| Abstrakt: |
High-resolution X-ray computed tomography (CT) is the standard for pulmonary imaging. Depending on the scanner hardware, CT can provide high spatial and high temporal resolution, excellent contrast resolution for the pulmonary structures and surrounding anatomy, and the ability to gather a complete three-dimensional (3D) volume of the human thorax in a single breath hold. Pulmonary CT images have been used for applications such as lung parenchyma density analysis, airway analysis, and lung and diaphragm mechanics analysis. A precursor to all of these quantitative analysis applications is lung segmentation. With the introduction of multi-slice spiral CT scanners, the number of volumetric studies of the lung is increasing, and it is critical to develop fast, accurate algorithms that require minimal to no human interaction to identify the precise boundaries of the lung. This paper presents the problem of inaccurate lung segmentation as observed in algorithms presented by researchers working in the area of medical image analysis. The different lung segmentation techniques have been tested using the data set of 9 patients consisting of a total of 413 CT scan slices. The slice thickness was 7.0 mm and the ages of patients varied from 30 to 73 years. We obtained data sets of patients from the Department of Radiology, Aga Khan Medical University Hospital, Karachi, Pakistan. After testing the algorithms against the data sets, the deficiencies of each algorithm have been highlighted. [ABSTRACT FROM AUTHOR] |
