A Hybrid Skin Lesions Segmentation Approach Based on Image Processing Methods

  • Hanae Moussaoui sidi mohamed ben abdellah university of Fez, Sciences and technologies Faculty of Fez
  • Nabil El Akkad Sidi Mohamed Ben Abdellah University of fez, Morocco
  • Mohamed Benslimane Sidi Mohamed Ben Abdellah University of fez, Morocco
Keywords: Image segmentation, skin cancer, fuzzy c-means, multi-Otsu, thresholding, marker-controlled watershed

Abstract

Presently image segmentation remains the most crucial stage in the image processing system. The main idea ofimage segmentation is to partition or divide a random image into several partitions depending on the problem to solve. In this paper, we will be presenting a new method of skin cancer detection based on Otsu’s thresholding algorithm and markercontrolled watershed method. This hybridization process is first of all started by segmenting the input image using fuzzy c-means algorithm which is a clustering method that gives the possibility to a pixel to belong to one or more clusters. After that, we will apply multi-Otsu which is a thresholding algorithm that separates the pixels of an image into a variety of classes depending on the intensity of the gray levels. The next step of this proposed method is the marker-controlled watershedalgorithm that divides the image into homogenous areas or regions by using edge-detection concepts including mathematical morphology. The proposed technique was applied and experienced using several images of different types of skin cancer that were collected and gathered from the web and also from the Kaggle dataset. To assess the worth of the achieved results, we used several evaluation metrics like dice coefficient, sensitivity, specificity as well as Jaccard similarity that all have shown good and satisfactory results.

References

S. Ramya Silpa, Chidvila V, A review on skin cancer, International Research Journal of Pharmacy, DOI: 10.7897/2230-8407.04814, 2013.

Rogers HW, Weinstock MA, Feldman SR, Coldiron BM Incidence Estimate of Nonmelanoma Skin Cancer, JAMA Dermatol. DOI:10.1001/jamadermatol.2015.1187, 2015.

Eleni.L, Katz. A, Graham A. Colditz, Skin Cancer—The Importance of Prevention, JAMA Internal Medicine, DOI:10.1001/jamainternmed.2016.5008, 2016.

N.Buchanan Lunsford, J.Berktold, Dawn M. Holman, k. Stein, A. Prempeh, A. Yerkes, Skin cancer knowledge, awareness, beliefs and preventive behaviors among black and Hispanic men and women, Preventive Medicine Reports. Volume 12, Pages 203-209, ISSN 2211-3355, https://DOI.org/10.1016/j.pmedr.2018.09.017, 2018.

M. Ciazynska, G. Winciorek, D. Lange et al, The incidence and clinical analysis of non melanoma skin cancer, Scientific reports, Rep 11, 4337. https://DOI.org/10.1038/s41598-021-83502-8, 2021.

Maryellen L. Giger, Machine Learning in Medical Imaging, Journal of the American College of Radiology, Volume 15, Issue 3, Part B, Pages 512-520, ISSN 1546-1440, https://DOI.org/10.1016/j.jacr.2017.12.028, 2018.

D.C.Castro, I. Walker, B. Glocker, Causality matters in medical imaging, Nature Communications 11, 3673, https://DOI.org/10.1038/s41467-020-17478-w, 2020.

H. Alquran et al, The melanoma skin cancer detection and classification using support vector machine, 2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), pp. 1-5, DOI:10.1109/AEECT.2017.8257738, 2017.

Thanh. D, Prasath, Hieu. L et al, Melanoma Skin Cancer Detection Method Based on Adaptive Principal Curvature, Colour Normalisation and Feature Extraction with the ABCD Rule, Journal of Digital Imaging, vol.33, pp.574–585, https://DOI.org/10.1007/s10278-019-00316-x, 2020.

