Open Access. Powered by Scholars. Published by Universities.®
Bioimaging and Biomedical Optics Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Bioimaging and Biomedical Optics
Using Deep Learning To Analyze Materials In Medical Images, Carson Molder
Using Deep Learning To Analyze Materials In Medical Images, Carson Molder
Computer Science and Computer Engineering Undergraduate Honors Theses
Modern deep learning architectures have become increasingly popular in medicine, especially for analyzing medical images. In some medical applications, deep learning image analysis models have been more accurate at predicting medical conditions than experts. Deep learning has also been effective for material analysis on photographs. We aim to leverage deep learning to perform material analysis on medical images. Because material datasets for medicine are scarce, we first introduce a texture dataset generation algorithm that automatically samples desired textures from annotated or unannotated medical images. Second, we use a novel Siamese neural network called D-CNN to predict patch similarity and build …
Clustered Microcalcification Detection Using Optimized Difference Of Gaussians, Edward M. Ochoa
Clustered Microcalcification Detection Using Optimized Difference Of Gaussians, Edward M. Ochoa
Theses and Dissertations
The objective of this thesis is to design an automated microcalcification detection system to be used as an aid in radiologic mammogram interpretation. This research proposes the following methodology for clustered microcalcification detection. First, preprocess the digitized film mammogram to reduce digitization noise. Second, spatially filter the image with a difference of Gaussians (DoG) kernel. To detect potential microcalcifications, segment the filtered image using global and local thresholding. Next, cluster and index these detections into regions of interest (ROIs). Identify ROIs on the digitized image (or hardcopy printout) for final radiologic diagnosis.