Melanoma recognition framework based on expert definition of ABCD for dermoscopic images.
OBJECTIVE
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.
Department of Computer Science, National Textile University, Faisalabad, 37610, Pakistan. qaisarabbasphd@gmail.com
Qaisar
Q
Abbas
M
M
Emre Celebi
Irene Fondón
IF
Garcia
Waqar
W
Ahmad
D000465:Algorithms; D016477:Artifacts; D003116:Color; D016208:Databases, Factual; D046169:Dermoscopy / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D003937:Diagnosis, Differential; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008545:Melanoma / Q000473:pathology*; D008954:Models, Biological; D018329:Nevus, Blue / Q000473:pathology; D018332:Nevus, Epithelioid and Spindle Cell / Q000473:pathology; D009508:Nevus, Pigmented / Q000473:pathology*; D012680:Sensitivity and Specificity; D012878:Skin Neoplasms / Q000473:pathology*