Retrieve similar cell images in OpenSlide file
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Computer-based image analysis system enables efficient retrieval of similar images from large-size pathology database. In such a system, images are expressed based on visual content characteristics, and similarities between images are obtained by comparing the features. A pathology image is usually very huge and expressed as several layer of image quality called OpenSlide. To find similar cells from a OpenSlide file, we have to use high performance computer equiped with multi-core and large size memory. In this paper, we propose a method to find similar cell images with resource limited computer. For this purpose, we exploit several technique to minimize system resource requirement and adapt imaging process scheme that enhances the accuracy of finding similar cell images from a OpenSlide file. We adapt a leveling, tiling and sub tiling to the OpenSlide file and extracting the feature points accurately using the hybrid feature extracting algorithm that adapts advantages of ORB and Blob algorithm. Furthermore, grayscale and histogram schemes are used to improve the accuracy of finding similar pathology cell images. Experiment results show that the proposed system improves the performance of the system and increases the accuracy of finding similar images efficiently.
KeywordsOpenSlide Leveling Tiling Image sync Feature extraction Image similarity
This research was supported by Hallym University Research Fund, 2016(HRF-201608-009) and this research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT and future Planning (2016H1D5A1910630).
- 5.Jyothi B, MadhaveeLatha Y, Mohan PGK (2015) An effective multiple visual features for Content Based Medical Image Retrieval. IEEE 9th International Conference on Intelligent Systems and Control, pp 1–5Google Scholar
- 8.Miksik O, Mikolajczyk K (2012) Evaluation of local detectors and descriptors for fast feature matching. Pattern Recognition (ICPR), 2012 21st International Conference on. IEEEGoogle Scholar
- 11.Rosten E, Drummond T (2006) Machine learning for high-speed corner detection. Computer Vision–ECCV 2006, pp 430–443Google Scholar
- 13.Rublee E et al (2011) ORB: An efficient alternative to SIFT or SURF. Computer Vision (ICCV), 2011 I.E. international conference on. IEEEGoogle Scholar
- 14.Xu H, Lu C, Berendt R, Jha N, Mandal M (2017) Automatic Nuclear Segmentation Using Multi-scale Radial Line Scanning with Dynamic Programming. IEEE Trans Biomed Eng 64(10):2475–2485Google Scholar
- 16.Zheng Y, Jiang Z, Shi J, Ma Y (2014) Retrieval of pathology image for breast cancer using PLSA model based on texture and pathological features. In Image Processing (ICIP), 2014 I.E. International Conference. IEEE, pp 2304–2308Google Scholar