automatic detection of pulmonary nodules in lung ct images

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Text of automatic detection of pulmonary nodules in lung ct images

  • 1. School of Information and MechatronicsSignal and Image Processing LaboratoryWook-Jin Choi

2. IntroductionLung Volume SegmentationGenetic Programming based ClassifierHierarchical Block-image AnalysisShape-based Feature DescriptorExperimental ResultsConclusions2 3. 3 4. Lung 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 increased4 5. 5(a) male (b) femaleTrends in death rates for selected cancers, United States, 1930-2008 6. Early detection of lung nodules is extremely important for the diagnosis and clinical management of lung cancerLung cancer had been commonly detected and diagnosed on chest radiographySince the early 1990s CT has been reported to improve detection and characterization of pulmonary nodules6 7. CT was introduced in 1971Sir Godfrey Hounsfield, United KingdomCT utilize computer-processed X-raysto produce tomographic images or 'slices' of specific areas of the bodyThe Hounsfield unit (HU) scale is a linear transformation of the original linear attenuation coefficient measurement into one in which the radio density of distilled water7waterwaterx1000 HU 8. 8The HU of common substancesSubstanceHUAir1000Lung500Fat84Water0Cerebrospinal Fluid15Blood+30 to +45Muscle+40Soft Tissue, Contrast Agent+100 to +300Bone+700(cancellous bone)to +3000 (dense bone)Nodule 9. Lung cancer screening is currently implemented using low-dose CT examinationsAdvanced in CT technologyRapid image acquisition with thinner image sectionsReduced motion artifacts and improved spatial resolutionThe typical examination generates large-volume data setsThese large data sets must be evaluated by a radiologistA fatiguing process9 10. The use of pulmonary nodule detection CAD system can provide an effective solutionCAD system can assist radiologists by increasing efficiency and potentially improving nodule detection10General structure of pulmonary nodule detection system 11. CAD systemsLung segmentationNodule Candidate DetectionFalse Positive ReductionSuzuki et al.(2003)[26]ThresholdingMultiple thresholdingMTANNRubin et al.(2005)[27]ThresholdingSurface normal overlapLantern transform and rule-based classifierDehmeshki et al.(2007)[28]Adaptive thresholdingShape-based GATMRule-based filteringSuarez-Cuenca et al.(2009)[29]Thresholding and 3-D connected component labeling3-D iris filteringMultiple rule-based LDA classifierGolosio et al.(2009)[30]Isosurface-triangulationMultiple thresholdingNeural networkYe et al.(2009)[31]3-D adaptive fuzzy segmentationShape based detectionRule-based filtering and weighted SVM classifierSousa et al.(2010)[32]Region growingStructure extractionSVM classifierMessay et al.(2010)[33]Thresholding and 3-D connected component labelingMultiple thresholding and morphological openingFisher linear discriminant and quadratic classifierRiccardi et al.(2011)[34]Iterative thresholding3-D fast radial filtering and scale space analysisZernike MIP classification based on SVMCascio et al.(2012)[35]Region growingMass-spring modelDouble-threshold cut and neural network11 12. To evaluate the performance of the proposed method, Lung Image Database Consortium (LIDC) database is appliedLIDC 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 nodulesThe database consists of 84 CT scans100-400 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 3mm12 13. 13 14. ThresholdingFixed thresholdOptimal threshold3-D adaptive fuzzy thresholdingLung region extraction3-D connectivity with seed point3-D connected component labelingContour correctionMorphological dilationRolling ball algorithmChain code representation14 15. 15Air has an attenuation of -1000 HUMost lung tissue is in the range of -910 HU to -500 HUThe chest wall, blood vessel, and bone are above -500 HUThe low and high intensities are differentiable around the intensity -500 HU(,,)(,,)500ixyzIxyzHUS 16. 16Input CT images, their intensity histograms, and thresholded images 17. A fixed threshold is applicable to segment lung areaThe intensity ranges of images are varied by different acquisition protocolsTo obtain optimal thresholdIterative approach continues until the threshold convergesThe initial threshold : is i th threshold and new threshold as17(0)500THU (1) 2iobT ()iT 18. 18Input CT images, their intensity histograms, and thresholded images 19. White areasnon-body voxelsincluding lung cavityBlack areasbody voxelsexcluding lung regionLung regions are extracted from the non- body voxels by using 3- D connected component labeling1918-connectivity voxels 20. 