CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

A Combined Expectation Maximization and Marker Controlled Watershed Driven Distance Regularized Level Sets for Nuclear Segmentation in Histopathological Images

by P. M. Shivamurthy, T. N. Nagabhushan, Vijaya Basavaraj
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 180 - Number 34
Year of Publication: 2018
Authors: P. M. Shivamurthy, T. N. Nagabhushan, Vijaya Basavaraj
10.5120/ijca2018916884

P. M. Shivamurthy, T. N. Nagabhushan, Vijaya Basavaraj . A Combined Expectation Maximization and Marker Controlled Watershed Driven Distance Regularized Level Sets for Nuclear Segmentation in Histopathological Images. International Journal of Computer Applications. 180, 34 ( Apr 2018), 1-8. DOI=10.5120/ijca2018916884

@article{ 10.5120/ijca2018916884,
author = { P. M. Shivamurthy, T. N. Nagabhushan, Vijaya Basavaraj },
title = { A Combined Expectation Maximization and Marker Controlled Watershed Driven Distance Regularized Level Sets for Nuclear Segmentation in Histopathological Images },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2018 },
volume = { 180 },
number = { 34 },
month = { Apr },
year = { 2018 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume180/number34/29263-2018916884/ },
doi = { 10.5120/ijca2018916884 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:02:36.570794+05:30
%A P. M. Shivamurthy
%A T. N. Nagabhushan
%A Vijaya Basavaraj
%T A Combined Expectation Maximization and Marker Controlled Watershed Driven Distance Regularized Level Sets for Nuclear Segmentation in Histopathological Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 180
%N 34
%P 1-8
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Detection of significant biomarker has been a major issue in the pathological study of cancerous tissue. The microscopic observation of H&E stained histopathological slides involves the study of various tissue objects and their significant traits influencing the cancer grading. The change in shape and size of the nuclei objects (nuclear pleomorphism) contributes much significantly in grading the cancer. With the advent of various imaging studies over a digitized Histopathological image, Active contour based segmentation approaches are considered to be much potential scheme in detecting the nuclei within occlusions and extracting their irregular boundaries. In this research, a novel approach of driving the contours of distance regularized level sets using an improved watershed transformation, has been presented. An Expectation Maximization based morphologically precomputed shape prior is used to extract the foreground markers, which controls the watershed transformation. The result of watershed transformation is used compute the centroid of nuclei to serve the initialization of the contour and the proposed gradient to drive the same efficiently. The study performed over the Benign and Malignant tissue images from BreakHis dataset has shown the efficacy of the methodology in terms of object detection accuracy and overlap resolution. The segmentation accuracy is compared to that of Geodesic active contours, based on the ground truth generated by expert pathologist.

