The document discusses medical image segmentation, focusing on its various methods and applications in radiology, particularly in tumor delineation and quantification of anatomical structures. It covers basic segmentation techniques such as thresholding, including Otsu's method, and highlights the importance of segmentation in diagnosing diseases using tools that aid in visualizing 3D anatomical relationships. The document also addresses different classification methods for segmentation and the challenges associated with each approach.

1. MEDICAL IMAGE COMPUTING (CAP 5937)
LECTURE 7: Medical Image Segmentation (I)
(Radiology Applications of Segmentation, and Thresholding)
Dr. Ulas Bagci
HEC 221, Center for Research in Computer
Vision (CRCV), University of Central Florida
(UCF), Orlando, FL 32814.
bagci@ucf.edu or bagci@crcv.ucf.edu
1SPRING 2017

2. Outline
• Introduction to Medical Image Segmentation, type of
segmentation methods, and definitions
– Recognition & Delineation
• Simplest Segmentation Method(s): Thresholding
– Otsu Thresholding
– Parametric Method
– PET Image Thresholding Methods
• ITM (Iterative Thresholding Method)
2

3. Motivation for Image Segmentation
In the last 20 years the computer vision and medical imaging
communities have produced a number of useful algorithms for
localizing object boundaries in images.
3

4. Motivation for Image Segmentation
• Content based image retrieval
• Machine Vision
• Medical Imaging applications (tumor delineation,..)
• Object detection (face detection,…)
• 3D Reconstruction
• Object/Motion Tracking
• Object-based measurements such as size and shape
• Object recognition (face recognition,…)
• Fingerprint recognition,
• Video surveillance
• …
4

5. Segmentation Tools in RadiologyApplications
• 3D views to visualize structural information and spatial
anatomic relationships is a difficult task, which is usually
carried out in the clinician’s mind.
5

6. Segmentation Tools in RadiologyApplications
• 3D views to visualize structural information and spatial
anatomic relationships is a difficult task, which is usually
carried out in the clinician’s mind.
• Image-processing tools provide the surgeon with interactively
displayed 3D visual information.
6

7. Segmentation Tools in RadiologyApplications
7
Credit: Kaus, et al. Radiology 2001.

8. • Determination of the volumes of abdominal solid organs and focal lesions
has great potential importance (liver, spleen, …).
• Monitoring the response to therapy and the progression of neoplastic
disease and preoperative examination of living liver donors are the most
common clinical applications of volume determination.
8
Segmentation Tools in RadiologyApplications
(credit: Farraher, et al.
Radiology 2005)

9. Segmentation Tools in RadiologyApplications
• Gross Tumor Volume in CT/MRI
• Metabolic Tumor Volume in PET/SPECT/
– Surgery/Therapy Planning
• Planning Tumor Volume (PTV)
– Tumor characterization
• Texture Extraction requires
segmentation to be done
• Shape analysis
9

10. Segmentation Tools in RadiologyApplications
• There is a strong interest in automatic and reproducible
techniques for detection and quantification of vascular
disease
• A first step toward an effective vessel analysis tool is
segmentation of the vasculature.
10
axial coronal sagittal
Credit: Manniesing, et al,
Radiology 2008
MIP: maximum intensity
Projection image of cerebral vessels (in CTA)

11. Segmentation Tools in RadiologyApplications
• MR volumetry of the
hippocampus can help
distinguish patients with
AD (Alzheimer’s
Disease) from elderly
controls with a high
degree of accuracy
(80%–90%).
11

12. Segmentation Tools in RadiologyApplications
• MR volumetry of the
hippocampus can help
distinguish patients with
AD (Alzheimer’s
Disease) from elderly
controls with a high
degree of accuracy
(80%–90%).
12
hippocampus
amygdala
Credit: Colliot et al, Radiology 2008.

13. Image Segmentation
Definition: Partitioning a picture/image into distinctive subsets is
called segmentation.
13

14. Image Segmentation
Definition: Partitioning a picture/image into distinctive subsets is
called segmentation.
14
Segmentation of an image entails the division or
separation of the image
into regions of similar attribute.

15. Image Segmentation
Definition: Partitioning a picture/image into distinctive subsets is
called segmentation.
15
Segmentation of an image entails the division or
separation of the image
into regions of similar attribute.
The most basic attributes:
-intensity
-edges
-texture
-other features…

16. Image Segmentation
Definition: Partitioning a picture/image into distinctive subsets is
called segmentation.
16
Purpose: To extractobject information
and representthis as a
hard/fuzzygeometric
structure.
Recognition: Determiningthe object’s
whereaboutsin the scene.
(humans> computer)
Delineation: Determining the object’s
spatial extent and
compositionin the scene.
(computers > humans)

17. Recognition - Example
17
(slice credit: J. Kim et al,
Signal Processing 2007)
Model is induced No Model is induced

18. Approaches to Recognition
18
• Model-based
• Knowledge-based - Non-interactive
• Atlas-based
• Human-assisted - Interactive

19. Approaches to Recognition
19
• Model-based
• Knowledge-based - Non-interactive
• Atlas-based
• Human-assisted - Interactive
- They all originate from human knowledge.
- Their relative efficacy is unknown.

