Lesion Preprocessing Techniques in Automatic Melanoma Detection System—A Comparative Study

Abstract

An automatic melanoma detection system is an image processing-based technique used to detect melanoma. From the infected skin area image, the automatic melanoma detection system produces classification results as benign or melanoma. The automatic melanoma detection system contains four steps. Preprocessing step removes the noise from the infected image. The segmentation step finds the region of interest. Feature extraction is used to obtain lesion features and the classification step predicts lesion image as benign or melanoma. The decision of melanoma detection from such an automatic system depends upon the quality of the input image. Therefore, preprocessing of lesion images is an essential step in the automatic melanoma detection system. It becomes a challenging task due to the presence of various outliers like glare, dust, and hairs on skin lesions. Preprocessing techniques are applied for noise and artifact removal from the lesion. A lot of preprocessing techniques are available in the literature. The selection of appropriate preprocessing techniques may improve the accuracy of the automatic melanoma detection system. Therefore, in this work, we have studied and compared different preprocessing techniques so that the researchers may select appropriate techniques for them. This paper highlights and compares the image enhancement preprocessing techniques based on SNR and PSNR using pepper, salt, and Gaussian noise.KeywordsLesion preprocessingPeak signal-to-noise ratioImage enhancement techniquesArtifacts removalAutomatic melanoma detection system