Connected components in binary images: the detection problem
Connected components in binary images: the detection problem
Connected component labeling of binary images on a mesh connected massively parallel processor
Computer Vision, Graphics, and Image Processing
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Machine Learning
A Simple and Efficient Connected Components Labeling Algorithm
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
Learning an Optimal Naive Bayes Classifier
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Lung nodule diagnosis using 3D template matching
Computers in Biology and Medicine
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In this paper, a Computer Aided Detection (CAD) system based on three-dimensional (3D) feature extraction is introduced to detect lung nodules. First, eight directional search was applied in order to extract regions of interests (ROIs). Then, 3D feature extraction was performed which includes 3D connected component labeling, straightness calculation, thickness calculation, determining the middle slice, vertical and horizontal widths calculation, regularity calculation, and calculation of vertical and horizontal black pixel ratios. To make a decision for each ROI, feed forward neural networks (NN), support vector machines (SVM), naïve Bayes (NB) and logistic regression (LR) methods were used. These methods were trained and tested via k-fold cross validation, and results were compared. To test the performance of the proposed system, 11 cases, which were taken from Lung Image Database Consortium (LIDC) dataset, were used. ROC curves were given for all methods and 100% detection sensitivity was reached except naïve Bayes.