Melanoma recognition framework based on expert definition of ABCD for dermoscopic images

Authors

  • Qaisar Abbas,

    Corresponding author
    1. Center for Biomedical Imaging and Bioinformatics, Key Laboratory of Image Processing, Faisalabad, Pakistan
    • Department of Computer Science, National Textile University, Faisalabad, Pakistan
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  • M. Emre Celebi,

    1. Department of Computer Science, Louisiana State University, Shreveport, LA, USA
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  • Irene Fondón Garcia,

    1. Department of Signal Theory and Communications, School of Engineering Path of Discovery, Seville, Spain
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  • Waqar Ahmad

    1. Department of Computer Science, National Textile University, Faisalabad, Pakistan
    2. Center for Biomedical Imaging and Bioinformatics, Key Laboratory of Image Processing, Faisalabad, Pakistan
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Address:

Dr Qaisar Abbas

Department of Computer Science

National Textile University

37610, Faisalabad

Pakistan

Tel: +92 41 9230081 Ext: 140

Fax: +92 (41) 9200764

e-mail : qaisarabbasphd@gmail.com; drqaisar@ntu.edu.pk

Abstract

Background/purpose

Melanoma Recognition based on clinical ABCD rule is widely used for clinical diagnosis of pigmented skin lesions in dermoscopy images. However, the current computer-aided diagnostic (CAD) systems for classification between malignant and nevus lesions using the ABCD criteria are imperfect due to use of ineffective computerized techniques.

Methods

In this study, a novel melanoma recognition system (MRS) is presented by focusing more on extracting features from the lesions using ABCD criteria. The complete MRS system consists of the following six major steps: transformation to the CIEL*a*b* color space, preprocessing to enhance the tumor region, black-frame and hair artifacts removal, tumor-area segmentation, quantification of feature using ABCD criteria and normalization, and finally feature selection and classification.

Results

The MRS system for melanoma-nevus lesions is tested on a total of 120 dermoscopic images. To test the performance of the MRS diagnostic classifier, the area under the receiver operating characteristics curve (AUC) is utilized. The proposed classifier achieved a sensitivity of 88.2%, specificity of 91.3%, and AUC of 0.880.

Conclusions

The experimental results show that the proposed MRS system can accurately distinguish between malignant and benign lesions. The MRS technique is fully automatic and can easily integrate to an existing CAD system. To increase the classification accuracy of MRS, the CASH pattern recognition technique, visual inspection of dermatologist, contextual information from the patients, and the histopathological tests can be included to investigate the impact with this system.

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