Hippocampus Segmentation Using Locally Integrated Prior-Based Level Set Guided By Assembled And Weighted Coherent Point Drift Registration

Segmentasi hipokampus daripada struktur-struktur subkortikal otak bersebelahan merupakan satu tugas yang sangat mencabar, terutamanya akibat sempadan pemisahan struktur-struktur ini adalah lemah atau kurang jelas, seterusnya menyebabkan pendekatan berasaskan sempadan tidak berkesan untuk segmenta...

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Main Author:	Achuthan, Anusha
Format:	Thesis
Language:	English
Published:	2016
Subjects:	QA75.5-76.95 Electronic computers Computer science
Online Access:	http://eprints.usm.my/31409/1/ANUSHA_ACHUTHAN_24.pdf
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id	my-usm-ep.31409
record_format	uketd_dc
spelling	my-usm-ep.314092019-04-12T05:25:22Z Hippocampus Segmentation Using Locally Integrated Prior-Based Level Set Guided By Assembled And Weighted Coherent Point Drift Registration 2016-03 Achuthan, Anusha QA75.5-76.95 Electronic computers. Computer science Segmentasi hipokampus daripada struktur-struktur subkortikal otak bersebelahan merupakan satu tugas yang sangat mencabar, terutamanya akibat sempadan pemisahan struktur-struktur ini adalah lemah atau kurang jelas, seterusnya menyebabkan pendekatan berasaskan sempadan tidak berkesan untuk segmentasi hipokampus yang betul. Disamping itu, kedudukan hipokampus yang hampir dengan amygdala menyukarkan lagi isu segmentasi. Walau bagaimanapun, trend terkini telah beralih dari bergantung semata-mata kepada ciri-ciri imej kepada penggunaan model-model terdahulu dalam segmentasi. Secara amnya, model-model terdahulu dibina menggunakan segmentasi berasaskan atlas. Walau bagaimanapun, pendekatan ini sangat data intensif kerana ia menggunakan kaedah berasaskan volumetri untuk pembinaan model terdahulu. Oleh yang demikian, tesis ini mencadangkan satu pendekatan pembinaan model terdahulu yang bukan sahaja mampu mewakilkan maklumat bentuk dan lokasi ruang secara berkesan, malah mempunyai keperluan data intensif yang lebih rendah berbanding pendekatan berasaskan atlas. Secara terperinci, satu kaedah pendaftaran set titik yang novel dicadangkan dan disahkan bagi pembinaan model terdahulu. Hippocampus segmentation from neighbouring brain subcortical structures is a very challenging task mainly because boundaries separating these structures are weak or unclear, rendering conventional edge-based approaches ineffective for proper hippocampus segmentation. Besides that, close proximity of the hippocampus with the amygdala further complicates the segmentation issue. Recent trends, however have shifted from sole reliance on image features to utilization of prior models in the segmentation. Predominantly, the prior models are constructed using atlas-based segmentation. This approach however, is highly data intensive due to the volumetric-based methods used for prior model construction. Consequently, this thesis proposes a prior model construction method that not only effectively represents shape and spatial location information, but also requires lower data intensiveness compared to atlas-based approaches. Specifically, a novel point set registration method is proposed and validated for prior model construction. 2016-03 Thesis http://eprints.usm.my/31409/ http://eprints.usm.my/31409/1/ANUSHA_ACHUTHAN_24.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Sains Komputer (School of Computer Sciences)
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	QA75.5-76.95 Electronic computers Computer science
spellingShingle	QA75.5-76.95 Electronic computers Computer science Achuthan, Anusha Hippocampus Segmentation Using Locally Integrated Prior-Based Level Set Guided By Assembled And Weighted Coherent Point Drift Registration
description	Segmentasi hipokampus daripada struktur-struktur subkortikal otak bersebelahan merupakan satu tugas yang sangat mencabar, terutamanya akibat sempadan pemisahan struktur-struktur ini adalah lemah atau kurang jelas, seterusnya menyebabkan pendekatan berasaskan sempadan tidak berkesan untuk segmentasi hipokampus yang betul. Disamping itu, kedudukan hipokampus yang hampir dengan amygdala menyukarkan lagi isu segmentasi. Walau bagaimanapun, trend terkini telah beralih dari bergantung semata-mata kepada ciri-ciri imej kepada penggunaan model-model terdahulu dalam segmentasi. Secara amnya, model-model terdahulu dibina menggunakan segmentasi berasaskan atlas. Walau bagaimanapun, pendekatan ini sangat data intensif kerana ia menggunakan kaedah berasaskan volumetri untuk pembinaan model terdahulu. Oleh yang demikian, tesis ini mencadangkan satu pendekatan pembinaan model terdahulu yang bukan sahaja mampu mewakilkan maklumat bentuk dan lokasi ruang secara berkesan, malah mempunyai keperluan data intensif yang lebih rendah berbanding pendekatan berasaskan atlas. Secara terperinci, satu kaedah pendaftaran set titik yang novel dicadangkan dan disahkan bagi pembinaan model terdahulu. Hippocampus segmentation from neighbouring brain subcortical structures is a very challenging task mainly because boundaries separating these structures are weak or unclear, rendering conventional edge-based approaches ineffective for proper hippocampus segmentation. Besides that, close proximity of the hippocampus with the amygdala further complicates the segmentation issue. Recent trends, however have shifted from sole reliance on image features to utilization of prior models in the segmentation. Predominantly, the prior models are constructed using atlas-based segmentation. This approach however, is highly data intensive due to the volumetric-based methods used for prior model construction. Consequently, this thesis proposes a prior model construction method that not only effectively represents shape and spatial location information, but also requires lower data intensiveness compared to atlas-based approaches. Specifically, a novel point set registration method is proposed and validated for prior model construction.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Achuthan, Anusha
author_facet	Achuthan, Anusha
author_sort	Achuthan, Anusha
title	Hippocampus Segmentation Using Locally Integrated Prior-Based Level Set Guided By Assembled And Weighted Coherent Point Drift Registration
title_short	Hippocampus Segmentation Using Locally Integrated Prior-Based Level Set Guided By Assembled And Weighted Coherent Point Drift Registration
title_full	Hippocampus Segmentation Using Locally Integrated Prior-Based Level Set Guided By Assembled And Weighted Coherent Point Drift Registration
title_fullStr	Hippocampus Segmentation Using Locally Integrated Prior-Based Level Set Guided By Assembled And Weighted Coherent Point Drift Registration
title_full_unstemmed	Hippocampus Segmentation Using Locally Integrated Prior-Based Level Set Guided By Assembled And Weighted Coherent Point Drift Registration
title_sort	hippocampus segmentation using locally integrated prior-based level set guided by assembled and weighted coherent point drift registration
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Sains Komputer (School of Computer Sciences)
publishDate	2016
url	http://eprints.usm.my/31409/1/ANUSHA_ACHUTHAN_24.pdf
_version_	1747820418773811200

Hippocampus Segmentation Using Locally Integrated Prior-Based Level Set Guided By Assembled And Weighted Coherent Point Drift Registration

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