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Algorithms for leukaemia image edge detection technique

Master of Science in Engineering Mathematics

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Bibliographic Details
Main Author: Moath Ali Mohammed, Alshorman
Other Authors: Ahmad Kadri, Junoh. Dr.
Format: Dissertation
Language:English
Published: Universiti Malaysia Perlis (UniMAP) 2017
Subjects:
Digital images
Pattern recognition systems
Image converters
Edge detection
Leukaemia
Online Access:http://dspace.unimap.edu.my:80/xmlui/handle/123456789/72570
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spelling my.unimap-725702021-12-17T03:10:52Z Algorithms for leukaemia image edge detection technique Moath Ali Mohammed, Alshorman Ahmad Kadri, Junoh. Dr. Digital images Pattern recognition systems Image converters Edge detection Leukaemia Master of Science in Engineering Mathematics Edge detection is an essential pre-processing operation in image processing and pattern recognition. It involves identifying and tracing the sharp sudden discontinuities to extract meaningful information from an image. Edge detection simplify the analysis of an image by drastically reducing the amount of data to be processed and filtering out inadequate information, while at the same time preserving useful structural information about object boundaries in an image. The discontinuities signify the sudden changes in pixel intensity which describes boundaries of objects in a scene. The purpose of the present study is to detect the leukaemia edges in the white blood cell image. Toward this end, two distinctive procedures are implemented which are Ant Colony Optimization Algorithm and the gradient edge detectors (Sobel, Prewitt and Robert). The latter involves image filtering, binarization, kernel convolution filtering and image transformation. Meanwhile, ACO involves filtering, enhancement, detection and localization of the edges. Finally, the performance of the edge detection methods ACO, Sobel, Prewitt and Robert is compared in order to determine the best edge detection method which yielded optimal true edges of leukaemia in the white blood cell image. The results revealed distinctive results whereby the Prewitt edge detection method produced optimal performance for detecting edges of leukaemia cells with a value of (5982) active pixels. Meanwhile, the ACO, Sobel and Robert yielded active pixels of (2970), (5318) and (3810) respectively. Overall findings indicated that the gradient edge detection methods are superior to the Ant Colony Optimization method. 2017 2021-10-21T02:45:03Z 2021-10-21T02:45:03Z Dissertation http://dspace.unimap.edu.my:80/xmlui/handle/123456789/72570 en Universiti Malaysia Perlis (UniMAP) Universiti Malaysia Perlis (UniMAP) Institute of Engineering Mathematics
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Digital images
Pattern recognition systems
Image converters
Edge detection
Leukaemia
spellingShingle Digital images
Pattern recognition systems
Image converters
Edge detection
Leukaemia
Moath Ali Mohammed, Alshorman
Algorithms for leukaemia image edge detection technique
description Master of Science in Engineering Mathematics
author2 Ahmad Kadri, Junoh. Dr.
author_facet Ahmad Kadri, Junoh. Dr.
Moath Ali Mohammed, Alshorman
format Dissertation
author Moath Ali Mohammed, Alshorman
author_sort Moath Ali Mohammed, Alshorman
title Algorithms for leukaemia image edge detection technique
title_short Algorithms for leukaemia image edge detection technique
title_full Algorithms for leukaemia image edge detection technique
title_fullStr Algorithms for leukaemia image edge detection technique
title_full_unstemmed Algorithms for leukaemia image edge detection technique
title_sort algorithms for leukaemia image edge detection technique
publisher Universiti Malaysia Perlis (UniMAP)
publishDate 2017
url http://dspace.unimap.edu.my:80/xmlui/handle/123456789/72570
_version_ 1724609922594766848
score 13.214268

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