Classification of breast and colorectal tumors based on percolation of color normalized images
Resumo
Percolation is a fractal descriptor that has been applied recently on computer vision problems. We applied this descriptor on 58 colored histological breast images, and 165 colored histological colorectal images, both stained with Hematoxylin and Eosin, in order to extract features to differentiate between benign and malignant cases. The experiments were also performed over normalized images, aiming to analyze the influence of different color normalization techniques on percolation-based features and whether they can provide better classification results. The feature sets obtained from the application of the method on the original images and on the normalized images with three different techniques were tested using 12 different classifiers. We compared the obtained results with other relevant methods in the area and observed significant contributions, with AUC rates above 0.900 in both normalized and non-normalized images. We also verified that color normalization does not contribute to the classification of breast tumors when associated with percolation features. However, color normalized images from the colorectal tumor's dataset provided better results than the original images.
Palavras-chave:
Percolation, Color normalization, Image classification, Colorectal tumors, Breast tumors
Referências
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M. Kandemir, C. Zhang, F.A. Hamprecht. Empowering multiple instance histopathology cancer diagnosis by cell graphs. Proceedings of the international conference on medical image computing and computer-assisted intervention, Springer (2014), pp. 228-235
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A. Vahadane, T. Peng, A. Sethi, S. Albarqouni, L. Wang, M. Baust, et al. Structure-preserving color normalization and sparse stain separation for histological images. IEEE Trans Med Imaging, 35 (8) (2016), pp. 1962-1971
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D.R. Chittajallu, N. Siekierski, S. Lee, S. Gerber, J. Beezley, D. Manthey, et al. Vectorized persistent homology representations for characterizing glandular architecture in histology images. Proceedings of the IEEE 15th international symposium on biomedical imaging (ISBI 2018), IEEE (2018), pp. 232-235
E. Reinhard, M. Adhikhmin, B. Gooch, P. Shirley. Color transfer between images. IEEE Comput Graph Appl, 21 (5) (2001), pp. 34-41
M.G. Ribeiro, L.A. Neves, M.Z. do Nascimento, G.F. Roberto, A.S. Martins, T.A.A. Tosta. Classification of colorectal cancer based on the association of multidimensional and multiresolution features. Expert Syst Appl, 120 (2019), pp. 262-278
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L. Sha, D. Schonfeld, A. Sethi. Color normalization of histology slides using graph regularized sparse NMF
Medical Imaging 2017: Digital Pathology, 10140, International Society for Optics and Photonics (2017), p. 1014010
A.M. Khan, N. Rajpoot, D. Treanor, D. Magee. A nonlinear mapping approach to stain normalization in digital histopathology images using image-specific color deconvolution. IEEE Trans Biomed Eng, 61 (6) (2014), pp. 1729-1738
E.D. Gelasca, J. Byun, B. Obara, B. Manjunath. Evaluation and benchmark for biological image segmentation
Proceedings of the IEEE international conference on image processing (2008)
K. Sirinukunwattana, J.P. Pluim, H. Chen, X. Qi, P.-A. Heng, Y.B. Guo, et al. Gland segmentation in colon histology images: the glas challenge contest. Med Image Anal, 35 (2017), pp. 489-502
