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A Computer Aided Detection System For Mammograms Based on Asymmetry and Feature Extraction Techniques. By Mohammed Jirari Benidorm, Spain Sept 9th, 2005. Why This Project?. Breast Cancer is the most common cancer and is the second leading cause of cancer deaths

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A Computer Aided Detection System For Mammograms Based on Asymmetry and Feature Extraction Techniques

By Mohammed Jirari

Benidorm, Spain

Sept 9th, 2005

why this project
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
basic components of the system
Basic Components of the System
  • Mammogram Normalization
  • Mammogram Registration
  • Mammogram Subtraction
  • Feature Extraction
    • Morphological Closing
    • Morphological Opening
    • Size Test
    • Border Test
  • ROC Analysis
what is a mammogram
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
mammograms cont
Mammograms (cont.)
  • In order to get a good image, the breast must be flattened or compressed
  • In a standard examination, two images of each breast are taken: one from the top (CC) and one from the side(MLO)
mammogram examples
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

purpose of cad
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
proposed method
Proposed Method
  • The proposed method will assist the physician by providing a second opinion on reading the mammogram, by pointing out area(s) that are different between the right and left breasts
  • 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
data used
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

normalization
Normalization

The images were corrected/normalized to avoid differences in brightness between the right and left mammograms

mammogram registration
Mammogram Registration
  • Thermodynamic concepts are used
  • Match a model M with a scene S(M must be deformed to resemble Sas much as possible)
  • Use diffusion process technique as follows:
mammogram registration cont
Mammogram Registration (cont.)

1. Select pixels to be demons

2. For each demon, store displacement then apply Gaussian filter

3. Use trilinear interpolation to estimate intermediate intensities

4. The demon force is given by optical flow

registration example
Registration Example

Mammogram of left breast

Mammogram of right breast

registration example cont
Registration Example (cont.)

Grid of displacement

Registered images

mammogram subtraction
Mammogram Subtraction
  • Simple linear subtraction is used
  • Flipped right – left
  • Most common gray level is 0
  • Masses in right breast are in lower gray level region of subtraction image histogram, while left breast masses are in the higher gray level region
mammogram subtraction example
Mammogram Subtraction Example

Flipped right breast

Left breast showing mass

mammogram subtraction example cont
Mammogram Subtraction Example (cont.)

Subtraction image

Superimposed subtraction image

feature extraction
Feature Extraction
  • Many features are not masses
  • Morphological filtering using a 3X3 kernel
  • Size test (100 pixels)
  • Border test for border misalignment
results
Results
  • 102 registered pairs of mammograms used
  • Verified by expert radiologists
future work
Future work
  • Use more features like brightness and directionality
  • 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