Stem lung$ (all stems)

29 papers:

ICPR-2014-ForsbergM #image #segmentation

Evaluating Cell Nuclei Segmentation for Use on Whole-Slide Images in Lung Cytology (DF, NM), pp. 3380–3385.

ICPR-2012-KockelkornSGRJVRSG #classification #interactive #simulation

Interactive classification of lung tissue in CT scans by combining prior and interactively obtained training data: A simulation study (TTJPK, CIS, JCG, RR, PAdJ, MAV, JR, CSP, BvG), pp. 105–108.

ICPR-2012-SakaiKMK #detection #robust

Robust detection of adventitious lung sounds in electronic auscultation signals (TS, MK, SM, SK), pp. 1993–1996.

ICPR-2012-YamashitaTHNH #detection #representation

Sparse representation of audio features for sputum detection from lung sounds (TY, ST, KH, YN, SH), pp. 2005–2008.

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-KovalevPV #image #mining

Mining Lung Shape from X-Ray Images (VK, AP, PV), pp. 554–568.

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.

KDD-2008-CuiDSAJ #learning

Learning methods for lung tumor markerless gating in image-guided radiotherapy (YC, JGD, GCS, BMA, SBJ), pp. 902–910.

KDD-2007-RaoBFSON #detection #machine learning #named

LungCAD: a clinically approved, machine learning system for lung cancer detection (RBR, JB, GF, MS, NO, DPN), pp. 1033–1037.

ICPR-v1-2006-ChenCLT #algorithm #automation #graph #image #segmentation #using

Automatic Segmentation of Lung Fields from Radiographic Images of SARS Patients Using a New Graph Cuts Algorithm (SC, LC, JL, XT), pp. 271–274.

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.

ICPR-v3-2006-KitasakaNMSMTN #analysis #recognition

Recognition of lung lobes and its application to the bronchial structure analysis (TK, YN, KM, YS, MM, HT, HN), pp. 288–291.

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.

SAC-2005-AntonelliLM #image #re-engineering #segmentation

Segmentation and reconstruction of the lung volume in CT images (MA, BL, FM), pp. 255–259.

ICPR-v3-2004-FaragEGF #detection #recognition #using

Detection and Recognition of Lung Abnormalities Using Deformable Templates (AAF, AEB, GLG, RF), pp. 738–741.

ICPR-v3-2004-MitaniMH #image

Artificial Images for Classifying Diffuse Lung Opacities in Thin-Section Computed Tomography Images (YM, NM, YH), pp. 530–533.

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-HiranoHTOE #3d #image #quantifier #using

Quantification of Shrinkage of Lung Lobe from Chest CT Images Using the 3D Extended Voronoi Division and its Application to the Benign/Malignant Discrimination of Tumor Shadows (YH, JiH, JiT, HO, KE), pp. 751–754.

ICPR-v1-2002-MitaniYKUMH

Combining the Gabor and Histogram Features for Classifying Diffuse Lung Opacities in Thin-Section Computed Tomography (YM, HY, SK, KU, NM, YH), pp. 53–56.

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-1998-OkumuraMKYMTIM #automation #detection #image

Automatic detection of lung cancers in chest CT images by variable N-Quoit filter (TO, TM, JiK, SY, MM, YT, TI, TM), pp. 1671–1673.

ICPR-1996-KanazawaKN #image

Computer aided diagnosis system for lung cancer based on helical CT images (KK, MK, NN), pp. 381–385.

ICPR-1996-TozakiKNOEM #3d #analysis #image #using

Three-dimensional analysis of lung areas using thin-slice CT images (TT, YK, NN, HO, KE, NM), pp. 548–552.

ICPR-1996-YamamotoMTI0 #automation #detection

Quoit filter-a new filter based on mathematical morphology to extract the isolated shadow, and its application to automatic detection of lung cancer in X-ray CT (SY, MM, YT, TI, TM), pp. 3–7.