Norbert Schuff Professor of Radiology VA Medical Center and UCSF


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1 Norbert Schuff Professor of Radiology Medical Center and UCSF Medical Imaging Informatics 2012, N.Schuff Course # Slide 1/67
2 Overview Definitions Role of Segmentation Segmentation methods Intensity based Shape based Texture based Summary & Conclusion Literature
3 The Concept Of Segmentation Identify classes (features) that characterize this image! Intensity: Bright  dark Shape: Squares, spheres, triangles Texture: homogeneous speckled Connectivity: Isolated  connected Topology: Closed  open
4 More On The Concept Of Segmentation: Deterministic versus probabilistic classes Can you still identify multiple classes in each image?
5 Segmentation Of Scenes Segment this scene! Hint: Use color composition and spatial features By J. Chen and T. Pappas; 2006, SPIE; DOI: /
6 Examples: Intensity, Texture, Topology Gray matter segmentation topology By intensity Segmentation of abdominal CT scan By texture image at: Stephen Cameron. Oxford U, Computing Laboratory
7 Definitions Segmentation is the partitioning of an image into regions that are homogeneous with respect to some characteristics. In medical context: Segmentation is the delineation of anatomical structures and other regions of interest, i.e. lesions, tumors.
8 Formal Definition If the domain of an image is, then the segmentation problem is to determine sets (classes) Z k, whose union represent the entire domain Sets are connected: N k 1 Z k Z k Z j ; k j;0
9 More Definitions When the constraint of connected regions is removed, then determining the sets Z k is termed pixel classification. Determining the total number of sets K can be a challenging problem. In medical imaging, the number of sets is often based on apriori knowledge of anatomy, e.g. K=3 (gray, white, CSF) for brain imaging.
10 Labeling Labeling is the process of assigning a meaningful designation to each region or pixel. This process is often performed separately from segmentation. Generally, computerautomated labeling is desirable Labeling and sets Z k may not necessarily share a onetoone correspondence
11 Dimensionality Dimensionality refers to whether the segmentation operates in a 2D or 3D domain. Generally, 2D methods are applied to 2D images and 3D methods to 3D images. In some instances, 2D methods can be applied sequentially to 3D images.
12 Characteristic and Membership Functions A characteristic function is an indicator whether a pixel at location j belongs to a particular class Z k. k j 1 if. element. of. class 0 otherwise This can be generalized to a membership function, which does not have to be binary valued. 0 j 1, for. all. pixels.&. classes k N 1 k k j 1, for. all. pixels The characteristic function describes a deterministic segmentation process whereas the membership function describes a probabilistic one.
13 Class y y y Simple Membership Functions binary : k j probabilistic : k j b=50 b=25 b= feature x x x1 sigmoid. function 1 y 1 exp a x * b
14 Segmentation Has An Important Role Computational diagnostic Surgical planning Database storage/retrieval SEGM Image registration Atlases informatics Image transmission Quantification Partial volume correction Super resolution
15 Segmentation Methods
16 Threshold Method Angiogram showing a right MCA aneurysm Histogram (fictitious) Threshold (T A ) (T B ) Dr. Chris Ekong; a.0
17 Threshold Method Original Threshold min/max Threshold standard deviation
18 Threshold Method Applied To Brain MRI White matter segmentation Major failures: Anatomically nonspecific Insensitive to global signal inhomogeneity
19 Threshold: Principle Limitations Works only for segmentation based on intensities Robust only for images with global uniformity and high contrast to noise Local variability causes distortions Intrinsic assumption is made that the probability of features is uniformly distributed
20 Region Growing  Edge Detection Seed point Region growing groups pixels or subregions into larger regions. A simple procedure is pixel aggregation, It starts with a seed point and progresses to neighboring pixels that have similar properties. Region growing is better than edge detection in noisy images. Guided e.g. by energy potentials: Similarity: Ii I j V i, j, equivalent. to. a. z score i, j Edges: V l, r Il Ir erf ( r) Ir t erf () r e dt 0 r 2
21 Gray scale intensity Region Growing Watershed Technique CT of different types of bone tissue (femur area) (a) WS oversegmentation (b) WS conditioned by regional density mean values (c) WS conditioned by hierarchical ordering of regional density mean values M. Straka, et al. Proceedings of MIT 2003
