A hybrid dermoscopy images segmentation approach based on neutrosophic clustering and histogram estimation
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Dosyalar
Tarih
2018
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
In this work, a novel skin lesion detection approach, called HBCENCM, is proposed using histogram-based clustering estimation (HBCE) algorithm to determine the required number of clusters in the neutrosophic c-means clustering (NCM) method. Initially, the dermoscopic images are mapped into the neutrosophic domain over three memberships, namely true, indeterminate, and false subsets. Then, an NCM algorithm is employed to group the pixels in the dermoscopy images, where the number of clusters in the dermoscopy images is determined using the HBCE algorithm. Lastly, the skin lesion is detected based on its intensity and morphological features. The public dataset (ISIC 2016) of 900 images for training and 379 images for testing are used in the present work. A comparative study of the original NCM clustering method is conducted on the same dataset. The results showed the superiority of the proposed approach to detect the lesion with 96.3% average accuracy compared to the average accuracy of 94.6% using the original NCM without HBCE algorithm. (C) 2018 Elsevier B.V. All rights reserved.
Açıklama
Polat, Kemal/0000-0003-1840-9958; Ashour, Amira/0000-0003-3217-6185; Guo, Yanhui/0000-0003-1814-9682
WOS: 000438775200024
WOS: 000438775200024
Anahtar Kelimeler
Dermoscopy images, Skin lesion, Neutrosophic clustering, Image histogram, Image segmentation, Neutrosophic c-means clustering, Histogram based cluster estimation (HBCE)
Kaynak
Applied Soft Computing
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
69
