A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Scale Control for Edge Detection and Blur Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
ISCV '95 Proceedings of the International Symposium on Computer Vision
Learning COPD Sensitive Filters in Pulmonary CT
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
| Hi-index | 0.00 |
Emphysema is characterized by the destruction and over distension of lung tissue, which manifest on high resolution computer tomography (CT) images as regions of low attenuation. Typically, it is diagnosed by clinical symptoms, physical examination, pulmonary function tests, and X-ray and CT imaging. In this paper we discuss a quantitative imaging approach to analyze emphysema which employs low-level segmentations of CT images that partition the data into perceptually relevant regions. We constructed multi-dimensional histograms of feature values computed over the image segmentation. For each region in the segmentation, we derive a rich set of feature measurements. While we can use any combination of physical and geometric features, we found that limiting the scope to two features – the mean attenuation across a region and the region area – is effective. The subject histogram is compared to a set of canonical histograms representative of various stages of emphysema using the Earth Mover’s Distance metric. Disease severity is assigned based on which canonical histogram is most similar to the subject histogram. Experimental results with 81 cases of emphysema at different stages of disease progression show good agreement against the reading of an expert radiologist.