Download or read book Computer-aided Detection of Malignant Lesions in Dynamic Contrast Enhanced MRI Breast and Prostate Cancer Datasets written by Brent James Woods and published by . This book was released on 2008 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is considered to have great potential in cancer diagnosis and monitoring. During the DCE-MRI procedure, repeated MRI scans are used to monitor contrast agent movement through the vascular system and into tissue. By observing the vascular permeability characteristics, radiologists can detect and classify malignant tissues. When used for diagnostic purposes, the DCE-MRI procedure often requires manual detection, classification, and marking of tumor tissues. This process can be time consuming and fatiguing especially when multiple DCE-MRI procedures must be processed to monitor the progress of a cancer therapy. Manual analysis also suffers from inter- and intra-observer variations which can lead to lesion segmentation inconsistencies. The goal of this dissertation research is to design and develop a tool to aid radiologists, researchers, and clinicians in the detection, segmentation, and analysis of malignant lesions from DCE-MRI datasets. The diagnostic tool presented in this research is model independent, speeds analysis, and provides more consistent segmentations. The approach of the project is to apply statistical 4-D image texture analysis features along with a classifier (such as a neural network) to analyze DCE-MRI datasets. Performance of the computer aided diagnosis (CAD) tool for this project is demonstrated with breast and prostate DCE-MRI data. Training methodology is reported so that extension to other types of cancers and anatomical regions is made possible. Results from the computer assisted diagnostic tool are compared with manual analysis performed by radiologists. The specific research aims of this dissertation are: a) provide a tool for quantitative and quick DCE-MRI analysis by providing radiologists a segmentation (for semi-automatic or automatic application), b) quantify inter- and intra-observer variations that occur during manual lesion segmentation and compare performance with computer-based segmentations, and c) explore the performance of the CAD system in different anatomic regions (including breast and prostate cancer datasets).