Abstract
To overcome low accuracy and high false positive of existing computer-aided lung nodules detection. We propose a novel lung nodule detection scheme based on the Gestalt visual cognition theory. The proposed scheme involves two parts which simulate human eyes cognition features such as simplicity, integrity and classification. Firstly, lung region was segmented from lung Computed tomography (CT) sequences. Then local threedimensional information was integrated into the Maximum intensity projection (MIP) images from axial, coronal and sagittal profiles. In this way, lung nodules and vascular are strengthened and discriminated based on pathologic image characteristics of lung nodules. The experimental database includes fifty-three high resolution CT images contained lung nodules, which had been confirmed by biopsy. The experimental results show that, the accuracy rate of the proposed algorithm achieves 91.29%. The proposed framework improves performance and computation speed for computer aided nodules detection.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
