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Detection of Pulmonary Nodules from CT Scans
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 KEYWORDS: computer aided diagnostic, image filtering scale space, mathematical morphology, lung nodules, elipsoid fitting, pattern recognition, Asymmetric AdaBoost.
 
 ABSTRACT: We are developing a complex computer aided diagnosis (CAD) system to detect small pulmonary nodules from helical CT scans. A pulmonary nodule is a small, round (parenchymal nodule) or worm (juxtapleural nodule) shaped lesion in the lungs. Both have greater radio-density than lung parenchyma, so they appear white on images. Lung nodules might indicate a lung cancer and their detection in the early stage improves the survival rate of patients. CT is considered to be the most accurate imaging modality for nodule detection. However, the large amount of data per examination makes the interpretation difficult. This leads to omission of nodules by human radiologist. The presented CAD system is designed to help lower the number of omissions and to decrease the time needed to examine the scan by a radiologist. Our system uses two different schemes to locate juxtapleural nodules and parenchymal nodules respectively. For juxtapleural nodules, morphological closing and thresholding is used to find nodule candidates. To locate non-pleural nodule candidates, we use a novel 3D detector based on multiscale filtration. To define which of the nodule candidates are in fact nodules, an additional classification step is applied. Our approach is based on an asymmetric Adaboost which enables us to give different weights to missed nodules (false negatives, FNs) and incorrectly detected structures (false positives, FPs). This is useful because there are noticeably more negative examples in the nodule candidate set than real nodules–true positives (TPs). The whole system is meant as a second opinion for a human radiologist to speed up reading the examination. That is why we should detect as many true nodules as possible, while a certain number of FPs is acceptable. The system was tested on 147 cases (36559 slices) containing 357 nodules marked by an expert radiologist. The new classifier significantly reduced the number of false positives, while only a few nodules were incorrectly omitted.
 
 This work was supported by the Czech Ministry of Health under project NR8314-3/2005, Czech Science Foundation-project 102/07/1317, and by Czech Ministry of Education-project 1M0567.
 
 
Last update: 19.1.2009