Pleuramesothelioma is a malignant tumor. A computer-assisted diagnosis system shall provide physicians with the assessment of the detected pleural thickening from the 3D CT data. This paper describes a new, improved method to assess the size of detected thickening using both the 2D and 3D thin plate spline. First, a coordinate transformation was applied. Next, set of landmarks was selected as input for the thin plate spline. The final step is the numerical area integration. The results show that the calculated area was more accurate than the former pixel counting technique, and promise further automatic development.
KEY WORDSThin plate spline interpolation, 3D modeling, pleural thickenings, pleural mesothelioma, 3D CT data
Pleuramesothelioma is malignant tumor on the pleura, caused by asbestos exposure. Computer-assisted diagnosis system shall support physicians to assess the growth rate of detected pleural thickenings from CT data. This paper describes a new, improved method to automatically assess the size of detected thickening using thin plate spline interpolation which then leads to the 3D modeling of thickening. First, we detect each pleural thickening. Second, an automatic coordinate transformation is applied to enable the numeric calculation of the spline. Next, the appropriate landmark points are then automatically selected by using the newly applied chain-code concavity analysis and used as the constraint points for the interpolation. Numerical integration is applied to calculate area by slice. In the final step, the spline interpolation between layers is applied to calculate volume of each thickening. The resultsshow that the thin plate spline interpolated boundary is suitable for 3D modeling of the thickening.
Pleural thickenings as biomarker of exposure to asbestos may evolve into malignant pleural mesothelioma. The diagnosis is based on a visual investigation of CT images, which is a time consuming and subjective procedure. Our image processing system identifies the pleural contours and detects pleural thickenings. In two algorithm steps, namely the detection of the thorax and the removal of air and trachea, 3D morphological operations were implemented and tested, in order to find an optimum of its application in the aspect of calculation expense and accuracy. After an evaluation, we obtained an experimental optimum for both algorithm steps.
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