Title: Medical Image Analysis
1Medical Image Analysis
Figures come from the textbook Medical Image
Analysis, by Atam P. Dhawan, IEEE Press, 2003.
2Edge-Based Image Segmentation
- Useful links
- The Berkeley Segmentation Dataset and Benchmark
- TurtleSeg
3Edge-Based Image Segmentation
- Edge-based approach
- Spatial filtering to compute the first-order or
second-order gradient information of the image
Sobel, Laplacian masks - Edges need to be linked to form closed regions
- Uncertainties in the gradient information due to
noise and artifacts in the image
Figures come from the textbook Medical Image
Analysis, by Atam P. Dhawan, IEEE Press, 2003.
4Edge Detection Operations
- Gradient magnitude and directional information
from the Sobel horizontal and vertical direction
masks
5Edge Detection Operations
- The second-order gradient operator Laplacian can
be computed by convolving one pf the following
masks
6Edge Detection Operations
- A smoothing filter first before taking a
Laplacian of the image - Combined into a single Laplacian of Gaussian
function as
7Edge Detection Operations
8Edge Detection Operations
- A Laplacian of Gaussian (LOG) mask of
pixels,
9Boundary Tracking
- Edge-linking
- Pixel-by-pixel search to find connectivity among
the edge segments - Connectivity can be defined using a similarity
criterion among edge pixels - Geometrical proximity or topographical properties
Figures come from the textbook Medical Image
Analysis, by Atam P. Dhawan, IEEE Press, 2003.
10Boundary Tracking
- The neighborhood search method
- edge magnitude
- edge orientation
- a boundary pixel
- a successor boundary pixel
- , , pre-determined thresholds
11Boundary Tracking
12Boundary Tracking
- A graph-based search method
- Find paths between the start and end nodes
minimizing a cost function that may be
established based on the distance and transition
probabilities - The start and end nodes are determined from
scanning the edge pixels based on some heuristic
criterion
13End Node
Figure 7.1. Top An edge map with magnitude and
direction information Bottom A graph derived
from the edge map with a minimum cost path
(darker arrows) between the start and end nodes.
14Boundary Tracking
- A search algorithm
- 1. Select an edge pixel as the start node of the
boundary and put all of the successor boundary
pixels in a list, OPEN - 2. If there is no node in the OPEN list, stop
otherwise continue - 3. For all nodes in the OPEN list, compute the
cost function and select the node
with the smallest cost . Remove the node
from the OPEN list and label it as CLOSED.
The cost function may be computed as
15Boundary Tracking
- A search algorithm
- 4. If is the end node, exit with the
solution path by backtracking the pointers
otherwise continue - 5. Expand the node by finding all
successors of . If there is no successor,
go to Step 2 otherwise continue
16Boundary Tracking
- A search algorithm
- 6. If a successor is not labeled yet in any
list, put it in the list OPEN with updated cost
as
and a pointer to its predecessor - 7. If a successor is already labeled as
CLOSED or OPEN, update its value by -
. Put those CLOSED successors
whose cost functions were lowered,
in the OPEN list and redirect to the
pointers from all nodes whose costs were lowered.
Go to Step 2
17Hough Transform
- Hough transform
- Similar to the Radon transform
- Detect straight lines and other parametric curves
such as circles, ellipses - A line in the image space forms a point
in the parameter space
18Hough Transform
Figure comes from the Wikipedia,
www.wikipedia.org.
19Gradient e
O
r
p
Figure 7.2. A model of the object shape to be
detected in the image using Hough transform. The
vector r connects the Centroid and a tangent
point p. The magnitude and angle of the vector r
are stored in the R-table at a location indexed
by the gradient of the tangent point p.
Figures come from the textbook Medical Image
Analysis, by Atam P. Dhawan, IEEE Press, 2003.
20Pixel-Based Direct Classification Methods
- Example the histogram for bimodal distribution
- Find the deepest valley point between the two
consecutive major peaks
Figures come from the textbook Medical Image
Analysis, by Atam P. Dhawan, IEEE Press, 2003.
21Figure 7.3. The original MR brain image (top),
its gray-level histogram (middle) and the
segmented image (bottom) using a gray value
threshold T12 at the first major valley point in
the histogram.
