A robust system for melanoma diagnosis using heterogeneous image databases

Khaled Taouil, Zied Chtourou, Nadra Ben Romdhane

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DOI: 10.4236/jbise.2010.36080                

Abstract

Early diagnosis of melanoma is essential for the fight against this skin cancer. Many melanoma detection systems have been developed in recent years. The growth of interest in telemedicine pushes for the development of offsite CADs. These tools might be used by general physicians and dermatologists as a second advice on submission of skin lesion slides via internet. They also can be used for indexation in medical content image base retrieval. A key issue inherent to these CADs is non-heterogeneity of databases obtained with different apparatuses and acquisition techniques and conditions. We hereafter address the problem of training database heterogeneity by developing a robust methodology for analysis and decision that deals with this problem by accurate choice of features according to the relevance of their discriminative attributes for neural network classification. The digitized lesion image is first of all segmented using a hybrid approach based on morphological treatments and active contours. Then, clinical descriptions of malignancy signs are quantified in a set of features that summarize the geometric and photometric features of the lesion. Sequential forward selection (SFS) method is applied to this set to select the most relevant features. A general regression network (GRNN) is then used for the classification of lesions. We tested this approach with color skin lesion images from digitized slides data base selected by expert dermatologists from the hospital “CHU de Rouen-France” and from the hospital “CHU Hédi Chaker de Sfax-Tunisia”. The performance of the system is assessed using the index area (Az) of the ROC curve (Receiver Operating Characteristic curve). The classification permitted to have an Az score of 89,10%.

Keywords

Melanoma; Computer Aided Diagnosis System; Segmentation; Feature Selection; Classification; Generalized Regression Neural Network

Conflicts of Interest

The authors declare no conflicts of interest.

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