22 Region Growing: Principle Limitations Segmentation results dependent on seed selection Local variability dominates the growth process Global features are ignored Generalization needed: Unsupervised segmentation (i.e.insensitive to selection of seeds) Exploitation of both local and global variability
23 Clustering Generalization using clustering Two commonly used clustering algorithms Kmean Fuzzy Cmean
24 Definitions: Clustering Clustering is a process for classifying patterns in such a way that the samples within a class Z k are more similar to one another than samples belonging to the other classes Z m, m k; m = 1 K. The kmeans algorithm attempts to cluster n patterns based on attributes (e.g. intensity) into k classes k < n. The objective is to minimize total intracluster variance in the leastsquare sense: for k clusters Z k, k= 1, 2,..., K. µ i is the mean point (centroid) of all pattern values j Z k. k K 1 j Sk j k 2
25 Fuzzy Clustering The fuzzy Cmeans algorithm is a generalization of K means. Rather than assigning a pattern to only one class, the fuzzy Cmeans assigns the pattern a number m, 0 <= m <= 1, described as membership function.
26 d2 K means Three classes d1
27 d2 K  means Four classes d1
28 d2 K  means Original d1
29 d2 K  means 2 clusters d1
30 d2 K means TRAPPED! Three classes d1
31 Component Fuzzy C  means Four classes Component 1 These two components explain 100 % of the point variability.
32 Fuzzy C Means Segmentation I Two classes Original Class I Class 2
33 Fuzzy Segmentation II Four classes Class I Class 2 Original Class 3 Class 4
34 Brain Segmentation With Fuzzy C Means 4T MRI, bias field inhomogeneity contributes to the problem of poor segmentation
35 Clustering: Principle Limitations Convergence to the optimal configuration is not guaranteed. Outcome depends on the number of clusters chosen. No easy control over balancing global and local variability Intrinsic assumption of a uniform feature probability is still being made Generalization needed: Relax requirement to predetermine number of classes Balance influence of global and local variability Possibility to including apriori information, such as nonuniform distribution of features.
36 Segmentation As Probabilistic Problem Treat both intensities Y and classes Z as random distributions The segmentation problems is to find the classes that maximize the likelihood to represent the image
37 Segmentation As A Probabilistic Problem Prior pz ( Y) p( Y Z) * p( Z) Likelihood Z is the segmented Y is the observed image p(z) is the prior probability p(y Z) is the likelihood P(Z Y) is the segmentation that best represents the observation Posterior
38 Probability In Spatial Context Model classes Z as Markov Random Fields (MRF) Rules: Classes exist: p(z) > 0 for all z in Z Probability of z at a location depends on the spatial neighborhood Observed intensities in the neighborhood are a random process
39 MRF Based Segmentation 1 st and 2 nd order MRFs (p,q) (p,q+1) Ising model of spin glasses (p+1,q) (p=1,q+1)
40 MRF Based Segmentation 1 st and 2 nd order MRFs Step I: Define prior class distribution energy: (p,q) (p,q+1) z s, z true 1, 1 z z s s z z true true (p+1,q) (p=1,q+1)
41 MRF Based Segmentation 1 st and 2 nd order MRFs (p,q) (p,q+1) Step I: Define prior class distribution energy: z s, z true 1, 1 z z s s z z true true (p+1,q) (p=1,q+1) Step II: Select distribution of conditional observation probability, e.g gaussian: E yp, q z p, q. yz 2 2 z y p,q is the pixel value at location (p,q) z and z are the mean value and variance of the class z 2
42 MRF Based Segmentation 1 st and 2 nd order MRFs (p,q) (p,q+1) Step III: Solve (iteratively) for the minimal distribution energy (p+1,q) (p=1,q+1) arg min E p, q z, z p. q y z s true 2 t N 2 z s y z 2 assignment similarity energy
43 probability The Process of MRF Based Segmentation 3 2 Update MRF classify Global Distribution 1 intensity Distribution model
44 MRF Based Segmentations 4T MRI, SPM2, priors for GM, WM based on 60 subjects
45 Generalization: Mixed Gaussian Distributions yp. q z arg min E p, q z, z. z y s t 2 t N 2 z s White matter 2 White matter White matter lesion arg min E p, q z, z z y s t t N s p. q Find solution iteratively using Expectation Maximization (EM) y w w y p. q wml wml
46 probability Expectation Maximization Of MRF Based Segmentation UpdateMRF classify Global Distribution intensity Update distribution model
47 EMS Standard 1.5 MRI, SPM2, tissue classes: GM, WM, CSF, WM Lesions
48 Limitations Of Energy Minimization Case where a simple energy minimization might not work: green: wrong segmentation red: correct segmentation The segmentation energy of green might be smaller than that of red based on simple separation energy
