A novel method of partitioning regions in lungs and their usage in feature extraction for reducing false positives

Mausumi Acharyya; Dinesh M. Siddu; Alexandra Manevitch; Jonathan Stoeckel

doi:10.1117/12.770603

27 March 2008 A novel method of partitioning regions in lungs and their usage in feature extraction for reducing false positives

Mausumi Acharyya, Dinesh M. Siddu, Alexandra Manevitch, Jonathan Stoeckel

Author Affiliations +

Proceedings Volume 6915, Medical Imaging 2008: Computer-Aided Diagnosis; 69150Z (2008) https://doi.org/10.1117/12.770603
Event: Medical Imaging, 2008, San Diego, California, United States

Abstract

Chest X-ray (CXR) data is a 2D projection image. The main drawback of such an image is that each pixel of it represents a volumetric integration. This poses a challenge in detection and estimation of nodules and their characteristics. Due to human anatomy there are a lot of lung structures which can be falsely identified as nodules in a projection data. Detection of nodules with a large number of false positives (FP) adds more work for the radiologists. With the help of CAD algorithms we aim to identify regions which cause higher FP readings or provide additional information for nodule detection based on the human anatomy. Different lung regions have different image characteristics we take advantage of this and propose an automatic lung partitioning into vessel, apical, basal and exterior pulmonary regions. Anatomical landmarks like aortic arch and end of cardiac-notch along-with inter intra-rib width and their shape characteristics were used for this partitioning. Likelihood of FPs is more in vessel, apical and exterior pulmonary regions due to rib-crossing, overlap of vessel with rib and vessel branching. For each of these three cases, special features were designed based on histogram of rib slope and the structural properties of rib segments information. These features were assigned different weights based on the partitioning. An experiment was carried out using a prototype CAD system 150 routine CXR studies were acquired from three institutions (24 negatives, rest with one or more nodules). Our algorithm provided a sensitivity of 70.4% with 5 FP/image for cross-validation without partition. Inclusion of the proposed techniques increases the sensitivity to 78.1% with 4.1 FP/image.

Citation Download Citation

Mausumi Acharyya, Dinesh M. Siddu, Alexandra Manevitch, and Jonathan Stoeckel "A novel method of partitioning regions in lungs and their usage in feature extraction for reducing false positives", Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 69150Z (27 March 2008); https://doi.org/10.1117/12.770603

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available