Digital image processing techniques for pre-diagnosis of psoriasis skin diseases / Rozita Jailani

This research involves the analysis of psoriasis skin lesions images. The images are captured using digital camera under controlled conditions. These images were collected from psoriasis patients at Hospital Universiti Kebangsaan Malaysia (HUKM) over five months starting from July 2002. The images i...

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Main Author:	Jailani, Rozita
Format:	Thesis
Language:	English
Published:	2004
Subjects:	Applications of electronics Pattern recognition systems
Online Access:	http://ir.uitm.edu.my/id/eprint/27493/ http://ir.uitm.edu.my/id/eprint/27493/1/TM_ROZITA%20JAILANI%20EE%2004_5.pdf

id	uitm-27493
recordtype	eprints
spelling	uitm-274932020-01-21T01:31:02Z http://ir.uitm.edu.my/id/eprint/27493/ Digital image processing techniques for pre-diagnosis of psoriasis skin diseases / Rozita Jailani Jailani, Rozita Applications of electronics Pattern recognition systems This research involves the analysis of psoriasis skin lesions images. The images are captured using digital camera under controlled conditions. These images were collected from psoriasis patients at Hospital Universiti Kebangsaan Malaysia (HUKM) over five months starting from July 2002. The images include three major types of psoriasis skin lesions. Two major works were carried out; one is done using image color and another one on skin lesion border segmentation. Color analysis was employed to distinguish the three major types of psoriasis skin diseases infecting the Malaysian population. Four color analysis techniques were applied; normalization techniques, Gaussian parameters', color spaces and pre-processing techniques. These color analyses produce a color model to distinguish the psoriasis skin disease. Second, border segmentation technique is introduced. Skin lesion border segmentation is a new technique to segment the psoriasis image into lesion, skin and other background. Accurate and reliable outline detection is important in order to segment the image into lesion, skin and other background, thereby ensuring that asymmetry and diameter measurement can be carried out only in the lesion image. Results from psoriasis skin lesion segmentation can be used in skin lesion shape, diameter and asymmetry calculation. From the results, a simple unified approach model for color analysis was constructed integrating significant normalization technique, Gaussian parameters', color space and pre-processing techniques. The mean value of red color component was found to be significant in pre-diagnosing the types of psoriasis skin diseases. In this color model, the plaque confidence interval is between 1.823 to 2.248, guttate confidence interval is between 1.169 to 1.594 and erythroderma confidence interval is between 2.974 to 3.399. Many combinations of the processing techniques had been tried to find robust border segmentation technique. From all the techniques, the proposed segmentation technique gives higher reliability and visually accurate continuous boundaries for a range of images. In this research, it produces visually accurate border segmentation more than 90% of psoriasis skin disease images. The results from border segmentation can be used for diameter calculation, asymmetry of lesion and recognition of the lesion border. 2004 Thesis NonPeerReviewed text en http://ir.uitm.edu.my/id/eprint/27493/1/TM_ROZITA%20JAILANI%20EE%2004_5.pdf Jailani, Rozita (2004) Digital image processing techniques for pre-diagnosis of psoriasis skin diseases / Rozita Jailani. Masters thesis, Universiti Teknologi MARA.
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Teknologi MARA
building	UiTM Institutional Repository
collection	Online Access
language	English
topic	Applications of electronics Pattern recognition systems
spellingShingle	Applications of electronics Pattern recognition systems Jailani, Rozita Digital image processing techniques for pre-diagnosis of psoriasis skin diseases / Rozita Jailani
description	This research involves the analysis of psoriasis skin lesions images. The images are captured using digital camera under controlled conditions. These images were collected from psoriasis patients at Hospital Universiti Kebangsaan Malaysia (HUKM) over five months starting from July 2002. The images include three major types of psoriasis skin lesions. Two major works were carried out; one is done using image color and another one on skin lesion border segmentation. Color analysis was employed to distinguish the three major types of psoriasis skin diseases infecting the Malaysian population. Four color analysis techniques were applied; normalization techniques, Gaussian parameters', color spaces and pre-processing techniques. These color analyses produce a color model to distinguish the psoriasis skin disease. Second, border segmentation technique is introduced. Skin lesion border segmentation is a new technique to segment the psoriasis image into lesion, skin and other background. Accurate and reliable outline detection is important in order to segment the image into lesion, skin and other background, thereby ensuring that asymmetry and diameter measurement can be carried out only in the lesion image. Results from psoriasis skin lesion segmentation can be used in skin lesion shape, diameter and asymmetry calculation. From the results, a simple unified approach model for color analysis was constructed integrating significant normalization technique, Gaussian parameters', color space and pre-processing techniques. The mean value of red color component was found to be significant in pre-diagnosing the types of psoriasis skin diseases. In this color model, the plaque confidence interval is between 1.823 to 2.248, guttate confidence interval is between 1.169 to 1.594 and erythroderma confidence interval is between 2.974 to 3.399. Many combinations of the processing techniques had been tried to find robust border segmentation technique. From all the techniques, the proposed segmentation technique gives higher reliability and visually accurate continuous boundaries for a range of images. In this research, it produces visually accurate border segmentation more than 90% of psoriasis skin disease images. The results from border segmentation can be used for diameter calculation, asymmetry of lesion and recognition of the lesion border.
format	Thesis
author	Jailani, Rozita
author_facet	Jailani, Rozita
author_sort	Jailani, Rozita
title	Digital image processing techniques for pre-diagnosis of psoriasis skin diseases / Rozita Jailani
title_short	Digital image processing techniques for pre-diagnosis of psoriasis skin diseases / Rozita Jailani
title_full	Digital image processing techniques for pre-diagnosis of psoriasis skin diseases / Rozita Jailani
title_fullStr	Digital image processing techniques for pre-diagnosis of psoriasis skin diseases / Rozita Jailani
title_full_unstemmed	Digital image processing techniques for pre-diagnosis of psoriasis skin diseases / Rozita Jailani
title_sort	digital image processing techniques for pre-diagnosis of psoriasis skin diseases / rozita jailani
publishDate	2004
url	http://ir.uitm.edu.my/id/eprint/27493/ http://ir.uitm.edu.my/id/eprint/27493/1/TM_ROZITA%20JAILANI%20EE%2004_5.pdf
first_indexed	2023-09-18T23:18:29Z
last_indexed	2023-09-18T23:18:29Z
_version_	1777419272866234368

Digital image processing techniques for pre-diagnosis of psoriasis skin diseases / Rozita Jailani

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