20. Approaches to Delineations
20
pI (purely image-based) approaches
• Rely mostlyon informationavailable in the given image
only.
• Recognition: manual

21. Approaches to Delineations
21
pI (purely image-based) approaches
• Rely mostlyon informationavailable in the given image
only.
• Recognition: manual
SM (shape model-based) approaches
• Employ models to codify object family shape info.
• Recognition: model-based/manual

22. Approaches to Delineations
22
pI (purely image-based) approaches
• Rely mostlyon informationavailable in the given image
only.
• Recognition: manual
SM (shape model-based) approaches
• Employ models to codify object family shape info.
• Recognition: model-based/manual
Hybrid approaches
• Combine among pI and SM approaches.
• Recognition: model-based, automatic.

23. Classification of Methods
23
Boundary-based (BpI):
• optimum boundary
• active boundary
• live wire
• level sets

24. Classification of Methods
24
Boundary-based (BpI):
• optimum boundary
• active boundary
• live wire
• level sets
Region-based (RpI):
• clustering – kNN, CM, FCM
• graph cut
• fuzzy connectedness
• MRF
• watershed
• optimum partitioning
• (Mumford-Shah)

25. Classification of Methods
25
Boundary-based (BpI):
• optimum boundary
• active boundary
• live wire
• level sets
Region-based (RpI):
• clustering – kNN, CM, FCM
• graph cut
• fuzzy connectedness
• MRF
• watershed
• optimum partitioning
• (Mumford-Shah)
SM Approaches
• manual tracing
• live wire
• active shape/appearance
• M-reps
• atlas-based

26. Classification of Methods
26
Boundary-based (BpI):
• optimum boundary
• active boundary
• live wire
• level sets
Region-based (RpI):
• clustering – kNN, CM, FCM
• graph cut
• fuzzy connectedness
• MRF
• watershed
• optimum partitioning
• (Mumford-Shah)
SM Approaches
• manual tracing
• live wire
• active shape/appearance
• M-reps
• atlas-based
Hybrid Approaches
• BpI + BpI
• RpI + RpI
• BpI + RpI
• BpI + SM
• RpI + SM
• SM + SM

27. Classification of Methods
27
pI Approaches
+ Where image info is good,
accuracy is good;
- Bad where it is poor/absent;
- Need recognition help;
+ Can determine degree of
match of model to image
well;
- Lack obj shape &
geographic info;

28. Classification of Methods
28
SMApproaches
- Even where image info is
good, accuracy suffers;
+ Where bad, model helps;
+ Can help in recognition;
- Need best match info;
+ Good models embody obj
shape & geographic info;

29. Purely Image Based Segmentation Methods
29

30. Thresholding – Simple Segmentation
• Image binarization
– mapping a scalar image I into a binary image J
30
J(x, y) =
(
0 if I(x, y) < T
1 otherwise.

31. Thresholding – Simple Segmentation
• Image binarization
– mapping a scalar image I into a binary image J
31
J(x, y) =
(
0 if I(x, y) < T
1 otherwise.

32. Thresholding – Simple Segmentation
32
Brighter objects
Darker objects

33. Thresholding – Simple Segmentation
33
Brighter objects
Darker objects
DIFFICULTIES
1. The valley may be so broad that
it is difficult to locate a
significant minimum
2. Number of minima due to type
of details in the image
3. Noise
4. No visible valley
5. Histogram may be multi-modal

34. Example: CT Scan
34

35. Example: CT Scan
35

36. Example: CT Scan
36

37. Example: CT Scan
37

38. Example: CT Scan
38

39. Thresholding Methods
• Huang
• Intermode
• Isodata
• Li
• MaxEntropy
• Mean
• MinError
• Otsu
• Percentile
• RenyiEntropy
• Moments
39

40. Thresholding Methods
• Huang
• Intermode
• Isodata
• Li
• MaxEntropy
• Mean
• MinError
• Otsu
• Percentile
• RenyiEntropy
• Moments
40

41. Thresholding Methods
PET Imaging
Fixed Thresholding
Adaptive Thresholding
Iterative Thresholding
41
• Huang
• Intermode
• Isodata
• Li
• MaxEntropy
• Mean
• MinError
• Otsu (non-parametric)
• Percentile
• RenyiEntropy
• Moments

