Image analysis of immunohistochemical stains for detection of parathyroid disease

• Autorzy: Płaczek B. , Orczyk T. , Bułdak R. , Michalski M. , Segiet O. , Polaniak R.
• Języki publikacji: EN

Abstrakty

EN

In this paper a method is introduced which enables automatic detection of parathyroid hyperplasia and parathyroid adenoma on the basis of immunohistochemical angiogenesis markers expression in micrographs. The proposed method uses digital image processing techniques and classification algorithms to detect diseased tissue. The disease detection is performed by classification of normalized color intensity histograms. Accuracy of this method was evaluated by using micrographs of parathyroid tissue sections obtained from patients that have undertaken surgery due to primary hyperparathyroidism. Use of different color models, various classifiers, and immunohistochemical markers was considered during the experiments. The experimental results show that the introduced method enables accurate detection of parathyroid disease. The most promising results were obtained for k-nearest neighbor and neural network classifiers.

Słowa kluczowe

EN

image processing; immunohistochemistry; hyperparathyroidism; light microscopy

PL

przetwarzanie obrazu; immunohistochemia; nadczynność przytarczyc; mikroskopia świetlna

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2015
• Tom: Vol. 24
• Strony: 141--146
• Opis fizyczny: Bibliogr. 17 poz., rys., tab.

Twórcy

autor

Płaczek B.

• Institute of Computer Science, University of Silesia, Będzińska 39, 41-200 Sosnowiec, Poland

autor

Orczyk T.

• Institute of Computer Science, University of Silesia, Będzińska 39, 41-200 Sosnowiec, Poland

autor

Bułdak R.

• Department of Physiology, School of Medicine with the Division of Dentistry, Medical University of Silesia
• Department of Human Nutrition, School of Public Health, Medical University of Silesia

autor

Michalski M.

• Department of Histology and Embryology, School of Medicine with the Division of Dentistry, Medical University of Silesia

autor

Segiet O.

• Department of Histology and Embryology, School of Medicine with the Division of Dentistry, Medical University of Silesia

autor

Polaniak R.

• Department of Human Nutrition, School of Public Health, Medical University of Silesia

Bibliografia

• [1] BERNAŚ M., PŁACZEK B., PORWIK P., PAMUŁA T. Segmentation of vehicle detector data for improved k-nearest neighbours-based traffic flow prediction. Intelligent Transport Systems, IET, 2015, Vol. 9. pp. 264–274.
• [2] BREY E. M., LALANI Z., JOHNSTON C., WONG M., MCINTIRE L. V., DUKE P. J., PATRICK C. W. Automated selection of DAB-labeled tissue for immunohistochemical quantification. The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society, May 2003, Vol. 51. pp. 575–84.
• [3] DONG J., LI J., FU A., LV H. Automatic segmentation for ovarian cancer immunohistochemical image based on yuv color space. Biomedical Engineering and Computer Science (ICBECS), 2010 International Conference on, April 2010. pp. 1–4.
• [4] FU R., MA X., BIAN Z., MA J. Digital separation of diaminobenzidine-stained tissues via an automatic color-filtering for immunohistochemical quantification. Biomedical optics express, Feb 2015, Vol. 6. pp. 544–58.
• [5] KACZMAREK E., GÓRNA A., MAJEWSKI P. Techniques of image analysis for quantitative immunohistochemistry. Roczniki Akademii Medycznej w Białymstoku (1995), 2004, Vol. 49 Suppl 1. pp. 155–8.
• [6] MUNSON P. Immunohistochemistry. Basic Science Techniques in Clinical Practice, 2007. Springer London, pp. 18–30.
• [7] PŁACZEK B. Rough sets in identification of cellular automata for medical image processing. Journal of Medical Informatics and Technologies, 2013, Vol. 22. pp. 161–168.
• [8] PŁACZEK B., BUŁDAK R. J., POLANIAK R. Automatic immunogold particle detection in transmission electron micrographs of cancer cells. Journal of Medical Imaging and Health Informatics, 2015, Vol. 5. pp. 1350–1357.
• [9] PHAM N.-A., MORRISON A., SCHWOCK J., AVIEL-RONEN S., IAKOVLEV V., TSAO M.-S., HO J., HEDLEY D. Quantitative image analysis of immunohistochemical stains using a cmyk color model. Diagnostic Pathology, 2007, Vol. 2. BioMed Central.
• [10] PRASAD K., PRABHU G. Image analysis tools for evaluation of microscopic views of immunohistochemically stained specimen in medical research–a review. Journal of Medical Systems, 2012, Vol. 36. Springer US, pp. 2621–2631.
• [11] RIEDMILLER M., BRAUN H. A direct adaptive method for faster backpropagation learning: the rprop algorithm. Neural Networks, 1993., IEEE International Conference on, 1993. pp. 586–591 vol.1.
• [12] RUIFROK A. C., JOHNSTON D. A. Quantification of histochemical staining by color deconvolution. Analytical and quantitative cytology and histology / the International Academy of Cytology [and] American Society of Cytology, Aug 2001, Vol. 23. pp. 291–9.
• [13] SCHACHT V., KERN J. S. Basics of immunohistochemistry. J Invest Dermatol, Mar 2015, Vol. 135. The Society for Investigative Dermatology, Inc, p. e30. Research Techniques Made Simple.
• [14] SEGIET O., PIECUCH A., MICHALSKI M., DESKA M., BRZOZOWA M., GAWRYCHOWSKI J., WOJNICZ R. Immunohistochemical assessment of angiogenesis markers in primary hyperparathyroidism. 48 Sympozjum Polskiego Towarzystwa Histochemików i Cytochemików "Od MAKRO do NANO. Nowe horyzonty i nowe możliwości w naukach podstawowych i klinicznych", 2014. p. 50.
• [15] SHAFER J., AGRAWAL R., MEHTA M. Sprint: A scalable parallel classifier for data mining. 1996. Morgan Kaufmann, pp. 544–555.
• [16] TADROUS P. J. Digital stain separation for histological images. Journal of microscopy, Nov 2010, Vol. 240. pp. 164–72.
• [17] VARGHESE F., BUKHARI A. B., MALHOTRA R., DE A. IHC Profiler: an open source plugin for the quantitative evaluation and automated scoring of immunohistochemistry images of human tissue samples. PloS one, 2014, Vol. 9. p. e96801.