Semi-automatic Segmentation of Skin Lesions based on Superpixels and Hybrid Texture Information
Resumo
This article exposes a semi-automatic method with the potential to aid the doctor while supervising the progression of skin lesions. The proposed methodology pre-segments skin lesions using the SLIC0 algorithm for the generation of superpixels. Following this, each superpixel is represented using a descriptor constructed of a mix from GLCM and Tamura texture features. The feature's gain ratios were utilized to choose the data applied in the semi-supervised clustering algorithm Seeded Fuzzy Cmeans. This algorithm uses certain specialist-marked regions to group the superpixels into lesion or background regions. Finally, the segmented image undergoes a post-processing step to eliminate sharp edges. The experiments were performed on a total of 3974 images. We used the 2995 images from PH2, DermIS and ISIC 2018 datasets to establish our method's specifications and the 979 images from ISIC 2016 and ISIC 2017 datasets for performance analysis. Our experiments demonstrate that by manually identifying a few percentages of the generated superpixels, the proposed approach reaches an average accuracy of 95.97%, thus giving a superior performance to the techniques presented in the literature. Even though the proposed method requires physicians' intervention, they can obtain segmentation results similar to manual segmentation from a significantly less time-consuming task.
Referências
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X. Tong, J. Wei, B. Sun, S. Su, Z. Zuo, and P. Wu, “Ascu-net: Attention gate, spatial and channel attention u-net for skin lesion segmentation,” Diagnostics, vol. 11, no. 3, p. 501, 2021.
T. Lee, V. Ng, R. Gallagher, A. Coldman, and D. McLean, “Dullrazor®: A software approach to hair removal from images,” Computers in biology and medicine, vol. 27, no. 6, pp. 533–543, 1997.
B. F. Nan and Z. C. Mu, “Slico-based superpixel segmentation method with texture fusion,” Chinese Journal of Scientific Instrument, vol. 35, no. 3, pp. 527–534, 2014.
R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for image classification,” IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-3, no. 6, pp. 610–621, 1973.
M. M. Galloway, “Texture analysis using gray level run lengths,” Computer graphics and image processing, vol. 4, no. 2, pp. 172–179, 1975.
A. Chu, C. M. Sehgal, and J. F. Greenleaf, “Use of gray value distribution of run lengths for texture analysis,” Pattern Recognition Letters, vol. 11, no. 6, pp. 415–419, 1990.
B. R. Dasarathyand and E. B. Holder, “Image characterizations based on joint gray-level run-length distributions,” Pattern Recognition Letters, vol. 12, no. 8, pp. 497–502, 1991.
A. C. Silva, P. C. P. Carvalho, and M. Gattass, “Analysis of spatial variability using geostatistical functions for diagnosis of lung nodule in computerized tomography images,” Pattern Analysis and Applications, vol. 7, no. 3, pp. 227–234, 2004.
J. Sousa, A. Paiva, J. Almeida, A. Silva, G. Junior, and M. Gattass, “Texture based on geostatistic for glaucoma diagnosis from fundus eye image,” Multimedia Tools and Applications, vol. 76, 09 2017.
T. Ojala, M. Pietik¨ainen, and D. Harwood, “A comparative study of texture measures with classification based on featured distributions,” Pattern recognition, vol. 29, no. 1, pp. 51–59, 1996.
H. Tamura, S. Mori, and T. Yamawaki, “Textural features corresponding to visual perception,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 8, no. 6, pp. 460–473, 1978.
L. E. Raileanu and K. Stoffel, “Theoretical comparison between the gini index and information gain criteria,” Annals of Mathematics and Artificial Intelligence, vol. 41, pp. 77 – 93, 2004.
V. Caselles, R. Kimmel, and G. Sapiro, “Geodesic active contours,” International Journal of Computer Vision, vol. 22, no. 1, pp. 61–79, 1997.
