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 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

 
 
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