Lung Segmentation and Rib Suppression for Enhancing Nodule Detection in Chest Radiographs using spatial Gabor filter

elaheh soleymanpour, Hamid reza Pourreza, Mehri Sadoghi yazdi

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Abstract


Computer-aided Diagnosis (CAD) systems can assist radiologists in several diagnostic tasks.  Lung segmentation is one of the mandatory steps for initial detection of lung cancer in Posterior-Anterior chest radiographs. On the other hand, many CAD schemes in projection chest radiography may benefit from the suppression of the bony structures that overlay the lung fields, e.g. ribs. The original images are enhanced by an adaptive contrast equalization and non-linear filtering. Then an initial estimation of lung area is obtained based on morphological operations and then it is improved by growing this region to find the accurate final contour, then for rib suppression, we use oriented spatial Gabor filter. The proposed method was tested on a publicly available database of 247 chest radiographs. Results show that this method outperformed greatly with accuracy of 96.25% for lung segmentation, also we will show improving the conspicuity of lung nodules by rib suppression with local nodule contrast measures. Because there is no additional radiation exposure or specialized equipment required, it could also be applied to bedside portable chest x-rays. In addition to simplicity of these fully automatic methods, lung segmentation and rib suppression algorithms are performed accurately with low computation time and robustness to noise because of the suitable enhancement procedure.


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