References
  1. Sahirzeeshan Ali and Anant Madabhushi. Active contour for overlap resolution using watershed based initialization (acorew): Applications to histopathology. In Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on, pages 614–617. IEEE, 2011.
  2. Sahirzeeshan Ali and Anant Madabhushi. Segmenting multiple overlapping objects via a hybrid active contour model incorporating shape priors: applications to digital pathology. In SPIE Medical Imaging, pages 79622W–79622W. International Society for Optics and Photonics, 2011.
  3. Sahirzeeshan Ali and Anant Madabhushi. An integrated region-, boundary-, shape-based active contour for multiple object overlap resolution in histological imagery. IEEE transactions on medical imaging, 31(7):1448–1460, 2012.
  4. Sahirzeeshan Ali, Robert Veltri, Jonathan Epstein, Christhunesa Christudass, and Anant Madabhushi. Adaptive energy selective active contour with shape priors for nuclear segmentation and gleason grading of prostate cancer. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2011, pages 661–669, 2011.
  5. Ewert Bengtsson, C Wahlby, and Joakim Lindblad. Robust cell image segmentation methods. Pattern Recognition and Image Analysis C/c of Raspoznavaniye Obrazov i Analiz Izobrazhenii., 14(2):157–167, 2004.
  6. Vicent Caselles, Ron Kimmel, and Guillermo Sapiro. Geodesic active contours. International journal of computer vision, 22(1):61–79, 1997.
  7. Tony Chan andWei Zhu. Level set based shape prior segmentation. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, volume 2, pages 1164–1170. IEEE, 2005.
  8. Tony F Chan and Luminita A Vese. Active contours without edges. IEEE Transactions on image processing, 10(2):266– 277, 2001.
  9. Cigdem Demir and B¨ulent Yener. Automated cancer diagnosis based on histopathological images: a systematic survey. Rensselaer Polytechnic Institute, Tech. Rep, 2005.
  10. Pegah Faridi, Habibollah Danyali, Mohammad Sadegh Helfroush, and Mojgan Akbarzadeh Jahromi. Cancerous nuclei detection and scoring in breast cancer histopathological images. arXiv preprint arXiv:1612.01237, 2016.
  11. Hussain Fatakdawala, Jun Xu, Ajay Basavanhally, Gyan Bhanot, Shridar Ganesan, Michael Feldman, John E Tomaszewski, and Anant Madabhushi. Expectation– maximization-driven geodesic active contour with overlap resolution (emagacor): Application to lymphocyte segmentation on breast cancer histopathology. IEEE Transactions on Biomedical Engineering, 57(7):1676–1689, 2010.
  12. Lei He, L Rodney Long, Sameer Antani, and George R Thoma. Histology image analysis for carcinoma detection and grading. Computer methods and programs in biomedicine, 107(3):538–556, 2012.
  13. Humayun Irshad, Antoine Veillard, Ludovic Roux, and Daniel Racoceanu. Methods for nuclei detection, segmentation, and classification in digital histopathology: a reviewcurrent status and future potential. IEEE reviews in biomedical engineering, 7:97–114, 2014.
  14. Michael Kass, Andrew Witkin, and Demetri Terzopoulos. Snakes: Active contour models. International journal of computer vision, 1(4):321–331, 1988.
  15. Chunming Li, Chenyang Xu, Changfeng Gui, and Martin D Fox. Distance regularized level set evolution and its application to image segmentation. IEEE Transactions on image processing, 19(12):3243–3254, 2010.
  16. Anant Madabhushi and Dimitris N Metaxas. Combining low- , high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions. IEEE transactions on medical imaging, 22(2):155–169, 2003.
  17. Antonio Morote. Nuclear detection in histopathology images. Thesis work, 2010.
  18. David Mumford and Jayant Shah. Optimal approximations by piecewise smooth functions and associated variational problems. Communications on pure and applied mathematics, 42(5):577–685, 1989.
  19. Shivang Naik, Scott Doyle, Shannon Agner, Anant Madabhushi, Michael Feldman, and John Tomaszewski. Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology. In Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008. 5th IEEE International Symposium on, pages 284–287. IEEE, 2008.
  20. Shivang Naik, Scott Doyle, Michael Feldman, John Tomaszewski, and Anant Madabhushi. Gland segmentation and computerized gleason grading of prostate histology by integrating low-, high-level and domain specific information. In MIAAB workshop, pages 1–8. Citeseer, 2007.
  21. Kien Nguyen, Bikash Sabata, and Anil K Jain. Prostate cancer grading: Gland segmentation and structural features. Pattern Recognition Letters, 33(7):951–961, 2012.
  22. Ale?s Proch´azka, Oldrich Vysata, and Eva Jerhotova. Wavelet use for reduction of watershed transform over-segmentation in biomedical images processing. In Information Technology and Applications in Biomedicine (ITAB), 2010 10th IEEE International Conference on, pages 1–4. IEEE, 2010.
  23. Nagabhushan T.N. Shivamurthy, P.M. and V. Basavaraj. A foreground marker based centroid initialized geodesic active contours for histopathological image segmentation. Cognitive Computing and Information Processing (CCIP), 2016 Second International Conference on. IEEE., pages 1–6, 2016.
  24. Mohammed M Siddeq and Dr Sadar Pirkhider Yaba. Using discrete wavelet transform and wiener filter for image denosing. Wasiit Journall for Sciience Mediiciine, 2(2):18–30, 2009.
  25. Ahmet Cagri Simsek, Akif Burak Tosun, Cevdet Aykanat, Cenk Sokmensuer, and Cigdem Gunduz-Demir. Multilevel segmentation of histopathological images using cooccurrence of tissue objects. IEEE Transactions on Biomedical engineering, 59(6):1681–1690, 2012.
  26. Korsuk Sirinukunwattana, Shan E Ahmed Raza, Yee-Wah Tsang, David RJ Snead, Ian A Cree, and Nasir M Rajpoot. Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE transactions on medical imaging, 35(5):1196–1206, 2016.
  27. Fabio A Spanhol, Luiz S Oliveira, Caroline Petitjean, and Laurent Heutte. A dataset for breast cancer histopathological image classification. IEEE Transactions on Biomedical Engineering, 63(7):1455–1462, 2016.
  28. Akif Burak Tosun and Cigdem Gunduz-Demir. Graph runlength matrices for histopathological image segmentation. IEEE Transactions on Medical Imaging, 30(3):721–732, 2011.
  29. Akif Burak Tosun, Melih Kandemir, Cenk Sokmensuer, and Cigdem Gunduz-Demir. Object-oriented texture analysis for the unsupervised segmentation of biopsy images for cancer detection. Pattern Recognition, 42(6):1104–1112, 2009.
  30. JP Vink, MB Van Leeuwen, CHM Van Deurzen, and G De Haan. Efficient nucleus detector in histopathology images. Journal of microscopy, 249(2):124–135, 2013.
  31. Carolina W¨ahlby, I-M SINTORN, Fredrik Erlandsson, Gunilla Borgefors, and Ewert Bengtsson. Combining intensity, edge and shape information for 2d and 3d segmentation of cell nuclei in tissue sections. Journal of Microscopy, 215(1):67– 76, 2004.
  32. Fuyong Xing and Lin Yang. Robust nucleus/cell detection and segmentation in digital pathology and microscopy images: a comprehensive review. IEEE reviews in biomedical engineering, 9:234–263, 2016.
  33. Fuyong Xing and Lin Yang. Robust nucleus/cell detection and segmentation in digital pathology and microscopy images: a comprehensive review. IEEE reviews in biomedical engineering, 9:234–263, 2016.
Index Terms

Computer Science
Information Sciences

Keywords

Nuclear pleomorphism Centroid Detection Watershed gradient Contour Evolution

Powered by PhDFocusTM