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RISS 인기검색어
- 검색키워드
- 저자 : Samsuddin Ahmed (검색결과 5 건)
- 무료
- 기관 내 무료
- 유료
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Siamese Network for Learning Robust Feature of Hippocampi
Samsuddin Ahmed,정호엽(Ho Yub Jung) 한국스마트미디어학회 2020 스마트미디어저널 Vol.9 No.3
Hippocampus is a complex brain structure embedded deep into the temporal lobe. Studies have shown that this structure gets affected by neurological and psychiatric disorders and it is a significant landmark for diagnosing neurodegenerative diseases. Hippocampus features play very significant roles in region-of-interest based analysis for disease diagnosis and prognosis. In this study, we have attempted to learn the embeddings of this important biomarker. As conventional metric learning methods for feature embedding is known to lacking in capturing semantic similarity among the data under study, we have trained deep Siamese convolutional neural network for learning metric of the hippocampus. We have exploited Gwangju Alzheimer’s and Related Dementia cohort data set in our study. The input to the network was pairs of three-view patches (TVPs) of size 32 × 32× 3. The positive samples were taken from the vicinity of a specified landmark for the hippocampus and negative samples were taken from random locations of the brain excluding hippocampi regions. We have achieved 98.72% accuracy in verifying hippocampus TVPs.
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Abol Basher,Samsuddin Ahmed,정호엽 (사)한국스마트미디어학회 2020 스마트미디어저널 Vol.9 No.2
The hippocampal volume atrophy is known to be linked with neuro-degenerative disorders and it is also one of the most important early biomarkers for Alzheimer's disease detection. The measurements of hippocampal pure volumes from Magnetic Resonance Imaging (MRI) is a crucial task and state-of-the-art methods require a large amount of time. In addition, the structural brain development is investigated using MRI data, where brain morphometry (e.g. cortical thickness, volume, surface area etc.) study is one of the significant parts of the analysis. In this study, we have proposed a patch-based ensemble model of 3-D convolutional neural network (CNN) to measure the hippocampal pure volume from MRI data. The 3-D patches were extracted from the volumetric MRI scans to train the proposed 3-D CNN models. The trained models are used to construct the ensemble 3-D CNN model and the aggregated model predicts the pure volume in one-step in the test phase. Our approach takes only 5 seconds to estimate the volumes from an MRI scan. The average errors for the proposed ensemble 3-D CNN model are 11.7±8.8 (error%±STD) and 12.5±12.8 (error%±STD) for the left and right hippocampi of 65 test MRI scans, respectively. The quantitative study on the predicted volumes over the ground truth volumes shows that the proposed approach can be used as a proxy
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Ensuring Quality in Biometric Systems
Md. Mahbubur Rahman,Amit Karmaker,Md.Mahmudul Hasan,Samsuddin Ahmed 보안공학연구지원센터 2015 International Journal of Security and Its Applicat Vol.9 No.4
Biometric system is using for personal recognition of people in many social and economical activities now a days. A good biometric trait should be measurable, distinctive and stable over time. Real-world deployment of biometric systems often has to contend with degraded signal quality and erratic behavior of the biometric data. For last few years biometric data quality measure become an important concern after poor pathological sample and other many causes. The user, sensor and environmental facts are causes to quality degradation of biometric system. This study approaches that have been used to extract additional information about the biometric data that can then be used to improve performance in degraded conditions and also discuss about the sensor and environmental facts .This study will also discuss how this problem can be overcome to maintain the quality in biometric system with a special emphasis on face, fingerprint, iris modalities with different organizational standards.
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H.M. Zakir Hossain,M. Sultan-Ul-Islam,Syed Samsuddin Ahmed,Ismail Hossain 한국지질과학협의회 2002 Geosciences Journal Vol.6 No.3
Lithofacies analysis of the Permian Gondwana sequencein borehole GDH-45 of the Khalaspir Basin was performed with aview to deduce the nature of depositional environments. On thebasis of dominant lithofacies association, the sequence is dividedinto six lithostratigraphic units (units A to F). Five lithofacies (con-glomerate, sandstone, siltstone, mudstone/shale and coal) are iden-tified within these units. Several sub-lithofacies, such as masive,crudely stratified, cross-stratified, ripple and parallel laminatedsandstones are also identified within these lithofacies. The sequenceforms a fining-upward trend with a rare coarsening-upward unit.The generalised Gondwana sequence is characterised mainly bychannel lags, pebbly massive to crudely cross-stratified sandstone,trough and planar cross-stratified sandstone, ripple laminatedsandstone/siltstone, massive to parallel laminated siltstone, mud-stone/shale and coal in ascending order. The facies associationsrepresent several repeated fining-upward units and cycles, indi-cating various sub-environments (channel, floodplain, flood basin/backswamp) in fluvial regime. The conglomerates might have bendeposited as debris flow or channel lag deposits. The sandstoneswere deposited mainly as multistoried channel and lateral bars inmoderately braided and sinuous streams. The siltstone and mud-stone lithofacies indicate bar top, natural levee or floodplain toflood basin environments. The coal lithofacies suggests depositionin low-lying, short to long persistent, moderately to well drainedand sparse to densely vegetated backswamps in fluvial channel-flood-plain complex. The overall succession of the Gondwana borehole sed-iments sugests that the depositional basin became, with time, gentlerin slope gradient, resulting in a more sinuous stream setting.
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