Equalisation of Archival Microscopic Images from Immunohistochemically Stained Tissue Sections

A method of image equalisation that reduces non-uniformity of light distribution caused by optical devices and dust on camera sensors is presented. The method explores non-uniformity which occurs in archival images captured by a typical optical set which consists of a light microscope and a digital camera. A sufficient number of images with low density of foreground objects has been used to extract a global map of non-uniformity of the particular microscope and camera. The proposed method consists of two steps: – (1) extraction of the map of non-uniformity based upon a set of chosen images and – (2) correction of images acquired by the optical set. The global map is created based upon a modified value layer, the third layer of HSV colour space. The proposed method has been tested on images of immunohistochemically (IHC) stained samples of a biopsy tissue, and it has been validated using an image segmentation method developed earlier. The results of the light distribution equalization, as well as the equalized images segmentation turn out to be more similar to the reference method results (namely the manual counting results), than the results of the original images segmentation. The equalization method can be used for other types of images, but all of them should be acquired by the same optical set.

[1]  Dorin Comaniciu,et al.  Cell image segmentation for diagnostic pathology , 2001 .

[2]  Andreas Hoppe,et al.  INVESTIGATION OF A COMBINED TEXTURE AND CONTOUR METHOD FOR SEGMENTATION OF LIGHT MICROSCOPY CELL IMAGES , 2003 .

[3]  Jerry L. Prince,et al.  A Survey of Current Methods in Medical Image Segmentation , 1999 .

[4]  H. Yaziji,et al.  Diagnostic Immunohistochemistry: What can Go Wrong? , 2006, Advances in anatomic pathology.

[5]  M. Iwanowski,et al.  Multistage morphological segmentation of bright-field and fluorescent microscopy images , 2012 .

[6]  Hai-Shan Wu,et al.  Applications of Image Analysis to Anatomic Pathology: Realities and Promises , 2003, Cancer investigation.

[7]  Jean Paul Frédéric Serra,et al.  The Morphological-Affine Object Deformation , 2000, ISMM.

[8]  Peter H. Bartels,et al.  Surrogate Endpoint Biomarkers for Clinical Trials of Prostate Cancer Chemoprevention , 1999 .

[9]  Pawel Badura,et al.  4D Segmentation of Ewing’s Sarcoma in MR Images , 2010 .

[10]  G. Kayser,et al.  Theory and implementation of an electronic, automated measurement system for images obtained from immunohistochemically stained slides. , 2006, Analytical and quantitative cytology and histology.

[11]  Stanley R. Sternberg,et al.  Biomedical Image Processing , 1983, Computer.

[12]  J. Suri,et al.  Advanced algorithmic approaches to medical image segmentation: state-of-the-art application in cardiology, neurology, mammography and pathology , 2001 .

[13]  Fritz Albregtsen,et al.  A Review of Caveats in Statistical Nuclear Image Analysis , 1998, Analytical cellular pathology : the journal of the European Society for Analytical Cellular Pathology.

[14]  Luc Vincent,et al.  Morphological grayscale reconstruction in image analysis: applications and efficient algorithms , 1993, IEEE Trans. Image Process..

[15]  Jerry L Prince,et al.  Current methods in medical image segmentation. , 2000, Annual review of biomedical engineering.

[16]  Stephen Lin,et al.  Single-Image Vignetting Correction , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Dan B. Goldman,et al.  Vignette and exposure calibration and compensation , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[18]  Masashi Baba,et al.  Photometric calibration of zoom lens systems , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[19]  Daniel Racoceanu,et al.  A cognitive virtual microscopic framework for knowlege-based exploration of large microscopic images in breast cancer histopathology , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[20]  Jean Serra,et al.  Image Analysis and Mathematical Morphology , 1983 .

[21]  Gloria Bueno,et al.  Review of imaging solutions for integrated quantitative immunohistochemistry in the Pathology daily practice. , 2010, Folia histochemica et cytobiologica.

[22]  Pierre Soille,et al.  Morphological Image Analysis , 1999 .

[23]  Carlos López,et al.  The Method of Immunohistochemical Images Standardization , 2010, IP&C.

