Slide 1 September 1999UCB The Analysis of Digital Mammograms: Spiculated Tumor Detection and Normal Mammogram Characterization Edward J. Delp Purdue University School of Electrical and Computer Engineering Video and Image Processing Laboratory (VIPER) West Lafayette, Indiana, USA

Slide 2 September 1999UCB Outline Breast Cancer and Mammography Multiresolution Detection of Spiculated Lesions Normal Mammogram Analysis and Characterization Future Research

Slide 3 September 1999UCB Research Team Charles Babbs - Department of Basic Medical Sciences Zygmunt Pizlo - Department of Psychological Sciences Sheng Lui - School of Electrical and Computer Engineering Valerie Jackson - IU Department of Radiology Funding - NSF, NIH, and Purdue Cancer Center

Slide 4 September 1999UCB 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

Slide 5 September 1999UCB 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%

Slide 6 September 1999UCB Screening Mammography

Slide 7 September 1999UCB A Digital Mammogram (normal)

Slide 8 September 1999UCB Three Types of Breast Abnormalities Micro- calcification Circumscribed Lesion Spiculated Lesion

Slide 9 September 1999UCB 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

Slide 10 September 1999UCB 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

Slide 11 September 1999UCB Multiresolution Detection of Spiculated Lesions in Digital Mammograms Spiculation or a 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

Slide 12 September 1999UCB Spiculated Lesions Spiculated lesions vary from a few millimeters to several centimeters in size Center masses of spiculated lesions are usually irregular with ill-defined borders Usually the larger the tumor center, the longer its spicules or “arms”

Slide 13 September 1999UCB Difficulties 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

Slide 14 September 1999UCB Appearance of A Spiculated Lesion at Multiple Resolutions

Slide 15 September 1999UCB Block Diagram of Multiresolution Detection of Spiculated Lesions

Slide 16 September 1999UCB 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

Slide 17 September 1999UCB 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

Slide 18 September 1999UCB Gradient Orientation Histogram Has a peak at the ductal structure orientation near a normal pixel Flat near a lesion pixel

Slide 19 September 1999UCB Example Histograms A normal region A spiculated region

Slide 20 September 1999UCB Notation (i, j) — spatial location at row i and column j f(i, j) — pixel intensity at (i, j)  S ij — some neighborhood of (i, j) M — the number of pixels within  S ij D y (i, j) and D x (i, j) — estimate of the vertical and horizontal spatial derivatives of f at (i, j), respectively  (i, j) = tan -1 {D y (i, j)/D x (i, j)}  (-  /2,  /2] — estimate of the gradient orientation at (i, j)

Slide 21 September 1999UCB Notation hist ij — histogram of  within  S ij using 256 bins hist ij (n) — # of pixels in  S ij that have gradient orientations, where n = 0, 1, …, 255 — average bin height of hist ij

Slide 22 September 1999UCB Folded Gradient Orientation M  + (i, j) and M  - (i, j) — number of positive and negative gradient orientations within  S ij, respectively and — average positive and negative gradient orientations, respectively — folded gradient orientation

Slide 23 September 1999UCB Why Folded Gradient Orientation? So that is not sensitive to the nominal value of , but to the actual gradient orientation variances The gradient orientation distance between  /2 and -  /4 is the same as that between  /2 and  /4, however –   ([  /2, -  /4]) = 2.8 –   ([  /2,  /4]) =  /4 folds to 3  /4, now   ’ ([  /2, -  /4]) =   ’ ([  /2,  /4]) = 0.3

Slide 24 September 1999UCB Features Differentiate Spiculated Lesions from Normal Tissue Mean pixel intensity in  S ij — Standard deviation of pixel intensities in  S ij —

Slide 25 September 1999UCB Features Differentiate Spiculated Lesions from Normal Tissue Standard deviation of gradient orientation histogram in  S ij — Standard deviation of the folded gradient orientations in  S ij —

Slide 26 September 1999UCB Multiresolution Feature Analysis 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

Slide 27 September 1999UCB Test Pattern at Multiple Resolutions An ideal spiculated lesion and normal duct structures embedded in uncorrelated Gaussian distributed noise

Slide 28 September 1999UCB Feature   ’ at Multiple Resolutions

Slide 29 September 1999UCB Feature  hist at Multiple Resolutions

Slide 30 September 1999UCB Feature at Multiple Resolutions

Slide 31 September 1999UCB Feature  f at Multiple Resolutions

Slide 32 September 1999UCB A Simple Binary Tree Classifier

Slide 33 September 1999UCB Advantages of Tree-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 (PAMI 1991)

