Institutional Repository [SANDBOX]
Technical University of Crete
EN  |  EL

Search

Browse

My Space

Assessment of automated brain structures segmentation based on the mean-shift algorithm: Application in brain tumor

Zervakis Michalis, Sakkalis, Vangelis, Cristina Farmaki, Kostas Mavrigiannakis, Kostas Marias

Full record


URI: http://purl.tuc.gr/dl/dias/22844100-23D3-45F4-BB35-B24F3A99D462
Year 2010
Type of Item Conference Poster
License
Details
Bibliographic Citation C.Farmaki, K. Mavrigiannakis, K. Marias, M. Zervakis, V. Sakkalis ,"Assessment of automated brain structures segmentation based on the mean-shift algorithm: Application in brain tumor ," in 2010 Intern.l Conf. on Inf. Technology and Appl. in Biomedicine (ITAB) ,pp. 1-5.doi:10.1109/ITAB.2010.5687634 https://doi.org/10.1109/ITAB.2010.5687634
Appears in Collections

Summary

Brain tomographic techniques, such as MRI provide a plethora of pathophysiological tissue information that assists the clinician in diagnosis, therapy design/monitoring and surgery. Robust segmentation of brain tissues is a very important task in order to perform a number of computational tasks including morphological measurements of brain structures, automatic detection of asymmetries and pathologies, and simulation of brain tissue growth. In this paper we present brain structure segmentation results based on our implementation of the mean-shift algorithm and compare them with a number of well-known brain-segmentation algorithms using an atlas dataset as ground truth. The results indicate that the mean-shift algorithm outperforms the other methods. Last, the value of this algorithm in automatic detection of abnormalities in brain images is also investigated.

Services

Statistics