Tensor Controlled Local Structure Enhancement of CT Images for Bone Segmentation
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Model-based three-dimensional medical image segmentation
Model-based three-dimensional medical image segmentation
Accurate Lung Segmentation For X-ray CT Images
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 02
Lung parenchyma segmentation from CT images based on material decomposition
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
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Lungs Segmentation from chest CT slices is a precursor for CAD applications. Most of the lungs segmentation methods are scanner dependent. We propose a fully automated machine independent method for segmenting lungs from CT images. The algorithm comprised of three main steps. In the first step, gray level threshold value has been selected by maximizing within class similarity. In the second step, binary mask has been developed using selected gray level threshold value and improved by morphological operations. In the third step, lungs have been segmented utilizing binary mask and original CT slice images. The method has been tested on data set of 25 slices collected from two different sources. Results have been compared with manually delineated lungs on CT images by a radiologist. Mean overlapping fraction, precision, sensitivity/recall, specificity, accuracy and F-measure have been recorded as 0.9929, 0.9962, 0.9966, 0.9997, 0.9995 and 0.9964 respectively.