42. Otsu Thresholding
• Definition: The method uses the grey-value histogram of the
given image I as input and aims at providing the best
threshold in the sense that the “overlap” between two
classes, set of object and background pixels, is minimized
(i.e., by finding the best balance).
42

43. Otsu Thresholding
• Definition: The method uses the grey-value histogram of the
given image I as input and aims at providing the best
threshold in the sense that the “overlap” between two
classes, set of object and background pixels, is minimized
(i.e., by finding the best balance).
• Otsu’s algorithm selects a threshold that maximizes the
between-class variance . In the case of two classes,
43
2
b
2
b = P1(µ1 µ)2
+ P2(µ2 µ)2
= P1P2(µ1 µ2)2

44. Otsu Thresholding
• Definition: The method uses the grey-value histogram of the
given image I as input and aims at providing the best
threshold in the sense that the “overlap” between two
classes, set of object and background pixels, is minimized
(i.e., by finding the best balance).
• Otsu’s algorithm selects a threshold that maximizes the
between-class variance . In the case of two classes,
• where P1 and P2 denote class probabilities, and μi the means
of object and background classes.
44
2
b
2
b = P1(µ1 µ)2
+ P2(µ2 µ)2
= P1P2(µ1 µ2)2

45. Otsu Thresholding
• Definition: The method uses the grey-value histogram of the
given image I as input and aims at providing the best
threshold in the sense that the “overlap” between two
classes, set of object and background pixels, is minimized
(i.e., by finding the best balance).
45
P1 =
uX
ı=0
p(i)
P2 =
GmaxX
ı=u+1
p(i)
u
u

46. Otsu Thresholding
• Definition: The method uses the grey-value histogram of the
given image I as input and aims at providing the best
threshold in the sense that the “overlap” between two
classes, set of object and background pixels, is minimized
(i.e., by finding the best balance).
46
P1 =
uX
ı=0
p(i)
P2 =
GmaxX
ı=u+1
p(i)
µ1 =
uX
ı=0
ip(i)/P1
µ2 =
GmaxX
ı=u+1
ip(i)/P2
CLASS MEANS

47. Otsu Thresholding-Algorithm
47
cI (u) 1 cI(u)
P1 P2
c indicates cumulative histogram,and P1 and P2
can be approximated well with cumulative density function.

48. Otsu Thresholding-Algorithm
48
cI (u) 1 cI(u)
P1 P2
c indicates cumulative histogram,and P1 and P2
can be approximated well with cumulative density function.
2
b = P1(µ1 µ)2
+ P2(µ2 µ)2
= P1P2(µ1 µ2)2

49. Otsu Thresholding-Algorithm
49
cI (u) 1 cI(u)
P1 P2
c indicates cumulative histogram,and P1 and P2
can be approximated well with cumulative density function.

50. Otsu Thresholding-Algorithm
50
cI (u) 1 cI(u)
P1 P2
c indicates cumulative histogram,and P1 and P2
can be approximated well with cumulative density function.

51. Otsu Thresholding-Algorithm
51
cI (u) 1 cI(u)
P1 P2
c indicates cumulative histogram,and P1 and P2
can be approximated well with cumulative density function.

52. Otsu Thresholding-Algorithm
52
cI (u) 1 cI(u)
P1 P2
c indicates cumulative histogram,and P1 and P2
can be approximated well with cumulative density function.

53. Otsu Thresholding-Algorithm
53
cI (u) 1 cI(u)
P1 P2
c indicates cumulative histogram,and P1 and P2
can be approximated well with cumulative density function.
optimal

54. Parametric Method for Optimal Thresholding
• Assuming again a two-class problem and assuming that the
distribution of gray levels for each class can be modeled by a
normal distribution with mean and variance
54

55. Parametric Method for Optimal Thresholding
• Assuming again a two-class problem and assuming that the
distribution of gray levels for each class can be modeled by a
normal distribution with mean and variance
• the overall normalized intensity histogram can be written as
the following mixture probability density function:
55

56. Parametric Method for Optimal Thresholding
• Assuming again a two-class problem and assuming that the
distribution of gray levels for each class can be modeled by a
normal distribution with mean and variance
• the overall normalized intensity histogram can be written as
the following mixture probability density function:
where P1 and P2 are class probabilities. The optimal threshold
(T) can be found as solving the quadratic equation à
56

57. Parametric Method for Optimal Thresholding
57

58. Parametric Method for Optimal Thresholding
58
In case, variances of both classes are equal, then->

59. Parametric Method for Optimal Thresholding
59
In case, variances of both classes are equal, then->

60. Thresholding methods for PET Image
Segmentation
• Due to the nature of PET images (i.e., low resolution with high
contrast), thresholding-based methods are suitable
– because the local or global intensity histogram usually provides a
sufficient level of information for separating the foreground (object of
interest) from the background. (Foster, Bagci, et al., CBM 2014)
60

