ENDA MOLLOY, ELECTRONIC ENG.

FINAL PRESENTATION, 31 /03 /09 .

Automated Image Analysis Techniques for Screening of

Mammography Images

Outline

Project Background

Project Overview

System Development

Conclusions

Background

Breast cancer can be missed on mammograms for anumber 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.

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.

Contrast Limited Adaptive Histogram Equalisation

Contrast Enhancement

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:i. Wavelet type and number of levels for decomposition are

selected, then the FWT of noisy image is computed. ii. A threshold is applied to the detail coefficients.iii. Wavelet reconstruction is performed to produce the de-

noised image.

Image Segmentation

Separating suspicious areas that may contain abnormalities from the image.

Two algorithms: Global Thresholding Region Growing

Feature Extraction

Two approaches examined in this system:

First order statistics : Calculated based on image intensity histogram. Previously used in literature, M. Alolfe et 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.

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.

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

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

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.

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.

Questions

Questions

Multilayer Perceptron