A Computer Aided Detection System For Digital Mammograms Based on Radial Basis Functions and Feature Extraction Techniques

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A Computer Aided Detection System For Digital
Mammograms Based on Radial Basis Functions and
Feature Extraction Techniques

• By Mohammed Jirari
• Shanghai, China
• Sept 3rd, 2005

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Why This Project?

• Breast Cancer is the most common cancer and is
  the second leading cause of cancer deaths
• Mammographic screening reduces the mortality of
  breast cancer
• But, mammography has low positive predictive
  value PPV (only 35 have malignancies)
• Goal of Computer Aided Detection CAD is to
  provide a second reading, hence reducing the
  false positive rate

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Basic Components of the System

• Preprocessing
• Cropping
• Enhancement (Histogram Equalization)
• Feature extraction
• Normalization
• Training
• Testing
• ROC Analysis

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What is a Mammogram?

• A Mammogram is an x-ray image of the breast.
  Mammography is the procedure used to generate a
  mammogram
• The equipment used to obtain a mammogram,
  however, is very different from that used to
  perform an x-ray of chest or bones

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Mammograms (cont.)

• In order to get a good image, the breast must
  also be flattened or compressed
• In a standard examination, two images of each
  breast are taken one from the top and one from
  the side

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Mammogram Examples

Mammogram of a left breast, cranio-caudal (from
the top) view
Mammogram of a left breast, medio-lateral oblique
(from the side) view
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Purpose of CAD

• Mammography is the most reliable method in early
  detection of breast cancer
• But, due to the high number of mammograms to be
  read, the accuracy rate tends to decrease
• Double reading of mammograms has been proven to
  increase the accuracy, but at high cost
• CAD can assist the medical staff to achieve high
  efficiency and effectiveness
• The physician/radiologist makes the call not CAD

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Proposed Method

• The proposed method will assist the physician by
  providing a second opinion on reading the
  mammogram, by pointing out an area (if one
  exists) delimited by its center coordinates and
  its radius
• If the two readings are similar, no more work is
  to be done
• If they are different, the radiologist will take
  a second look to make the final diagnosis

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Data Used

• The dataset used is the Mammographic Image
  Analysis Society (MIAS) MINIMIAS database
  containing Medio-Lateral Oblique (MLO) views for
  each breast for 161 patients for a total of 322
  images
• Each image is
• 1024 pixels X 1024 pixels

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Preprocessing

• Cropping cuts the black parts of the image
  (almost 50) based on a threshold
• Enhancement Histogram equalization to accentuate
  the features to be extracted by increasing the
  dynamic range of gray levels

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Preprocessing Result
After cropping
Original mammogram
After cropping and histogram equalization
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Co-occurrence Matrices to Calculate Features

• The joint probability of occurrence of gray level
  a and b for two pixels with a defined spatial
  relationship in an image
• The spatial relationship is defined in terms of
  distance d and angle ?
• From these matrices, a variety of features may be
  extracted

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Co-occurrence Matrices (cont.)

• In this project, the matrices are constructed at
  distance of d1 and d3 and for angles ?0, 45,
  90, 135
• For each matrix, seven features are extracted
• Can be formally represented as follows

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Features Used

• Energy or angular second moment
• Entropy
• Maximum Probability
• Inverse Difference moment
• ?2, ?1

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Features Used (cont.)

• Homogeneity
• Inertia or variance

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Features Used (cont.)

• Correlation

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Feature Extraction

• Calculate the co-occurrence matrices at distance
  d1 and d3
• The angles used are ?0, 45, 90, 135 with the
  fifth matrix being the mean of the 4 directions
• The co-occurrence matrices and seven statistical
  features are computed

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Example of Calculated Features
Feature 0 GLCM 45 GLCM 90 GLCM 135 GLCM Mean GLCM
Energy 1.62 e9 1.31 e9 1.73 e9 1.31 e9 1.48 e9
Inertia 2.29 e7 5.42 e7 4.22 e7 5.78 e7 4.43 e7
Entropy 4.76 e6 4.58 e6 4.84 e6 4.55 e6 4.66 e6
Homogeneity 2.98 e5 2.60 e5 3.24 e5 2.55 e5 2.84 e5
Max. Prob. 2.25 e4 1.99 e4 2.25 e4 2.00 e4 2.12 e4
Inv. Diff. Mom. 2.00 e5 1.83 e5 1.93 e5 1.77 e5 1.88 e5
Correlation 9.34 e5 1.16 e6 8.86 e5 1.15 e6 1.02 e6
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Radial Basis Network Used

• Radial basis networks may require more neurons
  than standard feed-forward backpropagation (FFBP)
  networks
• BUT, can be designed in a fraction of the time to
  train FFBP
• Work best with many training vectors

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Radial Basis Network with R Inputs
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Radbas Transfer Function Used
aradbas(n)e(-n2)
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Radial basis network consists of 2 layers a
hidden radial basis layer of S1 neurons and an
output linear layer of S2 neurons
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Training

• After normalizing the data, training begins
• The first training set was made up of 212
  mammograms with 81 abnormal ones, with features
  calculated at distances d1 and d3
• The second training set was made up of 163
  mammograms with 81 abnormal ones, with features
  calculated at distances d1 and d3

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Testing

• A mammogram is presented to the trained network
  and the output is a suspicious area denoted by
  its centers x and y coordinates and its radius.
  If the mammogram is considered to be normal then
  zeros are returned for the coordinates and radius
• The radiologist can then review his/her original
  assessment of the patient if some areas uncovered
  by the network were not originally looked at
  closely
• The whole database is tested and the accuracy is
  calculated
• The smaller dataset performed better than the
  larger one, and using d3 leads to better results
  than d1

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Results

• 2 training datasets 163 and 212
• 2 distance measures 1 and 3
• 3 spreads 0.1, 0.25, and 0.05
• 3 goals 0.00003, 0.008, 0.00005
• For 12 possible combinations
• The NN was sensitive to the unbalanced data
  collection that contained about 70-30 split in
  the larger training set. Therefore the smaller
  dataset was preferred
• Achieving a high recognition is not that
  appealing if the TPF is small

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Representative Preliminary Results
Network 1 2 3
Goal 0.00003 0.00005 0.008
Spread 0.1 0.05 0.25
TPF 0.0163 0.7297 0.9404
FPF 0.5939 0.0 0.1037
of Neurons 163 145 102
Recognition 0.3323 0.9068 0.8674
Az 0.5568 0.6522 0.9104
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Future work

• Use more features like standard deviation,
  skewness, and kurtosis
• Which feature(s) have the most impact
• Rank the features from best to worst (single
  input to NN)
• Select most significant feature(s) by using
  leave one out method
• Determine whether the area is benign or malignant
  by adding the severity of the abnormality to the
  training

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Future work (cont.)

• Try and reduce False Negatives on the basis of
  region characteristics size, difference in
  homogeneity and entropy
• Use larger database that contains both MLO and CC
  to train/learn, since most commercial CADs use
  hundreds of thousands of mammograms to try and
  recognize foreign samples

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• Thank you

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Questions