R. Hemalatha, T. Thamizhvani, A. J. A. Dhivya, J. E. Joseph, B. Babu, and R. Chandrasekaran, “Active contour based segmentation techniques for medical image analysis,” in Medical and Biological Image Analysis, R. Koprowski, Ed. Rijeka: IntechOpen, 2018, ch. 2, pp. 17–34.
T. Mendonça, P. M. Ferreira, J. S. Marques, A. R. Marcal, and J. Rozeira, “Ph2-a dermoscopic image database for research and benchmarking,” in Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE. IEEE, 2013, pp. 5437– 5440.
T. L. Diepgen and G. Yihune, “Dermatology information system – dermis,” http://dermis.net/, 2012, retrieved September 12, 2016.
N. C. F. Codella, D. Gutman, M. E. Celebi, B. Helba, M. A. Marchetti, S. W. Dusza, A. Kalloo, K. Liopyris, N. Mishra, H. Kittler, and A. Halpern, “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),” in 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), 2018, pp. 168–172.
D. Gutman, N. C. F. Codella, M. E. Celebi, B. Helba, M. A. Marchetti, N. K. Mishra, and A. Halpern, “Skin lesion analysis toward melanoma detection: A challenge at the international symposium on biomedical imaging (ISBI) 2016, hosted by the international skin imaging collaboration (ISIC),” arXiv, 2016. [Online]. Available: arXiv:1605.01397
N. C. F. Codella, D. Gutman, M. E. Celebi, B. Helba, M. A. Marchetti, S. W. Dusza, A. Kalloo, K. Liopyris, N. K. Mishra, H. Kittler, and A. Halpern, “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),” IEEE, 2017.
MATLAB, version 7.10.0 (R2010a). Natick, Massachusetts: The MathWorks Inc., 2010.
J. Scharcanski and M. E. Celebi, Computer vision techniques for the diagnosis of skin cancer. Springer, 2013.
M. Goyal, A. Oakley, P. Bansal, D. Dancey, and M. H. Yap, “Skin lesion segmentation in dermoscopic images with ensemble deep learning methods,” IEEE Access, vol. 8, pp. 4171–4181, 2020.
N. Moura, R. Veras, K. Aires, V. Machado, R. Silva, F. Araújo, and M. Claro, “Abcd rule and pre-trained cnns for melanoma diagnosis,” Multimedia Tools Appl., vol. 78, no. 6, p. 6869–6888, Mar. 2019. [Online]. Available: https://doi.org/10.1007/s11042-018-6404-8
S. Chan, V. Reddy, B. Myers, Q. Thibodeaux, N. Brownstone, and W. Liao, “Machine learning in dermatology: Current applications, opportunities, and limitations,” Dermatology and Therapy, p. 6869–6888, April 2020.
V. B.N., P. J. Shah, V. Shekar, H. R. Vanamala, and V. Krishna A., “Detection of melanoma using deep learning techniques,” in 2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM), 2020, pp. 391–394.
L. Santos, R. Veras, K. Aires, L. Britto, and V. Machado, “Medical image segmentation using seeded fuzzy c-means: A semi-supervised clustering algorithm.” in International Joint Conference on Neural Networks. IEEE, 2018, pp. 1–8.
M. Celebi, Q. Wen, H. Iyatomi, K. Shimizu, H. Zhou, and G. Schaefer, “A state-of-the-art survey on lesion border detection in dermoscopy images,” in Dermoscopy Image Analysis. CRC Press, sep 2015, pp. 97–129. [Online]. Available: https://doi.org/10.1201%2Fb19107-5
R. B. Oliveira, M. E. Filho, Z. Ma, J. P. Papa, A. S. Pereira, and J. M. R. Tavares, “Computational methods for the image segmentation of pigmented skin lesions: A review,” Computer Methods and Programs in Biomedicine, vol. 131, pp. 127–141, 2016.
Z. Ma and J. M. R. S. Tavares, “A novel approach to segment skin lesions in dermoscopic images based on a deformable model,” Journal of Biomedical and Health Informatics, vol. 20, no. 2, pp. 615–623, 2016.
