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Edward J. Delp Texture Analysis February 2000 Slide 1
Texture Analysis and Its Applications in Medical Imaging
Edward J. Delp
Purdue University School of Electrical and Computer Engineering
Video and Image Processing Laboratory (VIPER)West Lafayette, Indiana, USA
email: ace@ecn.purdue.edu
http://www.ece.purdue.edu/~ace
Edward J. Delp Texture Analysis February 2000 Slide 2
Breast Cancer
• Second major cause of cancer death among women in the United States (after lung cancer)
• Leading cause of nonpreventable cancer death
• 1 in 8 women will develop breast cancer in her lifetime
• 1 in 30 women will die from breast cancer
Edward J. Delp Texture Analysis February 2000 Slide 3
Mammography
• Mammograms are X-ray images of the breast
• Screening mammography is currently the best technique for reliable detection of early, non-palpable, potentially curable breast cancer
• Studies show that mammogram can reduce the overall mortality from breast cancer by up to 30%
Edward J. Delp Texture Analysis February 2000 Slide 4
Screening Mammography
Edward J. Delp Texture Analysis February 2000 Slide 5
A Digital Mammogram (normal)
Edward J. Delp Texture Analysis February 2000 Slide 6
Analysis of Mammograms
Density 1 Density 2 Density 3 Density 4
Edward J. Delp Texture Analysis February 2000 Slide 7
Digital Mammography
• Resolution - 50 pixel size
– 3000 x 4000 pixels (12,000,000 pixels)
– 8-16 bits/pixels• 8 bits/pixel (12 MB)
• 16 bits/pixel (24 MB)
• Each study consists of 48-96 MB!
• 200 patients per day can results to 20GB/day
• Problems with storage and retrieval
Edward J. Delp Texture Analysis February 2000 Slide 8
Problems in Screening Mammography
• Radiologists vary in their interpretation of the same mammogram
• False negative rate is 4 – 20% in current clinical mammography
• Only 15 – 34% of women who are sent for a biopsy actually have cancer
Edward J. Delp Texture Analysis February 2000 Slide 9
Current Research in Computer Aided Diagnosis (CAD)
• The goal is to increase diagnostic accuracy as well as the reproducibility of mammographic interpretation
• Most work aims at detecting one of the three abnormal structures
• Some have explored classifying breast lesions as benign or malignant
• The implementation of CAD systems in everyday clinical applications will change the practice of radiology
Edward J. Delp Texture Analysis February 2000 Slide 10
Three Types of Breast Abnormalities
Micro-calcification
Circumscribed Lesion
Spiculated Lesion
Edward J. Delp Texture Analysis February 2000 Slide 11
Malignant Microcalcifications
Extremely vary in form, size, density, and number, usually clustered within one area of the breast, often within one lobe
Granular:
dot-like or elongated, tiny, innumerable
Casting:
fragments with irregular contour, differ in length
Edward J. Delp Texture Analysis February 2000 Slide 12
Benign Microcalcifications
Homogenous, solid, sharply outlined,
spherical, pearl-like, very fine and dense
Crescent-shaped or elongate
Ring surrounds dilated duct, oval or elongated, varying lucent center, very dense periphery
Linear, often needle like, high and uniform density
Edward J. Delp Texture Analysis February 2000 Slide 13
Benign Microcalcifications (Cont.)
Ring-shaped, oval, center radiolucent, occur within skin
Egg-shell, center radiolucent or of parenchymal density
Coarse, irregular, sharply outlined and
very dense
Similar to raspberry, high density but often contain
small, oval-shaped lucent areas
Edward J. Delp Texture Analysis February 2000 Slide 14
Malignant Masses
High density radiopaque Solid tumor, may be smooth or lobulated, random orientation
Edward J. Delp Texture Analysis February 2000 Slide 15
Benign Masses
Halo: a narrow radiolucent ring or a segment of a ring around
the periphery of a lesion
Capsule: a thin, curved, radiopaque line that surrounds lesions containing
fat
Cyst: spherical or ovoid with smooth borders, orient in the direction of the
nipple following the trabecular structure of the breast
Edward J. Delp Texture Analysis February 2000 Slide 16
Benign Masses (Cont.)
