Segmenting microcalcification in fullfield digital mammogram using seed-based region growing and mathematical morphology / Aminah Abdul Malek

Nowadays screening mammography is widely recognized as the only imaging modality that is useful for the early detection of breast abnormalities. This is due to the fact that mammography is able to detect abnormality (microcalcifications) in the breast two years ahead before the abnormalities become...

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主要作者:	Abdul Malek, Aminah
格式:	Thesis
語言:	English
出版:	2012
主題:	Medical education Medical schools Research Diseases of the breast
在線閱讀:	https://ir.uitm.edu.my/id/eprint/65201/1/65201.pdf
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id	my-uitm-ir.65201
record_format	uketd_dc
spelling	my-uitm-ir.652012023-01-05T02:31:59Z Segmenting microcalcification in fullfield digital mammogram using seed-based region growing and mathematical morphology / Aminah Abdul Malek 2012 Abdul Malek, Aminah Medical education. Medical schools. Research Diseases of the breast Nowadays screening mammography is widely recognized as the only imaging modality that is useful for the early detection of breast abnormalities. This is due to the fact that mammography is able to detect abnormality (microcalcifications) in the breast two years ahead before the abnormalities become palpable. In order to detect these microcalcifications successfully, a good segmentation technique is required. Seed-Based Region Growing (SBRG) has been widely used as segmentation method for medical images. The first process in SBRG is to identify an initial seed point. Most of the SBRG methods identify the seed point manually which involve human interaction. In this study, the SBRG method is enhanced using Mathematical Morphology (MM) technique. Morphology involves a theory for the analysis of shape and spatial structures. The roles of MM are used in developing an automated initial seed point and for post processing to obtain a smooth shape and boundary of microcalcifications. These methods are tested on 50 region of interest (ROI) images confirmed by a radiologist to consist microcalcifications. The results were compared with manual seed point selection. The performance is evaluated using Receiving Operator Curve (ROC) based on level of detection and classification. Experimental results show that the level of detection and classification for automated seed point are 0.98 and 0.94, respectively. On the other hand, the level of detection and classification for manual seed point are both 0.93. Since the values for automated seed point are closer to 1 as compared to the values of manual seed point, it can be said that the use of automated seed point is better than manual seed point. Consequently, this implies that the automated seed point has successfully segmented the microcalcifications. 2012 Thesis https://ir.uitm.edu.my/id/eprint/65201/ https://ir.uitm.edu.my/id/eprint/65201/1/65201.pdf text en public masters Universiti Teknologi MARA (UiTM) Faculty of Computer and Mathematical Sciences Wan Abdul Rahman, Wan Eny Zarina
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Wan Abdul Rahman, Wan Eny Zarina
topic	Medical education Medical schools Research Diseases of the breast
spellingShingle	Medical education Medical schools Research Diseases of the breast Abdul Malek, Aminah Segmenting microcalcification in fullfield digital mammogram using seed-based region growing and mathematical morphology / Aminah Abdul Malek
description	Nowadays screening mammography is widely recognized as the only imaging modality that is useful for the early detection of breast abnormalities. This is due to the fact that mammography is able to detect abnormality (microcalcifications) in the breast two years ahead before the abnormalities become palpable. In order to detect these microcalcifications successfully, a good segmentation technique is required. Seed-Based Region Growing (SBRG) has been widely used as segmentation method for medical images. The first process in SBRG is to identify an initial seed point. Most of the SBRG methods identify the seed point manually which involve human interaction. In this study, the SBRG method is enhanced using Mathematical Morphology (MM) technique. Morphology involves a theory for the analysis of shape and spatial structures. The roles of MM are used in developing an automated initial seed point and for post processing to obtain a smooth shape and boundary of microcalcifications. These methods are tested on 50 region of interest (ROI) images confirmed by a radiologist to consist microcalcifications. The results were compared with manual seed point selection. The performance is evaluated using Receiving Operator Curve (ROC) based on level of detection and classification. Experimental results show that the level of detection and classification for automated seed point are 0.98 and 0.94, respectively. On the other hand, the level of detection and classification for manual seed point are both 0.93. Since the values for automated seed point are closer to 1 as compared to the values of manual seed point, it can be said that the use of automated seed point is better than manual seed point. Consequently, this implies that the automated seed point has successfully segmented the microcalcifications.
format	Thesis
qualification_level	Master's degree
author	Abdul Malek, Aminah
author_facet	Abdul Malek, Aminah
author_sort	Abdul Malek, Aminah
title	Segmenting microcalcification in fullfield digital mammogram using seed-based region growing and mathematical morphology / Aminah Abdul Malek
title_short	Segmenting microcalcification in fullfield digital mammogram using seed-based region growing and mathematical morphology / Aminah Abdul Malek
title_full	Segmenting microcalcification in fullfield digital mammogram using seed-based region growing and mathematical morphology / Aminah Abdul Malek
title_fullStr	Segmenting microcalcification in fullfield digital mammogram using seed-based region growing and mathematical morphology / Aminah Abdul Malek
title_full_unstemmed	Segmenting microcalcification in fullfield digital mammogram using seed-based region growing and mathematical morphology / Aminah Abdul Malek
title_sort	segmenting microcalcification in fullfield digital mammogram using seed-based region growing and mathematical morphology / aminah abdul malek
granting_institution	Universiti Teknologi MARA (UiTM)
granting_department	Faculty of Computer and Mathematical Sciences
publishDate	2012
url	https://ir.uitm.edu.my/id/eprint/65201/1/65201.pdf
_version_	1783735542892986368

Segmenting microcalcification in fullfield digital mammogram using seed-based region growing and mathematical morphology / Aminah Abdul Malek

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