Investigation of the FMRI based Carotid Occlusion Disease diagnostic System
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2017 by IJETT Journal|
|Year of Publication : 2017|
|Authors : Shailesh V. Bhalerao
|DOI : 10.14445/22315381/IJETT-V48P254|
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
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].
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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).