Multimodal Retrieval Framework for Brain Volumes in 3D MR Volumes
- 31 Downloads
The paper presents retrieval framework for extracting similar 3D tumor volumes in magnetic resonance brain volumes in response to a query tumor volume. Similar volumes correspond to closeness in spatial location of the brain structures. Query slice pertains to a new tumor volume of a patient and the output slices belong to the tumor volumes related to previous case histories stored in the database. The framework could be of immense help to the medical practitioners. It might prove to be a useful diagnostic aid for the medical expert and also serve as a teaching aid for researchers.
KeywordsSeed Key slice Tumor plot Brain volumes
The authors acknowledge the research facilities provided by Gautama Buddha University. The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the manuscript greatly.
- 1.Muda, S., & Mokji, M. (2011). Brain lesion segmentation from diffusion weighted MRI based on adaptive thresholding and gray level co-occurrence matrix. Journal of Telecommunication Electronic and Computer Engineering, 3(2), 1–13.Google Scholar
- 2.Cha, S. (2006). Review article: Update on brain tumor imaging: From anatomy to physiology. Journal of Neuro-radiology, 27, 475–487.Google Scholar
- 6.Harris, C., & Stephens, M. A. (1988). Combined corner and edge detector. In Proceedings of 4th alvey vision conference (pp. 147–151).Google Scholar
- 8.Moon, N., Bullitt, E., Leemput, K. V., & Gerig, G. (2002). Model based brain and tumor segmentation. In International conference on pattern recognition (ICPR) (pp. 528–531).Google Scholar
- 10.Mancas, M., & Gosselin, B. (2004). Toward an automatic tumor segmentation using iterative watersheds. In Medical imaging 2004: Image processing (SPIE2004) (Vol. 5370, pp. 1598–1608).Google Scholar
- 11.Zhou, J., Chan, K. L., Chong, V. F. H., & Krishnan, S. M. (2005). Extraction of brain tumor from MR images using one-class support vector machine. In IEEE conference on engineering in medicine and biology (pp. 6411–6414).Google Scholar
- 13.Gering, D. T. (2003). Recognizing deviations from normalcy for brain tumor segmentation. PhD thesis, Massachusetts Institute of Technology.Google Scholar
- 15.De Nunzio, G., Pastore, G., Donativi, M., Castellano, A., & Falini, A. (2011). A CAD system for cerebral glioma based on texture features in DT-MR images. Nuclear Instruments & Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors, and Associated Equipment, 648, 100–102.CrossRefGoogle Scholar
- 16.Uher, V., & Burget, R. (2012). Automatic 3D segmentation of human brain images using data-mining technique. In 35th international conference on telecommunications and signal processing (pp. 578–580).Google Scholar
- 17.Gooya, A., Pohl, K., Bilello, M., Biros, G., & Davatzikos, C. (2011). Joint segmentation and deformable registration of brain scans guided by a tumor growth model. In Medical image computing and computer assisted intervention—MICCAI (pp. 532–540).Google Scholar
- 19.Diaz, I., Boulanger, P., Greiner, R., Hoehn, B., Rowe, L., & Murtha, A. (2013). An automatic brain tumor segmentation tool. In Conference proceedings IEEE Eng Med Biol Soc. (pp. 3339–3342).Google Scholar
- 21.Vezhnevets, V., & Konouchine, V. (2005). Growcut-interactive multi-label N-D image segmentation by cellular automata. In Proceedings of graphicon (pp. 150–156).Google Scholar
- 22.Brain Tumor Detection. https://webdocs.cs.ualberta.ca/~nray1/MyWebsite/Codes.htm.
- 23.The Cancer Imaging Archive (TCIA). http://cancerimagingarchive.net/.
- 24.BRaTS: Multimodal Brain Tumor Segmentation Challenge MICCAI 2012. http://www.imm.dtu.dk/projects/BRATS2012.