Open AccessOff-the-shelf 3D Lung Segmentation in CT using Generalized Histogram Thresholding
Author(s) -
Marcelo Toledo,
Marina F. S. Rebelo,
José Eduardo Krieger,
Marco Antônio Gutierrez
Publication year - 2021
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/sbcas.2021.16054
Subject(s) - dice , thresholding , artificial intelligence , segmentation , computer science , histogram , balanced histogram thresholding , sørensen–dice coefficient , pattern recognition (psychology) , image segmentation , computer vision , deep learning , histogram equalization , mathematics , image (mathematics) , statistics
Computerized Tomography is very important for lung disease diagnostics, including computer assisted methods. Lung segmentation is usually a first step in further sophisticated methods of diagnosis. If in one hand, deep learning methods have state-of-the-art performance, they aren't as simple to apply compared to classical methods, sometimes requiring extra data and training. We designed a method specific for lung segmentation based on histogram thresholding. We observed that, in our proposed method, by changing from Otsu to the more recently developed GHT we got a significant improvement in segmentation, jumping from 77% to 91% average dice (from 90% to 95% median dice, respectively), approaching deep learning methods (UNet) results (94% average and 97% median dice). Even though our proposed method runs on CPU, it's still 2.6 times faster than UNet on GPU. Moreover, our proposed method is off-the-shelf, requiring no training or parameter calibration, being suitable as pre-processing for more sophisticated methods that aim specific diagnoses.