Abstract
Current best practice in the quantitative analysis of microscopy images dictates that image files should be saved in a lossless format such as TIFF. Use of lossy files, including those processed with the JPEG algorithm, is highly discouraged due to effects of compression on pixel characteristics. However, with the growing popularity of whole-slide imaging (WSI) and its attendant large file sizes, compressed image files are becoming more prevelent. This prompted us to perform a color-based quantitative pixel analysis of minimally compressed WSI images. Sections from three tissues stained with one of three reagents representing the colors blue (hematoxylin), red (Oil-Red-O), and brown (immunoperoxidase) were scanned with a whole slide imager in triplicate at 20x, 40x, and 63x magnifications. The resulting files were in the form of a BigTIFF with a JPEG compression automatically applied during acquisition. Images were imported into analysis software, six regions of interest were applied to various morphological locations, and the areas assessed for the color of interest. Whereas the number of designated weakly or strongly positive pixels was variable across the triplicate scans for the individual regions of interest, the total number of positive pixels was consistent. These results suggest that total positivity for a specific color representing a histochemical or immunohistochemical stain can be adequately quantitated on compressed images, but degrees of positivity (e.g., weak vs. strong) may not be as reliable. However, it is important to assess individual whole-slide imagers, file compression level and algorithm, and analysis software for reproducibility.
This is a preview of subscription content, access via your institution.
References
Abe T, Hashiguchi A, Yamazaki K, Ebinuma H, Saito H, Kumada H, Izumi N, Masaki N, Sakamoto M (2013) Quantification of collagen and elastic fibers using whole-slide images of liver biopsy specimens. Pathol Int 63:305–310. https://doi.org/10.1111/pin.12064
Bautista PA, Hashimoto N, Yagi Y (2014) Color standardization in whole slide imaging using a color calibration slide. J Pathol Inform 5:4. https://doi.org/10.4103/2153-3539.126153
Chiou PT, Sun Y, Young GS (2017) A complexity analysis of the JPEG image compression algorithm. In: IEEE 9th Computer Science and Electronic Engineering (CEEC), pp 65–70. https://doi.org/10.1109/CEEC.2017.8101601
Clunie DA, Dennison DK, Cram D, Persons KR, Bronkalla MD, Primo HR (2016) Technical challenges of enterprise imaging: HIMSS-SIIM collaborative white paper. J Digit Imaging 29:583–614. https://doi.org/10.1007/s10278-016-9899-4
Cromey DW (2013) Digital images are data: and should be treated as such. In: Taatjes DJ, Roth J (eds) Cell imaging techniques: methods and protocols, methods in molecular biology, vol 931, pp. 1-27. https://doi.org/10.1007/978-1-62703-056-4_1
Della Mea V, Baroni GL, Pilutti D, Di Loreto C (2017) SlideJ: an ImageJ plugin for automated processing of whole slide images. PLoS One 12(7):e0180540. https://doi.org/10.1371/journal.pone.01805-40
Diller RB, Kellar RS (2015) Validating whole slide digital morphometric analysis as a microscopy tool. Microsc Microanal 21:249–255. https://doi.org/10.1017/S1431927614013567
Farahani N, Parwani AV, Pantanowitz L (2015) Whole slide imaging in pathology: advantages, limitations, and emerging perspectives. Pathol Lab Med Int 7:23–33. https://doi.org/10.2147/PLMI.S59826
Gallas BD, Gavrielides MA, Conway CM, Ivansky SA, Keay TC, Cheng W-C, Hipp J, Hewitt SM (2014) Evaluation environment for digital and analog pathology: a platform for validation studies. J Med Imaging 1:1–9. https://doi.org/10.1117/1.JMI.1.3.037501
Ghaznavi F, Evans A, Madabhushi A, Feldman M (2013) Digital imaging in pathology: whole-slide imaging and beyond. Annu Rev pathol Mech Dis 8:331–359. https://doi.org/10.1146/annurev-pathol-011811-120902
Gray A, Wright A, Jackson P, Hale M, Treanor D (2015) Quantification of histochemical stains using whole slide imaging: development of a method and demonstration of its usefulness in laboratory quality control. J Clin Pathol 68:192–199. https://doi.org/10.1136/jclinpath-2014-202526
Hamilton PW, Bankhead P, Wang Y, Hutchinson R, Kieran D, McArt DG, James J, Salto-Tellez M (2014) Digital pathology and image analysis in tissue biomarker research. Methods 70:59–73. https://doi.org/10.1016/j.ymeth.2014.06.015
Hernandez-Cabronero M, Auli-Llinas F, Sanchez V, Serra-Sagrista J (2016a) Transform optimization for the lossy coding of pathology whole-slide images. In: Proceedings of the IEEE 2016 data compression conference, pp 131–140. https://doi.org/10.1109/DCC.2016.33
Hernandez-Cabronero M, Sanchez V, Auli-Llinas F, Serra-Sagrista J (2016b) Fast MCT optimization for the compression of whole-slide images. In: Proceedings of the 2016 IEEE conference on image processing (ICIP), pp 2370–2374.
