An Optimized Superpixel Clustering Approach for High-Resolution Chest CT Image Segmentation.
Lung segmentation is a fundamental step in many image analysis applications for lung diseases and abnormalities in thoracic computed tomography (CT). However, due to the large variations in pathology that may be present in thoracic CT images, it is difficult to extract the lung regions accurately. A major insight to deal with this problem is the existence of new approaches to cope with quality and performance. This poster presents an optimized superpixel clustering approach for high-resolution chest CT segmentation. The proposed algorithm is compared against some super-pixel algorithms while a performance evaluation is carried out in terms of boundary recall and under-segmentation error metrics. The over-segmentation results on a CT Emphysema Database demonstrate that our approach shows better performance than other three state-of-the-art superpixel methods.
Laboratório de Computação Aplicada, Universidade Federal de Santa Maria, Santa Maria-RS, Brasil.
Rafaelo
R
Pinheiro da Rosa
Laboratório de Computação Aplicada, Universidade Federal de Santa Maria, Santa Maria-RS, Brasil.
Marcos
M
Cordeiro d'Ornellas
D000465:Algorithms; D004646:Emphysema / Q000000981:diagnostic imaging*; D006801:Humans; D000069550:Machine Learning; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013902:Radiography, Thoracic / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*