49 A Case As Motivation EMS EMS gray matter segmentation areas where EMS has problems.
50 Potential Solution: Geocuts Algorithm The separation penalty is defined based on magnitude and direction of image gradient: E p, q Small gradient magnitude: Large separation penalty (no change in class) in gradient direction Small separation penalty (class change) in other directions Large gradient magnitude Small separation penalty in grad directions Large penalty in other directions. I 1 p I q Large penalty small
51 Using Geo Cuts Case where energy minimization did not work: green: wrong segmentation red: correct segmentation Using Geocuts leads to: higher separation energy long direction lower in other directions resulting overall in the correct red segmentation
52 EMS vs. Geo Cuts EMS gray matter GeoCuts gray matter
53 Deformable Models So far segmentation methods have not exploited knowledge of shape. In shape based methods, the segmentation problem is again formulated as an energyminimization problem. However, a curve evolves in the image until it reaches the lowest energy state instead of a MRF. External and internal forces deform the shape control the evolution of segmentation.
54 Deformable Template Segmentation A. Lundervold et al. Modelguided Segmentation of Corpus Callosum in MR Images
55 3D Deformable Surfaces Automated Delineation of Prostate Bladder and Rectum Costa, J. École Nationale Supérieure des Mines de Paris
56 Segmentation Via Texture Extraction Two classical methods for feature extraction Cooccurrence matrix (CM) Fractal dimensions (FD)
57 Pixel order CoOccurrance Matrix (CM) Definition: The CM is a tabulation of how often different combinations of pixel brightness values (gray levels) occur in an image. kernel CM Image Sharp Blurred image Pixel that stores calculated CM value More Blurred Pixel order
58 Evaluation of CM CM MRI with spatially varying intensity bias MRI with spatially homogenous intensity bias Medical Imaging Informatics 2011, N.Schuff Course # Slide 58/67
59 Fractal Dimensions Intuitive Idea: Many natural objects have structures that are repeated regardless of scale. Repetitive structures can be quantified by fractal dimensions (FD). Sierpinski Triangle Medical Imaging Informatics 2011, N.Schuff Course # Slide 59/67
60 Fractal Dimensions Definition: Box counting Sierpinski Triangle Medical Imaging Informatics 2011, N.Schuff Course # Slide 60/67
61 Fractal Dimensions MEDICAL IMAGE SEGMENTATION USING MULTIFRACTAL ANALYSIS Soundararajan Ezekiel; Medical Imaging Informatics 2011, N.Schuff Course # Slide 61/67
62 Summary Automated Semiautomated Initializing Threshold Region growing Cooccurrence MRF Kmeans EMS Shape Models Fuzzy CMeans Fractal Dimensions Manual Tracing Medical Imaging Informatics 2011, N.Schuff Course # Slide 62/67
63 Literature 1. Segmentation Methods I and II; in Handbook of Biomedical Imaging; Ed. J. S. Suri; Kluwer Academic WIKIBooks 1. SPM: FSLFAST: Medical Imaging Informatics 2011, N.Schuff Course # Slide 63/67
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