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Items: 1 to 20 of 265

1.

Effect of image compression and scaling on automated scoring of immunohistochemical stainings and segmentation of tumor epithelium.

Konsti J, Lundin M, Linder N, Haglund C, Blomqvist C, Nevanlinna H, Aaltonen K, Nordling S, Lundin J.

Diagn Pathol. 2012 Mar 21;7:29. doi: 10.1186/1746-1596-7-29.

2.

Quantitative comparison of immunohistochemical staining measured by digital image analysis versus pathologist visual scoring.

Rizzardi AE, Johnson AT, Vogel RI, Pambuccian SE, Henriksen J, Skubitz AP, Metzger GJ, Schmechel SC.

Diagn Pathol. 2012 Jun 20;7:42. doi: 10.1186/1746-1596-7-42.

3.

Digital image analysis in breast cancer: an example of an automated methodology and the effects of image compression.

López C, Lejeune M, Bosch R, Korzynska A, García-Rojo M, Salvadó MT, Alvaro T, Callau C, Roso A, Jaén J.

Stud Health Technol Inform. 2012;179:155-71.

PMID:
22925796
4.

Development and evaluation of a virtual microscopy application for automated assessment of Ki-67 expression in breast cancer.

Konsti J, Lundin M, Joensuu H, Lehtimäki T, Sihto H, Holli K, Turpeenniemi-Hujanen T, Kataja V, Sailas L, Isola J, Lundin J.

BMC Clin Pathol. 2011 Jan 25;11:3. doi: 10.1186/1472-6890-11-3.

5.
6.

JPEG2000 for automated quantification of immunohistochemically stained cell nuclei: a comparative study with standard JPEG format.

Lejeune M, López C, Bosch R, Korzyńska A, Salvadó MT, García-Rojo M, Neuman U, Witkowski Ł, Baucells J, Jaén J.

Virchows Arch. 2011 Feb;458(2):237-45. doi: 10.1007/s00428-010-1008-3. Epub 2010 Nov 18.

PMID:
21085985
7.

Lossless compression of threshold-segmented medical images.

Spelič D, Zalik B.

J Med Syst. 2012 Aug;36(4):2349-57. doi: 10.1007/s10916-011-9702-5. Epub 2011 Apr 15.

PMID:
21494853
8.

Using a visual discrimination model for the detection of compression artifacts in virtual pathology images.

Johnson JP, Krupinski EA, Yan M, Roehrig H, Graham AR, Weinstein RS.

IEEE Trans Med Imaging. 2011 Feb;30(2):306-14. doi: 10.1109/TMI.2010.2077308. Epub 2010 Sep 23.

9.

Technical note on the validation of a semi-automated image analysis software application for estrogen and progesterone receptor detection in breast cancer.

Krecsák L, Micsik T, Kiszler G, Krenács T, Szabó D, Jónás V, Császár G, Czuni L, Gurzó P, Ficsor L, Molnár B.

Diagn Pathol. 2011 Jan 18;6:6. doi: 10.1186/1746-1596-6-6.

10.

ImmunoRatio: a publicly available web application for quantitative image analysis of estrogen receptor (ER), progesterone receptor (PR), and Ki-67.

Tuominen VJ, Ruotoistenmäki S, Viitanen A, Jumppanen M, Isola J.

Breast Cancer Res. 2010;12(4):R56. doi: 10.1186/bcr2615. Epub 2010 Jul 27.

11.

Roundness variation in JPEG images affects the automated process of nuclear immunohistochemical quantification: correction with a linear regression model.

López C, Jaén Martinez J, Lejeune M, Escrivà P, Salvadó MT, Pons LE, Alvaro T, Baucells J, García-Rojo M, Cugat X, Bosch R.

Histochem Cell Biol. 2009 Oct;132(4):469-77. doi: 10.1007/s00418-009-0626-9. Epub 2009 Aug 4.

PMID:
19652993
12.

Identification of tumor epithelium and stroma in tissue microarrays using texture analysis.

Linder N, Konsti J, Turkki R, Rahtu E, Lundin M, Nordling S, Haglund C, Ahonen T, Pietikäinen M, Lundin J.

Diagn Pathol. 2012 Mar 2;7:22. doi: 10.1186/1746-1596-7-22.

13.

A Comparison of Visual Assessment and Automated Digital Image Analysis of Ki67 Labeling Index in Breast Cancer.

Zhong F, Bi R, Yu B, Yang F, Yang W, Shui R.

PLoS One. 2016 Feb 29;11(2):e0150505. doi: 10.1371/journal.pone.0150505. eCollection 2016.

14.

Effects of tissue decalcification on the quantification of breast cancer biomarkers by digital image analysis.

Gertych A, Mohan S, Maclary S, Mohanty S, Wawrowsky K, Mirocha J, Balzer B, Knudsen BS.

Diagn Pathol. 2014 Nov 25;9:213. doi: 10.1186/s13000-014-0213-9.

15.

Lossy three-dimensional JPEG2000 compression of abdominal CT images: assessment of the visually lossless threshold and effect of compression ratio on image quality.

Ringl H, Schernthaner RE, Kulinna-Cosentini C, Weber M, Schaefer-Prokop C, Herold CJ, Schima W.

Radiology. 2007 Nov;245(2):467-74. Epub 2007 Sep 21.

PMID:
17890355
16.

Digital immunohistochemistry platform for the staining variation monitoring based on integration of image and statistical analyses with laboratory information system.

Laurinaviciene A, Plancoulaine B, Baltrusaityte I, Meskauskas R, Besusparis J, Lesciute-Krilaviciene D, Raudeliunas D, Iqbal Y, Herlin P, Laurinavicius A.

Diagn Pathol. 2014;9 Suppl 1:S10. doi: 10.1186/1746-1596-9-S1-S10. Epub 2014 Dec 19.

17.

Acceptable compression ratio of full-field digital mammography using JPEG 2000.

Kang BJ, Kim HS, Park CS, Choi JJ, Lee JH, Choi BG.

Clin Radiol. 2011 Jul;66(7):609-13. doi: 10.1016/j.crad.2011.02.004. Epub 2011 Mar 29.

PMID:
21450282
18.

Image analysis algorithms for immunohistochemical assessment of cell death events and fibrosis in tissue sections.

Krajewska M, Smith LH, Rong J, Huang X, Hyer ML, Zeps N, Iacopetta B, Linke SP, Olson AH, Reed JC, Krajewski S.

J Histochem Cytochem. 2009 Jul;57(7):649-63. doi: 10.1369/jhc.2009.952812. Epub 2009 Mar 16.

19.

[Application study of JPEG2000 image compression in nuclear medicine].

Zhou L, Su X, Qin W, Li L, Kuang A, Mo T.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2006 Feb;23(1):52-5. Chinese.

PMID:
16532809
20.

Perceived sufficiency of full-field digital mammograms with and without irreversible image data compression for comparison with next-year mammograms.

Destounis S, Somerville P, Murphy P, Seifert P.

J Digit Imaging. 2011 Feb;24(1):66-74. doi: 10.1007/s10278-010-9277-6.

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