Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
A survey of image registration techniques
ACM Computing Surveys (CSUR)
An analysis of histogram-based thresholding algorithms
CVGIP: Graphical Models and Image Processing
Evaluation and comparison of different segmentation algorithms
Pattern Recognition Letters
Pattern Recognition Letters
An efficient algorithm for image segmentation, Markov random fields and related problems
Journal of the ACM (JACM)
Digital Picture Processing
A Class of Discrete Multiresolution Random Fields and Its Application to Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Image Segmentation Using Markov Random Field Models
EMMCVPR '97 Proceedings of the First International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Registration of Reconstructed Post Mortem Optical Data with MR Scans of the Same Patient
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Co-registration of Histological, Optical and MR Data of the Human Brain
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part I
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Robust Fuzzy Segmentation of Magnetic Resonance Images
CBMS '01 Proceedings of the Fourteenth IEEE Symposium on Computer-Based Medical Systems
Hidden Markov Random Field Model Selection Criteria Based on Mean Field-Like Approximations
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Colour Image Processing Handbook (Optoelectronics, Imaging and Sensing)
The Colour Image Processing Handbook (Optoelectronics, Imaging and Sensing)
A Convergence Theorem for the Fuzzy ISODATA Clustering Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
A cellular coevolutionary algorithm for image segmentation
IEEE Transactions on Image Processing
Hi-index | 0.00 |
Building a digital three dimensional representation of a human brain is a challenging task. Such a model provides insights into the microstructure of cortical layering and columns. The presented work is based on a complete dissected and preserved human brain that has been serially sectioned at a coronal resolution that is suitable for single cell detection. More than 6000 sections have been generated and exist as digital images. To obtain a valuable three dimensional representation, morphology preserving affine linear and nonlinear registration schemes are necessary steps. To rebuild a serially sectioned brain, reference images derived from a non deformed object, e.g., MRI or block face images, are necessary for a faithful affine linear and nonlinear registration. In the case of block face images the brain regions must be separated from highly variable background regions to obtain a suitable stack of segmentation images. Among the image segmentation algorithms we found fuzzy c-means techniques as a promising starting point for a sophisticated segmentation framework of either gray level or color images within 2- and 3-dimensions. With respect to algorithmic complexity and computation cost, two fuzzy c-means algorithms were implemented. A proper image preprocessing strategy turned out to be necessary for accurate and robust segmentation results. Primarily, the algorithms work in a parametric resp. supervised mode. Additionally, an automatic mode helps to explore the parameter space within a reasonable range and to compare the segmentation result with an optimal one, provided by an expert. By minimizing the differences we can set up parameters that are used for series of adjacent images. So, it is possible to obtain optimal segmentations independent of illumination disturbances, artifacts and defocusing. We present a complete high resolution and accurate segmentation of the first complete human brain that was sectioned, photographed and digitized at histologic resolution. Based on these images, a succeeding 3D representation is presented. Finally, a segmented and spatially correct straightened data set is available now for coregistration tasks together with the high resolution histologic data set.