Abstract
Automated centroblast (CB) detection in Follicular Lymphoma (FL) tissue samples has recently attracted significant research interest. Most of the methods described in the literature are based on the use of Hematoxilin and Eosin (H&E) stain. However, the automated detection of CBs from H&E stained images remains a challenging issue. To this end, this paper presents a novel approach which is based on the use of both PAX5 and H&E stains in tissue sections sliced at the thickness of 1μm. The goal of PAX5 is three-fold: to facilitate the segmentation of nuclei, to remove a number of follicular dendritic cells and finally to extract morphological characteristics of nuclei. Furthermore, the use of H&E stain enables us to extract textural information related to histological characteristics used by pathologists in diagnosis of FL grading. In our method we propose a novel algorithm for the separation of overlapped nuclei inspired by the clustering of large scale visual vocabularies. Finally, aiming to model pathologists’ knowledge used in FL grading, we use a Bayesian Network classifier to combine the morphological and textural characteristics. Experiments conducted on a dataset of ten pairs of PAX5 and H&E images demonstrate the potential of the proposed approach providing an average detection rate of 93.46%.
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References
S H Swerdlow et al., Classification of Tumours of Haemaopoietic and Lymphoid Tissues, Fourth Edition ed. Lyon: World Health Organization, 2008.
R B Mann and C W Berard, “Criteria for the cytologic subclassification of follicular lymphomas: a proposed alternative method,” Hematl. Oncol., vol. 1, no. 2, pp. 187-192, 1983.
A Freedman, “Follicular Lymphoma: 2012 Update on Diagnosis and Managemet,” Am. J. Hematol., vol. 87, no. 10, pp. 988-995, September 2012.
O Sertel et al., “Histopathological Image Analysis Using Model-Based Intermediate Representations and Color Texture: Follicular Lymphoma Grading,” Signal Processing Systems, vol. 55, no. 1-3, pp. 169-183, April 2009.
K Belkacem-Boussaid, O Sertel, G Lozanski, A Ahana’aah, and M Gurcan, “Extraction of color features in the spectral domain to recognize centroblasts in histopathology,” in Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis, USA, 2009.
O Seterl, G Lozanski, A Shana’ah, and M Gurcan, “Computer aided detection of centroblasts for follicular lymphoma grading using adaptive likelihood-based cell segmentation,” IEEE Trans. Biomed. Eng., vol. 57, no. 10, pp. 2613-2616, 2010.
E N Kornaropoulos, M Niazi, G Lozanski, and M N Gurcan, “Histopathological image analysis for centroblasts classification through dimensionality reduction approaches,” Cytometry Part A, vol. 85, no. 3, pp. 242-255, 2014.
Y Song et al., “Unsupervised content classification based non-rigid registration of differently stained histology images,” IEEE Trans. Biomed. Eng., vol. 96, no. 1, pp. 96-108, Janouary 2014.
M Horcher, A Souabni, and M Busslinger, “Pax5/BSAP Maintains the Identity of B Cells in Late B Lymphopoiesis,” Immunity, vol. 14, pp. 779-790, 2001.
K C Jensen et al., “The utility of PAX5 immunohistochemistry in the diagnosis of undifferentiated malignant neoplasms,” Modern Pathology, vol. 20, no. 8, pp. 871-877, August 2007.
Y Boykov, O Veksler, and R Zabih, “Fast Approximate Energy Minimization via Graph Cuts,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 11, pp. 1222-1239, 2001.
P Soille, Morphological Image Analysis: Principles and Applications. Berlin, Germany: Springer-Verlag, 1999.
Y Avrithis and Y Kalantidis, “Approximate Gaussian Mixtures for Large Scale Vocabularies,” in European Conference on Computer Vision, Florence, Italy, 2012.
Mohamed Bouguessa, Shengrui Wang, and Haojun Sun, “An objective approach to cluster validation,” Pattern Recognition Letters, vol. 27, no. 13, pp. 1419-1430, 2006.
Jos B.T.M. Roerdink and Arnold Meijster, “The Watershed Transform: Definitions, Algorithms and Parallelization Strategies,” Fundamenta Informaticae, vol. 41, pp. 187-228, 2001.
W Gander, G H Golub, and R Strebel, “Least-square fitting of circles and ellipses,” BIT, vol. 34, no. 4, pp. 558-578, 1994.
Kosmas Dimitropoulos, Emmanouil Michail, Triantafyllia Koletsa, Ioannis Kostopoulos, and Nikos Grammalidis, “Using adaptive neuro-fuzzy inference systems for the detection of centroblasts in microscopic images of follicular lymphoma,” Signal, Image and Video Processing, vol. 8, no. 1, pp. 33-40, December 2014.
P Kevin Murphy, “The Bayes Net Toolbox for MATLAB,” Computing Science and Statistics, vol. 33, 2001.
MathWorks. (2015, June) R2013a imregister. [Online]. http://www.mathworks.com/help/images/ref/imregister.html
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Dimitropoulos, K., Barmpoutis, P., Koletsa, T., Kostopoulos, I., Grammalidis, N. (2016). Classification of Nuclei in Follicular Lyphoma Tissue Sections Using Different Stains and Bayesian Networks. In: Kyriacou, E., Christofides, S., Pattichis, C. (eds) XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016. IFMBE Proceedings, vol 57. Springer, Cham. https://doi.org/10.1007/978-3-319-32703-7_47
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DOI: https://doi.org/10.1007/978-3-319-32703-7_47
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