L. Khrissi, N. Elakkad, H. Satori and K. Satori, Image Segmentation based on k-means and genetic algorithms, Advances in Intelligent Systems and Computing. Vol. 1076, pp. 489 - 497, 2020. DOI: 10.1007/978-981-15-0947-6_4

X. Zheng, Q. Lei, R. Yao, Y. Gong, and Q. Yin, Image segmentation based on adaptive Kmeans algorithm, EURASIP Journal on Image and Video Processing, Article number: 68. DOI: 10.1186/s13640-018-0309-3, 2018.

Nida M. Zaitouna, Musbah J. Aqelb, Survey on Image Segmentation Techniques, ScienceDirect. International Conference on Communication, Management and Information Technology (ICCMIT), 2015.

S. Ghosh, N. Das, I. Das, U. Maulik, Understanding Deep Learning Techniques for Image Segmentation, ACM Computing Surveys Vol. 52, No. 4. https://DOI.org/10.1145/3329784, 2019.

N.El akkad, S.El Hazzat, A.Saaidi and k.Satori, Reconstruction of 3D Scenes by Camera Self-Calibration and Using Genetic Algorithms, 3D Research, 6 (7): 1-17. DOI: 10.1007/s13319-016-0082-y, 2016.

M. Merras, A. Saaidi, N. El Akkad, K. Satori, Multi-view 3D reconstruction and modeling of the unknown 3D scenes using genetic algorithms, Soft Computing, 22(19), pp. 6271-6289, 2016.

N. El akkad, M. Merras, A. Baataoui, A. Saaidi and K. Satori, Camera Self-calibration having the varying parameters

and based on homography of the plane at infinity, Multimedia Tools and Applications (Springer). 77(11), pp. 14055-14075, 2017.

N. El akkad, M. Merras, A. Saaidi and K. Satori, Camera Self-Calibration with Varying Intrinsic Parameters by an Unknown Three-Dimensional Scene The Visual Computer (Springer), Vol. 30, No. 5, pp. 519-530, 2014.

N. El Akkad, M. Merras, A. Saaidi, K. Satori, Robust method for self-calibration of cameras having the varying intrinsic parameters, . Journal of Theoretical and Applied Information Technology. 50(1), pp. 57-67, 2013.

N. El Akkad, M. Merras, A. Saaidi, K. Satori, Camera self-Calibration with Varying Parameters from Two views, WSEAS Transactions on Information Science and Applications. 10(11), pp. 356-367, 2013.

N. El Akkad, A. Saaidi, K. Satori, Self-calibration based on a circle of the cameras having the varying intrinsic parameters, Proceedings of 2012 International Conference on Multimedia Computing and Systems, ICMCS. pp. 161- 166, 2012.

Mohammad D. Hossain, D. Chen, Segmentation for Object-Based Image Analysis (OBIA): A review of algorithms and challenges from remote sensing perspective, ISPRS Journal of Photogrammetry and Remote Sensing, Volume 150, Pages 115-134, ISSN 0924-2716, https://DOI.org/10.1016/j.isprsjprs.2019.02.009, 2019.

Deep Gupta, R.S. Anand, A hybrid edge-based segmentation approach for ultrasound medical images, Biomedical Signal Processing and Control, Volume 31, Pages 116-126, ISSN 1746-8094, https://DOI.org/10.1016/j.bspc.2016.06.012, 2017.

X. Zhi, H. Shen, Saliency driven region-edge-based top-down level set evolution reveals the asynchronous focus in image segmentation, Pattern Recognition, Volume 80, Pages 241-255, ISSN 0031-3203, https://DOI.org/10.1016/j.patcog.2018.03.010, 2018.

R. Suganya, An automated computer aided diagnosis of skin lesions detection and classification for dermoscopy images, International Conference on Recent Trends in Information Technology (ICRTIT), pp. 1-5, DOI:10.1109/ICRTIT.2016.7569538, 2016.

A. Chiem, A. Al-Jumaily and R. Khushaba, A Novel Hybrid System for Skin Lesion Detection, 3rd International Conference on Intelligent Sensors, Sensor Networks and Information, 567-572, 2007.