Radiolucent density Radiolucent and radiopaque combined
Low density radiopaque
Edward J. Delp Texture Analysis February 2000 Slide 17
Malignant Spiculated Lesions
Scirrhous carcinoma:
distinct central tumor mass, dense spicules radiate in all directions, spicule length
increases with tumor size
Early stage scirrhous carcinoma:
tumor center small, may be imperceptible, only a lace-like, fine reticular radiating
structure which causes parenchymal distortion and/or asymmetry
Edward J. Delp Texture Analysis February 2000 Slide 18
Benign Spiculated Lesions
Sclerosing duct hyperplasia:
translucent, oval or circular center, the longest spicules are very thin and long, spicules close to the lesion center become numerous and clumped
together in thick aggregates
Traumatic fat necrosis:
translucent areas are within a loose, reticular structure, spicules are fine and of low density
Edward J. Delp Texture Analysis February 2000 Slide 19
Statistical Segmentation of Mammograms
Mary L. Comer, Sheng Liu, and Edward J. Delp
• Abnormalities in mammograms are disruptions of the normal structures
• It is desirable to partition a mammogram into texture regions
• Study the use of a new statistical method for the detection of abnormalities in mammograms
Edward J. Delp Texture Analysis February 2000 Slide 20
Non-statistical Approaches
• Use a series of heuristics, such as filtering, thresholding, and texture analysis
• Suffer from a lack of robustness when the number of images to be classified is large
Edward J. Delp Texture Analysis February 2000 Slide 21
EM/MPM Algorithm
• Assign each pixel in the mammogram membership to one of 3 texture classes: tumor, normal tissue, and background, depending on statistical properties of the pixel and its neighborhood
• Both the original mammogram and its class labels are modeled as discrete parameter random fields
• Use a combination of the expectation-maximization and the maximization of the posterior marginals (EM/MPM) algorithms to segment mammograms
Edward J. Delp Texture Analysis February 2000 Slide 22
Image Models
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Segmentation Algorithm
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Advantages
• The values of all parameters of the MPM algorithm need not be known a priori
• Provide indication of the reliability of each classified pixel
• Detect various types of tumors within the same framework
Edward J. Delp Texture Analysis February 2000 Slide 25
Database
• Images used in this research were provided courtesy of the Center for Engineering and Medical Image Analysis at the University of South Florida
• Abnormal mammograms have an interpretation file that indicates the types and positions of abnormalities
• 220 micron resolution
Edward J. Delp Texture Analysis February 2000 Slide 26
Experiments
• The spatial interaction parameter and cost parameters were determined experimentally using a variety of mammography images
• a priori knowledge is used to initialize the model parameter vector
• The reliability information is displayed as an image where pixel values are proportional to the estimated marginal conditional probability mass function of the label field:
larger graylevel higher reliability of classification
Edward J. Delp Texture Analysis February 2000 Slide 27
Experimental Results
Original mammogram
Segmented image
Ground truth Reliability image
Edward J. Delp Texture Analysis February 2000 Slide 28
Experimental Results (Cont.)
Original mammogram
Segmented image
Ground truth Reliability image
Edward J. Delp Texture Analysis February 2000 Slide 29
Multiresolution Detection of Spiculated Lesions in Digital Mammograms
Sheng Liu and Edward J. Delp
• Spiculations or a more stellate appearance in mammograms indicates with near certainty the presence of breast cancer
• Detection of spiculated lesions is very important in the characterization of breast cancer
Edward J. Delp Texture Analysis February 2000 Slide 30
Difficulties
• Center masses of spiculated lesions are usually irregular with ill-defined borders
• In some cases, the center masses are too small to be perceptible
• Spiculated lesions vary from a few millimeters to several centimeters in size
Edward J. Delp Texture Analysis February 2000 Slide 31
Difficulties (Cont.)
