Floor of log: a novel intelligent algorithm for 3D lung segmentation in computer tomography images

• Published in: Multimedia systems Vol. 28; no. 4; pp. 1151 - 1163
• Main Authors: Peixoto, Solon Alves, Medeiros, Aldísio G., Hassan, Mohammad Mehedi, Dewan, M. Ali Akber, Albuquerque, Victor Hugo C. de, Filho, Pedro P. Rebouças
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2022; Springer Nature B.V
• Subjects: Algorithms; Artificial neural networks; Clustering; Computed tomography; Computer Communication Networks; Computer Graphics; Computer Science; Correlation coefficients; Cryptology; Data Storage Representation; Deep Learning for Multimedia Healthcare; Image segmentation; Lungs; Machine learning; Metric space; Multimedia Information Systems; Operating Systems; Performance indices; Sensitivity; Special Issue Paper; Support vector machines; Tomography; Deep learning; Floor of Log; Lung Segmentation; Clustering; Image Processing
• ISSN: 0942-4962; 1432-1882
• DOI: 10.1007/s00530-020-00698-x

This work presents a high-performance approach for 3D lung segmentation tasks in computer tomography images using a new intelligent machine learning algorithm called Floor of Log(FoL). The Support Vector Machine was used to learn the better parameter of the FoL algorithm using the parenchyma and its border as labels. Sensitivity, Matthews Correlation Coefficient (MCC), Hausdorff Distance (HD), Dice, Accuracy (ACC), and Jaccard were used to evaluate the proposed algorithm. The FoL was compared with recent 3D region growing, 3D Adaptive Crisp Active Contour, 3D OsiriX toolbox, and Level-set algorithm based on the coherent propagation method algorithms. The FoL algorithm achieves good results with approximately 19 s in the most significant result in an exam with 430 slices and presents similarity indexes achieving HD 3.5, DICE 83.63, and Jaccard 99.73 and qualitative indexes achieving Sensitivity 83.87, MCC 83.08, and ACC 99.62. The proposed approach of this work showed a simple and powerful algorithm to segment lung in computer tomography images of the chest region by combining similar textures, highlighting the lung structure. The FoL was presented as a new supervised clustering algorithm which can be trained to achieve better results and attached to other approaches as Convolutional Deep Neural Networks applications.