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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. Initial Presentation, 7/10/08. Outline. Background Project Overview Initial Work Project Schedule. Background. Breast cancer can be missed on mammograms for a number of reasons:

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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. Initial Presentation, 7/10/08.

  2. Outline • Background • Project Overview • Initial Work • Project Schedule

  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. • This will be achieved by developing a system comprising of feature extraction and a classification architecture. • It is also planned to provide functionality for remote access to the data via a web browser.

  5. Project Overview Image from database of mammograms MATLAB will be used to carry out image processing Web server and database

  6. Initial Work • Contrast Enhancement: • Contrast Limited Adaptive Histogram Equalisation (CLAHE) algorithm separates images into contextual regions and histogram equalisation is applied to each. This evens out the used grey values and brings out hidden features in the image.

  7. CLAHE Example • Applying CLAHE to an image in MATLAB:

  8. Canny Edge Detector • Image is smoothed by Gaussian convolution. • Compute the x and y derivatives of the image using a 2-D first derivative operator. • From x and y derivative, compute the edge magnitude. • Suppress non-maximum edges. • Hysteresis process.

  9. Canny Edge Detector

  10. Project Schedule • Now – Oct 27th • Continuing with research on basic image processing techniques for feature extraction. • Familiarise myself with MATLAB. • Test the different processing techniques on a subset of mammographic images. • Oct 27th – Nov 10th • Add to work already done on feature extraction. • Include techniques to reduce noise e.g. wavelet analysis

  11. Project Schedule • Nov 10th– Nov 28th • Investigate and research available options for classification techniques. • Choose a suitable classification architecture for screening. • Build and test a basic system for screening of mammograms. • Christmas Break • Time will be used to catch up if I have fallen behind. • Start research on MySQL.

  12. Project Schedule • Jan 12th– Jan 26th • Using MySQL develop a simple online database that would allow a doctor remote access to the data. • Jan 26th– Feb 16th • Work on a second classification architecture and compare the results between this architecture and the previous one developed, in terms of performance and complexity. • Feb 16th– March 2nd • Test and debug the overall system.

  13. Questions

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