Document Type : Research Paper

Authors

Electrical Engineering Department, University of Technology - Iraq

Abstract

Automatic skin lesion segmentation on skin images is an essential component in diagnosing skin cancer. Image de-noising in skin cancer lesion is a description of processing image which refers to image restoration techniques to develop an image in predefined touch. Then de-noising is the crucial step of image processing to restore the right quality image after that which can use in many processes like segmentation, detection. This work proposes a new technique for skin lesion tumor denoising and segmentation. Initially, using Deep Convolution Neural Network (CNN) to eliminate noise and undesired structures for the images. Then, a new mechanism is proposed to segment the skin lesion into skin images based on active_contour straight with morphological processes. Different noise removal and segmentation techniques on skin lesion images are applying and comparing. The proposed algorithm shows improvement in the results of both noise reduction and segmentation

Keywords

Main Subjects

[1] A.H.H. Alasadi, and B.M. Alsafy, ―Diagnosis of
Malignant Melanoma of Skin Cancer Types,‖
International Journal of Interactive Multimedia and
Artificial Intelligence, Vol. 4, No.5, pp.44-49, 2017.
[2] A. Victor, and M. Ghalib, ―Automatic detection
and classification of skin cancer,‖ International
Journal of Intelligent Engineering and Systems, vol.
10, no. 3, pp.444-451, 2017.
[3] M.A. Sheha, M.S. Mabrouk, and A.Sharawy,
―Automatic detection of melanoma skin cancer using
texture analysis,” International Journal of Computer
Applications, vol. 42, no. 20, pp.22-26, March 2012.
[4] H.N. Abdullah, B.H. Abd, and S.H. Muhi, ―HighResolution Systems for Automated Diagnosis of
Hepatitis,‖ In 2018 Third Scientific Conference of
Electrical Engineering (SCEE), pp. 39-44, April 2019,
IEEE.
[5] D. Sharma, and S. Srivastava, ―Automatically
detection of skin cancer by classification of neural
network,‖ International Journal of Engineering and
Technical Research, Vol. 4, Issue 1, pp.15-18, 2016.
[6] H.N.Abdullah, J.S. Aziz, and A.N. Mohammed,
―Complex discrete wavelet transform-based image
denoising,‖ Engineering and Technology Journal, vol.
29, no. 5, pp.833-850, 2011.
[7] H.T. Lau, and A. Al-Jumaily, ―Automatically
early detection of skin cancer: Study based on neural
network classification,‖ In 2009 International
Conference of Soft Computing and Pattern
Recognition, pp. 375-380, December 2009, IEEE.
[8] H.R. Mhaske, and D.A. Phalke, ―Melanoma skin
cancer detection and classification based on supervised
and unsupervised learning,‖ In 2013 International Conference on Circuits, Controls and
Communications (CCUBE), pp. 1-5, December 2013,
IEEE.
[9] H.N. Abdullah, and M.A. Habtr, ―Brain Tumor
Extraction Approach in MRI Images Based on Soft
Computing Techniques,‖ In 2015 8th International
Conference on Intelligent Networks and Intelligent
Systems (ICINIS), pp. 21-24, November 2015, IEEE.
[10] M.K.A. Mahmoud, A. Al-Jumaily, and M.
Takruri, ―The automatic identification of melanoma by
wavelet and curvelet analysis: a study based on neural
network classification,‖ In 2011 11th International
Conference on Hybrid Intelligent Systems (HIS), pp.
680-685, December 2011, IEEE.
[11] J. Bai, Y. Sun, T. Fan, S. Song, and X. Zhang,
Medical image denoising based on improving K-SVD
and block-matching 3D filtering, In 2016 IEEE Region
10 Conference (TENCON), pp. 1624-1627, November
2016, IEEE.
[12] F. Xu, J. G. Lu, and Y. X. Sun, ―Application of
neural network in image processing,‖ Information and
Control, vol. 32, no. 4, pp. 344– 351, 2003.
[13] R. Garnavi, M. Aldeen, M.E. Celebi, A. Bhuiyan,
C. Dolianitis, and G. Varigos, ―Automatic
segmentation of dermoscopy images using histogram
thresholding on optimal color channels, International
Journal of Medicine and Medical Sciences, vol. 1, no.
2, pp.126-134,2010.
[14] Y. Liu, ―Image denoising method based on a
threshold, wavelet transform and genetic algorithm,‖
International Journal of Signal Processing, Image
Processing and Pattern Recognition, vol. 8, no. 2,
pp.29-40,2015.
[15] S. Schulte, B. Huysmans, A. Pižurica, E.E. Kerre,
and W. Philips, ―A new fuzzy-based wavelet shrinkage
image denoising technique,‖ In International
Conference on Advanced Concepts for Intelligent
Vision Systems, pp. 12-23, Springer, Berlin,
Heidelberg, September 2006.
[16] K. Zhang, W. Zuo, Y. Chen, D. Meng, and L.
Zhang, ―Beyond a gaussian denoiser: Residual
learning of deep CNN for image denoising,‖ IEEE
Transactions on Image Processing, vol. 26, no. 7, pp.
3142-3155,2017.
[17] ―Addi project,‖
https://www.fc.up.pt/addi/ph2%20database.html