edward j. delp texture analysis february 2000 slide 1 texture analysis and its applications in...

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: [email protected]

http://www.ece.purdue.edu/~ace


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


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%


Screening Mammography


A Digital Mammogram (normal)


Analysis of Mammograms

Density 1 Density 2 Density 3 Density 4


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


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


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


Three Types of Breast Abnormalities

Micro-calcification

Circumscribed Lesion

Spiculated Lesion


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


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


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


Malignant Masses

High density radiopaque Solid tumor, may be smooth or lobulated, random orientation


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


Benign Masses (Cont.)

Radiolucent density Radiolucent and radiopaque combined

Low density radiopaque


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


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


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


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


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


Image Models


Segmentation Algorithm


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


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


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


Experimental Results

Original mammogram

Segmented image

Ground truth Reliability image


Experimental Results (Cont.)

Original mammogram

Segmented image

Ground truth Reliability image


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


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


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


Appearance of A Spiculated Lesion at Multiple Resolutions


Block Diagram of Multiresolution Detection of Spiculated Lesions


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


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


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


Gradient Orientation Histogram

• Has a peak at the ductal structure orientation near a normal pixel

• Flat near a lesion pixel


Example Histograms

A normal region

A spiculated region


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)


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


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(


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(


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(


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


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.


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


Test Pattern at Multiple ResolutionsAn ideal spiculated lesion and normal duct structures embedded in uncorrelated Gaussian distributed noise


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


Feature ’ at Multiple Resolutions


Feature hist at Multiple Resolutions


Feature at Multiple Resolutionsf


Feature f at Multiple Resolutions


A Simple Binary Tree Classifier


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


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


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


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


Detection Results

A 35.0mm lesion detected at the coarsest resolution

Automatic detection Ground truth


Detection Results

Automatic Detection Ground truth

A 12.4mm lesion detected at the second coarsest resolution


Detection ResultsA 6.6mm lesion detected at the finest resolution

Automatic Detection Ground truth


FROC Analysis

• 100% TP detection at 2.2 FP per image

• 84.2% TP detection at less than 1 FP per image


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

edward j. delp texture analysis february 2000 slide 1 texture analysis and its applications in...

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