An improved segmentation method for lung cancer detection

Segmentation is one of the image processing technique which is use to segments an object from the background of an image. Problems may occur when segmenting an object from background normally due to cases such inhomogeneity intensity and others. This research proposed new segmentation method to be u...

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Main Author: Ahmad Amiruddin Almukhtar, Kamarudin
Format: Undergraduates Project Papers
Language:English
Published: 2019
Subjects:
Online Access:http://umpir.ump.edu.my/id/eprint/26921/
http://umpir.ump.edu.my/id/eprint/26921/
http://umpir.ump.edu.my/id/eprint/26921/1/An%20improved%20segmentation%20method%20for%20lung%20cancer.pdf
id ump-26921
recordtype eprints
spelling ump-269212019-12-17T02:20:33Z http://umpir.ump.edu.my/id/eprint/26921/ An improved segmentation method for lung cancer detection Ahmad Amiruddin Almukhtar, Kamarudin QA75 Electronic computers. Computer science Segmentation is one of the image processing technique which is use to segments an object from the background of an image. Problems may occur when segmenting an object from background normally due to cases such inhomogeneity intensity and others. This research proposed new segmentation method to be use in lung cancer diagnosis or detection purpose. Methods of image segmentation from every article are analyzed basically for advantage, features and drawbacks of each proposed method. Research suggest a combination of model, which have been proposed in other thesis or article with their results. Best model from reviewed articles which suggest best results is chosen, model A and model B to be combined and produce much better results in terms of accuracy and efficiency. 2019 Undergraduates Project Papers NonPeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/26921/1/An%20improved%20segmentation%20method%20for%20lung%20cancer.pdf Ahmad Amiruddin Almukhtar, Kamarudin (2019) An improved segmentation method for lung cancer detection. Faculty of Computer System & Software Engineering, Universiti Malaysia Pahang. http://fypro.ump.edu.my/ethesis/index.php
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Ahmad Amiruddin Almukhtar, Kamarudin
An improved segmentation method for lung cancer detection
description Segmentation is one of the image processing technique which is use to segments an object from the background of an image. Problems may occur when segmenting an object from background normally due to cases such inhomogeneity intensity and others. This research proposed new segmentation method to be use in lung cancer diagnosis or detection purpose. Methods of image segmentation from every article are analyzed basically for advantage, features and drawbacks of each proposed method. Research suggest a combination of model, which have been proposed in other thesis or article with their results. Best model from reviewed articles which suggest best results is chosen, model A and model B to be combined and produce much better results in terms of accuracy and efficiency.
format Undergraduates Project Papers
author Ahmad Amiruddin Almukhtar, Kamarudin
author_facet Ahmad Amiruddin Almukhtar, Kamarudin
author_sort Ahmad Amiruddin Almukhtar, Kamarudin
title An improved segmentation method for lung cancer detection
title_short An improved segmentation method for lung cancer detection
title_full An improved segmentation method for lung cancer detection
title_fullStr An improved segmentation method for lung cancer detection
title_full_unstemmed An improved segmentation method for lung cancer detection
title_sort improved segmentation method for lung cancer detection
publishDate 2019
url http://umpir.ump.edu.my/id/eprint/26921/
http://umpir.ump.edu.my/id/eprint/26921/
http://umpir.ump.edu.my/id/eprint/26921/1/An%20improved%20segmentation%20method%20for%20lung%20cancer.pdf
first_indexed 2023-09-18T22:42:13Z
last_indexed 2023-09-18T22:42:13Z
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