N. N. Sultana and N. B. Puhan, “Recent deep learning methods for melanoma detection: A review,” in Mathematics and Computing, D. Ghosh, D. Giri, R. N. Mohapatra, E. Savas, K. Sakurai, and L. P. Singh, Eds. Singapore: Springer Singapore, 2018, pp. 118–132.
X. Li, L. Yu, H. Chen, C. Fu, and P. Heng, “Semi-supervised skin lesion segmentation via transformation consistent self-ensembling model,” arXiv:1808.03887, 2018.
Y. Li and L. Shen, “Skin lesion analysis towards melanoma detection using deep learning network,” Sensors, vol. 18, no. 2, p. 556, Feb 2018. [Online]. Available: http://dx.doi.org/10.3390/s18020556
H. M. Ünver and E. Ayan, “Skin lesion segmentation in dermoscopic images with combination of yolo and grabcut algorithm,” Diagnostics, vol. 9, no. 3, p. 72, Jul 2019. [Online]. Available: http://dx.doi.org/10.3390/diagnostics9030072
I. Filali and M. Belkadi, “Multi-scale contrast based skin lesion segmentation in digital images,” Optik, vol. 185, pp. 794 – 811, 2019. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0030402619304917
N. Nida, A. Irtaza, A. Javed, M. H. Yousaf, and M. Mahmood, “Melanoma lesion detection and segmentation using deep region based convolutional neural network and fuzzy c-means clustering,” International Journal of Medical Informatics, vol. 124, 04 2019.
M. E.-V. Fulgencio Navarro and J. Bescós, “Accurate segmentation and registration of skin lesion images to evaluate lesion change,” IEEE Journal of Biomedical and Health Inf, vol. 23, no. 2, pp. 501 – 508, 2019.
J. L. Garcia-Arroyo and B. Garcia-Zapirain, “Segmentation of skin lesions in dermoscopy images using fuzzy classification of pixels and histogram thresholding,” Computer Methods and Programs in Biomedicine, vol. 168, pp. 11 – 19, 2019.
Y. Xie, J. Zhang, Y. Xia, and C. Shen, “A mutual bootstrapping model for automated skin lesion segmentation and classification,” IEEE Transactions on Medical Imaging, pp. 1–1, 2020.
B. Lei, Z. Xia, F. Jiang, X. Jiang, Z. Ge, Y. Xu, J. Qin, S. Chen, T. Wang, and S. Wang, “Skin lesion segmentation via generative adversarial networks with dual discriminators,” Medical Image Analysis, vol. 64, p. 101716, 2020.
R. Arora, B. Raman, K. Nayyar, and R. Awasthi, “Automated skin lesion segmentation using attention-based deep convolutional neural network,” Biomedical Signal Processing and Control, vol. 65, p. 102358, 2021.
S. Qamar, P. Ahmad, and L. Shen, “Dense encoder-decoder–based architecture for skin lesion segmentation,” Cognitive Computation, vol. 13, no. 2, pp. 583–594, 2021.
X. Tong, J. Wei, B. Sun, S. Su, Z. Zuo, and P. Wu, “Ascu-net: Attention gate, spatial and channel attention u-net for skin lesion segmentation,” Diagnostics, vol. 11, no. 3, p. 501, 2021.
T. Lee, V. Ng, R. Gallagher, A. Coldman, and D. McLean, “Dullrazor®: A software approach to hair removal from images,” Computers in biology and medicine, vol. 27, no. 6, pp. 533–543, 1997.
B. F. Nan and Z. C. Mu, “Slico-based superpixel segmentation method with texture fusion,” Chinese Journal of Scientific Instrument, vol. 35, no. 3, pp. 527–534, 2014.
R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for image classification,” IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-3, no. 6, pp. 610–621, 1973.
M. M. Galloway, “Texture analysis using gray level run lengths,” Computer graphics and image processing, vol. 4, no. 2, pp. 172–179, 1975.
