Automated Hippocampus Segmentation
Lahari Hariram1, Karthik Kota2, J. Vedha3, K. Bharathwaj4
1Lahari Hariram, Department of Computer Science Engineering, Koneru Lakshmiah Education Foundation, Vijayawada (Andhra Pradesh), India.
2Karthik Kota, Department of Computer Science Engineering, Koneru Lakshmiah Education Foundation, Vijayawada (Andhra Pradesh), India.
3J.Vedha, Department of Computer Science Engineering, Koneru Lakshmiah Education Foundation, Vijayawada (Andhra Pradesh), India.
4K.Bharathwaj, Department of Computer Science Engineering, Koneru Lakshmiah Education Foundation, Vijayawada (Andhra Pradesh), India.
Manuscript received on 22 April 2019 | Revised Manuscript received on 04 May 2019 | Manuscript Published on 17 May 2019 | PP: 81-85 | Volume-7 Issue-6S4 April 2019 | Retrieval Number: F10160476S419/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Alzheimer’s malady (AD) is the most widely recognized reason for dementia; its initial and exact conclusion is testing. The hippocampus is a dark issue structure of the fleeting projection known to be influenced at the soonest phase of AD, even before the determination can be made, at the phase of Mild Cognitive Impairment .High goals MRI is progressively used to research hippocampal subfields, however most investigations depend on manual division which is work escalated. Hippocampal MRI volumetry is a potential biomarker for AD yet is blocked by the confinements of manual division. Up to now, hippocampal volumetry for the most part depends on very tedious manual division, which is rater-subordinate, and not possible in clinical daily schedule. Programmed division of the hippocampus would defeat these impediments and give a valuable biomarker of AD. This can be solved by a deep learning method stacked auto encoder for hippocampus segmentation. The layers in the auto encoder help us get accurate segmentation with minimal error or deviation.
Keywords: Automated Segmentation Manual Deep Learning Method.
Scope of the Article: Automated Software Specification