Digital Image Processing
Complete Cross-Validation for Nearest Neighbor Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Computer-aided interpretation of medical images
Computer-aided interpretation of medical images
Lung nodule diagnosis using 3D template matching
Computers in Biology and Medicine
Lung nodule detection in low-dose and thin-slice computed tomography
Computers in Biology and Medicine
Lung structure classification using 3D geometric measurements and SVM
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Automatic segmentation of lung nodules with growing neural gas and support vector machine
Computers in Biology and Medicine
Using experts feedback in clinical case resolution and arbitration as accuracy diagnosis methodology
Computers in Biology and Medicine
Computer Methods and Programs in Biomedicine
Automated pulmonary nodule detection based on three-dimensional shape-based feature descriptor
Computer Methods and Programs in Biomedicine
A novel method for pulmonary embolism detection in CTA images
Computer Methods and Programs in Biomedicine
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Lung cancer is a disease with significant prevalence in several countries around the world. Its difficult treatment and rapid progression make the mortality rates among people affected by this illness to be very high. Aiming to offer a computational alternative for helping in detection of nodules, serving as a second opinion to the specialists, this work proposes a totally automatic methodology based on successive detection refining stages. The automated lung nodules detection scheme consists of six stages: thorax extraction, lung extraction, lung reconstruction, structures extraction, tubular structures elimination, and false positive reduction. In the thorax extraction stage all the artifacts external to the patient's body are discarded. Lung extraction stage is responsible for the identification of the lung parenchyma. The objective of the lung reconstruction stage is to prevent incorrect elimination of portions belonging to the parenchyma. Structures extraction stage comprises the selection of dense structures from inside the lung parenchyma. The next stage, tubular structures elimination eliminates a great part of the pulmonary trees. Finally, the false positive stage selects only structures with great probability to be nodule. Each of the several stages has very specific objectives in detection of particular cases of lung nodules, ensuring good matching rates even in difficult detection situations. We use 33 exams with diversified diagnosis and slices numbers for validating the methodology. We obtained a false positive per exam rate of 0.42 and false negative rate of 0.15. The total classification sensitivity obtained, measured out of the nodule candidates, was 84.84%. The specificity achieved was 96.15% and the total accuracy of the method was 95.21%.