A. Chu, C. M. Sehgal, and J. F. Greenleaf, “Use of gray value distribution of run lengths for texture analysis,” Pattern Recognition Letters, vol. 11, no. 6, pp. 415–419, 1990.
B. R. Dasarathyand and E. B. Holder, “Image characterizations based on joint gray-level run-length distributions,” Pattern Recognition Letters, vol. 12, no. 8, pp. 497–502, 1991.
A. C. Silva, P. C. P. Carvalho, and M. Gattass, “Analysis of spatial variability using geostatistical functions for diagnosis of lung nodule in computerized tomography images,” Pattern Analysis and Applications, vol. 7, no. 3, pp. 227–234, 2004.
J. Sousa, A. Paiva, J. Almeida, A. Silva, G. Junior, and M. Gattass, “Texture based on geostatistic for glaucoma diagnosis from fundus eye image,” Multimedia Tools and Applications, vol. 76, 09 2017.
T. Ojala, M. Pietik¨ainen, and D. Harwood, “A comparative study of texture measures with classification based on featured distributions,” Pattern recognition, vol. 29, no. 1, pp. 51–59, 1996.
H. Tamura, S. Mori, and T. Yamawaki, “Textural features corresponding to visual perception,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 8, no. 6, pp. 460–473, 1978.
L. E. Raileanu and K. Stoffel, “Theoretical comparison between the gini index and information gain criteria,” Annals of Mathematics and Artificial Intelligence, vol. 41, pp. 77 – 93, 2004.
V. Caselles, R. Kimmel, and G. Sapiro, “Geodesic active contours,” International Journal of Computer Vision, vol. 22, no. 1, pp. 61–79, 1997.
R. Hemalatha, T. Thamizhvani, A. J. A. Dhivya, J. E. Joseph, B. Babu, and R. Chandrasekaran, “Active contour based segmentation techniques for medical image analysis,” in Medical and Biological Image Analysis, R. Koprowski, Ed. Rijeka: IntechOpen, 2018, ch. 2, pp. 17–34.
T. Mendonça, P. M. Ferreira, J. S. Marques, A. R. Marcal, and J. Rozeira, “Ph2-a dermoscopic image database for research and benchmarking,” in Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE. IEEE, 2013, pp. 5437– 5440.
T. L. Diepgen and G. Yihune, “Dermatology information system – dermis,” http://dermis.net/, 2012, retrieved September 12, 2016.
N. C. F. Codella, D. Gutman, M. E. Celebi, B. Helba, M. A. Marchetti, S. W. Dusza, A. Kalloo, K. Liopyris, N. Mishra, H. Kittler, and A. Halpern, “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),” in 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), 2018, pp. 168–172.
D. Gutman, N. C. F. Codella, M. E. Celebi, B. Helba, M. A. Marchetti, N. K. Mishra, and A. Halpern, “Skin lesion analysis toward melanoma detection: A challenge at the international symposium on biomedical imaging (ISBI) 2016, hosted by the international skin imaging collaboration (ISIC),” arXiv, 2016. [Online]. Available: arXiv:1605.01397
N. C. F. Codella, D. Gutman, M. E. Celebi, B. Helba, M. A. Marchetti, S. W. Dusza, A. Kalloo, K. Liopyris, N. K. Mishra, H. Kittler, and A. Halpern, “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),” IEEE, 2017.
MATLAB, version 7.10.0 (R2010a). Natick, Massachusetts: The MathWorks Inc., 2010.
Publicado
18/10/2021
Como Citar
SANTOS, Elineide Silva dos; VERAS, Rodrigo de Melo Souza.
Semi-automatic Segmentation of Skin Lesions based on Superpixels and Hybrid Texture Information. In: WORKSHOP DE TESES E DISSERTAÇÕES - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 34. , 2021, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 119-125.
DOI: https://doi.org/10.5753/sibgrapi.est.2021.20023.