B. Ghanbarian, A.G. Hunt, T.E. Skinner, R.P. Ewing. Saturation dependence of transport in porous media predicted by percolation and effective medium theories. Fractals, 23 (01) (2015), p. 1540004
M. Ivanovici, N. Richard. Fractal dimension of color fractal images. IEEE Trans Image Process, 20 (1) (2011), pp. 227-235
M. Ivanovici, N. Richard. The lacunarity of colour fractal images. Proceedings of the 16th IEEE international conference on image processing (ICIP), IEEE (2009), pp. 453-456
J. Martín-Herrero, J. Peón-Fernández. Alternative techniques for cluster labelling on percolation theory. J Phys A Math Gen, 33 (9) (2000), p. 1827
A. Căliman, M. Ivanovici. Psoriasis image analysis using color lacunarity. Proceedings of the 13th international conference on optimization of electrical and electronic equipment (OPTIM), IEEE (2012), pp. 1401-1406
T.A.A. Tosta, et al. Computational normalization of H&E-stained histological images: Progress, challenges and future potential. Artif Intell Med (2019), pp. 118-132
M. Karabatak. A new classifier for breast cancer detection based on Naïve Bayesian. Measurement, 72 (2015), pp. 32-36
W. Perrizo, Q. Ding, A. Denton. Lazy classifiers using p-trees. Proceedings of the CAINE (2002), pp. 176-179
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D.M. Farid, M.A. Al-Mamun, B. Manderick, A. Nowe. An adaptive rule-based classifier for mining big biological data. Expert Syst Appl, 64 (2016), pp. 305-316
J.R. Quinlan. Learning decision tree classifiers. ACM Comput Surv (CSUR), 28 (1) (1996), pp. 71-72
A.Y. Ng. Preventing “overfitting” of cross-validation data. Proceedings of the ICML, 97 (1997), pp. 245-253
I. Kononenko, E. Šimec, M. Robnik-Šikonja. Overcoming the myopia of inductive learning algorithms with Relieff
Appl Intell, 7 (1) (1997), pp. 39-55
M. Robnik-Sikonja, I. Kononenko. An adaptation of relief for attribute estimation in regression. Fisher D.H. (Ed.), Proceedings of the fourteenth international conference on machine learning, Morgan Kaufmann (1997), pp. 296-304
D.R. Nayak, R. Dash, B. Majhi, V. Prasad. Automated pathological brain detection system: a fast discrete Curvelet transform and probabilistic neural network based approach. Expert Syst Appl, 88 (2017), pp. 152-164
D. Jain, V. Singh. An efficient hybrid feature selection model for dimensionality reduction. Procedia Comput Sci, 132 (2018), pp. 333-341
K. Faust, Q. Xie, D. Han, K. Goyle, Z. Volynskaya, U. Djuric, et al. Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction. BMC Bioinformat, 19 (1) (2018), p. 173
R. Turkki, et al. Breast cancer outcome prediction with tumour tissue images and machine learning. Breast Cancer Res Treat, 177 (1) (2019), pp. 1-12
H. Lu, L. Yang, K. Yan, Y. Xue, Z. Gao. A cost-sensitive rotation forest algorithm for gene expression data classification. Neurocomputing, 228 (2017), pp. 270-276
O. Simon, R. Yacoub, S. Jain, J.E. Tomaszewski, P. Sarder. Multi-radial LBP features as a tool for rapid glomerular detection and assessment in whole slide histopathology images Sci Rep, 8 (1) (2018), p. 2032
T. Löfstedt, P. Brynolfsson, T. Asklund, T. Nyholm, A. Garpebring. Gray-level invariant Haralick texture features
PloS One, 14 (2) (2019), p. e0212110
I.K.N.S. dos Santos, P.F.I. Goiozo, A.F. de Brito, G.H. de Melo, C. de Pauli Pereira, R.M.B. Nogueira. Fractal dimension in liver histological findings of Wistar rats experimentally intoxicated with venom of Crotalus durissus terrificus. Acta Scientiae Veterinariae, 47 (2019)
