPUs), Fuzzy C-Means (FCM), Magnetic Resonance Angiography (MRA).