Breast Histopathological Whole Slide Image For Retrieval Using Latent Dirichlet Allocation
DOI:
https://doi.org/10.61841/ke470n46Keywords:
Clustering, Features extraction, Fuzzy C-Means, Histopathological Images, Image Retrieval, Image Segmentation, LDA.Abstract
Whole Slide Image (WSI) has become the major carrier of visual and diagnostic information. Recovery of content-based images among WSIs may help to diagnose an unknown pathological image by finding its similar regions in WSIs with diagnostic information based on deep learning and local morphology nuclei statistical function. This research work implements the breast histopathological image approach, extracting the features such as Gabor and LSFN from the given image and splits the region based on the threshold (detha, beta). Based on threshold value, the matched regions are selected called candidate region. Then, the LDA model is utilized to obtain a histogram for each feature of every region. For this training and testing images are utilized. In this proposed system focused on clustering method such as NN- clustering algorithm that has been widely used for medical image segmentation. Local statistical feature of nuclei (LSFN) is presented to describe morphology and distribution pattern of nuclei is utilized for texture information. The algorithms have been implemented and tested on WSI images. The comparison is made with existing conventional NN-clustering method
Downloads
References
[1] X. Zhang, H. Su, L. Yang, and S. Zhang, “Fine-grained histopathological image analysis via robust segmentation and large-scale retrieval,” inProc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2015, pp. 5361–5368.
[2] S. Naik, S. Doyle, S. Agner, A. Madabhushi, M. Feldman, and J. Tomaszewski, “Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology,” inProc. 5th IEEE Int. Symp. Biomed. Imag., Nano Macro, May 2008, pp. 284– 287.
[3] H. Fatakdawalaet al., “Expectation-maximization-driven geodesic active contour with overlap resolution (EMaGACOR): Application to lymphocyte segmentation on breast cancer histopathology,”IEEE Trans. Biomed. Eng., vol. 57, no. 7, pp. 1676–1689, Jul. 2010.
[4] H. Kong, M. Gurcan, and K. Belkacem-Boussaid, “Partitioning histopathological images: An integrated framework for supervised colortexture segmentation and cell splitting,”IEEE Trans. Med. Imag., vol. 30, no. 9, pp. 1661–1677, Sep. 2011
[5] R. Gutirrez, F. Gmez, L. Roa-Pea, and E. Romero, “A supervised visual model for finding regions of interest in basal cell carcinoma images,”Diagnostic. Pathol., vol. 6, no. 26, Mar. 2011. [Online]. Availableat: http://diagnosticpathology.biomedcentral.com/articles/ 10.1186/1746-1596-6-26
[6] C.-R. Angel, D. Gloria, R. Eduardo, and G. Fabio, “Automatic annotation of histopathological images using a latent topic model based on nonnegative matrix factorization,”J. Pathol. Informat., vol. 2, no. 2, p. 4, 2011.
[7] P. Ghosh, S. Antani, L. Long, and G. Thoma, “Review of medical image retrieval systems and future directions,” inProc. 24th Int. Symp. Comput.-Based Med. Syst., Jun. 2011,
pp. 1–6.
[8] A. Kumar, J. Kim, W. Cai, M. Fulham, and D. Feng, “Content-based medical image retrieval: A survey of applications to multidimensional and multimodality data,”J. Digital Imag., vol. 26, no. 6, pp. 1025–1039, 2013.
[9] [22] X. Zhang, W. Liu, M. Dundar, S. Badve, and S. Zhang, “Towards largescale histopathological image analysis: Hashing-based image retrieval,” IEEE Trans. Med. Imag., vol. 34, no. 2, pp. 496–506, Feb. 2015
[10] J. Caicedo, F. Gonzalez, and E. Romero, “A semantic content-based retrieval method for histopathology images,” in Information Retrieval Technology (Series Lecture Notes in Computer Science 4993), Berlin, Germany: Springer-Verlag, 2008, pp. 51–60
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
