Automated Image Analysis Techniques for Screening of Mammography Images

1. Automated Image Analysis Techniques for Screening of Mammography Images Enda Molloy, Electronic Eng. Final Presentation, 31/03/09.

2. Outline • Project Background • Project Overview • System Development • Conclusions

3. Background Breast cancer can be missed on mammograms for a number of reasons: • Cancer blends into the background of glandular tissue and is missed at screening. • Breast tissue is simply too dense and cancer cannot be seen on the mammogram. • Human error, where the radiologist misinterprets the mammogram.

4. Project Overview • The project aims to investigate analysis techniques for the screening of mammography images, which may be used in automated screening of a large set of images. MIAS database is used for testing. • Provide functionality for remote access to the data via a web browser.

5. Contrast Enhancement • Contrast Limited Adaptive Histogram Equalisation

6. Image de-noising • Often Mammograms can be affected by Gaussian noise. Although the images in the MIAS database are not affected, noise is added to the images to simulate the effect. • Wavelet Analysis is used to remove the noise: • Wavelet type and number of levels for decomposition are selected, then the FWT of noisy image is computed. • A threshold is applied to the detail coefficients. • Wavelet reconstruction is performed to produce the de-noised image.

7. Image Segmentation • Separating suspicious areas that may contain abnormalities from the image. • Two algorithms: • Global Thresholding • Region Growing

8. Feature Extraction • Two approaches examined in this system: • First order statistics : • Calculated based on image intensity histogram. • Previously used in literature, M. Alolfeet al. • Six statistics chosen – Mean, Standard Deviation, Third Moment, Uniformity, Entropy, Kurtosis. • Textural features using wavelet decomposition: • DWT is applied to a 64 x 64 pixel window with abnormality centered. • DB4 was the chosen wavelet, one level of decomposition performed. • The hundred biggest approximation coefficients were kept.

9. Classifier • An Artificial Neural Network (ANN) is used as a classification architecture for screening regions of interest. • The Multilayer Perceptron (MLP) was the architecture chosen. • The output signal indicates the appropriate class for the input data i.e. Benign, Malignant, Normal.

10. Classification Results • Accuracy of models calculated in terms of • Performance – percentage of correctly identified cases. • Specificity – percentage of TP which are identified as such. • Sensitivity – percentage of TN correctly identified. • First Order Statistics • Performance: 92.3% • Specificity: 95.0% • Sensitivity: 83.3% Confusion Matrix

11. Classification Results • Wavelet Coefficients: Tumour V Normal • Performance: 92.3% • Specificity: 100.0% • Sensitivity: 83.3% • Wavelet Coefficients: Benign V Malignant • Performance: 83.3% • Specificity: 83.3% • Sensitivity: 83.3% • Combining the results above gives an overall performance of 76.9%, specificity of 83.3% and sensitivity of 69.4%. Tumour V Normal confusion matrix Benign V Malignant confusion matrix

12. Online Database • MySQL database used to store user login, patient and image information. • PHP is the scripting language used to query the database and generate dynamic web pages. • All the patient and image information is displayed in the form of a HTML table. • Functionality is also provided to allow a user to upload an image to the database.

13. Conclusions • Image processing techniques investigated unfortunately no adaptive segmentation algorithm was developed. • Features were extracted and used as inputs to a classification architecture. A model was built and tested for the screening of mammograms. • A basic database accessible from a web browser was implemented.

14. Questions

15. Questions

16. Multilayer Perceptron