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Full-Text Articles in Radiology
Image Enhancement Of Cancerous Tissue In Mammography Images, Richard Thomas Richardson
Image Enhancement Of Cancerous Tissue In Mammography Images, Richard Thomas Richardson
CCE Theses and Dissertations
This research presents a framework for enhancing and analyzing time-sequenced mammographic images for detection of cancerous tissue, specifically designed to assist radiologists and physicians with the detection of breast cancer. By using computer aided diagnosis (CAD) systems as a tool to help in the detection of breast cancer in computed tomography (CT) mammography images, previous CT mammography images will enhance the interpretation of the next series of images. The first stage of this dissertation applies image subtraction to images from the same patient over time. Image types are defined as temporal subtraction, dual-energy subtraction, and Digital Database for Screening Mammography …
Presenting A Simplified Assistant Tool For Breast Cancer Diagnosis In Mammography To Radiologists, Ping Zhang, Jenny Doust, Kuldeep Kumar
Presenting A Simplified Assistant Tool For Breast Cancer Diagnosis In Mammography To Radiologists, Ping Zhang, Jenny Doust, Kuldeep Kumar
Kuldeep Kumar
This paper proposes a method to simplify a computational model from logistic regression for clinical use without computer. The model was built using human interpreted features including some BI-RADS standardized features for diagnosing the malignant masses. It was compared with the diagnosis using only assessment categorization from BI-RADS. The research aims at assisting radiologists to diagnose the malignancy of breast cancer in a way without using automated computer aided diagnosis system.
Presenting A Simplified Assistant Tool For Breast Cancer Diagnosis In Mammography To Radiologists, Ping Zhang, Jenny Doust, Kuldeep Kumar
Presenting A Simplified Assistant Tool For Breast Cancer Diagnosis In Mammography To Radiologists, Ping Zhang, Jenny Doust, Kuldeep Kumar
Jenny Doust
This paper proposes a method to simplify a computational model from logistic regression for clinical use without computer. The model was built using human interpreted features including some BI-RADS standardized features for diagnosing the malignant masses. It was compared with the diagnosis using only assessment categorization from BI-RADS. The research aims at assisting radiologists to diagnose the malignancy of breast cancer in a way without using automated computer aided diagnosis system.
Near-Infrared Characterization Of Breast Tumors In Vivo Using Spectrally-Constrained Reconstruction, Subhadra Srinivasan, Brian W. Pogue, Ben Brooksby, Shudong Jiang, Hamid Dehghani, Christine Kogel, Wendy A. Wells, Steven P. Poplack, Keith D. Paulsen
Near-Infrared Characterization Of Breast Tumors In Vivo Using Spectrally-Constrained Reconstruction, Subhadra Srinivasan, Brian W. Pogue, Ben Brooksby, Shudong Jiang, Hamid Dehghani, Christine Kogel, Wendy A. Wells, Steven P. Poplack, Keith D. Paulsen
Dartmouth Scholarship
Multi-wavelength Near-Infrared (NIR) Tomography was utilized in this study to non-invasively quantify physiological parameters of breast tumors using direct spectral reconstruction. Frequency domain NIR measurements were incorporated with a new spectrally constrained direct chromophore and scattering image reconstruction algorithm, which was validated in simulations and experimental phantoms. Images of total hemoglobin, oxygen saturation, water, and scatter parameters were obtained with higher accuracy than previously reported. Using this spectral approach, in vivo NIR images are presented and interpreted through a series of case studies (n=6 subjects) having differing abnormalities. The corresponding mammograms and ultrasound images are also evaluated. Three of six …