M. A. Farooq, M. A. M. Azhar and R. H. Raza, Automatic Lesion Detection System (ALDS) for Skin Cancer Classification Using SVM and Neural Classifiers, IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE), 2016, pp. 301-308, DOI: 10.1109/BIBE.2016.53, 2016.

M. W. Rashad and M. Takruri, Automatic non-invasive recognition of melanoma using Support Vector Machines, International Conference on Bio-engineering for Smart Technologies (BioSMART), DOI:10.1109/BIOSMART.2016.7835462, 2016.

M. Yang, Y. Nataliani, Robust-learning fuzzy c-means clustering algorithm with unknown number of clusters, Pattern Recognition, Volume 71, Pages 45-59, ISSN 0031-3203, https://DOI.org/10.1016/j.patcog.2017.05.017, 2017

U. Erkan, L. Gokrem, S. Enginoglu, Different applied median filter in salt and pepper noise, Computers and Electrical Engineering, Volume 70, Pages 789-798, ISSN 0045-7906, https://DOI.org/10.1016/j.compeleceng.2018.01.019, 2018.

Zhan, Yantong, and G. Zhang, An Improved OTSU Algorithm Using Histogram Accumulation Moment for Ore Segmentation, Symmetry 11, no. 3: 431. https://DOI.org/10.3390/sym11030431, 2019.

H. Zhang, Z. Tang, Y. Xie, X. Gao, Q. Chen, A watershed segmentation algorithm based on an optimal marker for bubble size measurement, Measurement, Volume 138, Pages 182-193, ISSN 0263-2241, https://DOI.org/10.1016/j.measurement.2019.02.005, 2019.

H. Moussaoui, M. Benslimane, N. El Akkad, Image Segmentation Approach Based on Hybridization Between K-Means

and Mask R-CNN, WITS 2020, Proceedings of the sixth International Conference on Wireless Technologies, Embedded, and Intelligent Systems, July 2021, pages 821-830; ISBN: 978-981-33-6892-7.DOI: 10.1007/978-3-030-73882-2_79

L. Khrissi, N. El Akkad,H.Satori and k.Satori, IA Performant Clustering Approach Based on An Improved Sine Cosine Algorithm, International Journal of Computing, https://doi.org/10.47839/ijc.21.2.2584

L. Khrissi, N. El Akkad,H.Satori and k.Satori, Color image segmentation based on hybridization between Canny and k-means, 2019 7th Mediterranean Congress of Telecommunications (CMT), 2019, pp. 1-4, doi:10.1109/CMT.2019.8931358.

L. Khrissi, N. El Akkad,H.Satori and k.Satori, Simple and Efficient Clustering Approach Based on Cuckoo Search Algorithm, 2020 Fourth Int. Conf. Intell. Comput. Data Sci., pp. 1–6, Oct. 2020, doi: 10.1109/ICDS50568.2020.9268754.

L. Khrissi, N. El Akkad,H.Satori and k.Satori, Clustering method and sine cosine algorithm for image segmentation, Evol. Intell., Jan. 2021, doi: 10.1007/s12065-020-00544-z.

L. Khrissi,H.Satori,k.Satori and N. El Akkad, An Efficient Image Clustering Technique based on Fuzzy Cmeans and Cuckoo Search Algorithm, Int. J.Adv. Comput. Sci. Appl., vol. 12, no. 6, pp. 423–432, 2021, doi:10.14569/IJACSA.2021.0120647

Published
2023-01-23
How to Cite
Moussaoui, H., Nabil El Akkad, & Mohamed Benslimane. (2023). A Hybrid Skin Lesions Segmentation Approach Based on Image Processing Methods. Statistics, Optimization & Information Computing, 11(1), 95-105. https://doi.org/10.19139/soic-2310-5070-1549
Section
Research Articles