22Figure 7.3. The original MR brain image (top),
its gray-level histogram (middle) and the
segmented image (bottom) using a gray value
threshold T12 at the first major valley point in
the histogram.
23Figure 7.4. Two segmented MR brain images using a
gray value threshold T166 (top) and T225
(bottom).
24Optimal Global Thresholding
- Assume
- The histogram of an image to be segmented has two
Gaussian distributions belonging to two
respective classes such as background and object - The histogram
25Optimal Global Thresholding
- The error probabilities of misclassifying a pixel
26Optimal Global Thresholding
- Assume the Gaussian probability density functions
27Optimal Global Thresholding
- The optimal global threshold
28Pixel Classification Through Clustering
- Feature vector of pixels
- Gray value, contrast, local texture measure, red,
green, or blue components - Clusters in the multi-dimensional feature space
- Group data points with similar feature vectors
together in a single cluster - Distance measure Euclidean or Mahalanobis
distance - Post-processing
- Region growing, pixel connectivity
29K-Means Clustering
- 1. Select the number of clusters with
initial cluster centroids - 2. Partition the input data points into
clusters by assigning each data point to
the closest cluster centroid using the
selected distance measure - 3. Compute a cluster assignment matrix
representing the partition of the data points
with the binary membership value of the th data
point to the th cluster such that
30K-Means Clustering
- 4. Re-compute the centroids using the membership
values as - 5. If cluster centroids or the assignment matrix
does not change from the previous iteration,
stop otherwise go to Step 2.
31K-Means Clustering
32Fuzzy c-Means Clustering
33Region-Based Segmentation
- Region-growing based segmentation
- Examine pixels in the neighborhood based on a
pre-defined similarity criterion - The neighborhood pixels with similar properties
are merged to form closed regions - Region splitting
- The entire image or large regions are split into
two or more regions based on a heterogeneity or
dissimilarity criterion
34Region-Growing
- Two criteria
- A similarity criterion that defines the basis for
inclusion of pixels in the growth of the region - A stopping criterion that stops the growth of the
region
35Figure 7.5. A pixel map of an image (top) with
the region-growing process (middle) and the
segmented region (bottom).
36Figure 7.6. A T-2 weighted MR brain image (top)
and the segmented ventricles (bottom) using the
region-growing method.
37Region-Splitting
- The following conditions are met
- 1. Each region,
is connected - 2.
- 3. for all ,
- 4. TRUE for
- 5. FALSE for
, where is a logical predicate
for the homogeneity criterion on the region
38Figure 7.7. An image with quad region-splitting
process (top) and the corresponding quad-tree
structure (bottom).
39Recent Advances in Segmentation
- Model-based estimation methods
- Rule-based systems
- Automatic segmentation
40Estimation-Model Based Adaptive Segmentation
- A multi-level adaptive segmentation (MAS) method
Figures come from the textbook Medical Image
Analysis, by Atam P. Dhawan, IEEE Press, 2003.
41Figure 7.8 The overall approach of the MAS
method.
42Figure 7.9 (a) Proton Density MR and (b)
perfusion image of a patient 48 hours after
stroke.
43Figure 7.10. Results of MAS method with 4x4 pixel
probability cell size and 4 pixel wide averaging.
(a) pixel classification as obtained on the
basis of maximum probability, (b) as obtained
with pgt0.9.
44Image Segmentation Using Neural Networks
- Backpropagation neural network for classification
- Radial basis function (RBF) network
- Segmentation of arterial structure in digital
subtraction angiograms
Figures come from the textbook Medical Image
Analysis, by Atam P. Dhawan, IEEE Press, 2003.
45Figure 7.11. A basic computational neural element
or Perceptron for classification.
46Figure 7.12. A feedforward Backpropagation neural
network with one hidden layer.
47Figure 7.13. An RBF network classifier for image
segmentation.
48Figure 7.14. RBF Segmentation of Angiogram Data
of Pig-cast Phantom image (top left) with using a
set of 10 clusters (top right) and 12 clusters
(bottom) respectively.
49Figure 7.14. RBF Segmentation of Angiogram Data
of Pig-cast Phantom image (top left) with using a
set of 10 clusters (top right) and 12 clusters
(bottom) respectively.