Sampling with level set for pigmented skin lesion segmentation
- 26 Downloads
Melanoma is the deadliest form of skin cancer, and its incidence is increasing. The first step in automated melanoma analysis of dermoscopy images is to segment the area of the lesion from the surrounding skin. To improve the accuracy and adaptability of segmentation, an algorithm called sampling with level set by integrating color and texture (SLS-CT) is proposed that not only designs a new way to incorporate textural and color features in the definition of the energy functional but also utilizes an index called texture level, proposed in this work, to automatically decide the weight of each feature in the combined energies. First, at the preprocessing stage, hair and black frame removal is applied, and a potential lesion area is then obtained using Otsu thresholding and entropy maximization. Thereafter, the probability distribution of prior color in this potential lesion area is calculated as well. Second, Gabor wavelet-based texture features are extracted and integrated with the prior color into the evolving energies of the level set based on the texture level. To achieve global optimization, a Markov chain Monte Carlo sampling approach guided by the combined energy is adopted in evolving the level set, which ultimately defines a border in the image to segment a lesion from normal skin. Finally, morphological operations are used for postprocessing. The experimental results of the proposed algorithm are compared with those of other state-of-the-art algorithms, demonstrating that the proposed algorithm outperforms the other tested ones in terms of accuracy and adaptability to different databases.
KeywordsPigmented skin lesion Level set Texture Markov chain Monte Carlo Image segmentation
This research was partly supported by the Guangdong Provincial Key Laboratory of Digital Signal and Image Processing Techniques (2013GDDSIPL-03), the Guangxi Natural Science Foundation (2018JJB170004), the Guangxi young and middle-aged teachers basic ability promotion project (2017KY0247), the Project of Cultivating a Thousand Young and Middle-aged Teachers in Guangxi Universities and the Guangxi Key Laboratory Fund of Embedded Technology and Intelligent System under Grant No. 2018A-07.
- 12.Li, W., Li, F., Du, J.: A level set image segmentation method based on a cloud model as the priori contour. Signal Image Video Process. (2018). https://doi.org/10.1007/s11760-018-1334-5
- 14.Oliveira, R.B., Pereira, A.S., Tavares, J.M.R.S.: Pattern recognition in macroscopic and dermoscopic images for skin lesion diagnosis. In: VipIMAGE 2017, Lecture Notes in Computational Vision and Biomechanics, vol. 27, pp. 504–514. Springer, Cham (2018)Google Scholar
- 15.Roth, H.R., Lu, L., Farag, A., Shin, H.C., Liu, J., Turkbey, E.B., Summers, R.M.: DeepOrgan: Multi-level deep convolutional networks for automated pancreas segmentation. In: Medical Image Computing and Computer-Assisted Intervention, vol. 9349, pp. 556–564. Munich (2015)Google Scholar
- 16.Hu, P., Yang, T.J.: Pigmented skin lesions detection using random forest and wavelet based texture. In: Proceeding of SPIE 10024, pp. 1X1–1X7 (2016)Google Scholar
- 17.Jafari, M.H., Nasresfahani, E., Karimi, N., Soroushmehr, S.M.R., Samavi, S., Najarian, K.: Extraction of skin lesions from non-dermoscopic images using deep learning. CoRR abs/1609.02374 (2016)Google Scholar
- 24.Chang, J., Fisher, J.W.: Efficient MCMC sampling with implicit shape representations. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2081–2088. Providence (2011)Google Scholar
- 25.Oliveira, R.B., Pereira, A.S., Tavares, J.M.R.S.: Computational diagnosis of skin lesions from dermoscopic images using combined features. Neural Comput. Appl. (2018). https://doi.org/10.1007/s00521-018-3439-8
- 30.Mokrzycki, W.S., Tatol, M.: Color difference Delta E—A survey. Mach. Graph. Vis. 20, 383–411 (2011)Google Scholar
- 34.Lee, T.S.: Image representation using 2D Gabor wavelet. IEEE Trans. Pattern Anal. Mach. Intell. 18, 959–971 (2002)Google Scholar
- 38.Mendonca, T., Ferreira, P.M., Marques, J.S., Marcal, A.R.S., Rozeira, J.: PH2—A dermoscopic image database for research and benchmarking. In: 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5437–5440 (2013). http://www.fc.up.pt/addi/ph2%20database.html