computer aided detection of pulmonary nodules in ct scans

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computer aided detection of pulmonary nodules in ct scans

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  • 1. Computer Aided Detection ofPulmonary Nodules in CT ScansWookjin Choi, PhD

2. IntroductionLung cancer is the leading cause of cancer deaths.Most patients diagnosed with lung cancer already have advanced disease40% are stage IV and 30% are IIIThe current five-year survival rate is only 16%Defective nodules are detected at an early stageThe survival rate can be increased(a) male (b) femaleTrends in death rates for selected cancers, United States, 1930-2008 [1] 3. Pulmonary Nodule Detection CAD systemThe use of pulmonary nodule detection CAD system can provide an effective solutionCAD system can assist radiologists by increasing efficiency and potentially improving nodule detectionGeneral structure of pulmonary nodule detection system 4. Pulmonary Nodule Detection CAD systemCAD systemsLung segmentationNodule Candidate DetectionFalse Positive ReductionSuzuki et al.(2003)[3]ThresholdingMultiple thresholdingMTANNRubin et al.(2005)[4]ThresholdingSurface normal overlapLantern transform and rule- based classifierDehmeshki et al.(2007)[5]Adaptive thresholdingShape-based GATMRule-based filteringSuarez-Cuenca et al.(2009)[6]Thresholding and 3-D connected component labeling3-D iris filteringMultiple rule-based LDA classifierGolosio et al.(2009)[7]Isosurface-triangulationMultiple thresholdingNeural networkYe et al.(2009)[8]3-D adaptive fuzzy segmentationShape based detectionRule-based filtering and weighted SVM classifierSousa et al.(2010)[9]Region growingStructure extractionSVM classifierMessay et al.(2010)[10]Thresholding and 3-D connected component labelingMultiple thresholding and morphological openingFisher linear discriminant and quadratic classifierRiccardi et al.(2011)[11]Iterative thresholding3-D fast radial filtering and scale space analysisZernike MIP classification based on SVMCascio et al.(2012)[12]Region growingMass-spring modelDouble-threshold cut and neural network 5. Experimental Data SetLung Image Database Consortium (LIDC) database [2] is applied to evaluate the performance of the proposed method.LIDC database, National Cancer Institute (NCI), United StatesThe LIDC is developing a publicly available database of thoracic computed tomography (CT) scans as a medical imaging research resource to promote the development of computer-aided detection or characterization of pulmonary nodules.The database consists of 84 CT scans (up to 2007) [2]100-400 Digital Imaging and Communication (DICOM) imagesAn XML data file containing the physician annotations of nodules148 nodulesThe pixel size in the database ranged from 0.5 to 0.76 mmThe reconstruction interval ranged from 1 to 3mm 6. Genetic Programming based Classifier for Detection of Pulmonary nodulesWook-Jin Choi, Tae-Sun Choi, Genetic programming-based feature transform and classification for the automatic detection of pulmonary nodules on computed tomography images, Information Sciences, Vol. 212, pp. 57-78, December 2012, doi: http://dx.doi.org/10.1016/j.ins.2012.05.008Feature spaces for four types of features2-D geometric feature3-D geometric feature2-D intensity-based statistical feature3-D intensity-based statistical featureGenetic programming classifier learningClassification spaceGP based classification expression in tree shape 7. Hierarchical Block-image Analysis for Pulmonary Nodule DetectionWook-Jin Choi, Tae-Sun Choi, Automated Pulmonary Nodule Detection System in Computed Tomography Images: A Hierarchical Block Classification Approach, Entropy, Vol. 15, No. 2, pp. 507-523, February 2013, doi:http://dx.doi.org/10.3390/e15020507ROC curves of the SVM classifiers with respect to three different kernel functions, SVM-r: radial basis function, SVM-p: polynomial function, and SVM-m: Minkowski distance function; (a) p = 0:25 and (b) p = 1.FROC curves of the proposed CAD system with respect to three different kernel parameters of SVM-r classifiers 8. Pulmonary Nodule Detection using Three-dimensional Shape- based Feature DescriptorWook-Jin Choi, Tae-Sun Choi, Automated Pulmonary Nodule Detection based on Three-dimensional Shape-based Feature Descriptor, Computer Methods and Programs in Biomedicine, Vol. 113, No. 1, January 2014, pp. 3754, doi: http://dx.doi.org/10.1016/j.cmpb.2013.08.015Surface saliency weighted surface normal vectorsTwo angular histograms of the surface normal vectorsROC curves of the SVM classifiers with respect to three different kernelfunctions, SVM-r: radial basis function, SVM-p: polynomial function, and SVM-m: Minkowski distance function; (a) p = 0:25 and (b) p = 1.FROC curves of the proposed CAD system with respect to three different dimensions of AHSN featuresFeature optimization with wall detection and elimination algorithm3D shape-based feature descriptor 9. Comparative AnalysisCAD systemsNodule sizeFPs per caseSensitivitySuzuki et al.(2003)[3]8 - 20 mm16.180.3%Rubin et al.(2005)[4]>3 mm376%Dehmeshki et al.(2007)[5]3 - 20 mm14.690%Suarez-Cuenca et al.(2009)[6]4 - 27 mm7.780%Golosio et al.(2009)[7]3 - 30 mm4.079%Ye et al.(2009)[8]3 - 20 mm8.290.2%Sousa et al.(2010)[9]3 - 40.93 mm-84.84%Messay et al.(2010)[10]3-30 mm382.66%Riccardi et al.(2011)[11]>3 mm6.571.%Cascio et al.(2012)[12]3-30 mm6.197.66%Genetic Programming3-30 mm5.4590.9%Hierarchical Block Analysis3-30 mm2.2795.2%Shape-based Feature3-30 mm2.4395.4% 10. ConclusionsAutomated pulmonary nodule detection system is studiedPulmonary nodule detection CAD system is an effective solution for early detection of lung cancerThe proposed systems are based onGenetic programming based classifierFeature transform to classification spaceHierarchical block-image analysisLocally optimized nodule segmentation3-D shape-based feature descriptorShape feature without nodule segmentationThe performance of the proposed CAD systems is evaluated on the LIDC database of NCIThe proposed methods have significantly reduced the false positives in nodule candidates 11. Future workClinically applicable computer aided diagnosis and image guided radiation therapy system for lung cancer (long term goal)Multi-modal imagesClinical and gene informationQuantitative analysis of lung images based on image processing techniquesImproved segmentation, registration, classification, and etc.Lung cancer, COPD and other lung diseasesCT, Dual-energy CT, PET/CT, 4DCT 12. 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