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Automatic MRI brain segmentation using local features, Self-Organizing Maps, and watershed
1. 1
Automatic MRI Brain Segmentation Using
Local Features, Self-Organizing Maps,
and Watershed
Slides by:
Mehryar Emambakhsh
Sahand University of Technology
2. 2
Table of Contents
Image segmentation: applications and algorithms
– Watershed
The proposed method
– Feature extraction
– Dimension reduction
– Clustering by SOM
– Edge detection and watershed transformation
Simulation results
Summary
References
3. 3
Outline
Image segmentation: applications and
algorithms
– Watershed
The proposed method
– Feature extraction
– Dimension reduction
– Clustering by SOM
– Edge detection and watershed transformation
Simulation results
Summary
References
4. 4
Image segmentation: applications and
algorithms
Image segmentation is the art of partitioning an
image, into non-overlapping/disjoint regions.
Each of the regions should have a uniformity in their
predefined feature space.
There are various applications for image
segmentation, such as:
– Computer aided medical diagnosis
– Military purposes
– Object detection and computer vision
– Automation and remote sensing
5. 5
Image segmentation: applications and
algorithms
There are numerous methods for image
segmentation:
– Active contour
Level set and snakes [1 and 2]
– Unsupervised segmentation
Clustering algorithms [3 and 4]
– Supervised segmentation
Machine learning algorithms [5]
– Watershed transformation [6]
Shape prior incorporation and edge map construction
6. 6
Image segmentation: applications and
algorithms watershed
– The input image is considered to be a topographic surface.
– Imagine that there is a hole in each regional minima.
– The entire topography is filling with water entering the hole
at uniform rate.
– Dams will be made at higher altitudes.
– Watershed lines are the dams.
– Consequently, basins must be located at image objects.
11. 11
The proposed method: Feature
extraction
Our proposed feature space consists of
(cont.):
–
12. 12
The proposed method: Feature
extraction
The feature vector for pixel (i , j) will be:
The final feature space will be:
13. 13
The proposed method: Dimension
reduction
The feature space dimension is:
–
– Is high and increases the computational
complexity.
14. 14
The proposed method: Dimension
reduction
Principal Component Analysis (PCA) is used
to reduce the feature space dimensionality.
– Feature space mean is subtracted:
– Covariance is computed:
– K eigenvectors of the corresponding K largest
eigenvalues are extracted:
15. 15
The proposed method: Clustering by
SOM
A two-stage SOM network is used to cluster the feature
space:
– The first stage, performs feature mapping
– The second stage, performs classification bone, soft
tissue, fat, and background
16. 16
The proposed method: Edge detection
and watershed transformation
Sobel edge detection is used because of its
simplicity and smoothing effects.
Watershed transformation is utilized on the
edge map.
17. 17
Outline
Image segmentation: applications and algorithms
– Watershed
The proposed method
– Feature extraction
– Dimension reduction
– Clustering by SOM
– Edge detection and watershed transformation
Simulation results
Summary
References
18. 18
Simulation results
Material:
– T1 human MRI brain sequence, consisting of 256 human MRI
[7] and 94 rat MRI brain [8 and 9].
Parameters:
300 epochs for training the first, and 200 epochs for training
the second SOM network, are used.
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25. 25
Outline
Image segmentation: applications and algorithms
– Watershed
The proposed method
– Feature extraction
– Dimension reduction
– Clustering by SOM
– Edge detection and watershed transformation
Simulation results
Summary
References
26. 26
Summary
In this work a watershed based method is proposed for MRI brain
segmentation.
The trained SOM network can be used to cluster other MRI
sequences.
Our approach is robust against
– Linear additive Gaussian noise,
– Non-linear salt and pepper noise,
– And multiplicative noise speckle noise.
Compared to other feature extraction methods, such as, Gabor and
non-linear diffusion, our algorithm is simpler and much faster.
Unlike usual watershed segmentation methods, de-noising and region
merging algorithms, which are so time-consuming, are not employed.
27. 27
References
[1] L. Jonasson, P. Hagmann, C. Pollo, X. Bresson, C. R. Wilson, R. Meuli, and J. Thiran. "A level set
method for segmentation of the thalamus and its nuclei in DT-MRI," Signal Processing, Tensor Signal
Processing, Volume 87, Issue 2, February 2007, Pages 309-321
[2] K. Seongjai and Lim. Hyeona, " A hybrid level set segmentation for medical imagery," IEEE Nuclear
Science Symposium Conference Record, Volume: 3, On page(s): 5 pp.-, Oct 2005
[3] J. Wang, J. Kong, Y. Lu, M. Qi, and B. Zhang, 2008. “A modified FCM algorithm for MRI brain image
segmentation using both local and non-local spatial constraints,”Computerized Medical Imaging and
Graphics (PMID: 18818051).
[4] C. Lai and C. Chang, "A hierarchical evolutionary algorithm for automatic medical image
segmentation," Expert Systems with Applications, Volume 36, Issue 1, Pages 248-259, January 2009
[5] Y. Lu, J. Wang, J. Kong, B. Zhang, and J. Zhang. “An integrated algorithm for MRI brain images
segmentation, "In Computer Vision Approaches to Medical Image Analysis, pages 132–142. Springer,
Berlin, 2006.
[6] W. Kuo, C. Lin, and Y. Sun. Brain MR images segmentation using statistical ratio: Mapping between
watershed and competitive Hopfield clustering network algorithms. Computer Methods and Programs in
Biomedicine, 91(3):191 – 198, 2008.
[7] www.mri-tip.com, April 2009.
[8] http://www.egr.msu.edu/~raguin/home.html, access time: 2009-02-10
[9] http://www.dotynmr.com/mri/mri_saippg.htm, access time: 2009-02-10
