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Segmentation of Cortical Spreading Depression Wavefronts Through Local Similarity Metric
- Source :
- ICIP
- Publication Year :
- 2019
- Publisher :
- IEEE, 2019.
-
Abstract
- In this paper, we present a novel region-based segmentation method for cortical spreading depressions in 2-photon microscopy images. Fluorescent microscopy has become an important tool in neuroscience, but segmentation approaches are challenged by the opaque properties and structures of brain tissue. These challenges are made more extreme when segmenting events such as cortical spreading depressions, where low signal-to-noise ratios and intensity inhomogeneity dominate images. The method we propose uses a local intensity similarity measure that takes advantage of normalized Euclidean and geodesic distance maps of the image. This method provides a smooth segmentation boundary which is robust to the noise and inhomogeneity within cortical spreading depression images. Experimental results yielded a DICE index of 0.9859, an increase of 6% over the current state-of-the-art, and a reduction of root mean square error by 79.9%.
- Subjects :
- Normalization (statistics)
Similarity (geometry)
Geodesic
Computer science
02 engineering and technology
Brain tissue
Similarity measure
03 medical and health sciences
0302 clinical medicine
FOS: Electrical engineering, electronic engineering, information engineering
0202 electrical engineering, electronic engineering, information engineering
Segmentation
Quantitative Biology::Neurons and Cognition
business.industry
Image and Video Processing (eess.IV)
020206 networking & telecommunications
Pattern recognition
Image segmentation
Electrical Engineering and Systems Science - Image and Video Processing
Computer Science::Computer Vision and Pattern Recognition
Cortical spreading depression
Metric (mathematics)
Artificial intelligence
Noise (video)
business
030217 neurology & neurosurgery
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2019 IEEE International Conference on Image Processing (ICIP)
- Accession number :
- edsair.doi.dedup.....747533f84237ccb4bf9f101b1ba56ecf