Abstract
Computed tomography (CT) can contribute to the early detection of lung diseases like emphysema, a chronic and progressive disease. Texture-based methods can be explored to classify regions of interest (ROI's) into emphysematous areas and normal areas. In this work we evaluated the importance of a set of parameters in the classification of lung CT images, such as the size of the ROIs, the quantization level, and textural features used in classification. A support vector machine was used as classifier. The performance of the designed classifier was evaluated using a 10-fold cross validation method and the results compared based on overall accuracy, sensibility and specificity. This study shows that textural features have a good discriminatory power in the classification of lung emphysema in CT images.

Abstract
The purpose of the described system is to aid radiologists on their daily routine in the task of analyzing HRCT lung images and to contribute to a more accurate and fast diagnosis. We developed a framework - Study Lung Tool-with the objective of gather information from radiologists, in a systematic way. Using Study Lung Tool framework, the radiologist analyzes HRCT scans, outlines regions of typical pattern and characterizes the patterns. A database of typical patterns associated with common pulmonary diseases was created. The information gathered can be a valuable teaching tool to every one that intends to understand HRCT lung parenchyma. The proposed system discriminates between normal and abnormal patterns of lung parenchyma based on statistical texture analysis extracted from HRCT lung scans. An overall accuracy of 89,2%, a sensitivity of 92,7% and a specificity of 83,6% were achieved.

Abstract
In this paper is presented a project -CAD Lung System- whose objective is to help radiologists in their daily routine, in the HRCT images analysis of lung parenchyma. A database of abnormal lung patterns and their reliable classification is a determinant part of the system. We developed a framework -Study Lung Tool- with the objective of gather information from radiologist, in a systematic way. Using this framework, the radiologist analyses HRCT scans, outlines regions of typical pattern and characterizes the pattern. This framework can also be used as a learning tool through the observation of the HRCT scans previously characterized by experts.