N. Rajkovic, M. Radulovic, B. Stojadinovic, D.N. Vukosavljevic, K. Kranjer, N.T. Miloševic. Analysis of histopathology images by the use of monofractal and multifractal algorithms. Proceedings of the 21st international conference on control systems and computer science (CSCS), IEEE (2017), pp. 350-355
J.L. Mueller, J.E. Gallagher, R. Chitalia, M. Krieger, A. Erkanli, R.M. Willett, et al. Rapid staining and imaging of subnuclear features to differentiate between malignant and benign breast tissues at a point-of-care setting
J Cancer Res Clin Oncol, 142 (7) (2016), pp. 1475-1486
J.L. Dobbs, J.L. Mueller, S. Krishnamurthy, D. Shin, H. Kuerer, W. Yang, et al. Micro-anatomical quantitative optical imaging: toward automated assessment of breast tissues. Breast Cancer Res, 17 (1) (2015), p. 105
K. Shukla, A. Tiwari, S. Sharma, et al. Classification of histopathological images of breast cancerous and non cancerous cells based on morphological features. Biomed Pharmacol J, 10 (1) (2017), pp. 353-366
X. Li, K.N. Plataniotis. Color model comparative analysis for breast cancer diagnosis using h and e stained images
Medical Imaging 2015: Digital Pathology, 9420, International Society for Optics and Photonics (2015), p. 94200L
M. Kandemir, C. Zhang, F.A. Hamprecht. Empowering multiple instance histopathology cancer diagnosis by cell graphs. Proceedings of the international conference on medical image computing and computer-assisted intervention, Springer (2014), pp. 228-235
G.F. Roberto, L.A. Neves, M.Z. Nascimento, T.A. Tosta, L.C. Longo, A.S. Martins, et al. Features based on the percolation theory for quantification of non-Hodgkin lymphomas. Comput Biol Med, 91 (2017), pp. 135-147
J.A.A. Jothi, V.M.A. Rajam. A survey on automated cancer diagnosis from histopathology images. Artif Intell Rev, 48 (1) (2017), pp. 31-81
X. Li, K.N. Plataniotis. A complete color normalization approach to histopathology images using color cues computed from saturation-weighted statistics. IEEE Trans Biomed Eng, 62 (7) (2015), pp. 1862-1873
A. Vahadane, T. Peng, A. Sethi, S. Albarqouni, L. Wang, M. Baust, et al. Structure-preserving color normalization and sparse stain separation for histological images. IEEE Trans Med Imaging, 35 (8) (2016), pp. 1962-1971
M.G. Ribeiro, L.A. Neves, G.F. Roberto, T.A. Tosta, A.S. Martins, M.Z. do Nascimento. Analysis of the influence of color normalization in the classification of non-Hodgkin Lymphoma images. Proceedings of the 31st SIBGRAPI conference on graphics, patterns and images (SIBGRAPI), IEEE (2018), pp. 369-376
S.I. McClelland, K.J. Holland, J.J. Griggs. Quality of life and metastatic breast cancer: the role of body image, disease site, and time since diagnosis. Q Life Res, 24 (12) (2015), pp. 2939-2943
Society AC. About breast cancer. https://www.cancer.org/content/dam/CRC/PDF/Public/8577.00.pdf; Accessed: 2018-09-24.
Society AC. Cancer statistics center. https://cancerstatisticscenter.cancer.org; Accessed: 2017-02-09.
D.Ç. Olgun, B. Korkmazer, F. Kılıç, A.S. Dikici, M. Velidedeoğlu, F. Aydoğan, et al. Use of shear wave elastography to differentiate benign and malignant breast lesions. Diagn Interv Radiol, 20 (3) (2014), p. 239
A. Rakhlin, A. Shvets, V. Iglovikov, A.A. Kalinin. Deep convolutional neural networks for breast cancer histology image analysis. Proceedings of the international conference image analysis and recognition, Springer (2018), pp. 737-744
Ilse M, Tomczak JM, Welling M. Attention-based deep multiple instance learning. arXiv:180204712 2018.
