A mammogram and breast ultrasound-based expert system with image processing features for breast diseases
| dc.contributor.author | Ngah, Umi Kalthum | |
| dc.date.accessioned | 2014-11-11T01:19:54Z | |
| dc.date.available | 2014-11-11T01:19:54Z | |
| dc.date.issued | 2007 | |
| dc.description | PhD | en_US |
| dc.description.abstract | Survival rates for breast cancer patients may be increased when the disease is detected in its earliest stage through mammography. A thorough assessment during breast screening would also include clinical, physical examination and ultrasound. The implementation of mass screening would result in increased caseloads for radiologists which would incur chances of improper diagnosis. Diagnosticians with the training and experience to interpret mammographic images and breast ultrasounds are scarce. The existence of an expert system would facilitate computer aided study and learning and produce more experts in the area and would also prove to be useful in the training of radiologists in the early part of their career. The archiving of knowledge gathered in this area with patient cases would also promote the interpretation of images in a more consistent manner and may be referred to from time to time. This study focuses on developing expert systems based on the interpretation of mammographic (MAMMEX) and ultrasound (SOUNDEX) images that may be used by expert and non-expert doctors to deduce cases (according to the BI-RADS ‘Breast Imaging Recording and Data System’) based upon patients’ history, physical and clinical assessment as well as mammograms and breast ultrasound images. Digital enhancement of mammograms and breast ultrasound through the existence of image processing routines may help to accentuate images in the process of analyzing procedures. Image based extension of the expert systems have also been built. A total of 179 retrospective cases from the Radiology Department, Hospital Universiti Sains Malaysia were tested, producing an accuracy, sensitivity and specificity of 97%, 96% and 92% respectively for MAMMEX and 99%, 98% and 100% for SOUNDEX. The Receiver Operating Characteristics (ROC) curve analysis produced an Area Under the Curve (AUC) with values of 0.997(±0.003) for MAMMEX and 0.996(±0.004) for SOUNDEX. The Randomized Complete Block Design (RCBD) and the Two-Way Analysis of Variants (ANOVA) proved that the results of MAMMEX and SOUNDEX are consistent with the radiologists’ opinion. Two extensions of image processing algorithms, namely the Fuzzy-Count Image Processing (FCIP) and the Automated Modified Seed-Based Region Growing (AMSBRG) techniques are also implemented to facilitate the detection of microcalcifications | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/387 | |
| dc.language.iso | en | en_US |
| dc.subject | Radiology | en_US |
| dc.subject | Mammogram | en_US |
| dc.subject | Breast diseases | en_US |
| dc.title | A mammogram and breast ultrasound-based expert system with image processing features for breast diseases | en_US |
| dc.type | Thesis | en_US |
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