Next Article in Journal
Symmetry Reduction and Numerical Solution of Von K a ´ rm a ´ n Swirling Viscous Flow
Next Article in Special Issue
Neutrosophic Triplet Cosets and Quotient Groups
Previous Article in Journal
A Simple Method for Measuring the Bilateral Symmetry of Leaves
Previous Article in Special Issue
An Extension of Neutrosophic AHP–SWOT Analysis for Strategic Planning and Decision-Making
Open AccessArticle

A Novel Skin Lesion Detection Approach Using Neutrosophic Clustering and Adaptive Region Growing in Dermoscopy Images

1
Department of Computer Science, University of Illinois at Springfield, Springfield, IL 62703, USA
2
Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Tanta 31527, Egypt
3
Department of Mathematics, University of New Mexico, 705 Gurley Ave., Gallup, NM 87301, USA
*
Author to whom correspondence should be addressed.
Symmetry 2018, 10(4), 119; https://doi.org/10.3390/sym10040119
Received: 26 March 2018 / Revised: 9 April 2018 / Accepted: 14 April 2018 / Published: 18 April 2018
This paper proposes novel skin lesion detection based on neutrosophic clustering and adaptive region growing algorithms applied to dermoscopic images, called NCARG. First, the dermoscopic images are mapped into a neutrosophic set domain using the shearlet transform results for the images. The images are described via three memberships: true, indeterminate, and false memberships. An indeterminate filter is then defined in the neutrosophic set for reducing the indeterminacy of the images. A neutrosophic c-means clustering algorithm is applied to segment the dermoscopic images. With the clustering results, skin lesions are identified precisely using an adaptive region growing method. To evaluate the performance of this algorithm, a public data set (ISIC 2017) is employed to train and test the proposed method. Fifty images are randomly selected for training and 500 images for testing. Several metrics are measured for quantitatively evaluating the performance of NCARG. The results establish that the proposed approach has the ability to detect a lesion with high accuracy, 95.3% average value, compared to the obtained average accuracy, 80.6%, found when employing the neutrosophic similarity score and level set (NSSLS) segmentation approach. View Full-Text
Keywords: neutrosophic clustering; image segmentation; neutrosophic c-means clustering; region growing; dermoscopy; skin cancer neutrosophic clustering; image segmentation; neutrosophic c-means clustering; region growing; dermoscopy; skin cancer
Show Figures

Figure 1

MDPI and ACS Style

Guo, Y.; Ashour, A.S.; Smarandache, F. A Novel Skin Lesion Detection Approach Using Neutrosophic Clustering and Adaptive Region Growing in Dermoscopy Images. Symmetry 2018, 10, 119. https://doi.org/10.3390/sym10040119

AMA Style

Guo Y, Ashour AS, Smarandache F. A Novel Skin Lesion Detection Approach Using Neutrosophic Clustering and Adaptive Region Growing in Dermoscopy Images. Symmetry. 2018; 10(4):119. https://doi.org/10.3390/sym10040119

Chicago/Turabian Style

Guo, Yanhui; Ashour, Amira S.; Smarandache, Florentin. 2018. "A Novel Skin Lesion Detection Approach Using Neutrosophic Clustering and Adaptive Region Growing in Dermoscopy Images" Symmetry 10, no. 4: 119. https://doi.org/10.3390/sym10040119

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop