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Computerized Histopathological Image Analysis: A review on Multiple Instances

Vilas S.Gaikwad1 , Anilkumar N.Holambe2

  1. Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, India.
  2. Department of Computer Science, Engineering, TPCT COE, Osmanabad, India.

Correspondence should be addressed to: vilasgaikwad11@gmail.com.

Section:Review Paper, Product Type: Journal Paper
Volume-5 , Issue-12 , Page no. 237-242, Dec-2017

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v5i12.237242

Online published on Dec 31, 2017

Copyright © Vilas S.Gaikwad, Anilkumar N.Holambe . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

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IEEE Style Citation: Vilas S.Gaikwad, Anilkumar N.Holambe, “Computerized Histopathological Image Analysis: A review on Multiple Instances,” International Journal of Computer Sciences and Engineering, Vol.5, Issue.12, pp.237-242, 2017.

MLA Style Citation: Vilas S.Gaikwad, Anilkumar N.Holambe "Computerized Histopathological Image Analysis: A review on Multiple Instances." International Journal of Computer Sciences and Engineering 5.12 (2017): 237-242.

APA Style Citation: Vilas S.Gaikwad, Anilkumar N.Holambe, (2017). Computerized Histopathological Image Analysis: A review on Multiple Instances. International Journal of Computer Sciences and Engineering, 5(12), 237-242.

BibTex Style Citation:
@article{S.Gaikwad_2017,
author = {Vilas S.Gaikwad, Anilkumar N.Holambe},
title = {Computerized Histopathological Image Analysis: A review on Multiple Instances},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {12 2017},
volume = {5},
Issue = {12},
month = {12},
year = {2017},
issn = {2347-2693},
pages = {237-242},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1609},
doi = {https://doi.org/10.26438/ijcse/v5i12.237242}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v5i12.237242}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1609
TI - Computerized Histopathological Image Analysis: A review on Multiple Instances
T2 - International Journal of Computer Sciences and Engineering
AU - Vilas S.Gaikwad, Anilkumar N.Holambe
PY - 2017
DA - 2017/12/31
PB - IJCSE, Indore, INDIA
SP - 237-242
IS - 12
VL - 5
SN - 2347-2693
ER -

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Abstract

This review paper deals with the most recent expertise developed on Digital assisted examination for histopathology images. The development on digital assisted examination for locating, analyzing and classification of fatal diseases like cancer, using histopathology. The previously, the observer is completely based on the proficiency level of the pathologist, is done by the physical processes. The organizational structure of the analysis of digital slides, cell distribution and the shape of the cell is based on the action. The entire interior of this process informal for the observer as well as the external observer. Histopathological diagnosis of tissue-paper images from the quantitative analysis of the process to evaluate the computerized Image. Histopathology of digital image processing techniques that can be applied to the area of digital slide analysis is presented in the summary. Histopathology of discrimination in the automated retrieval of the digital slides is an important area of research in image processing.

Key-Words / Index Term

Multiple Instances; Histopathology; Image Preprocessing; Classification

References

[1] J. P. Monaco, J. E. Tomaszewski, M. D. Feldman, I. Hagemann, M. Moradi, P. Mousavi, A. Boag, C. Davidson, P. Abolmaesumi, and A. Madabhushi, “High-throughput detection of prostate cancer in histological sections using probabilistic pairwise Markov models,” Medical Image Analysis, Elsevier, vol. 14(4), pp. 617–629, Aug. 2010.
[2] ["Cell culture supplements," http://www.integrated-bio.com/pro-RecombinantProteins.php, September 2015
[3] "How we make slides", http://bio-path.com/slides.htm, September 2015
[4] What is Histotechnology?," http://www.nyhisto.com/home/what-is-histotechnology/, September 2015.
[5] ["ParaffinProcessingofTissue," http://protocolsonline.com/histology/sample-preparation/paraffin-processing-of-tissue/, September
[6] "Tissue Processing For Histology: What Exactly Happens,?", http://bitesizebio.com/13469/tissue-processing-for-histology-what- exactly-happens/, September 2015.
[7] "How we make slides", http://bio-path.com/slides.htm, September 2015
[8] Simple Stain Kit for Plant Tissue," https://www.sciencelabsupplies.com/Simple-Stain-Kit-for-Plant-Tissue.html, September 2015.
[9] "H&E on Liver," http://sharedresources.fredhutch.org/images/he-liver, September 2015
Yassine Aribi, Ali Wali, Mohamed Chakroun, Adel M.Alimi, “Automatic definition of regions of interest on renal scintigraphic images,” AASRI Procedia, Elsevier, vol. 4, pp. 37-42, April 2013.
[10] M.T. McCann, J.A. Ozolek, C.A. Castro, B. Parvin, J. Kovacevic, "Automated histology analysis: opportunities for signal processing," Signal Processing Magazine, IEEE, vol 32(1), pp. 78-87, January 2015
[11] "CAD in pathology," http://www.healthcare-in-europe.com/en/article/15180-cad-in-pathology.html, September 2015
[12] J. P. Monaco, J. E. Tomaszewski, M. D. Feldman, I. Hagemann, M. Moradi, P. Mousavi, A. Boag, C. Davidson, P. Abolmaesumi, and A. Madabhushi, “High-throughput detection of prostate cancer in histological sections using probabilistic pairwise markov models.” Medical Image Analysis, Elsevier, vol. 14(4), pp. 617–629, August 2010.
[13] Lamia Jaafar Belaid and Walid Mourou, "Image segmentation: a watershed transformation algorithm," Image Anal Stereol, vol. 28, pp. 93-102, 2009.
[14] [Gowri Srinivasa, Matthew C. Fickus, Yusong Guo, Adam D. Linstedt, Jelena Kovacevic, "Active mask segmentation of fluorescence microscope images," IEEE Transactions On Image Processing, vol. 18(8), pp. 1817-1829, August 2009.
[15] P. Ghosh, S.K. Antani, L.R. Long, , G.R. Thoma,"Unsupervised Grow-Cut: Cellular Automata-Based Medical Image Segmentation," First IEEE International Conference on Healthcare Informatics, Imaging and Systems Biology, pp. 40-47, July2011.
[16] Rolf Adams and Leanne Bischof, "seeded region growing," IEEE Transaction on Pattern Analysis and Machine Intelligence, vol.
16(6), pp. 641-647, June 1994
[17] Chao-Hui Huang, Antoine Veillard, Ludovic Roux, Nicolas Lomenie, and Daniel Racoceanu, “Time-efficient sparse analysis ofhistopathological whole slide images,” Computerized Medical Imaging and Graphics, Elsevier, vol. 35, pp. 579–591, December
2011.
[18] [Vincent Roullier, Olivier Lézoray, Vinh-Thong Ta, and Abderrahim Elmoataz, “Multi-resolution graph-based analysis of histopathological whole slide images: application to mitotic cell extraction and visualization.” Computerized Medical Imaging and Graphics,Elsevier, vol. 35, pp. 603–615, May 2011.
[19] J. P. Monaco, J. E. Tomaszewski, M. D. Feldman, I. Hagemann, M. Moradi, P. Mousavi, A. Boag, C. Davidson, P. Abolmaesumi, and A. Madabhushi, “High-throughput detection of prostate cancer in histological sections using probabilistic pairwise Markov models,” Medical Image Analysis, Elsevier, vol. 14(4), pp. 617–629, Aug. 2010
[20] [Bilge Karacali and Aydin Tozeren, “Automated detection of regions of interest for tissue microarray experiments: an image textureanalysis,” BMC Med Imaging, March 2007.
[21] [2Ezgi Mercan, Selim Aksoy, Linda G. Shapiro, Donald L. Weaver, Tad Brunye, Joann G. Elmore, “Localization of diagnostically relevant regions of interest in whole slide images,” 22nd IAPR International Conference on Pattern Recognition, Stockholm, Sweden, August 2014.
[22] [Inoue Y, Yoshikawa K, Yoshioka N, Watanabe T, Saigua S, Kaneko Y, Yokoyama I, Ohtomo K, “Evaluation of renal function with99m-TC-MAG3 using semiautomated regions of interest,” J Nucl Med., vol. 41, pp. 1947–1954, December 2000.
[23] Y. Aribi, A. Wali, A.M.Alimi, "A system based on the fast marching method for analysis and processing DICOM images: the case of renal scintigraphy dynamic," International Conference on Computer Medical Applications, IEEE, pp. 1-6, January 2013.
[24] [Daniel Ståhl, Kalle Åström, Niels Christian Overgaard, Matilda Landgren, Karl Sjöstrand, Lars Edenbrandt, "Automatic compartment modelling and segmentation for dynamical renal scintigraphies," SCIA`11 Proceedings of the 17th Scandinavianconference on Image analysis, Springer, pp. 557–568, 2011
[25] [Ernest V. Garcia, Russell Folks, Samuel Pak and Andrew Taylor, "Totally automatic definition of renal regions-of-interest from TC-99m MAG3 renograms: validation in patients with normal kidneys and in patients with suspected renal obstruction," Nucl Med Commun., Vol. 31(5), pp.366-374, may 2010.
[26] Sushmita Mitra, B. Uma Shankar, “Medical image analysis for cancer management in natural computing framework,” Information Sciences, Elsevier, vol. 306, pp. 111-131, June 2015.
[27] Michael Derde, Laura Antanas, Luc De Raedt, Fabian Guiza Grandas, “An interactive learning approach to histology imagesegmentation,” 24th Benelux Conference on Artificial Intelligence,Benelux, pp. 1-8, October 2012
[28] [3N.R. Pal and S.K. Pal, “A review on image segmentation techniques”,Pattern Recognition Society, Printed in Great Britain, vol.26(9): pp. 1277–1294, March 1993.
[29] M.N. Gurcan, L.E. Boucheron, A. Can, A. Madabhushi, N.M. Rajpoot, and B.Yener, “Histopathological image analysis: a review”,IEEE Reviews in Biomedical Engineering, vol.2, pp. 147-171, October 2009
[30] “Features for histology images”, http://www.informed.unal.edu.co/jccaicedo/docs/review.pdf, November 2009
[31] Thomas Brox, Yoo-Jin Kim, Joachim Weickert, and Wolfgang Feiden,"Fully automated analysis of muscle fiber images with combined region and edge-based active contours," In Bildverarbeitung fur die Medizin, Springer pp. 86–90, 2006
[32] Alison G. Todman, and Ela Claridge, "Cell segmentation in histological images of striated muscle tissue- a perceptual grouping approach", Medical Image Understanding and Analysis, January 1997.
[33] Radu Rogojanu, Giovanna Bises, Cristian Smochina, and Vasile Manta, "Segmentation of cell nuclei within complex configuration s in images with colon sections," International Conference on Intelligent Computer Communication and Processing, IEEE, pp. 243–246, August 2010.
[34] R. Szeliski, "Computer vision: algorithms and applications," Springer-Verlag New York Inc, 2010
[35] Ching-Wei Wang, "A bayesian learning application to automated tumour segmentation for tissue microarray analysis," MachineLearning in Medical Imaging, Lecture Notes in Computer Science, Springer, vol. 6357, pp. 100-107, September 2010.
[36] Zhaozheng Yin, Ryoma Bise, Mei Chen, and Takeo Kanade, "Cell segmentation in microscopy imagery using a bag of local bayesian classifiers," International Symposium on Biomedical Imaging: From Nano to Macro, IEEE, pp. 125-128, April 2010.
[37] Marie Dumont, Raphael Maree, LouisWehenkel, and Pierre Geurts, "Fast multi class image annotation with random subwindows and multiple output randomized trees," In VISAPP, vol. 2, pp. 196–203. INSTICC, February 2009.