Diagnostic techniques for improved segmentation, feature extraction, and classification of malignant melanoma.

Diagnostic techniques for improved segmentation, feature extraction, and classification of malignant melanoma.

Publication date: Feb 01, 2020

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.

Concepts Keywords
Benign Cancer
Lesion Melanoma
Malignant RTT
Malignant Melanoma Image segmentation
Melanoma Thresholding
Melanomas Medicine
Otsu
Receiver Operating Characteristic
Thresholding
Variegation

Semantics

Type Source Name
disease MESH malignant melanoma
disease MESH diagnosis
disease MESH separated
pathway KEGG Melanoma
disease MESH skin cancer

Original Article

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