Automatic Microcalcifications Detection and Its Performance of Computer-Aided Diagnosis in Stereo Imaging Mammograms
Date Issued
2009
Date
2009
Author(s)
Wu, Yo-Ching
Abstract
The identification of micro-calcifications can be useful in the effective detection of breast cancer. Their various location, shape, and size can reveal malignant cancers from benign cysts. The procedure of mammography, a specific type of X-ray radiograph, can digitally capture the contrasting imageries of both micro-calcifications as well as normal breast tissues. However, different examiners of the same digitized images may arrive at divergent diagnoses due to the varying experience and background of each examiner as well as his/her own subjectivity. Especially the mammographic films are atypical cases. The present thesis, therefore, endeavors to introduce an automatic computer-aided diagnosis (CAD) that will provide additional, more objective data based on certain characteristics within the images from mammography. It is hoped that this data will ultimately assist physicians in their diagnosis of breast diseases in a more objective way.ince micro-calcifications, on mammograms, show up as bright spots against the predominantly black background, we shall combine different types of wavelet transforms as our methodology to identify them in our study. Subsequently we shall devise two primary methods to eliminate false traits of micro-calcification: 1). by establishing thresholds within the computer-aid system, and 2). by examining the images from different angles.n reality, patients might exhibit slight movements during the stereo imaging procedure. In our study a formula will be established to normalize discrepancies resulting from these extraneous patient movements. The normalized results will then be used to derive three-dimensional coordinates. These coordinates will then ultimately be used to show clusters of micro-calcification. inally, the specific characteristics of the micro-calcifications (shape, pattern, and distance) can be revealed through the process of forward select. These characteristics are then subjected to logistic regression function to aid in the useful interpretation of visual data, and to ultimately help in determining whether the cancer is malign or benign. he data contained in this present study are provided by the NTU Hospital-- BI-RAID 4 level cases--. The data is photographed from biopsy of patients using the Senographe DS. The present study analyzes the visual data from the three initially fixed angles/positions.
Subjects
mammograms
micro-calcification clusters
wavelet transforms
stereo imaging system
logistic regression function
SDGs
Type
thesis
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