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Colorectal Cancer Diagnostic Algorithm Based on Sub-Patch Weight Color Histogram in Combination of Improved Least Squares Support Vector Machine for Pathological Image

  • Image & Signal Processing
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

A Correction to this article was published on 12 November 2019

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Abstract

In order to improve the diagnostic accuracy of colon cancer, a novel classification algorithm based on sub-patch weight color histogram and improved SVM is proposed, which has good approximation ability for complex pathological image. Our proposed algorithm combines wavelet kernel SVM with color histogram to classify pathological image. Firstly, the pathological image is divided into non-overlapping sub-patches, and the features of sub-patch histogram are extracted. Then, the global and local features are fused by the sub-patch weighting algorithm. Then, the RelicfF based forward selection algorithm is used to integrate color features and texture features so as to enhance the characterization capabilities of the tumor cell. Finally, Morlet wavelet kernel-based least squares support vector machine method is adopted to enhance the generalization ability of the model for small sample with non-linear and high-dimensional pattern classification problems. Experimental results show that the proposed pathological diagnostic algorithm can gain higher accuracy compared with existing comparison algorithms.

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Change history

  • 12 November 2019

    The original article unfortunately contained a mistake. The corresponding author’s name should be corrected as “Yingsheng Cheng”.

References

  1. Hong, Y., Wei, H, and Zeng-Li, L., Research for the colon cancer based on the EMD and LS-SVM[C]. Fourth International Conference on Intelligent Computation Technology & Automation. IEEE Computer Society, 24(7):329-331, 2011.

  2. Wang, H., and Huang, G., Application of support vector machine in cancer diagnosis[J]. Med. Oncol. 28(1 Supplement):613–618, 2011.

    Article  CAS  Google Scholar 

  3. Chen, H., Tan, C., Wu, H. et al., Feasibility of rapid diagnosis of colorectal cancer by near-infrared spectroscopy and support vector machine[J]. Anal. Lett. 47(15):2580–2593, 2014.

    Article  Google Scholar 

  4. Mizaku, A, and Land, W. H., Biomolecular feature selection of colorectal cancer microarray data using GA-SVM hybrid and noise perturbation to address overfitting[J]. Dissertations & Theses -Gradworks, 12(1):64-76, 2009.

  5. Tamaki, T., Yoshimuta, J., Kawakami, M. et al., Computer-aided colorectal tumor classification in NBI endoscopy using local features[J]. Med. Image Anal. 17(1):123-129, 2013.

    Article  Google Scholar 

  6. Li, S., Fevens, T., Krzy Ak, A. et al., Automatic clinical image segmentation using pathological modeling, PCA and SVM[J]. Eng. Appl. Artif. Intell. 19(4):403–410, 2006.

    Article  Google Scholar 

  7. Shi, W., Dongkai, J., Ke, W., Application of modified wavelet features and multi-class sphere SVM to pathological vocal detection[C]. Seventh International Conference on Natural Computation. IEEE, pp:1290-1298, 2011.

  8. Majhi, B., Dash, R., and Nayak, D. R., Stationary wavelet transform and AdaBoost with SVM based pathological brain detection in MRI scanning[J]. CNS Neurol. Disord. Drug Targets 16(2):32-44, 2017.

  9. Cataldo, S. D., Ficarra, E., and Macii, E., Automated discrimination of pathological regions in tissue images: Unsupervised clustering vs. supervised SVM classification[C]. International Joint Conference on Biomedical Engineering Systems and Technologies. Springer, Berlin, Heidelberg, pp:2100-2111, 2008.

  10. Cataldo,Wang S, Huo J, et al. Bayesian Framework with Non-local and Low-rank Constraint for Image Reconstruction[C]// Journal of Physics Conference Series. pp:1-11, 2017.

  11. Shunji, T., Junji, T., Atsuko, H. et al., Role of early phase helical CT images in the evaluation of wall invasion of colorectal cancer: Pathological correlation[J]. Nihon Igaku Hōshasen Gakkai Zasshi Nippon Acta Radiologica 60(3):87, 2000.

    CAS  PubMed  Google Scholar 

  12. None, Colorectal cancer pathology reporting: A regional audit[J]. J. Clin. Pathol. 50(4):358–358, 1997.

    Article  Google Scholar 

  13. Xia, Kai Jian, H. S. Yin, and J. Q. Wang. "A novel improved deep convolutional neural network model for medical image fusion." Cluster Computing, 23(20):1-13, 2018.

  14. Song, B., Zhang, G., Wang, H., et al., A feasibility study of high order texture features with application to pathological diagnosis of colon lesions for CT Colonography[C]. Nuclear Science Symposium & Medical Imaging Conference. IEEE, 2013.