Slide 34 September 1999UCB 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

Slide 35 September 1999UCB Database MIAS database provided by the Mammographic Image Analysis Society in the UK 50  resolution A total of 19 mammograms containing spiculated lesions Smallest lesion extends 3.6 mm in radius Largest lesion extends 35 mm in radius

Slide 36 September 1999UCB 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

Slide 37 September 1999UCB Detection Results A 35.0mm lesion detected at the coarsest resolution Automatic DetectionGround Truth

Slide 38 September 1999UCB Detection Results Automatic DetectionGround Truth A 12.4mm lesion detected at the second coarsest resolution

Slide 39 September 1999UCB Detection Results A 6.6mm lesion detected at the finest resolution Automatic DetectionGround Truth

Slide 40 September 1999UCB 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

Slide 41 September 1999UCB Normal Mammograms Characterization Better understanding of normal mammograms can greatly help reduce the “misses” in cancer detection Little work has been done on characterizing normal mammograms

Slide 42 September 1999UCB Very Different Normal Mammograms Density 1Density 2Density 3Density 4

Slide 43 September 1999UCB General Normal Characteristics Unequivocally normal areas have lower density than abnormal ones –no spikes indicating microcalcifications –no large bright areas indicating masses Normal areas have “quasi-parallel” linear markings

Slide 44 September 1999UCB Normal Linear Markings Shadow of normal ducts and connective tissue elements Appear slightly curved Approximately linear over short segments Can be observed as straight line segments of dimensions 1 to 2 mm or greater in length and 0.1 to 1.0 mm in width Low contrast in very noisy background

Slide 45 September 1999UCB Recognizing Normal Structures

Slide 46 September 1999UCB Background Subtraction

Slide 47 September 1999UCB Problems in Extracting Linear Markings Edge detection based line detectors –generate very dense edge maps due to small spatial extent of most local edge operators –miss “thick” lines Hough transform based line detectors –do not provide locations of lines –not suitable for grayscale images

Slide 48 September 1999UCB A New Model For Lines There exists a string of pixels with similar graylevels along a certain direction The surrounding pixels have different graylevels The length of a line is greater than its width

Slide 49 September 1999UCB Line Detection Block Diagram

Slide 50 September 1999UCB Advantages Given minimum length l, our new line detector can detect –lines of very different width, from single pixel wide up to l –lines of any length that is greater than l –lines with varying width, provided that the changes are “slower” than l –curves, provided that over short segment, they can be approximated as lines of length greater than l

Slide 51 September 1999UCB Detect Lines in Noisy Image

Slide 52 September 1999UCB Detect Lines Narrower Than l Test Imagel = 5l = 20

Slide 53 September 1999UCB Normal Structure Removal

Slide 54 September 1999UCB Database Digital Database for Screening Mammography (DDSM) provided by Massachusetts General Hospital, University of South Florida, and Sandia National Laboratories 42  / 50  More than 650 cases available now Each case consists of 4 images: left and right MLO and CC views Have pixel level “ground truth” information

Slide 55 September 1999UCB Residual Image of Circumscribed Lesions Original Mammogram Background Subtracted Image Residual Image

Slide 56 September 1999UCB Residual Image of Spiculated Lesion Original Mammogram Background Subtracted Image Residual Image

Slide 57 September 1999UCB Residual Image of Microcalcifications Original Mammogram Background Subtracted Image Residual Image

Slide 58 September 1999UCB Compare with Strickland’s Microcalcification Enhancement Algorithm Original Mammogram Strickland’s Enhanced Image Residual Image

Slide 59 September 1999UCB Compare with Strickland’s Microcalcification Enhancement Algorithm Original Mammogram Strickland’s Enhanced Image Residual Image

Slide 60 September 1999UCB Summary Normal mammogram characterization is fundamentally simpler — characteristics of normal tissue are relatively simpler than characteristics of tumors of various types, sizes, and stages of development Suppressing normal structures essentially enhances abnormal structures — facilitates the classification of abnormalities

Slide 61 September 1999UCB Future Research Detect stellate patterns in the “Normal” mask before removal so that spiculation remains in the residual image Detect normal mammogram based on obtained residual images