15 papers:
CASE-2014-ChiuC #bound #detection #image- A variance-reduction method for thyroid nodule boundary detection on ultrasound images (LYC, AC), pp. 681–685.
MLDM-2012-NascimentoPS #classification #image #using- Lung Nodules Classification in CT Images Using Shannon and Simpson Diversity Indices and SVM (LBN, ACdP, ACS), pp. 454–466.
ICPR-2010-FaragGEF #data-driven #detection #modelling #robust- Data-Driven Lung Nodule Models for Robust Nodule Detection in Chest CT (AAF, JHG, SE, AAF), pp. 2588–2591.
- MLDM-2009-SilvaSNPJN #classification #geometry #image #metric #using
- Lung Nodules Classification in CT Images Using Simpson’s Index, Geometrical Measures and One-Class SVM (CAdS, ACS, SMBN, ACdP, GBJ, RAN), pp. 810–822.
- ICPR-2008-El-BazGFE #3d #analysis #approach #automation #detection #image #monitoring
- A new approach for automatic analysis of 3D low dose CT images for accurate monitoring the detected lung nodules (AEB, GLG, RF, MAEG), pp. 1–4.
- ICPR-2008-SuzukiSZ #network
- Supervised enhancement of lung nodules by use of a massive-training artificial neural network (MTANN) in computer-aided diagnosis (CAD) (KS, ZS, JZ), pp. 1–4.
- ICPR-v3-2006-El-BazFGFEE #automation #framework #segmentation
- A Framework for Automatic Segmentation of Lung Nodules from Low Dose Chest CT Scans (AEB, AAF, GLG, RF, MAEG, TE), pp. 611–614.
- MLDM-2005-SilvaJNP #geometry #learning #metric #using
- Diagnosis of Lung Nodule Using Reinforcement Learning and Geometric Measures (ACS, VRdSJ, AdAN, ACdP), pp. 295–304.
- MLDM-2005-SilvaPO #comparison
- Comparison of FLDA, MLP and SVM in Diagnosis of Lung Nodule (ACS, ACdP, ACMdO), pp. 285–294.
- ICPR-v4-2004-NakamuraFTMYMTI #image #recognition #using
- Eigen Nodule: View-Based Recognition of Lung Nodule in Chest X-ray CT Images Using Subspace Method (YN, GF, HT, SM, SY, TM, YT, TI), pp. 681–684.
SAC-2004-SilvaCG #image #using- Diagnosis of lung nodule using Gini coefficient and skeletonization in computerized tomography images (ACS, PCPC, MG), pp. 243–248.
- ICPR-v1-2002-TakizawaYMTIM #3d #image #markov #modelling #random #recognition #using
- Recognition of Lung Nodules from X-ray CT Images Using 3D Markov Random Field Models (HT, SY, TM, YT, TI, MM), pp. 99–102.
- ICPR-v4-2000-KawataNOKKKMMNE #analysis
- Computerized Analysis of Pulmonary Nodules in Topological and Histogram Feature Spaces (YK, NN, HO, RK, MK, MK, NM, KM, HN, KE), pp. 4332–4335.
- ICPR-1998-KanazawaKNSOK #image
- Computer-aided diagnosis for pulmonary nodules based on helical CT images (KK, YK, NN, HS, HO, RK), pp. 1683–1685.
- ICPR-1998-KawataNOKMEKM #analysis #image #using
- Curvature based analysis of pulmonary nodules using thin-section CT images (YK, NN, HO, RK, KM, KE, MK, NM), pp. 361–363.