• Computer aided diagnosis of digital mammograms generally consists of feature extraction followed by classification
• It is very difficult to determine the neighborhood size that should be used to extract features which are local
• If the neighborhood is too large, small lesions may be missed
• If the neighborhood is too small, one may not be able to capture features of larger lesions
Edward J. Delp Texture Analysis February 2000 Slide 32
Appearance of A Spiculated Lesion at Multiple Resolutions
Edward J. Delp Texture Analysis February 2000 Slide 33
Block Diagram of Multiresolution Detection of Spiculated Lesions
Edward J. Delp Texture Analysis February 2000 Slide 34
Multiresolution Decomposition
• Linear phase nonseparable 2D perfect reconstruction wavelet transform
– does not introduce phase distortions in the decomposed images
– no bias is introduced in the horizontal and vertical directions as a separable transform would
• The impulse response of the analysis low pass filter
0125.00
125.05.0125.0
0125.00
)n,n(h 21
Edward J. Delp Texture Analysis February 2000 Slide 35
Advantages ofMultiresolution Approach
• Overcomes the difficulty of choosing a neighborhood size a priori (variable lesion size)
• Requires less computation by
– starting with the least amount of data
– propagating detection results to finer resolutions
Edward J. Delp Texture Analysis February 2000 Slide 36
A Spiculated Lesion Distorts the Normal Breast Duct Structure
• Normal duct structures of the breast radiate from the nipple to the chest wall
• Spiculated lesion radiates spicules in all directions
Edward J. Delp Texture Analysis February 2000 Slide 37
Gradient Orientation Histogram
• Has a peak at the ductal structure orientation near a normal pixel
• Flat near a lesion pixel
Edward J. Delp Texture Analysis February 2000 Slide 38
Example Histograms
A normal region
A spiculated region
Edward J. Delp Texture Analysis February 2000 Slide 39
Notation
• (i, j) — spatial location at row i and column j
• f(i, j) — pixel intensity at (i, j)
Sij — some neighborhood of (i, j)
• M — the number of pixels within Sij
• Dy(i, j) and Dx(i, j) — estimate of the vertical and horizontal spatial derivatives of f at (i, j), respectively
(i, j) = tan-1{Dy(i, j)/Dx(i, j)} (-/2, /2] — estimate of the gradient orientation at (i, j)
Edward J. Delp Texture Analysis February 2000 Slide 40
Notation (Cont.)
• histij — histogram of within Sij using 256 bins
• histij(n) — # of pixels in Sij that have gradient
orientations , where n = 0, 1,
…, 255
• — average bin height of histij
255
0nij )n(hist
2561
)j,i(hist
256)1n(
2,
256n
2
Edward J. Delp Texture Analysis February 2000 Slide 41
Folded Gradient Orientation
• M+(i, j) and M-
(i, j) — number of positive and negative
gradient orientations within Sij, respectively
• and — average positive and negative
gradient orientations, respectively
•
— folded gradient orientation
)j,i( )j,i(
otherwise)j,i(
)j,i(M)j,i(M,2/)j,i()j,i(if)j,i(
)j,i(M)j,i(M,2/)j,i()j,i(if)j,i(
)j,i(
Edward J. Delp Texture Analysis February 2000 Slide 42
Features Differentiate Spiculated Lesions from Normal Tissue
ijS)n,m(
)n,m(fM1
)j,i(f
• Mean pixel intensity in Sij —
• Standard deviation of pixel intensities in Sij —
ijS)n,m(
2f ))j,i(f)n,m(f(
1M
1)j,i(
Edward J. Delp Texture Analysis February 2000 Slide 43
Features Differentiate Spiculated Lesions from Normal Tissue (Cont.)
• Standard deviation of gradient orientation histogram in Sij —
• Standard deviation of the folded gradient orientations in Sij —
255
0n
2ijijhist ))n(hist)n(hist(
255
1)j,i(
ijS)n,m(
2))j,i()n,m((1M
1)j,i(
Edward J. Delp Texture Analysis February 2000 Slide 44
Why Folded Gradient Orientation?
So that is not sensitive to the nominal value of , but to the actual gradient orientation variances
)j,i(
• The gradient orientation distance between /2 and -/4 is the same as that between /2 and /4, however ([/2, -/4]) = 2.8 ([/2, /4]) = 0.3
• -/4 folds to 3/4, now
’([/2, -/4]) = ’([/2, /4]) = 0.3
Edward J. Delp Texture Analysis February 2000 Slide 45
Multiresolution Feature Analysis
An MM region at a coarser spatial resolution N/nN/n corresponds to an nMnM region in the original mammogram with spatial resolution NN
if a set of features extracted within an 88 window at the original resolution NN can capture spiculated lesions of size 1mm, then the same set of features extracted at the coarser resolution N/4N/4, using the same sized 88 window, should be able to detect spiculated lesions of size 4mm.
