Thoracic image matching with appearance and spatial distribution

Annu Int Conf IEEE Eng Med Biol Soc. 2011:2011:4469-72. doi: 10.1109/IEMBS.2011.6091108.

Abstract

Positron emission tomography--computed tomography (PET-CT) produces co-registered anatomical (CT) and functional (PET) patient information (3D image set) from a single scanning session, and is now accepted as the best imaging technique to accurately stage the most common form of primary lung cancer--non-small cell lung cancer (NSCLC). This paper presents a content-based image retrieval (CBIR) method for retrieving similar images as a reference dataset to potentially aid the physicians in PET-CT scan interpretation. We design a spatial distribution to describe the spatial information of each region-of-interest (ROI), and a pairwise ROI mapping scheme between images to compute the image matching level. Similar images are then retrieved based on the local and spatial information of the detected ROIs, and a learned weighted sum of ROI distances. Our evaluation on clinical data shows good image retrieval performance.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Carcinoma, Non-Small-Cell Lung / diagnostic imaging*
  • Humans
  • Lung Neoplasms / diagnostic imaging*
  • Multimodal Imaging
  • Positron-Emission Tomography
  • Tomography, X-Ray Computed