Quantitative evaluation of binary digital region asymmetry with application to skin lesion detection
Abstract Background The performance of Computer Aided Diagnosis Systems for early melanoma detection relies mainly on quantitative evaluation of the geometric features corresponding to skin lesions. In these systems, diagnosis is carried out by analyzing four geometric characteristics: asymmetry (A)...
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| Format: | Article |
| Language: | English |
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BMC
2018-06-01
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| Series: | BMC Medical Informatics and Decision Making |
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| Online Access: | http://link.springer.com/article/10.1186/s12911-018-0641-7 |
| _version_ | 1811270364858155008 |
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| author | Agustin Sancen-Plaza Raul Santiago-Montero Humberto Sossa Francisco J. Perez-Pinal Juan J. Martinez-Nolasco Jose A. Padilla-Medina |
| author_facet | Agustin Sancen-Plaza Raul Santiago-Montero Humberto Sossa Francisco J. Perez-Pinal Juan J. Martinez-Nolasco Jose A. Padilla-Medina |
| author_sort | Agustin Sancen-Plaza |
| collection | DOAJ |
| description | Abstract Background The performance of Computer Aided Diagnosis Systems for early melanoma detection relies mainly on quantitative evaluation of the geometric features corresponding to skin lesions. In these systems, diagnosis is carried out by analyzing four geometric characteristics: asymmetry (A), border (B), color (C) and dimension (D). The main objective of this study is to establish an algorithm for the measurement of asymmetry in biological entities. Methods Binary digital images corresponding to lesions are divided into 8 segments from their centroid. For each segment, the discrete compactness value is calculated using Normalized E-Factor (NEF). The asymmetry value is obtained from the sum of the square difference of each NEF value and corresponding value of its opposite by the vertex. Two public skin cancer databases were used. 1) Lee’s database with 40 digital regions evaluated by fourteen dermatologists. 2) The PH2 database which consists of 200 images in an 8-bit RGB format. This database provides a pre-classification of asymmetry carried out by experts, and it also indicates if the lesion is a melanoma. Results The measure was applied using two skin lesion image databases. 1) In Lee’s database, Spearman test provided a value of 0.82 between diagnosis of dermatologists and asymmetry values. For the 12 binary images most likely to be melanoma, the correlation between the measurement and dermatologists was 0.98. 2) In the PH2 database a label is provided for each binary image where the type of asymmetry is indicated. Class 0–1 corresponds to symmetry and one axis of symmetry shapes, the completely asymmetrical were assigned to Class 2, the values of sensitivity and specificity were 59.62 and 85.8% respectively between the asymmetry measured by a group of dermatologists and the proposed algorithm. Conclusions Simple image digital features such as compactness can be used to quantify the asymmetry of a skin lesion using its digital binary image representation. This measure is stable taking into account translations, rotations, scale changes and can be applied to non-convex regions, including areas with holes. |
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| id | doaj.art-5f1458e4b6904016968020caf2b3b520 |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-04-12T21:59:40Z |
| publishDate | 2018-06-01 |
| publisher | BMC |
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| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-5f1458e4b6904016968020caf2b3b5202022-12-22T03:15:11ZengBMCBMC Medical Informatics and Decision Making1472-69472018-06-0118111110.1186/s12911-018-0641-7Quantitative evaluation of binary digital region asymmetry with application to skin lesion detectionAgustin Sancen-Plaza0Raul Santiago-Montero1Humberto Sossa2Francisco J. Perez-Pinal3Juan J. Martinez-Nolasco4Jose A. Padilla-Medina5Department of Engineering Sciences, Tecnologico Nacional de Mexico-Instituto Tecnologico de CelayaDepartment of Computer Science, Tecnológico Nacional de México-Instituto Tecnológico de LeónDepartment of Computer Science, Instituto Politécnico Nacional-Centro de Investigación en ComputaciónDepartment of Engineering Sciences, Tecnologico Nacional de Mexico-Instituto Tecnologico de CelayaDepartment of Engineering Sciences, Tecnologico Nacional de Mexico-Instituto Tecnologico de CelayaDepartment of Engineering Sciences, Tecnologico Nacional de Mexico-Instituto Tecnologico de CelayaAbstract Background The performance of Computer Aided Diagnosis Systems for early melanoma detection relies mainly on quantitative evaluation of the geometric features corresponding to skin lesions. In these systems, diagnosis is carried out by analyzing four geometric characteristics: asymmetry (A), border (B), color (C) and dimension (D). The main objective of this study is to establish an algorithm for the measurement of asymmetry in biological entities. Methods Binary digital images corresponding to lesions are divided into 8 segments from their centroid. For each segment, the discrete compactness value is calculated using Normalized E-Factor (NEF). The asymmetry value is obtained from the sum of the square difference of each NEF value and corresponding value of its opposite by the vertex. Two public skin cancer databases were used. 1) Lee’s database with 40 digital regions evaluated by fourteen dermatologists. 2) The PH2 database which consists of 200 images in an 8-bit RGB format. This database provides a pre-classification of asymmetry carried out by experts, and it also indicates if the lesion is a melanoma. Results The measure was applied using two skin lesion image databases. 1) In Lee’s database, Spearman test provided a value of 0.82 between diagnosis of dermatologists and asymmetry values. For the 12 binary images most likely to be melanoma, the correlation between the measurement and dermatologists was 0.98. 2) In the PH2 database a label is provided for each binary image where the type of asymmetry is indicated. Class 0–1 corresponds to symmetry and one axis of symmetry shapes, the completely asymmetrical were assigned to Class 2, the values of sensitivity and specificity were 59.62 and 85.8% respectively between the asymmetry measured by a group of dermatologists and the proposed algorithm. Conclusions Simple image digital features such as compactness can be used to quantify the asymmetry of a skin lesion using its digital binary image representation. This measure is stable taking into account translations, rotations, scale changes and can be applied to non-convex regions, including areas with holes.http://link.springer.com/article/10.1186/s12911-018-0641-7Melanoma asymmetry measurementShape analysisDigital binary regionImage processingComputer aided diagnosis system |
| spellingShingle | Agustin Sancen-Plaza Raul Santiago-Montero Humberto Sossa Francisco J. Perez-Pinal Juan J. Martinez-Nolasco Jose A. Padilla-Medina Quantitative evaluation of binary digital region asymmetry with application to skin lesion detection BMC Medical Informatics and Decision Making Melanoma asymmetry measurement Shape analysis Digital binary region Image processing Computer aided diagnosis system |
| title | Quantitative evaluation of binary digital region asymmetry with application to skin lesion detection |
| title_full | Quantitative evaluation of binary digital region asymmetry with application to skin lesion detection |
| title_fullStr | Quantitative evaluation of binary digital region asymmetry with application to skin lesion detection |
| title_full_unstemmed | Quantitative evaluation of binary digital region asymmetry with application to skin lesion detection |
| title_short | Quantitative evaluation of binary digital region asymmetry with application to skin lesion detection |
| title_sort | quantitative evaluation of binary digital region asymmetry with application to skin lesion detection |
| topic | Melanoma asymmetry measurement Shape analysis Digital binary region Image processing Computer aided diagnosis system |
| url | http://link.springer.com/article/10.1186/s12911-018-0641-7 |
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