20Labeled images after applying 3-D connected component labeling 21. To extract lung volumeRemove rim attached to boundaries of imageThe first and the second largest volumes are selected as the lung regionThe lung region contains small holesTo remove these holesMorphological hole filling operations are applied21|lungfirstsecondSll 22. 22Binary images of the selected lung regionLung mask images after hole filling 23. The contour of the lung volume is needed to correctTo include wall side nodule (juxta-pleural nodule)23Extracted lung region using 3D connected component labeling and contour corrected lung region (containing wall side nodule) 24. 24Contour correction using chain-code representation 25. 25 26. 26 27. 27 28. 28 29. 29 30. Detection of nodule candidates is importantThe performance of nodule detection system relies on the accuracy of candidate detectionROI extractionOptimal multi-thresholdingNodule candidates detection and segmentationRule-based pruning30 31. The traditional multi-thresholding method needs many steps of grey levelsAn iterative approach is applied to select the threshold valueThe optimal threshold value is calculated on median slice of lung CT scan31(1) 2iobT 32. The optimal threshold valueA base threshold for multi-thresholdingAdditional six threshold values are obtainedBase threshold + 400,+ 300,+ 200,+ 100, - 100, and - 20032 33. Rule based classifier removes vessels and noiseVessel removingVolume is extremely bigger than noduleElongated objectNoise removingRadius of ROI is smaller than 3mmBigger than 30mmRemaining ROIs are nodule candidates33 34. 34RuleDescriptionR1Small noiseR2VesselR3Large noiseR4NodulePruning rules for nodule candidate detection 35. 35 36. 36(d) (e) (f) The results of nodule candidate detection: (a,d) ROIs, (b,e) vessel, and (c,f) nodule candidates after rule-based pruning(a) (b) (c) 37. The features are useful information that describe characteristics of the nodule candidatesIn the proposed CAD system, these features will be used to train the GPCThe proposed feature extraction process consists of two stagesThe variety types of features are extracted from the nodule candidatesSubsets of features are selected and combined into sub-groups37 38. 38Index Feature Index Feature2-D geometric features Mean insideArea Mean outsideDiameter Variance insidePerimeter Skewness insideCircularity Kurtosis inside3-D geometric features EigenvaluesVolume 3-D intensity based statistical featuresCompactness Minimum value insideBounding Box Dimensions Mean insidePrincipal Axis Length Mean outsideElongation Variance inside2-D intensity based statistical features Skewness insideMinimum value inside Kurtosis inside1 f2 f3 f4 f5 f6 f7 9 f ~ f10 12 f ~ f13 f14 f15 f16 f17 f18 f19 f20 27 f ~ f28 f29 f30 f31 f32 f33 fFeatures for nodule detection 39. Feature vector Description2-D geometric features3-D geometric features2-D intensity-based statistical features3-D intensity-based statistical features2-D features3-D featuresGeometric featuresIntensity-based statistical featuresAll features391 1 4 f { f ,..., f }2 5 13 f { f ,..., f }3 14 27 f { f ,..., f }4 28 33 f { f ,..., f }5 1 3 f f f6 2 4 f f f7 1 2 f f f8 3 4 f f f1 2 3 4 f f f f fEight different groups of feature vectors 40. Genetic Programming (GP)An evolutionary optimization techniqueThe basic structure of GP is very similar to Genetic Algorithm(GA)The chromosomeGA : variable (binary digit or string)GP : program (tree or graph)40A function represented as a tree structure 41. GP chromosomeThe terminal setThe elements of feature vector extracted from nodule candidate imagesRandomly generated constants with in the range 0,1The function setFour standard arithmetic operator namely plus, minus, multiply and divisionAdditional mathematical operators log, exp, abs, sin and cosAll operators in the function set are protected to avoid exceptionGP evolves combination of the terminal set and function set41 42. Fitness Function evaluate every individuals in GP generation True positive rate (TPR) Specificity (SPC) SPC is the value subtracted from 1 to FPR and also called true negativerate (TNR) Area under the ROC curve (AUC) ROC curve is plotted between TP and FP for different threshold values AUC is area under the ROC curve and a good measure of classifierperformance in different condition42TPTPRTP FN1 1TN FPSPC FPRTN FP FP TN f TPRFPR AUC 43. 43ObjectiveTo evolve a optimum classifier with a maximum TPR, SPC and AUCFunction Set+,-,*,protected division, log, exp, abs, sin and cosTerminal SetElements of a feature vector and randomly generated constantsFitnessFit(B)=TPRSPCAUCSelectionGenerationalWrapperPositive if , else negativePopulation Size300Generation


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