Learning COPD Sensitive Filters in Pulmonary CT

TY - GEN

T1 - Learning COPD Sensitive Filters in Pulmonary CT

AU - Sørensen, Lauge Emil Borch Laurs

AU - Lo, Pechin Chien Pau

AU - Ashraf, Haseem

AU - Sporring, Jon

AU - Nielsen, Mads

AU - de Bruijne, Marleen

N1 - Conference code: 12

PY - 2009

Y1 - 2009

N2 - The standard approaches to analyzing emphysema in computed tomography (CT) images are visual inspection and the relative area of voxels below a threshold (RA). The former approach is subjective and impractical in a large data set and the latter relies on a single threshold and independent voxel information, ignoring any spatial correlation in intensities. In recent years, supervised learning on texture features has been investigated as an alternative to these approaches, showing good results. However, supervised learning requires labeled samples, and these samples are often obtained via subjective and time consuming visual scoring done by human experts. In this work, we investigate the possibility of applying supervised learning using texture measures on random CT samples where the labels are based on external, non-CT measures. We are not targeting emphysema directly, instead we focus on learning textural differences that discriminate subjects with chronic obstructive pulmonary disease (COPD) from healthy smokers, and it is expected that emphysema plays a major part in this. The proposed texture based approach achieves an 69% classification accuracy which is significantly better than RA’s 55% accuracy.

AB - The standard approaches to analyzing emphysema in computed tomography (CT) images are visual inspection and the relative area of voxels below a threshold (RA). The former approach is subjective and impractical in a large data set and the latter relies on a single threshold and independent voxel information, ignoring any spatial correlation in intensities. In recent years, supervised learning on texture features has been investigated as an alternative to these approaches, showing good results. However, supervised learning requires labeled samples, and these samples are often obtained via subjective and time consuming visual scoring done by human experts. In this work, we investigate the possibility of applying supervised learning using texture measures on random CT samples where the labels are based on external, non-CT measures. We are not targeting emphysema directly, instead we focus on learning textural differences that discriminate subjects with chronic obstructive pulmonary disease (COPD) from healthy smokers, and it is expected that emphysema plays a major part in this. The proposed texture based approach achieves an 69% classification accuracy which is significantly better than RA’s 55% accuracy.

U2 - 10.1007/978-3-642-04271-3_85

DO - 10.1007/978-3-642-04271-3_85

M3 - Article in proceedings

BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2009

T2 - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2009

Y2 - 20 September 2009 through 24 September 2009

ER -