[24]  Stanislaw Osowski,et al.  Image processing for accurate cell recognition and count on histologic slides. , 2006, Analytical and quantitative cytology and histology.

[25]  Stephen Lin,et al.  Single-image vignetting correction using radial gradient symmetry , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[26]  G. Kayser,et al.  Towards an automated virtual slide screening: theoretical considerations and practical experiences of automated tissue-based virtual diagnosis to be implemented in the Internet , 2006, Diagnostic pathology.

[27]  Klaus Kayser,et al.  Introducing Diagnostic Pathology , 2006, Diagnostic pathology.

[28]  Marylène Lejeune,et al.  Automated quantification of nuclear immunohistochemical markers with different complexity , 2008, Histochemistry and Cell Biology.

[29]  Jeroen van der Laak,et al.  Ki-67 as a prognostic marker in mantle cell lymphoma—consensus guidelines of the pathology panel of the European MCL Network , 2009, Journal of hematopathology.

[30]  Klaus-Jürgen Bathe.  What Can Go Wrong , 1998 .

[31]  Peter Devilee,et al.  Pathology and Genetics of Tumours of the Breast and Female Genital Organs , 2003 .

[32]  Sing Bing Kang,et al.  Can We Calibrate a Camera Using an Image of a Flat, Textureless Lambertian Surface? , 2000, ECCV.

[33]  S. Osowski,et al.  New automated image analysis method for the assessment of Ki-67 labeling index in meningiomas. , 2010, Folia histochemica et cytobiologica.

[34]  S. Yenidunya,et al.  Predictive value of pathological and immunohistochemical parameters for axillary lymph node metastasis in breast carcinoma , 2011, Diagnostic pathology.

[35]  Yoav Y. Schechner,et al.  Addressing radiometric nonidealities: a unified framework , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[36]  Alexander A. Sawchuk,et al.  Real-Time Correction of Intensity Nonlinearities in Imaging Systems , 1977, IEEE Transactions on Computers.

[37]  Pierre Soille,et al.  Morphological Refinement of an Image Segmentation , 2005, CAIP.

[38]  King-Sun Fu,et al.  A survey on image segmentation , 1981, Pattern Recognit..

[39]  Wonpil Yu,et al.  Practical anti-vignetting methods for digital cameras , 2004, IEEE Trans. Consumer Electron..

[40]  T. Seidal,et al.  Interpretation and Quantification of Immunostains , 2001, The American journal of surgical pathology.

[41]  Heung-Kook Choi,et al.  Color image analysis for quantifying renal tumor angiogenesis. , 2005, Analytical and quantitative cytology and histology.

[42]  J. Serra,et al.  An overview of morphological filtering , 1992 .

[43]  Marylène Lejeune,et al.  Immunohistochemical patterns of reactive microenvironment are associated with clinicobiologic behavior in follicular lymphoma patients. , 2006, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[44]  Zygmunt Wróbel,et al.  The Cell Structures Segmentation , 2005, CORES.

[45]  Tomasz Markiewicz.  Using MATLAB software with Tomcat server and Java platform for remote image analysis in pathology , 2011, Diagnostic pathology.

[46]  Stanislaw Osowski,et al.  Comparative analysis of methods for accurate recognition of cells through nuclei staining of Ki-67 in neuroblastoma and estrogen/progesterone status staining in breast cancer. , 2009, Analytical and quantitative cytology and histology.

[47]  Anna Korzynska,et al.  Segmentation of Stained Lymphoma Tissue Section Images , 2010 .

[48]  Haishan Wu,et al.  Image analysis and morphometry in the diagnosis of breast cancer , 2002, Microscopy research and technique.

[49]  Peter Hufnagl,et al.  Image standards in Tissue-Based Diagnosis (Diagnostic Surgical Pathology) , 2008, Diagnostic pathology.

[50]  David Wertheim,et al.  Segmentation of microscope images of living cells , 2007, Pattern Analysis and Applications.

[51]  Pierre Soille,et al.  Morphological Image Analysis: Principles and Applications , 2003 .

[52]  Sumeet Dua,et al.  Segmentation of Fluorescence Microscopy Cell Images Using Unsupervised Mining , 2010, The open medical informatics journal.