CAD system for lung cancer
Principal Investigator
Name
Xiaoting Dai
Degrees
Bachelor's degree
Institution
College of medical devices and food science, University of Shanghai for Science and Technology
Position Title
student
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-213
Initial CDAS Request Approval
May 3, 2016
Title
CAD system for lung cancer
Summary
Our study focuses on detection of pulmonary nodules with image features in CT scans. Our main concern is sub-solid nodules, particularly ground glass nodules. By establishing different mathematical models based on the gray value distribution of sub-solid pulmonary nodules, we will detect candidate nodules by using a template matching algorithm. Then the image features of candidates will be extracted and selected by applying an algorithm for extraction and selection of image characteristics. The features contain two-dimensional and three-dimensional features in CT scans. Finally, a classifier algorithm will be applied to further discriminate the sub-solid nodules.
Aims
Computer-aided detection (CAD) technology serves as a powerful tool to assist radiologists in detecting pulmonary nodules, especially at their early stages. In this project, a novel scheme for automatic detection of pulmonary nodules in thoracic computed tomography images will be evaluated extensively with a large data set.
Collaborators
Gong Jing, College of medical devices and food science, University of Shanghai for Science and Technology
Liu Jiyu,College of medical devices and food science, University of Shanghai for Science and Technology
He Xingyi,College of medical devices and food science, University of Shanghai for Science and Technology
Duan Huihong, College of medical devices and food science, University of Shanghai for Science and Technology