Abstract
In this paper, we present a novel segmentation framework for glandular structures in Hematoxylin and Eosin stained histology images, choosing poorly differentiated colon tissue as an example. The proposed framework’ target is to identify precise epithelial nuclei objects. We start with staining separate to detect all nuclei objects, and deploy multi-resolution morphology operation to map the initial epithelial nuclei positions. We proposed a new bag of words scheme using sparse random feature to classify epithelial nuclei and stroma nuclei objects to adjust the rest nuclei positions. Finally, we can use the boundary of optimized epithelial nuclei objects to segment the glandular structure.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Gleason, D.F., Mellinger, G.T.: Prediction of prognosis for prostatic adenocarcinoma by combined histological grading and clinical staging. J. Urol. 167(2), 953–958 (2002)
Hamilton, S.R., Aaltonen, L.A.: International Agency for Research on Cancer, World Health Organization, and others, Pathology and genetics of tumours of the digestive system (2000)
Farjam, R., Soltanian-Zadeh, H., Jafari-Khouzani, K., Zoroofi, R.A.: An image analysis approach for automatic malignancy determination of prostate pathological images. Cytometry Part B Clin. Cytometry 72(4), 227–240 (2007)
Naik, S., Doyle, S., Madabhushi, A., Tomaszewski, J., Feldman, M.: Gland segmentation and gleason grading of prostate histology by integrating low-, high-level and domain specific information. In: Workshop on Microscopic Image Analysis with Applications in Biology (2007)
Nguyen, K., Jain, A.K., Allen, R.L.: Automated gland segmentation and classification for gleason grading of prostate tissue images. In: 2010 20th International Conference on Pattern Recognition (ICPR), pp. 1497–1500. IEEE (2010)
Gunduz-Demir, C., Kandemir, M., Tosun, A.B., Sokmensuer, C.: Automatic segmentation of colon glands using objectgraphs. Med. Image Anal. 14(1), 1–12 (2010)
Varma, M., Zisserman, A.: A statistical approach to material classification using image patches. IEEE Trans. Pattern Anal. Mach. Intell. 31(11), 2032–2047 (2009)
Zhang, J., Marszalek, M., Lazebnik, S., Schmid, C.: Local features and kernels for classification of texture and object categories: a comprehensive study. Int. J. Comput. Vis. 73(2), 213–238 (2007)
Liu, L., Fieguth, P.: Texture classification from random features. IEEE Trans. Pattern Anal. Mach. Intell. 34(3), 574–586 (2012)
Indyk, P.: Sparse recovery using sparse random matrices. In: López-Ortiz, A. (ed.) LATIN 2010. LNCS, vol. 6034, p. 157. Springer, Heidelberg (2010). doi:10.1007/978-3-642-12200-2_15
Lu, W., Li, W., Kpalma, K., et al.: Sparse matrix-based random projection for classification. Comput. Sci. 12, 581–607 (2013)
Macenko, M., Niethammer, M., Marron, J., Borland, D., Woosley, J.T., Guan, X., Schmitt, C., Thomas, N.E.: A method for normalizing histology slides for quantitative analysis. In: International Symposium on Biomedical Imaging, vol. 9, pp. 1107–1110 (2009)
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)
Zhang, K., Crookes, D., Diamond, J., Fei, M., Wu, J., Zhang, P., Zhou, H.: Multi-scale colorectal tumour segmentation using a novel coarse to fine strategy. In: British Machine Vision Conference (BMVC) (2016)
Sirinukunwattana, K., Pluim, J.P., Chen, H., et al.: Gland segmentation in colon histology images: the glas challenge contest. Med. Image Anal. 35, 489 (2016)
Cohen, A., Rivlin, E., Shimshoni, I., Sabo, E.: Memory based active contour algorithm using pixel-level classified images for colon crypt segmentation. Comput. Med. Imaging Graph. 43, 150–164 (2015)
Gunduz-Demir, C., Kandemir, M., Tosun, A., Sokmensuer, C.: Automatic segmentation of colon glands using object-graphs. Med. Image Anal. 14(1), 112 (2010)
Cheikh, B.B., Bertheau, P., Racoceanu, D.: A structure-based approach for colon gland segmentation in digital pathology. In: SPIE Medical Imaging, vol. 9791, p. 97910J (2016)
Acknowledgements
This work was financially supported by the Natural Science Foundation of Jiangsu Province, China under grant No. BK20170443. Nantong Research Program of Application Foundation under Grant No. GY12016022 and Dr. H Zhou is currently supported by UK EPSRC under Grant EP/N011074/1, and Newton Advanced Fellowship under Grant NA160342.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhang, K., Zhou, H., Chen, L., Fei, M., Wu, J., Zhang, P. (2017). A Novel Segmentation Framework Using Sparse Random Feature in Histology Images of Colon Cancer. In: Fei, M., Ma, S., Li, X., Sun, X., Jia, L., Su, Z. (eds) Advanced Computational Methods in Life System Modeling and Simulation. ICSEE LSMS 2017 2017. Communications in Computer and Information Science, vol 761. Springer, Singapore. https://doi.org/10.1007/978-981-10-6370-1_17
Download citation
DOI: https://doi.org/10.1007/978-981-10-6370-1_17
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-6369-5
Online ISBN: 978-981-10-6370-1
eBook Packages: Computer ScienceComputer Science (R0)