Learning COPD Sensitive Filters in Pulmonary CT
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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Learning COPD Sensitive Filters in Pulmonary CT. / Sørensen, Lauge Emil Borch Laurs; Lo, Pechin Chien Pau; Ashraf, Haseem; Sporring, Jon; Nielsen, Mads; de Bruijne, Marleen.
Medical Image Computing and Computer-Assisted Intervention - MICCAI 2009. 2009.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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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
Y2 - 20 September 2009 through 24 September 2009
ER -
ID: 38540035