Pemprosesan Imej Dan Pengekstrakan Ciri-Ciri Morfologi Secara Automatik Untuk Sel Lesi Payudara Teraspirasi Jarum Halus

Fine needle aspiration cytology (FNAC) has been known as one of the compatible method for detecting breast cancer at its early stage. This method is not only simple and fast; it is also low in cost. However, the process needs to be handled by highly skilled and experienced pathologists. Application...

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Main Author:	Mohd. Salleh, Nuryanti
Format:	Thesis
Language:	English
Published:	2015
Subjects:	TK1-9971 Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.usm.my/40940/1/NURYANTI_BINTI_MOHD._SALLEH_24_pages.pdf
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id	my-usm-ep.40940
record_format	uketd_dc
spelling	my-usm-ep.409402018-07-06T08:21:32Z Pemprosesan Imej Dan Pengekstrakan Ciri-Ciri Morfologi Secara Automatik Untuk Sel Lesi Payudara Teraspirasi Jarum Halus 2015 Mohd. Salleh, Nuryanti TK1-9971 Electrical engineering. Electronics. Nuclear engineering Fine needle aspiration cytology (FNAC) has been known as one of the compatible method for detecting breast cancer at its early stage. This method is not only simple and fast; it is also low in cost. However, the process needs to be handled by highly skilled and experienced pathologists. Application in medical imaging has been found to be very useful to assist this process. For solid tumours, the morphological features have been shown to be more sensitive and very useful to diagnose breast cancer. Identification of these morphological features has often been performed either by visualization or using manual computational system. The main objective of this research is to identify automated processing techniques to extract morphological features from fine needle aspirated cell images of breast lesion. The processing techniques studied were firstly, image preprocessing techniques to enhance focal areas. Secondly, image segmentation techniques which concentrate on focal areas. Finally, feature extraction techniques to extract morphological features from the images. In this research, 13 morphological features were extracted from FNAC images which contain single cell while another 40 morphological features were extracted from images which contain cluster cells. The information based on area, boundary, grey level concentration of pixel, compactness, diameter, roundness, nuclear pleomorphism index (NPI), nuclear pleomorphism measurement (NPM), optical density (OD) and integrated nuclear density of breast cells in each image were used to obtain the morphological data. Those image morphological data were then analyzed and compared with manual data through correlation test. Based on the test correlation results obtained show that 9 out of 13 types of correlation data extracted from images of single cells has a strong linear correlation of more than 0.8. As for the image of the cells cluster, only 11 out of 40 types of data extracted has a strong linear correlation relationship. 2015 Thesis http://eprints.usm.my/40940/ http://eprints.usm.my/40940/1/NURYANTI_BINTI_MOHD._SALLEH_24_pages.pdf application/pdf en public masters Universiti Sains Malaysia Pusat Pengajian Kejuruteraan Elektrik dan Elektronik
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	TK1-9971 Electrical engineering Electronics Nuclear engineering
spellingShingle	TK1-9971 Electrical engineering Electronics Nuclear engineering Mohd. Salleh, Nuryanti Pemprosesan Imej Dan Pengekstrakan Ciri-Ciri Morfologi Secara Automatik Untuk Sel Lesi Payudara Teraspirasi Jarum Halus
description	Fine needle aspiration cytology (FNAC) has been known as one of the compatible method for detecting breast cancer at its early stage. This method is not only simple and fast; it is also low in cost. However, the process needs to be handled by highly skilled and experienced pathologists. Application in medical imaging has been found to be very useful to assist this process. For solid tumours, the morphological features have been shown to be more sensitive and very useful to diagnose breast cancer. Identification of these morphological features has often been performed either by visualization or using manual computational system. The main objective of this research is to identify automated processing techniques to extract morphological features from fine needle aspirated cell images of breast lesion. The processing techniques studied were firstly, image preprocessing techniques to enhance focal areas. Secondly, image segmentation techniques which concentrate on focal areas. Finally, feature extraction techniques to extract morphological features from the images. In this research, 13 morphological features were extracted from FNAC images which contain single cell while another 40 morphological features were extracted from images which contain cluster cells. The information based on area, boundary, grey level concentration of pixel, compactness, diameter, roundness, nuclear pleomorphism index (NPI), nuclear pleomorphism measurement (NPM), optical density (OD) and integrated nuclear density of breast cells in each image were used to obtain the morphological data. Those image morphological data were then analyzed and compared with manual data through correlation test. Based on the test correlation results obtained show that 9 out of 13 types of correlation data extracted from images of single cells has a strong linear correlation of more than 0.8. As for the image of the cells cluster, only 11 out of 40 types of data extracted has a strong linear correlation relationship.
format	Thesis
qualification_level	Master's degree
author	Mohd. Salleh, Nuryanti
author_facet	Mohd. Salleh, Nuryanti
author_sort	Mohd. Salleh, Nuryanti
title	Pemprosesan Imej Dan Pengekstrakan Ciri-Ciri Morfologi Secara Automatik Untuk Sel Lesi Payudara Teraspirasi Jarum Halus
title_short	Pemprosesan Imej Dan Pengekstrakan Ciri-Ciri Morfologi Secara Automatik Untuk Sel Lesi Payudara Teraspirasi Jarum Halus
title_full	Pemprosesan Imej Dan Pengekstrakan Ciri-Ciri Morfologi Secara Automatik Untuk Sel Lesi Payudara Teraspirasi Jarum Halus
title_fullStr	Pemprosesan Imej Dan Pengekstrakan Ciri-Ciri Morfologi Secara Automatik Untuk Sel Lesi Payudara Teraspirasi Jarum Halus
title_full_unstemmed	Pemprosesan Imej Dan Pengekstrakan Ciri-Ciri Morfologi Secara Automatik Untuk Sel Lesi Payudara Teraspirasi Jarum Halus
title_sort	pemprosesan imej dan pengekstrakan ciri-ciri morfologi secara automatik untuk sel lesi payudara teraspirasi jarum halus
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Kejuruteraan Elektrik dan Elektronik
publishDate	2015
url	http://eprints.usm.my/40940/1/NURYANTI_BINTI_MOHD._SALLEH_24_pages.pdf
_version_	1747820844170608640

Pemprosesan Imej Dan Pengekstrakan Ciri-Ciri Morfologi Secara Automatik Untuk Sel Lesi Payudara Teraspirasi Jarum Halus

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