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

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.

[1]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[2]  Alan C. Bovik,et al.  Automatic segmentation of dermoscopy images using self-generating neural networks seeded by genetic algorithm , 2013, Pattern Recognit..

[3]  Palaniappan Mirunalini,et al.  Automatic Skin Lesion Segmentation using Semi-supervised Learning Technique , 2017, ArXiv.

[4]  Gerald Schaefer,et al.  Anisotropic Mean Shift Based Fuzzy C-Means Segmentation of Dermoscopy Images , 2009, IEEE Journal of Selected Topics in Signal Processing.

[5]  M. Binder,et al.  Epiluminescence microscopy. A useful tool for the diagnosis of pigmented skin lesions for formally trained dermatologists. , 1995, Archives of dermatology.

[6]  I Zalaudek,et al.  Slow‐growing melanoma: a dermoscopy follow‐up study , 2010, The British journal of dermatology.

[7]  E. Kazerooni,et al.  Automated iterative neutrosophic lung segmentation for image analysis in thoracic computed tomography. , 2013, Medical physics.

[8]  V. Krishnaveni,et al.  A New Neutrosophic Approach of Wiener Filtering for MRI Denoising , 2013 .

[9]  Demetrio Labate,et al.  Characterization and Analysis of Edges Using the Continuous Shearlet Transform , 2009, SIAM J. Imaging Sci..

[10]  J. Wolfe,et al.  Changing your mind: on the contributions of top-down and bottom-up guidance in visual search for feature singletons. , 2003, Journal of experimental psychology. Human perception and performance.

[11]  Yi-Ping Phoebe Chen,et al.  Skin cancer extraction with optimum fuzzy thresholding technique , 2013, Applied Intelligence.

[12]  Shehzad Khalid,et al.  Segmentation of skin lesion using Cohen–Daubechies–Feauveau biorthogonal wavelet , 2016, SpringerPlus.

[13]  Hao Chen,et al.  Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks , 2017, IEEE Transactions on Medical Imaging.

[14]  Ashfaq A Marghoob,et al.  Instruments and new technologies for the in vivo diagnosis of melanoma. , 2003, Journal of the American Academy of Dermatology.

[15]  M MercyTheresa,et al.  Computer aided diagnostic (CAD) for feature extraction of lungs in chest radiograph using different transform features , 2017 .

[16]  Noel C. F. Codella,et al.  Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC) , 2016, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

[17]  V. Krishnaveni,et al.  Automated brain tumor segmentation on MR images based on neutrosophic set approach , 2015, 2015 2nd International Conference on Electronics and Communication Systems (ICECS).

[18]  W. Stoecker,et al.  Unsupervised border detection in dermoscopy images , 2007, Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging.

[19]  Zhen Ma,et al.  A Novel Approach to Segment Skin Lesions in Dermoscopic Images Based on a Deformable Model , 2016, IEEE Journal of Biomedical and Health Informatics.

[20]  Huiyu Zhou,et al.  A State-of-the-Art Survey on Lesion Border Detection in Dermoscopy Images , 2015 .

[21]  Jun Zhou,et al.  Shearlet transform based anomaly detection for hyperspectral image , 2012, Other Conferences.

[22]  Mohammad Aldeen,et al.  Border detection in dermoscopy images using hybrid thresholding on optimized color channels , 2011, Comput. Medical Imaging Graph..

[23]  Junji Maeda,et al.  Comparison of Segmentation Methods for Melanoma Diagnosis in Dermoscopy Images , 2009, IEEE Journal of Selected Topics in Signal Processing.

[24]  Jiawei Tian,et al.  A novel breast ultrasound image segmentation algorithm based on neutrosophic similarity score and level set , 2018, Comput. Methods Programs Biomed..

[25]  Demetrio Labate,et al.  Optimally Sparse Multidimensional Representation Using Shearlets , 2007, SIAM J. Math. Anal..

[26]  Wang-Q Lim,et al.  Sparse multidimensional representation using shearlets , 2005, SPIE Optics + Photonics.

[27]  Heng-Da Cheng,et al.  A NOVEL IMAGE SEGMENTATION APPROACH BASED ON NEUTROSOPHIC SET AND IMPROVED FUZZY C-MEANS ALGORITHM , 2011 .

[28]  Abdulkadir Sengür,et al.  NCM: Neutrosophic c-means clustering algorithm , 2015, Pattern Recognit..

[29]  K. Jeganathan,et al.  MRI denoising based on neutrosophic wiener filtering , 2012, 2012 IEEE International Conference on Imaging Systems and Techniques Proceedings.

[30]  Gerald Schaefer,et al.  Lesion border detection in dermoscopy images , 2009, Comput. Medical Imaging Graph..

[31]  Yanhui Guo,et al.  A novel image segmentation approach based on neutrosophic c-means clustering and indeterminacy filtering , 2017, Neural Computing and Applications.

[32]  Jacob Scharcanski,et al.  Automated prescreening of pigmented skin lesions using standard cameras , 2011, Comput. Medical Imaging Graph..

[33]  Xuan Liu,et al.  Image fusion based on shearlet transform and regional features , 2014 .

[34]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[35]  Jorge S. Marques,et al.  Development of a clinically oriented system for melanoma diagnosis , 2017, Pattern Recognit..

[36]  Yanhui Guo,et al.  Color texture image segmentation based on neutrosophic set and wavelet transformation , 2011, Comput. Vis. Image Underst..