  15. Robnik-ŠIkonja, M., and Kononenko, I., Theoretical and empirical analysis of ReliefF and RReliefF[J]. Mach. Learn. 53(1–2):23–69, 2003.

    Article  Google Scholar 

  16. Beretta, L., Santaniello et al., Implementing ReliefF filters to extract meaningful features from genetic lifetime datasets[J]. J. Biomed. Inform. 44(2):361–369, 2011.

    Article  Google Scholar 

  17. Wang, C., Guan, Y., Zuo, C., et al., Value of the texture feature for solitary pulmonary nodules and mass lesions based on PET/CT[C]. International Conference on Bioinformatics & Biomedical Engineering. 2010.

  18. Song, B., Zhang, G., Zhu, H., et al., A feasibility study of high order volumetric texture features for computer aided diagnosis of polyps via CT colonography[C]. Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). pp:719-724, 2012.

  19. Song, B., Zhang, G., Lu, H. et al., Volumetric texture features from higher-order images for diagnosis of colon lesions via CT colonography[J]. Int. J. Comput. Assist. Radiol. Surg. 9(6):1021–1031, 2014.

    Article  Google Scholar 

  20. Thon, N., Haas, C. A., Rauch, U. et al., The chondroitin sulphate proteoglycan brevican is upregulated by astrocytes after entorhinal cortex lesions in adult rats.[J]. Eur. J. Neurosci. 12(7):2547–2558, 2010.

    Article  Google Scholar 

  21. Jiang, X., Liang, Q., and Shen, T., A new color information entropy retrieval method for pathological cell image[C]// Computer & Computing Technologies in Agriculture Iv-ifip Tc 12 Conference. 0.

  22. Jiang, X., Liang, Q., and Shen, T., A new color information entropy retrieval method for pathological cell image[J]. Computer and Computing Technologies in Agriculture IV, 22(12):872-880, 2016.

  23. Xia K J, Yin H S, Zhang Y D. Deep Semantic Segmentation of Kidney and Space-Occupying Lesion Area Based on SCNN and ResNet Models Combined with SIFT-Flow Algorithm[J]. Journal of Medical Systems, 2019, 43(1):2.

  24. Sammouda, M, and Mukai, K., Diagnosis of liver cancer based on the analysis of pathological liver color images[C]. Medical Imaging: Image Processing. International Society for Optics and Photonics, pp:12-21, 2000.

  25. Malekian, V., Mokhtari, M., Sadri, S., et al., Detection of collagenous colitis based on histopathology image segmentation of colon[C]// Iranian Conference on Machine Vision & Image Processing. IEEE, 2011.

  26. Sammouda, M., Sammouda, R., Niki, N. et al., Cancerous nuclei detection on digitized pathological lung color images[J]. J. Biomed. Inform. 35(2):92–98, 2002.

    Article  Google Scholar 

  27. Zheng, L., Wetzel, A. W., Gilbertson, J. et al., Design and analysis of a content-based pathology image retrieval system[J]. IEEE Trans. Inf. Technol. Biomed. 7(4):249–255, 2004.

    Article  Google Scholar 

  28. Kande, G. B., Subbaiah, P. V., and Savithri, T. S., Unsupervised fuzzy based vessel segmentation in pathological digital fundus images[J]. J. Med. Syst. 34(5):849–858, 2010.

    Article  Google Scholar 

  29. Ramella, G., Baja, G. S. D. Color histogram-based image segmentation[M]. Computer Analysis of Images and Patterns. Springer Berlin Heidelberg, 2011.

    Chapter  Google Scholar 

  30. Jin, Y., Fayad, L., and Laine, A. F., Contrast enhancement by multi-scale adaptive histogram equalization[J]. Proc. SPIE Int. Soc. Opt. Eng. 4478:206–213, 2001.

    Google Scholar 

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Acknowledgments

This study is supported by the National Natural Science Foundation of China (No.81571773, 81781771943 81771943), Shanghai municipal health and Family Planning Commission (No.201640191).

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Authors and Affiliations

  1. Department of Radiological Intervention, Shanghai Sixth People’s Hospital East Campus Affiliated to Shanghai University of Medicine & Health Science, Shanghai, 201306, China

    Kai Yang, Bi Zhou, Fei Yi, Yan Chen & Yingsheng Chen

  2. Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China

    Kai Yang & Yingsheng Chen

Authors
  1. Kai Yang
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  2. Bi Zhou
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  3. Fei Yi
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  4. Yan Chen
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  5. Yingsheng Chen
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Correspondence to Yingsheng Chen.

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Yang, K., Zhou, B., Yi, F. et al. Colorectal Cancer Diagnostic Algorithm Based on Sub-Patch Weight Color Histogram in Combination of Improved Least Squares Support Vector Machine for Pathological Image. J Med Syst 43, 306 (2019). https://doi.org/10.1007/s10916-019-1429-8

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