Journal of Medical Signals & Sensors

SHORT COMMUNICATION
Year
: 2018  |  Volume : 8  |  Issue : 3  |  Page : 184--194

Employing the local radon transform for melanoma segmentation in dermoscopic images


Alireza Amoabedini1, Mahsa Saffari Farsani2, Hamidreza Saberkari3, Ehsan Aminian1 
1 Department of Computer Engineering, Safadasht Branch, Islamic Azad University, Tehran, Iran
2 Department of Electrical Engineering, Yazd University, Yazd, Iran
3 Department of Electrical Engineering, Sahand University of Technology, Tabriz, Iran

Correspondence Address:
Alireza Amoabedini
Department of Computer Engineering, Safadasht Branch, Islamic Azad University, Tehran
Iran

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.


How to cite this article:
Amoabedini A, Farsani MS, Saberkari H, Aminian E. Employing the local radon transform for melanoma segmentation in dermoscopic images.J Med Signals Sens 2018;8:184-194


How to cite this URL:
Amoabedini A, Farsani MS, Saberkari H, Aminian E. Employing the local radon transform for melanoma segmentation in dermoscopic images. J Med Signals Sens [serial online] 2018 [cited 2023 Jan 28 ];8:184-194
Available from: https://www.jmssjournal.net/article.asp?issn=2228-7477;year=2018;volume=8;issue=3;spage=184;epage=194;aulast=Amoabedini;type=0