A Computer Aided Detection System For Mammograms Based on Asymmetry and Feature Extraction Techniques - PowerPoint PPT Presentation

About This Presentation
Title:

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

Description:

Most common gray level is 0 ... are in lower gray level region of subtraction image histogram, while left breast masses are in the higher gray level region ... – PowerPoint PPT presentation

Number of Views:507
Avg rating:3.0/5.0
Slides: 25
Provided by: mohamme8
Learn more at: https://www.cs.kent.edu
Category:

less

Transcript and Presenter's Notes

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


1
A Computer Aided Detection System For Mammograms
Based on Asymmetry and Feature Extraction
Techniques
  • By Mohammed Jirari
  • Benidorm, Spain
  • Sept 9th, 2005

2
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

3
Basic Components of the System
  • Mammogram Normalization
  • Mammogram Registration
  • Mammogram Subtraction
  • Feature Extraction
  • Morphological Closing
  • Morphological Opening
  • Size Test
  • Border Test
  • ROC Analysis

4
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

5
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)

6
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
7
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

8
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

9
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

10
Normalization
The images were corrected/normalized to avoid
differences in brightness between the right and
left mammograms
11
Mammogram Registration
  • Thermodynamic concepts are used
  • Match a model M with a scene S (M must be
    deformed to resemble S as much as possible)
  • Use diffusion process technique as follows

12
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

13
Registration Example
Mammogram of left breast
Mammogram of right breast
14
Registration Example (cont.)
Grid of displacement
Registered images
15
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

16
Mammogram Subtraction Example
Flipped right breast
Left breast showing mass
17
Mammogram Subtraction Example (cont.)
Subtraction image
Superimposed subtraction image
18
Feature Extraction
  • Many features are not masses
  • Morphological filtering using a 3X3 kernel
  • Size test (100 pixels)
  • Border test for border misalignment

19
Avg. of areas after each stage of the detection
process
Stage in detection process Avg. of detected areas
After subtraction 13.65
After morphological filtering 7.80
After size test 5.42
After border test 2.17
20
Results
Recognition 93
False positive 1.26
TPF 0.9605
FPF 0.0962
Az 0.95
  • 102 registered pairs of mammograms used
  • Verified by expert radiologists

21
ROC curve showing Az0.95
22
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

23
  • Thank you

24
Questions
Write a Comment
User Comments (0)
About PowerShow.com