Automatic detection of pulmonary nodules in lung ct images

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Automatic detection of pulmonary nodules in lung ct images

Text of Automatic detection of pulmonary nodules in lung ct images

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

2. Introduction Lung Volume Segmentation Genetic Programming based Classifier Hierarchical Block-image Analysis Shape-based Feature Descriptor Experimental Results Conclusions2 3. 3 4. Lung cancer is the leading cause of cancerdeaths. Most patients diagnosed with lung canceralready have advanced disease 40% are stage IV and 30% are III The current five-year survival rate is only 16% Defective nodules are detected at an earlystage The 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 isextremely important for the diagnosis andclinical management of lung cancer Lung cancer had been commonly detectedand diagnosed on chest radiography Since the early 1990s CT has beenreported to improve detection andcharacterization of pulmonary nodules6 7. CT was introduced in 1971 Sir Godfrey Hounsfield, United Kingdom CT utilize computer-processed X-rays to produce tomographic images or 'slices' of specificareas of the body The Hounsfield unit (HU) scale is a lineartransformation of the original linear attenuationcoefficient measurement into one in which theradio density of distilled water7 x water 1000waterHU 8. 8Substance HUAir 1000Lung 500Fat 84Water 0Cerebrospinal Fluid 15Blood +30 to +45Muscle +40Soft Tissue, Contrast Agent +100 to +300Bone +700(cancellous bone)to +3000 (dense bone)The HU of common substancesNodule 9. Lung cancer screening is currently implementedusing low-dose CT examinations Advanced in CT technology Rapid image acquisition with thinner image sections Reduced motion artifacts and improved spatialresolution The typical examination generates large-volumedata sets These large data sets must be evaluated by aradiologist A fatiguing process9 10. The use of pulmonary nodule detection CADsystem can provide an effective solution CAD system can assist radiologists by increasingefficiency and potentially improving noduledetection10General structure of pulmonary nodule detection system 11. CAD systems Lung segmentation Nodule Candidate Detection False Positive ReductionSuzuki et al.(2003)[26] Thresholding Multiple thresholding MTANNRubin et al.(2005)[27] Thresholding Surface normal overlapLantern transform and rule-based classifierDehmeshki et al.(2007)[28] Adaptive thresholding Shape-based GATM Rule-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-triangulation Multiple thresholding Neural networkYe et al.(2009)[31]3-D adaptive fuzzy segmentationShape based detectionRule-based filtering and weighted SVM classifierSousa et al.(2010)[32] Region growing Structure extraction SVM classifierMessay et al.(2010)[33]Thresholding and 3-D connected component labelingMultiple thresholding and morphological openingFisher linear discriminant andquadratic 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 growing Mass-spring modelDouble-threshold cut and neural network11 12. To evaluate the performance of the proposed method, Lung ImageDatabase Consortium (LIDC) database is applied LIDC database, National Cancer Institute (NCI), United States The LIDC is developing a publicly available database of thoraciccomputed tomography (CT) scans as a medical imaging researchresource to promote the development of computer-aideddetection or characterization of pulmonary nodules The database consists of 84 CT scans 100-400 Digital Imaging and Communication (DICOM) images An XML data file containing the physician annotations of nodules 148 nodules The pixel size in the database ranged from 0.5 to 0.76 mm The reconstruction interval ranged from 1 to 3mm12 13. 13 14. Thresholding Fixed threshold Optimal threshold 3-D adaptive fuzzy thresholding Lung region extraction 3-D connectivity with seed point 3-D connected componentlabeling Contour correction Morphological dilation Rolling ball algorithm Chain code representation14 15. Air has an attenuation of -1000 HU Most lung tissue is in the range of -910 HU to-500 HU The chest wall, blood vessel, and bone areabove -500 HU The low and high intensities are differentiablearound the intensity -500 HU15( Si x, y, z) I (x, y, z) 500HU 16. 16Input CT images, their intensity histograms, and thresholded images 17. A fixed threshold is applicable to segment lungarea The intensity ranges of images are varied by differentacquisition protocols To obtain optimal threshold Iterative approach continues until the thresholdconverges The initial threshold : is i th threshold and new threshold as17T(0) 500HU T( i 1)o b 2(i) T 18. 18Input CT images, their intensity histograms, and thresholded images 19. White areas non-body voxels including lung cavity Black areas body voxels excluding lung region Lung regions areextracted from the non-bodyvoxels by using 3-D connectedcomponent labeling1918-connectivity voxels 20. 20Labeled images after applying 3-D connected component labeling 21. To extract lung volume Remove rim attached to boundaries of image The first and the second largest volumes areselected as the lung region The lung region contains small holes To remove these holes Morphological hole filling operations are applied21Slung l first | lsecond 22. 22Binary images of the selected lung regionLung mask images after hole filling 23. The contour of the lung volume is needed tocorrect To include wall side nodule (juxta-pleural nodule)23Extracted lung region using 3D connected component labeling and contourcorrected 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 nodulecandidates is important The performance of noduledetection system relies onthe accuracy of candidatedetection ROI extraction Optimal multi-thresholding Nodule candidatesdetection and segmentation Rule-based pruning30 31. The traditional multi-thresholding methodneeds many steps of grey levels An iterative approach is applied to selectthe threshold value i o b T The optimal threshold value is calculatedon median slice of lung CT scan31( 1)2 32. The optimal threshold value A base threshold for multi-thresholding Additional six threshold values are obtained Base threshold + 400,+ 300,+ 200,+ 100, - 100,and - 20032 33. Rule based classifier removes vessels andnoise Vessel removing Volume is extremely bigger than nodule Elongated object Noise removing Radius of ROI is smaller than 3mm Bigger than 30mm Remaining ROIs are nodule candidates33 34. 34Rule DescriptionR1 Small noiseR2 VesselR3 Large noiseR4 NodulePruning rules for nodule candidate detection 35. 35 36. 36(a) (b) (c)(d) (e) (f)The results of nodule candidate detection: (a,d) ROIs, (b,e) vessel, and (c,f) nodulecandidates after rule-based pruning 37. The features are useful information thatdescribe characteristics of the nodulecandidates In the proposed CAD system, these featureswill be used to train the GPC The proposed feature extraction processconsists of two stages The variety types of features are extracted fromthe nodule candidates Subsets of features are selected and combinedinto 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 inside1f2 f3 f4 f5 f6 f7 9 f ~ f10 12 f ~ f13 f14 f15 f16f17f18f19f20 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 features39f1 { f1,..., f4}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 evolutionaryoptimization technique The basic structure of GPis very similar to GeneticAlgorithm(GA) The chromosome GA : variable (binary digitor string) GP : program (tree or graph)A function represented as a tree structure40 41. GP chromosome The terminal set The elements of feature vector extracted from nodulecandidate images Randomly generated constants with in the range 0,1 The function set Four standard arithmetic operator namely plus, minus,multiply and division Additional mathematical operators log, exp, abs, sin and cos All operators in the function set are protected to avoidexception GP evolves combination of the terminal set andfunction set41 42. Fitness Function evaluate every individuals in GP generationf TPRFPR AUC True positive rate (TPR) Specificity (SPC) SPC is the value subtracted from 1 to FPR and also called true negativerate (TNR)TN FPSPC FPR 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 FP FP TN 43. 43Objective To evolve a optimum classifier with a maximum TPR, SPC and AUCFunction Set +,-,*,protected division, log, exp, abs, sin and cosTerminal Set Elements of a feature vector and randomly generated constantsFitness Fit(B)=TPRSPCAUCSelection Gener