ENDA MOLLOY, ELECTRONIC ENG.

PROGRESS PRESENTATION, 22/01/09.

Automated Image Analysis Techniques for Screening of

Mammography Images

Outline

Project Overview

Current Progress

Future Plans

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.

This will be achieved by developing a system comprising of feature extraction and a classification architecture.

Provide functionality for remote access to the data via a web browser.

Contrast Enhancement

Contrast Limited Adaptive Histogram Equalisation

Image Segmentation

Global Thresholding

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.

Neural Networks

An Artificial Neural Network is being used as a classification architecture for screening regions of interest.

A Multilayer Perceptron is currently being implemented using ROI textural statistics as inputs to the input layer.

The output signal will indicate the appropriate class for the input data i.e. Benign, malignant, normal.

MLP Overview

Mean

Standard

DeviationThird

Moment

Uniformity

Entropy

Kurtosis

Input Layer

Hidden Layer Output Layer

Benign

Malignant

Normal

Online Database

MySQL database, with a table holding usernames and passwords of registered users and a second table holding image information. The images are uploaded directly to the server with the filename stored in the database.

Future Plans

Jan 22th– Jan 30th Continuing with work on MLP i.e. Training and testing

it.

Jan 30th –Feb 6th Complete the online database.

Feb 6th – Feb 27th Research and implement a second classification

architecture.

Questions