T. Papastergiou, E.I. Zacharaki, V. Megalooikonomou. Tensor decomposition for multiple-instance classification of high-order medical data. Complexity, 2018 (2018), Article 8651930
C. Yu, H. Chen, Y. Li, Y. Peng, J. Li, F. Yang. Breast cancer classification in pathological images based on hybrid features. Multimed Tools Appl (2019), pp. 1-21
K. Sirinukunwattana, S. e Ahmed Raza, Y.-W. Tsang, D.R. Snead, I.A. Cree, N.M. Rajpoot. Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans Med Imaging, 35 (5) (2016), pp. 1196-1206
B. Korbar, A.M. Olofson, A.P. Miraflor, C.M. Nicka, M.A. Suriawinata, L. Torresani, et al. Deep learning for classification of colorectal polyps on whole-slide images. J Pathol Informat, 8 (2017)
R. Awan, K. Sirinukunwattana, D. Epstein, S. Jefferyes, U. Qidwai, Z. Aftab, et al. Glandular morphometrics for objective grading of colorectal adenocarcinoma histology images. Sci Rep, 7 (1) (2017), p. 16852
L.F.S. dos Santos, L.A. Neves, G.B. Rozendo, M.G. Ribeiro, M.Z. do Nascimento, T.A.A. Tosta. Multidimensional and fuzzy sample entropy (SampEnMF) for quantifying h&e histological images of colorectal cancer. Comput Biol Med, 103 (2018), pp. 148-160
D.R. Chittajallu, N. Siekierski, S. Lee, S. Gerber, J. Beezley, D. Manthey, et al. Vectorized persistent homology representations for characterizing glandular architecture in histology images. Proceedings of the IEEE 15th international symposium on biomedical imaging (ISBI 2018), IEEE (2018), pp. 232-235
E. Reinhard, M. Adhikhmin, B. Gooch, P. Shirley. Color transfer between images. IEEE Comput Graph Appl, 21 (5) (2001), pp. 34-41
M.G. Ribeiro, L.A. Neves, M.Z. do Nascimento, G.F. Roberto, A.S. Martins, T.A.A. Tosta. Classification of colorectal cancer based on the association of multidimensional and multiresolution features. Expert Syst Appl, 120 (2019), pp. 262-278
Tellez D, Litjens G, Bandi P, Bulten W, Bokhorst J.-M, Ciompi F, et al. Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology. arXiv:190206543 2019.
L. Sha, D. Schonfeld, A. Sethi. Color normalization of histology slides using graph regularized sparse NMF
Medical Imaging 2017: Digital Pathology, 10140, International Society for Optics and Photonics (2017), p. 1014010
A.M. Khan, N. Rajpoot, D. Treanor, D. Magee. A nonlinear mapping approach to stain normalization in digital histopathology images using image-specific color deconvolution. IEEE Trans Biomed Eng, 61 (6) (2014), pp. 1729-1738
E.D. Gelasca, J. Byun, B. Obara, B. Manjunath. Evaluation and benchmark for biological image segmentation
Proceedings of the IEEE international conference on image processing (2008)
K. Sirinukunwattana, J.P. Pluim, H. Chen, X. Qi, P.-A. Heng, Y.B. Guo, et al. Gland segmentation in colon histology images: the glas challenge contest. Med Image Anal, 35 (2017), pp. 489-502
B. Ghanbarian, A.G. Hunt, T.E. Skinner, R.P. Ewing. Saturation dependence of transport in porous media predicted by percolation and effective medium theories. Fractals, 23 (01) (2015), p. 1540004
M. Ivanovici, N. Richard. Fractal dimension of color fractal images. IEEE Trans Image Process, 20 (1) (2011), pp. 227-235
M. Ivanovici, N. Richard. The lacunarity of colour fractal images. Proceedings of the 16th IEEE international conference on image processing (ICIP), IEEE (2009), pp. 453-456
J. Martín-Herrero, J. Peón-Fernández. Alternative techniques for cluster labelling on percolation theory. J Phys A Math Gen, 33 (9) (2000), p. 1827
A. Căliman, M. Ivanovici. Psoriasis image analysis using color lacunarity. Proceedings of the 13th international conference on optimization of electrical and electronic equipment (OPTIM), IEEE (2012), pp. 1401-1406
