Mammography is used to aid early detection and diagnosis systems. It takes an x-ray
image of the breast and can provide a second opinion for radiologists. The earlier
detection is made, the better treatment works. Digital mammograms are dealt with by
Computer Aided Diagnosis (CAD) systems that can detect and analyze abnormalities in
a mammogram. The purpose of this study is to investigate how to categories cropped
regions of interest (ROI) from digital mammogram images into two classes; normal and
abnormal regions (which contain microcalcifications).
The work proposed in this thesis is divided into three stages to provide a concept
system for classification between normal and abnormal cases. The first stage is the
Segmentation Process, which applies thresholding filters to separate the abnormal
objects (foreground) from the breast tissue (background). Moreover, this study has been
carried out on mammogram images and mainly on cropped ROI images from different
sizes that represent individual microcalcification and ROI that represent a cluster of
microcalcifications. The second stage in this thesis is feature extraction. This stage
makes use of the segmented ROI images to extract characteristic features that would
help in identifying regions of interest. The wavelet transform has been utilized for this
process as it provides a variety of features that could be examined in future studies. The
third and final stage is classification, where machine learning is applied to be able to
distinguish between normal ROI images and ROI images that may contain
microcalcifications. The result indicated was that by combining wavelet transform and
SVM we can distinguish between regions with normal breast tissue and regions that
include microcalcifications.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/4429 |
| Date | January 2010 |
| Creators | Al-Osta, Husam E.I. |
| Contributors | Qahwaji, Rami S.R., Ipson, Stanley S. |
| Publisher | University of Bradford, Department of Electronic Imaging and Media Communications |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Thesis, doctoral, PhD |
| Rights | <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/"><img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by-nc-nd/3.0/88x31.png" /></a><br />The University of Bradford theses are licenced under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/">Creative Commons Licence</a>. |
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