Classification of Hematoxylin and Eosin Images Using Local Binary Patterns and 1-D SIFT Algorithm †
Abstract
:1. Introduction
2. Classification of Hematoxylin and Eosin (H&E) Stained Images
2.1. Local Binary Patterns
2.2. One Dimensional Scale Invariant Feature Transform (1-D SIFT) Algorithm
3. Experimental Approaches and Results
- Experiment I: The uniform LBP histogram of the image is extracted by using gird approach without weights [14]. Then, we used 1-D SIFT feature vector extraction process.
- (Experiment II): The uniform LBP histogram is again obtained with grid approach without weights but feature vectors are extracted with M-1-D SIFT algorithm.
- (Experiment III): In this experiment, the best color histogram combination for H&E image classification given in [16] is combined with Uniform LBP histogram which extracted with grid approach. Feature vectors are again constructed with M-1-D SIFT method.
- (Experiment IV): Lastly, the same procedure in experiment III is applied but now instead of using grid approach the uniform LBP histogram is extracted from the whole image itself.
4. Conclusions
References
- Stewart, B.; Wild, C.P. World Cancer Report 2014; International Agency for Research on Cancer: Lyon, France, 2014. [Google Scholar]
- Monaco, J.; Tomaszewski, J.; Feldman, M.; Moradi, M.; Mousavi, P.; Boag, A.; Davidson, C.; Abolmaesumi, P.; Madabhushi, A. Detection of prostate cancer from whole-mount histology images using markov random fields. In Proceedings of the Workshop on Microscopic Image Analysis with Applications in Biology (in Conjunction with MICCAI), New York, NY, USA, 5–6 September 2008. [Google Scholar]
- Basavanhally, A.; Agner, S.; Alexe, G.; Bhanot, G.; Ganesan, S.; Madabhushi, A. Manifold learning with graph-based features for identifying extent of lymphocytic infiltration from high grade, her2+ breast cancer histology. 2008. Available online: http://www. miaab. org/miaab-2008-papers/27-miaab-2008-paper-21.pdf (accessed on 1 March 2010).
- Graham, R.L.; Hell, P. On the history of the minimum spanning tree problem. Ann. Hist. Comput. 1985, 7, 43–57. [Google Scholar] [CrossRef]
- Gurcan, M.N.; Boucheron, L.E.; Can, A.; Madabhushi, A.; Rajpoot, N.M.; Yener, B. Histopathological image analysis: A review. IEEE Rev. Biomed. Eng. 2009, 2, 147–171. [Google Scholar] [CrossRef] [PubMed]
- Kiang, T. Random fragmentation in two and three dimensions. Z. Astrophys. 1966, 64, 433–439. [Google Scholar]
- Aurenhammer, F. Voronoi diagrams—A survey of a fundamental geometric data structure. ACM Comput. Surv. 1991, 23, 345–405. [Google Scholar] [CrossRef]
- Schindelin, J.; Rueden, C.T.; Hiner, M.C.; Eliceiri, K.W. The imagej ecosystem: An open platform for biomedical image analysis. Mol. Reprod. Dev. 2015, 82, 518–529. [Google Scholar] [CrossRef] [PubMed]
- Schindelin, J.; Arganda-Carreras, I.; Frise, E.; Kaynig, V.; Longair, M.; Pietzsch, T.; Preibisch, S.; Rueden, C.; Saalfeld, S.; Schmid, B.; et al. Fiji: An open-source platform for biological-image analysis. Nat. Methods 2012, 9, 676–682. [Google Scholar] [CrossRef] [PubMed]
- Ojala, T.; Pietikäinen, M.; Harwood, D. A comparative study of texture measures with classification based on featured distributions. Pattern Recognit. 1996, 29, 51–59. [Google Scholar] [CrossRef]
- Li, W.; Chen, C.; Su, H.; Du, Q. Local binary patterns and extreme learning machine for hyperspectral imagery classification. IEEE Trans. Geosci. Remote Sens. 2015, 53, 3681–3693. [Google Scholar] [CrossRef]
- Nosaka, R.; Fukui, K. Hep-2 cell classification using rotation invariant co-occurrence among local binary patterns. Pattern Recognit. 2014, 47, 2428–2436. [Google Scholar] [CrossRef]
- Zhao, G.; Pietikainen, M. Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans. Pattern Anal. Mach. Intell. 2007, 29, 915–928. [Google Scholar] [CrossRef] [PubMed]
- Ahonen, T.; Hadid, A.; Pietikäinen, M. Face recognition with local binary patterns. In Proceedings of the Computer Vision-Eccv 2004, Prague, Czech Republic, 11–14 May 2004; pp. 469–481. [Google Scholar]
- Yorulmaz, O.; Oğuz, O.; Akhan, E.; Tuncel, D.; Atalay, R.Ç.; Çetin, A.E. Multi-resolution super-pixels and their applications on fluorescent mesenchymal stem cells images using 1-d sift merging. In Proceedings of the 2015 IEEE International Conference on Image Processing (ICIP), Quebec, QC, Canada, 27–30 September 2015; pp. 2495–2499. [Google Scholar]
- Oğuz, O.; Akbaş, C.E.; Mallah, M.; Taşdemir, K.; Güzelcan, E.A.; Muenzenmayer, C.; Wittenberg, T.; Üner, A.; Cetin, A.; Atalay, R.Ç. Mixture of learners for cancer stem cell detection using cd13 and h and e stained images. In Proceedings of the SPIE Medical Imaging, International Society for Optics and Photonics, San Diego, CA, USA, March 2016. [Google Scholar]
- Ojala, T.; Pietikainen, M.; Maenpaa, T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 2002, 24, 971–987. [Google Scholar] [CrossRef]
- Ahonen, T.; Hadid, A.; Pietikainen, M. Face description with local binary patterns: Application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 2006, 28, 2037–2041. [Google Scholar] [CrossRef] [PubMed]
- Lowe, D.G. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 2004, 60, 91–110. [Google Scholar] [CrossRef]
- Biomax. Available online: http://www.biomax.us/tissue-arrays.
- Vedaldi, A.; Fulkerson, B. Vlfeat: An open and portable library of computer vision algorithms. In Proceedings of the 18th ACM international conference on Multimedia, Firenze, Italy, 25–29 October 2012. [Google Scholar]
Experiment | KM (%) | ENNI (%) |
---|---|---|
Experiment I | 77.97 | 77.97 |
Experiment II | 79.95 | 79.73 |
Experiment III | 82.15 | 82.15 |
Experiment IV | 88.12 | 88.10 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Oğuz, O.; Çetin, A.E.; Atalay, R.Ç. Classification of Hematoxylin and Eosin Images Using Local Binary Patterns and 1-D SIFT Algorithm. Proceedings 2018, 2, 94. https://doi.org/10.3390/proceedings2020094
Oğuz O, Çetin AE, Atalay RÇ. Classification of Hematoxylin and Eosin Images Using Local Binary Patterns and 1-D SIFT Algorithm. Proceedings. 2018; 2(2):94. https://doi.org/10.3390/proceedings2020094
Chicago/Turabian StyleOğuz, Oğuzhan, A. Enis Çetin, and Rengul Çetin Atalay. 2018. "Classification of Hematoxylin and Eosin Images Using Local Binary Patterns and 1-D SIFT Algorithm" Proceedings 2, no. 2: 94. https://doi.org/10.3390/proceedings2020094