T.A.A. Tosta, et al. Computational normalization of H&E-stained histological images: Progress, challenges and future potential. Artif Intell Med (2019), pp. 118-132
M. Karabatak. A new classifier for breast cancer detection based on Naïve Bayesian. Measurement, 72 (2015), pp. 32-36
W. Perrizo, Q. Ding, A. Denton. Lazy classifiers using p-trees. Proceedings of the CAINE (2002), pp. 176-179
P. Melville, R.J. Mooney. Constructing diverse classifier ensembles using artificial training examples. Proceedings of the IJCAI (2003), pp. 505-510
D.M. Farid, M.A. Al-Mamun, B. Manderick, A. Nowe. An adaptive rule-based classifier for mining big biological data. Expert Syst Appl, 64 (2016), pp. 305-316
J.R. Quinlan. Learning decision tree classifiers. ACM Comput Surv (CSUR), 28 (1) (1996), pp. 71-72
A.Y. Ng. Preventing “overfitting” of cross-validation data. Proceedings of the ICML, 97 (1997), pp. 245-253
I. Kononenko, E. Šimec, M. Robnik-Šikonja. Overcoming the myopia of inductive learning algorithms with Relieff
Appl Intell, 7 (1) (1997), pp. 39-55
M. Robnik-Sikonja, I. Kononenko. An adaptation of relief for attribute estimation in regression. Fisher D.H. (Ed.), Proceedings of the fourteenth international conference on machine learning, Morgan Kaufmann (1997), pp. 296-304
D.R. Nayak, R. Dash, B. Majhi, V. Prasad. Automated pathological brain detection system: a fast discrete Curvelet transform and probabilistic neural network based approach. Expert Syst Appl, 88 (2017), pp. 152-164
D. Jain, V. Singh. An efficient hybrid feature selection model for dimensionality reduction. Procedia Comput Sci, 132 (2018), pp. 333-341
K. Faust, Q. Xie, D. Han, K. Goyle, Z. Volynskaya, U. Djuric, et al. Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction. BMC Bioinformat, 19 (1) (2018), p. 173
R. Turkki, et al. Breast cancer outcome prediction with tumour tissue images and machine learning. Breast Cancer Res Treat, 177 (1) (2019), pp. 1-12
H. Lu, L. Yang, K. Yan, Y. Xue, Z. Gao. A cost-sensitive rotation forest algorithm for gene expression data classification. Neurocomputing, 228 (2017), pp. 270-276
O. Simon, R. Yacoub, S. Jain, J.E. Tomaszewski, P. Sarder. Multi-radial LBP features as a tool for rapid glomerular detection and assessment in whole slide histopathology images Sci Rep, 8 (1) (2018), p. 2032
T. Löfstedt, P. Brynolfsson, T. Asklund, T. Nyholm, A. Garpebring. Gray-level invariant Haralick texture features
PloS One, 14 (2) (2019), p. e0212110
I.K.N.S. dos Santos, P.F.I. Goiozo, A.F. de Brito, G.H. de Melo, C. de Pauli Pereira, R.M.B. Nogueira. Fractal dimension in liver histological findings of Wistar rats experimentally intoxicated with venom of Crotalus durissus terrificus. Acta Scientiae Veterinariae, 47 (2019)
N. Rajkovic, M. Radulovic, B. Stojadinovic, D.N. Vukosavljevic, K. Kranjer, N.T. Miloševic. Analysis of histopathology images by the use of monofractal and multifractal algorithms. Proceedings of the 21st international conference on control systems and computer science (CSCS), IEEE (2017), pp. 350-355
Publicado
28/10/2019
Como Citar
ROBERTO, Guilherme Freire; TOSTA, Thaína A. ; FARIA, Paulo R. ; MARTINS, Alessandro S. ; NEVES, Leandro A. ; NASCIMENTO, Marcelo Z. .
Classification of breast and colorectal tumors based on percolation of color normalized images. In: CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 32. , 2019, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
DOI: https://doi.org/10.5753/sibgrapi.2019.9818.