61. Thresholding methods for PET Image
Segmentation
• Due to the nature of PET images (i.e., low resolution with high
contrast), thresholding-based methods are suitable
– because the local or global intensity histogram usually provides a
sufficient level of information for separating the foreground (object of
interest) from the background. (Foster, Bagci, et al., CBM 2014)
61
Fixed
Thresholding
Adaptive
Thresholding
Iterative
Thresholding

62. Fixed Thresholding Methods
• Due to the nature of PET images (i.e., low resolution with high
contrast), thresholding-based methods are suitable
– because the local or global intensity histogram usually provides a
sufficient level of information for separating the foreground (object of
interest) from the background. (Foster, Bagci, et al., CBM 2014)
62

63. Thresholding methods for PET Image
Segmentation
• Due to the nature of PET images (i.e., low resolution with high
contrast), thresholding-based methods are suitable
– because the local or global intensity histogram usually provides a
sufficient level of information for separating the foreground (object of
interest) from the background. (Foster, Bagci, et al., CBM 2014)
63
Fixed
Thresholding
Adaptive
Thresholding
Iterative
Thresholding
Phantom
Based
Image Quality
metrics based

64. Adaptive Thresholding 64

65. Thresholding methods for PET Image
Segmentation
• Due to the nature of PET images (i.e., low resolution with high
contrast), thresholding-based methods are suitable
– because the local or global intensity histogram usually provides a
sufficient level of information for separating the foreground (object of
interest) from the background. (Foster, Bagci, et al., CBM 2014)
65
Fixed
Thresholding
Adaptive
Thresholding
Iterative
Thresholding
Phantom
Based
Image Quality
metrics based

66. Iterative Thresholding Method (ITM)
66
S/B: Source to background ratio.
The method is based on calibrated
threshold-volume curves at varying
S/B ratio acquired by phantom
measurements using spheres of known
volumes.

67. Iterative Thresholding Method (ITM)
67
S/B: Source to background ratio.
The method is based on calibrated
threshold-volume curves at varying
S/B ratio acquired by phantom
measurements using spheres of known
volumes.

68. Iterative Thresholding Method (ITM)
68
S/B: Source to background ratio.
The method is based on calibrated
threshold-volume curves at varying
S/B ratio acquired by phantom
measurements using spheres of known
volumes.
The measured S/B ratios of the
lesions are then estimated from
PET images, and their volumes are
iteratively calculated using the
calibrated S/B-threshold-volume curves

69. Iterative Thresholding Method (ITM)
69
S/B: Source to background ratio.
The method is based on calibrated
threshold-volume curves at varying
S/B ratio acquired by phantom
measurements using spheres of known
volumes.
The measured S/B ratios of the
lesions are then estimated from
PET images, and their volumes are
iteratively calculated using the
calibrated S/B-threshold-volume curves
The resulting PET volumes are then
compared with the known sphere volume
and CT volumes of tumors that served
as gold standards.

70. ITM Example Result on PET Images/Lung
70

71. Another Example for PET Thresholding
71
ITM for tumor segmentation/FDG PET

72. Another Example for PET Thresholding
72

73. Further Thresholding Example – CT Bones
73

74. Further Thresholding Example – CT Bones
74

75. Head-Neck CT – Thresholding for Skull
Modeling
75
(Slice Credit: P.Seutens)
Segmentation of the skull and the mandibula in CT images using thresholding.(a) Original CT
image of the head. (b) Result with a threshold value of 276 Hounsfield units. The segmented bony
structures are represented in color. (c) 3D rendering of the skull shows a congenital growth
deficiency of the mandibula in this 8-year-old patient. This information was used preoperatively to
plan a repositioning of the mandibula.

76. Multiple Thresholds – MRI Thresholding
76
Thresholding can be done interactively and separates the image into different
regions. Valleys in the histogram indicate potentially useful threshold values
Credit: Toeonies,K.

77. Summary of today’s lecture
• Introduction into the Medical Image Segmentation
• Recognition and Delineation concepts in Segmentation
• Simplest Segmentation method: Thresholding
– Otsu
– Parametric method for optimal thresholding
– PET Image thresholding
• ITM, fixed thresholding,etc.
77

78. Slide Credits and References
• Jayaram K. Udupa, MIPG of University of Pennsylvania, PA.
• P. Suetens, Fundamentals of Medical Imaging, Cambridge
Univ. Press.
• Foster, B., et al. CBM, Review paper, 2014.
• Kaus, et al. Radiology 2001.
• Toeonies, K., Medical Image Analysis.
• Farraher, et al., Radiology 2005
• Zaidi, H., Quantitative Analysis in Nuclear Medicine Imaging.
• Bailey et al. Positron Emission Tomography, Springer.
• Dawood, M., et al. Correction Techniques in Emission
Tomography
78