Open AccessDiagnostic techniques for improved segmentation, feature extraction, and classification of malignant melanoma
Author(s) -
Hyunju Lee,
Kiwoon Kwon
Publication year - 2019
Publication title -
biomedical engineering letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.709
H-Index - 26
eISSN - 2093-985X
pISSN - 2093-9868
DOI - 10.1007/s13534-019-00142-8
Subject(s) - thresholding , segmentation , artificial intelligence , pattern recognition (psychology) , variegation (histology) , feature (linguistics) , image segmentation , computer science , feature extraction , ground truth , region growing , scale space segmentation , computer vision , image (mathematics) , linguistics , philosophy , botany , biology
A typical diagnosis of malignant melanoma involves three major steps: segmentation of a lesion from the input color image, feature extraction from the separated lesion, and classification to distinguish malignant from benign melanomas based on features obtained. We suggest new methods for segmentation, feature extraction, and classification compared. We replaced edge-imfill method with U-Otsu method for segmentation, the previous features with new features for the criteria ABCD (asymmetry, border irregularity, color variegation, diameter) criteria, and the median thresholding with weighted receiver operating characteristic thresholding for classification. We used 88 melanoma images and expert's segmentation. All the three steps in the suggested method were compared with the steps in the previous method, with respect to sensitivity, specificity, and accuracy of the 88 samples. For segmentation, the previous and the suggested segmentations were also compared assuming the skin cancer expert's segmentation as a ground truth. All three steps resulted in remarkable improvement in the suggested method.