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Automated Image Analysis Techniques for Screening of Mammography Images

Automated Image Analysis Techniques for Screening of Mammography Images. Enda Molloy, Electronic Eng. Progress Presentation, 22/01/09. Outline. Project Overview Current Progress Future Plans. Project Overview.

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Automated Image Analysis Techniques for Screening of Mammography Images

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  1. Automated Image Analysis Techniques for Screening of Mammography Images Enda Molloy, Electronic Eng. Progress Presentation, 22/01/09.

  2. Outline • Project Overview • Current Progress • Future Plans

  3. 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.

  4. Contrast Enhancement • Contrast Limited Adaptive Histogram Equalisation

  5. Image Segmentation • Global Thresholding

  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. 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.

  8. MLP Overview Mean Standard Deviation Benign Third Moment Malignant Uniformity Normal Entropy Kurtosis Input Layer Hidden Layer Output Layer

  9. 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.

  10. 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.

  11. Questions

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