CT scan of the thorax is widely used to diagnose and evaluate numerous lung diseases. These scans yield a large amount of image data. The expanding volume of thoracic CT studies along with the increase of image data, elucidates the need of computer-aided diagnosis (CAD) schemes to assist the radiologists. The preprocessing step of most CAD systems for identifying the lung diseases is lung segmentation. Since several lung diseases are diagnosed based on the patterns of lung tissue, texture evaluation is one of the most important parts of CAD schemes to classify healthy lung images from unhealthy ones.

The following topics will be addressed in this talk.
- Introduction to pulmonary CT images
- Discussion of patterns of abnormal lung parenchyma
- Segmentation of lung in CT images using Watershed Transform
- Texture feature extraction and evaluation using wavelet transform and statistical measures
- Discussions and future directions

Keywords: Medical image processing, lung segmentation, texture analysis, pattern recognition, tissue classification, watershed transform, wavelet transform, feature extraction.

Rushin Shojaii received her B.A.Sc. degree in electrical and computer engineering from Iran University of Science and Technology, Tehran, Iran, in 1993. She is currently an M.A.Sc. candidate in the Department of Electrical and Computer Engineering at Ryerson University. Her research interests include, medical imaging, signal and image processing, and pattern recognition. Rushin is an active member of the Computer Vision and Image Processing (CVIP) laboratory at Ryerson University