Open AccessMelanoma recognition framework based on expert definition of ABCD for dermoscopic images
Author(s) -
Abbas Qaisar,
Emre Celebi M.,
Garcia Irene Fondón,
Ahmad Waqar
Publication year - 2013
Publication title -
skin research and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.521
H-Index - 69
eISSN - 1600-0846
pISSN - 0909-752X
DOI - 10.1111/j.1600-0846.2012.00614.x
Subject(s) - artificial intelligence , preprocessor , computer science , segmentation , pattern recognition (psychology) , classifier (uml) , feature selection , cad , computer aided diagnosis , melanoma , receiver operating characteristic , normalization (sociology) , computer vision , medicine , machine learning , engineering drawing , engineering , cancer research , sociology , anthropology
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.