Edward J. Delp Texture Analysis February 2000 Slide 46
Multiresolution Feature Analysis (Cont.)
• Choose a neighborhood that is small enough to capture the smallest possible spiculated lesion in the finest resolution
• Fix this neighborhood size for feature extraction at all resolutions
• Larger lesions will be detected at a coarser resolution
• Smaller lesions can be detected at a finer resolution
Edward J. Delp Texture Analysis February 2000 Slide 47
Test Pattern at Multiple ResolutionsAn ideal spiculated lesion and normal duct structures embedded in uncorrelated Gaussian distributed noise
Edward J. Delp Texture Analysis February 2000 Slide 48
Multiresolution Feature Extraction
• Each feature at different resolutions is extracted within same sized circular neighborhoods
• Features are able to discriminate a spiculated lesion from complex background when extracted within an appropriate neighborhood whose size matches to that of the lesion
• Fail when the sizes mismatch
Edward J. Delp Texture Analysis February 2000 Slide 49
Feature ’ at Multiple Resolutions
Edward J. Delp Texture Analysis February 2000 Slide 50
Feature hist at Multiple Resolutions
Edward J. Delp Texture Analysis February 2000 Slide 51
Feature at Multiple Resolutionsf
Edward J. Delp Texture Analysis February 2000 Slide 52
Feature f at Multiple Resolutions
Edward J. Delp Texture Analysis February 2000 Slide 53
A Simple Binary Tree Classifier
Edward J. Delp Texture Analysis February 2000 Slide 54
Advantages ofTree-Structured Approach
• Robust with respect to outliers and misclassified points in the training set
• The classifier can be efficiently represented
• Once trained, classification is very fast
• Provides easily understood and interpreted information regarding the predictive structure of the data
• Classifier used is described in a paper by Gelfand, Ravishankar, and Delp
Edward J. Delp Texture Analysis February 2000 Slide 55
Multiresolution Detection
• At each resolution, five features are used: the four features extracted at that resolution plus the feature hist extracted from the next coarser resolution
• Detection starts from the second coarsest resolution
• A positive detection at a coarser resolution eliminates the need for both feature extraction and detection at the corresponding pixel locations at all finer resolutions
• A negative result at a coarser resolution will be combined with those at finer resolutions via weighted sum
Edward J. Delp Texture Analysis February 2000 Slide 56
Database
• MIAS database provided by the Mammographic Image Analysis Society in the UK
• 50 micron resolution
• A total of 19 mammograms containing spiculated lesions
• Smallest lesion extends 3.6mm in radius
• Largest lesion extends 35mm in radius
Edward J. Delp Texture Analysis February 2000 Slide 57
Half/half Training Methodology
• The 19 mammograms containing spiculated lesions together with another 19 normal mammograms are random split into two sets with approximately an equal number of lesion and normal mammograms in each set
• Each set was used separately as a training set to generate two BCTs
• A BCT trained by one set was used to classify mammograms in the other set, and vice versa
Edward J. Delp Texture Analysis February 2000 Slide 58
Detection Results
A 35.0mm lesion detected at the coarsest resolution
Automatic detection Ground truth
Edward J. Delp Texture Analysis February 2000 Slide 59
Detection Results
Automatic Detection Ground truth
A 12.4mm lesion detected at the second coarsest resolution
Edward J. Delp Texture Analysis February 2000 Slide 60
Detection ResultsA 6.6mm lesion detected at the finest resolution
Automatic Detection Ground truth
Edward J. Delp Texture Analysis February 2000 Slide 61
FROC Analysis
• 100% TP detection at 2.2 FP per image
• 84.2% TP detection at less than 1 FP per image
Edward J. Delp Texture Analysis February 2000 Slide 62
Summary
• Multiresolution detection eliminates the problem of choosing a neighborhood size a priori to capture features of lesions of varying sizes
• Using features across resolutions simultaneously helps capture spiculated lesions of sizes that exist between the resolutions
• Top-down approach requires less computation by starting with the least amount of data and propagating detection results to finer resolutions