OJMI  Vol.3 No.4 , December 2013
Segmentation of Sinusoids in Hematoxylin and Eosin Stained Liver Specimens Using an Orientation-Selective Filter
ABSTRACT

The liver comprises cell layers of hepatocytes called trabeculae, which are separated by vascular sinusoids. Under- standing the structure of hepatic trabeculae and liver sinusoids in hematoxylin and eosin (HE)-stained liver specimens is important for the differential diagnosis of liver diseases. In this study, we develop an approach to extracting liver sinusoids from HE-stained images. The proposed approach involves: 1) a new orientation-selective filter (OS filter) for edge enhancement and image denoising, 2) the clustering of image pixels to identify candidate sinusoids, and 3) a classification procedure that discards unlikely candidates and selects the final sinusoid areas. Experimental studies using a database of 16 images with a resolution of 512 × 512 pixels showed that the proposed approach could segment liver sinusoid pixels with 81% of specificity and 94% of sensitivity. A comparison with a method based on bilateral filters showed that this method improved the sensitivity for all images with an average improvement of 4% and no difference in specificity. The results were presented to a group of pathologists and they confirmed that the images were highly representative of the tissue morphology features.


Cite this paper
M. Ishikawa, S. Taha Ahi, F. Kimura, M. Yamaguchi, H. Nagahashi, A. Hashiguchi and M. Sakamoto, "Segmentation of Sinusoids in Hematoxylin and Eosin Stained Liver Specimens Using an Orientation-Selective Filter," Open Journal of Medical Imaging, Vol. 3 No. 4, 2013, pp. 144-155. doi: 10.4236/ojmi.2013.34022.
References
[1]   M. Ogura, A. Saito, H. Graf, E. Cosatto, C. Malon, A. Marugame, T. Kiyuna, Y. Yamashita and M. Fukumoto, “The E-Pathologist Cancer Diagnosis Assistance System for Gastric Biopsy Tissues,” Analytical Cellular Pathol- ogy, Vol. 4, 2011, p. 34.

[2]   A. Tabesh, M. Teverovskiy, H.-Y. Pang, V. P. Kumar, D. Verbel, A. Kotsianti and O. Saidi, “Multifeature Prostate Cancer Diagnosis and Gleason Grading of Histological Images,” IEEE Transactions on Medical Imaging, Vol. 26, No. 10, 2007, pp. 1366-1378.
http://dx.doi.org/10.1109/TMI.2007.898536

[3]   S. Naik, S. Doyle, M. Feldman, J. Tomaszewski and A. Madabhushi, “Gland Segmentation and Computerized Gleason Grading of Prostate Histology by Integrating Low-, High-Level and Domain Specific Information,” 2007.

[4]   N. R. Muhammad Arif, “Classification of Potential Nuclei in Prostate Histology Images Using Shape Manifold Learning,” International Conference on Machine Vision, 2007, pp. 113-118.

[5]   N. Metin, A. M. Gurcan and N. Rajpoot, “Pattern Recognition in Histopathological Images: An ICPR 2010 Contest,” International Conference on ICPR, Islamabad, 28-29 December 2010, pp. 226-234.

[6]   P.-W. Huang and Y.-H. Lai, “Effective Segmentation and Classification for HCC Biopsy Images,” Pattern Recognition, Vol. 43, No. 4, 2010, pp. 1550-1563.
http://dx.doi.org/10.1016/j.patcog.2009.10.014

[7]   C. Atupelage, H. Nagahashi, M. Yamaguchi, T. Abe, A. Hashiguchi and M. Sakamoto, “Computational Grading of Hepatocellular Carcinoma Using Multifractal Feature Description,” Journal of Computerized Medical Imaging and Graphics, Vol. 37, No. 1, 2012, pp. 61-71.
http://dx.doi.org/10.1016/j.compmedimag.2012.10.001

[8]   F. T. Bosman, F. Carneiro, R. H. Hruban and N. D. Theise, “WHO Classification of Tumours of the Digestive System,” World Health Organization, 4th Edition, 2010.

[9]   P. J. Scheuer and J. H. Lefkowitch, “Scheuer’s Liver Biopsy Interpretation,” Saunders Elsevier, 8th Edition, 2010.

[10]   C. Tomasi and R. Manduchi, “Bilateral Filtering for Gray and Color Images,” IEEE International Conference on Computer Vision, Bombay, 1998.

[11]   B. H. Hall, M. Ianosi-Irimie, P. Javidian, W. Chen, S. Ganesan and D. J. Foran, “Computer-Assisted Assessment of the Human Epidermal Growth Factor Receptor 2 Immunohistochemical Assay in Imaged Histologic Sections Using a Membrane Isolation Algorithm and Quantitative Analysis of Positive Controls,” BMC Medical Imaging, Vol. 8, 2008, p. 11.

[12]   W. Wang, J. Ozolek, D. Slepcev, A. Lee, C. Chen and G. Rohde, “An Optimal Transportation Approach for Nuclear Structure-Based Pathology,” IEEE Transactions on Medical Imaging, Vol. 99, 2011, p. 1.

[13]   J. A. Bilmes, “A Gentle Tutorial of the EM Algorithm and Its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models,” Tech. Rep. TR-97021, International Computer Science Institute, Berkeley, 1998.

[14]   K.-R. Müller, S. Mika, G. Ratsch, K. Tsuda and B. Scholkopf, “An Introduction to Kernel-Based Learning Algorithms,” IEEE Transactions on Neural Networks, Vol. 12, No. 2, 2001, pp. 181-201.
http://dx.doi.org/10.1109/72.914517

[15]   C.-C. Chang and C.-J. Lin, “Libsvm: A Library for Support Vector Machines,” ACM Transactions on Intelligent Systems and Technology, Vol. 2, No. 3, 2011, pp. 1-7.
http://dx.doi.org/10.1145/1961189.1961199

[16]   H. A. Edmondson and P. E. Steiner, “Primary Carcinoma of the Liver: A Study of 100 Cases among 48,900 Necropsies,” Cancer, Vol. 7, No. 3, 1954, pp. 462-503.
http://dx.doi.org/10.1002/1097-0142(195405)7:3<462::AID-CNCR2820070308>3.0.CO;2-E

 
 
Top