ABCD rules segmentation on Malignant tumor and Benign skin lesion images
Skin lesion is defined as a superficial growth or patch of the skin that is visually different than its surrounding area. Skin lesions appear for many reasons such as the symptoms indicative of diseases, birthmarks, allergic reactions, and so on. Images of skin lesions are analyzed by computer to ca...
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| Format: | Conference or Workshop Item |
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Institute of Electrical and Electronics Engineers Inc.
2016
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| _version_ | 1796861992309358592 |
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| author | Azmi, N. F. M. Sarkan, H. M. Yahya, Y. Chuprat, S. |
| author_facet | Azmi, N. F. M. Sarkan, H. M. Yahya, Y. Chuprat, S. |
| author_sort | Azmi, N. F. M. |
| collection | ePrints |
| description | Skin lesion is defined as a superficial growth or patch of the skin that is visually different than its surrounding area. Skin lesions appear for many reasons such as the symptoms indicative of diseases, birthmarks, allergic reactions, and so on. Images of skin lesions are analyzed by computer to capture certain features to be characteristic of skin diseases. These activities can be defined as automated skin lesion diagnosis (ASLD). ASLD involves five steps including image acquisition, pre-processing to remove occluding artifacts (such as hair), segmentation to extract regions of interest, feature selection and classification. This paper present analysis of automated segmentation called the ABCD rules (Asymmetry, Border irregularity, Color variegation, Diameter) in image segmentation. The experiment was carried on Malignant tumor and Benign skin lesion images. The study shows that the ABCD rules has successfully classify the images with high value of total dermatoscopy score (TDS). Although some of the analysis shows false alarm result, it may give the significant input to search suitable segmentation measure. |
| first_indexed | 2024-03-05T20:04:49Z |
| format | Conference or Workshop Item |
| id | utm.eprints-72932 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T20:04:49Z |
| publishDate | 2016 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| record_format | dspace |
| spelling | utm.eprints-729322017-11-21T08:17:09Z http://eprints.utm.my/72932/ ABCD rules segmentation on Malignant tumor and Benign skin lesion images Azmi, N. F. M. Sarkan, H. M. Yahya, Y. Chuprat, S. QA75 Electronic computers. Computer science Skin lesion is defined as a superficial growth or patch of the skin that is visually different than its surrounding area. Skin lesions appear for many reasons such as the symptoms indicative of diseases, birthmarks, allergic reactions, and so on. Images of skin lesions are analyzed by computer to capture certain features to be characteristic of skin diseases. These activities can be defined as automated skin lesion diagnosis (ASLD). ASLD involves five steps including image acquisition, pre-processing to remove occluding artifacts (such as hair), segmentation to extract regions of interest, feature selection and classification. This paper present analysis of automated segmentation called the ABCD rules (Asymmetry, Border irregularity, Color variegation, Diameter) in image segmentation. The experiment was carried on Malignant tumor and Benign skin lesion images. The study shows that the ABCD rules has successfully classify the images with high value of total dermatoscopy score (TDS). Although some of the analysis shows false alarm result, it may give the significant input to search suitable segmentation measure. Institute of Electrical and Electronics Engineers Inc. 2016 Conference or Workshop Item PeerReviewed Azmi, N. F. M. and Sarkan, H. M. and Yahya, Y. and Chuprat, S. (2016) ABCD rules segmentation on Malignant tumor and Benign skin lesion images. In: 3rd International Conference on Computer and Information Sciences, ICCOINS 2016, 15 August 2016 through 17 August 2016, Kuala Lumpur; Malaysia. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010443026&doi=10.1109%2fICCOINS.2016.7783190&partnerID=40&md5=cf2834d81cc82acfa4071e507d1ffb52 |
| spellingShingle | QA75 Electronic computers. Computer science Azmi, N. F. M. Sarkan, H. M. Yahya, Y. Chuprat, S. ABCD rules segmentation on Malignant tumor and Benign skin lesion images |
| title | ABCD rules segmentation on Malignant tumor and Benign skin lesion images |
| title_full | ABCD rules segmentation on Malignant tumor and Benign skin lesion images |
| title_fullStr | ABCD rules segmentation on Malignant tumor and Benign skin lesion images |
| title_full_unstemmed | ABCD rules segmentation on Malignant tumor and Benign skin lesion images |
| title_short | ABCD rules segmentation on Malignant tumor and Benign skin lesion images |
| title_sort | abcd rules segmentation on malignant tumor and benign skin lesion images |
| topic | QA75 Electronic computers. Computer science |
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