Hernandez-Cabronero M, Sanchez V, Blanes I, Auli-Llinas F, Marcellin MW, Serra-Sagrista J (2018) Mosaic-based color-transform optimization for lossy and lossy-to-lossless compression of pathology whole-slide images. IEEE Trans Med Imaging. https://doi.org/10.1109/TMI.2018.2852685
Keay T, Conway CM, O’Flaherty N, Hewitt SM, Shea K, Gavrielides MA (2013) Reproducibility in the automated quantitative assessment of HER2/neu for breast cancer. J Pathol Inform 4:19. https://doi.org/10.4103/2153-3539.115879
Kiernan JA (2015) Histological and histochemical methods—theory and practice, 5th edn. Scion Publishing Ltd., Banbury
Konsti J, Lundin M, Linder N, Haglund C, Blomqvist C, Nevanlinna H, Aaltonen K, Nordling S, Lundin J (2012) Effect of image compression and scaling on automated scoring of immunohistochemical stainings and segmentation of tumor epithelium. Diagn Pathol 7:29. https://doi.org/10.1186/1746-1596-7-29
Lejeune M, Lopez C, Bosch R, Korzynska A, Salvado MT, Garcia-Rojo M, Neuman U, Witkowski L, Baucells J, Jaen J (2011) JPEG2000 for automated quantification of immunohistochemically stained cell nuclei: a comparative study with standard JPEG format. Virchows Arch 458:237–245. https://doi.org/10.1007/s00428-010-1008-3
Liu F, Hernandez-Cabronero M, Sanchez V, Marcellin MW, Bilgin A (2017) The current role of image compression standards in medical imaging. Information 8(131):1–26. https://doi.org/10.3390/info8040131
Lopez C, Lejeune M, Escriva P, Bosch R, Salvado MT, Pons LE, Baucells J, Cugat X, Alvaro T, Jaen J (2008) Effects of image compression on automatic count of immunohistochemically stained nuclei in digital images. J Am Med Inform Assoc 15:794–798. https://doi.org/10.1197/jamia.M2747
Lopez C, Martinez JJ, Lejeune M, Escriva P, Salvado MT, Pons LE, Alvaro T, Baucells J, Garcia-Rojo M, Cugat X, Bosch R (2009) Roundness variation in JPEG images affects the automated process of nuclear immunohistochemical quantification: correction with a linear regression model. Histochem Cell Biol 132:469–477. https://doi.org/10.1007/s00418-009-0626-9
Opsahl AC, Tengowski MW (2007) Effects of compression in digital images used for image analysis. J Biocommun 33:E28–E33
Pantanowitz L, Sharma A, Carter AB, Kurc T, Sussman A, Saltz J (2018) Twenty years of digital pathology: an overview of the road travelled, what is on the horizon, and the emergence of vendor-neutral archives. J Pathol Inform 9:40. https://doi.org/10.4103/jpi.jpi_69_18
Raid AM, Khedr WM, El-dousky MA, Ahmed W (2014) Jpeg image compression using discrete cosine transform—a survey. Int J Comp Sci Engin Sur 5:39–47. https://doi.org/10.5121/ijcses.2014.5204
Roy S, Jain AK, Lal S, Kini J (2018) A study about color normalization for histopathology images. Micron 114:42–61. https://doi.org/10.1016/j.micron.2018.07.005
Russ JC, Neal FB (2015) The image processing handbook, 7th edn. CRC Press, New York
Sarode VR, Xiang QD, Christie A, Collins R, Rao R, Leitch M, Euhus D, Haley B (2015) Evaluation of HER2/neu status by immunohistochemistry using computer-based image analysis and correlation with gene amplification by fluorescence in situ hybridization. Arch Pathol Lab Med 139:922–928. https://doi.org/10.5858/arpa.2014-0127-OA
Shrestha P, Kneepkens R, Vrijnsen J, Vossen D, Abels E, Hulsken B (2016) A quantitative approach to evaluate image quality of whole slide imaging scanners. J Pathol Inform 7:56. https://doi.org/10.4103/2153-3539.197205
Tengowski MW (2004) Image compression in morphometry studies requiring 21 CFR Part 11 compliance: Procedure is key with TIFFs and various JPEG compression strengths. Toxicol Pathol 32:258–263. https://doi.org/10.1080/01926230490274399
Webster JD, Dunstan RW (2014) Whole-slide imaging and automated image analysis: considerations and opportunities in the practice of pathology. Vet Pathol 5:211–233. https://doi.org/10.1177/0300985813503570
Zarella MD, Bowman D, Aeffner F, Farahani N, Xthona A, Absar SF, Parwani A, Bui M, Hartman DJ (2018) A practical guide to whole slide imaging. A white paper from the digital pathology association. Arch Pathol Lab Med. https://doi.org/10.5858/arpa.2018-0343-RA
Acknowledgements
The Aperio VERSA 8 whole slide imager was purchased with funds provided through a Shared Instrumentation Grant awarded by the Dean’s Office of the Larner College of Medicine (to DJT). We thank Joan Skelly of Department of Medical Biostatisitics University of Vermont for statistical advice, and Drs. Mercedes Rincon and Marilyn Cipolla for allowing us to use their tissue slides. We also thank Leica Technical Support for discussions and assistance.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Taatjes, D.J., Bouffard, N.A., Barrow, T. et al. Quantitative pixel intensity- and color-based image analysis on minimally compressed files: implications for whole-slide imaging. Histochem Cell Biol 152, 13–23 (2019). https://doi.org/10.1007/s00418-019-01783-7
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00418-019-01783-7
Keywords
- Whole-slide imaging
- Image analysis
- Image focus
- Image compression
- BigTiff
- Image data