Quantitative measure in image segmentation for skin lesion images: a preliminary study

Automatic Skin Lesion Diagnosis (ASLD) allows skin lesion diagnosis by using a computer or mobile devices. The idea of using a computer to assist in diagnosis of skin lesions was first proposed in the literature around 1985. Images of skin lesions are analyzed by the computer to capture certain feat...

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Main Authors: Mohd. Azmi, Nurulhuda Firdaus, Md. Sarkan, Haslina, Ibrahim, Mohd. Hakimi Aiman, Lau, Hui Keng, Ibrahim, Nuzulha Khilwani
Format: Article
Published: American Institute of Physics Inc.dx 2014
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Online Access:http://eprints.utm.my/id/eprint/62377/
http://dx.doi.org/10.1063/1.4903564
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Institution: Universiti Teknologi Malaysia
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spelling my.utm.623772017-06-14T00:41:36Z http://eprints.utm.my/id/eprint/62377/ Quantitative measure in image segmentation for skin lesion images: a preliminary study Mohd. Azmi, Nurulhuda Firdaus Md. Sarkan, Haslina Ibrahim, Mohd. Hakimi Aiman Lau, Hui Keng Ibrahim, Nuzulha Khilwani QA75 Electronic computers. Computer science Automatic Skin Lesion Diagnosis (ASLD) allows skin lesion diagnosis by using a computer or mobile devices. The idea of using a computer to assist in diagnosis of skin lesions was first proposed in the literature around 1985. Images of skin lesions are analyzed by the computer to capture certain features thought to be characteristic of skin diseases. These features (expressed as numeric values) are then used to classify the image and report a diagnosis. Image segmentation is often a critical step in image analysis and it may use statistical classification, thresholding, edge detection, region detection, or any combination of these techniques. Nevertheless, image segmentation of skin lesion images is yet limited to superficial evaluations which merely display images of the segmentation results and appeal to the reader's intuition for evaluation. There is a consistent lack of quantitative measure, thus, it is difficult to know which segmentation present useful results and in which situations they do so. If segmentation is done well, then, all other stages in image analysis are made simpler. If significant features (that are crucial for diagnosis) are not extracted from images, it will affect the accuracy of the automated diagnosis. This paper explore the existing quantitative measure in image segmentation ranging in the application of pattern recognition for example hand writing, plat number, and colour. Selecting the most suitable segmentation measure is highly important so that as much relevant features can be identified and extracted. American Institute of Physics Inc.dx 2014 Article PeerReviewed Mohd. Azmi, Nurulhuda Firdaus and Md. Sarkan, Haslina and Ibrahim, Mohd. Hakimi Aiman and Lau, Hui Keng and Ibrahim, Nuzulha Khilwani (2014) Quantitative measure in image segmentation for skin lesion images: a preliminary study. International Conference on Quantitative Sciences and its Applications (ICOQSIA 2014), 1635 . pp. 65-71. ISSN 0094-243X http://dx.doi.org/10.1063/1.4903564 DOI:10.1063/1.4903564
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Mohd. Azmi, Nurulhuda Firdaus
Md. Sarkan, Haslina
Ibrahim, Mohd. Hakimi Aiman
Lau, Hui Keng
Ibrahim, Nuzulha Khilwani
Quantitative measure in image segmentation for skin lesion images: a preliminary study
description Automatic Skin Lesion Diagnosis (ASLD) allows skin lesion diagnosis by using a computer or mobile devices. The idea of using a computer to assist in diagnosis of skin lesions was first proposed in the literature around 1985. Images of skin lesions are analyzed by the computer to capture certain features thought to be characteristic of skin diseases. These features (expressed as numeric values) are then used to classify the image and report a diagnosis. Image segmentation is often a critical step in image analysis and it may use statistical classification, thresholding, edge detection, region detection, or any combination of these techniques. Nevertheless, image segmentation of skin lesion images is yet limited to superficial evaluations which merely display images of the segmentation results and appeal to the reader's intuition for evaluation. There is a consistent lack of quantitative measure, thus, it is difficult to know which segmentation present useful results and in which situations they do so. If segmentation is done well, then, all other stages in image analysis are made simpler. If significant features (that are crucial for diagnosis) are not extracted from images, it will affect the accuracy of the automated diagnosis. This paper explore the existing quantitative measure in image segmentation ranging in the application of pattern recognition for example hand writing, plat number, and colour. Selecting the most suitable segmentation measure is highly important so that as much relevant features can be identified and extracted.
format Article
author Mohd. Azmi, Nurulhuda Firdaus
Md. Sarkan, Haslina
Ibrahim, Mohd. Hakimi Aiman
Lau, Hui Keng
Ibrahim, Nuzulha Khilwani
author_facet Mohd. Azmi, Nurulhuda Firdaus
Md. Sarkan, Haslina
Ibrahim, Mohd. Hakimi Aiman
Lau, Hui Keng
Ibrahim, Nuzulha Khilwani
author_sort Mohd. Azmi, Nurulhuda Firdaus
title Quantitative measure in image segmentation for skin lesion images: a preliminary study
title_short Quantitative measure in image segmentation for skin lesion images: a preliminary study
title_full Quantitative measure in image segmentation for skin lesion images: a preliminary study
title_fullStr Quantitative measure in image segmentation for skin lesion images: a preliminary study
title_full_unstemmed Quantitative measure in image segmentation for skin lesion images: a preliminary study
title_sort quantitative measure in image segmentation for skin lesion images: a preliminary study
publisher American Institute of Physics Inc.dx
publishDate 2014
url http://eprints.utm.my/id/eprint/62377/
http://dx.doi.org/10.1063/1.4903564
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