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 article belongs to the Special Issue Algebraic Structures of Neutrosophic Triplets, Neutrosophic Duplets, or Neutrosophic Multisets)
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.
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Keywords:
neutrosophic clustering; image segmentation; neutrosophic c-means clustering; region growing; dermoscopy; skin cancer
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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 StyleGuo, 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
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