Computerized lung nodule detection using 3D Feature extraction and learning based algorithms
Küçük Resim Yok
Tarih
2010
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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. © Springer Science + Business Media, LLC 2008.
Açıklama
PubMed ID: 20433057
Anahtar Kelimeler
3D feature extraction, Feed-forward neural networks, Logistic regression, Naïve Bayes, Support vector machines
Kaynak
Journal of Medical Systems
WoS Q Değeri
Q3
Scopus Q Değeri
Q1
Cilt
34
Sayı
2
