Employing the local radon transform for melanoma segmentation in dermoscopic images

Abstract

In recent years, the number of patients suffering from melanoma, as the deadliest type of skin cancer, has grown significantly in the world. The most common technique to observe and diagnosis of such cancer is the use of noninvasive dermoscope lens. Since this approach is based on the expert ocular inference, early stage of melanoma diagnosis is a difficult task for dermatologist. The main purpose of this article is to introduce an efficient algorithm to analyze the dermoscopic images. The proposed algorithm consists of four stages including converting the image color space from the RGB to CIE, adjusting the color space by applying the combined histogram equalization and the Otsu thresholding-based approach, border extraction of the lesion through the local Radon transform, and recognizing the melanoma and nonmelanoma lesions employing the ABCD rule. Simulation results in the designed user-friendly software package environment confirmed that the proposed algorithm has the higher quantities of accuracy, sensitivity, and approximation correlation in comparison with the other state-of-the-art methods. These values are obtained 98.81 (98.92), 94.85 (89.51), and 90.99 (86.06) for melanoma (nonmelanoma) lesions, respectively.

Accuracy of Computer-Aided Diagnosis of Melanoma

“…We identified 1694 potentially eligible articles, of which 132 were included in the qualitative analysis and 70 provided sufficient data for a quantitative meta-analysis (Figure 1, Figure 2, and Figure 3). We attributed 105 articles to the field of computer science and 27 to the field of medicine. The methods used were computer vision (n = 58), deep learning (n = 55), and hardware based (n = 19) .…”

“…10 Summary receiver operating characteristic (ROC) curves and mean estimates of sensitivity and specificity and the corresponding 95% CIs were calculated by the bivariate model of Reitsma et al 11 Heterogeneity and the presence of outliers were visually checked and the presence of betweenstudy variance was tested. 12 A bivariate meta-regression with potential covariables was modeled to reduce any heterogeneity noted between the studies. For all studies, the use of independent test sets, of proprietary or public test sets, and the method of analysis (computer vision, deep learning, or hardware based) were available and investigated.…”