Optimal fuzzy rule based pulmonary nodule detection

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

2. Introduction Lung Segmentation Nodule Candidates Detection Optimal Fuzzy Rule-based Pruning Experimental Results Conclusions2 3. 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 increased3 4. 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 nodules4 5. 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 water5 x water 1000waterHU 6. 6Substance 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 7. 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 process7 8. The use of pulmonary nodule detection CADsystem can provide an effective solution CAD system can assist radiologists by increasingefficiency and potentially improving noduledetection8General structure of pulmonary nodule detection system 9. 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 network9 10. 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 representation10 11. 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 as11T(0) 500HU T( i 1)o b 2(i) T 12. 12Input CT images, their intensity histograms, and thresholded images 13. 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 labeling1318-connectivity voxels 14. 14Labeled images after applying 3-D connected component labeling 15. 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 applied15Slung l first | lsecond 16. 16Binary images of the selected lung regionLung mask images after hole filling 17. The contour of the lung volume is needed tocorrect To include wall side nodule (juxta-pleural nodule)17Extracted lung region using 3D connected component labeling and contourcorrected lung region (containing wall side nodule) 18. 18Contour correction using chain-code representation 19. 19 20. 20 21. 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 pruning21 22. 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 scan22( 1)2 23. The optimal threshold value A base threshold for multi-thresholding Additional six threshold values are obtained Base threshold + 400,+ 300,+ 200,+ 100, - 100,and - 20023 24. 24Optimal Fuzzy Rulebased on GAROIs NoduleCandidates 25. Fuzzy rule based classifier removes vessels and noise 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 candidates25Index Feature1 Area2 Diameter3 Circularity4 Volume5-8 Bounding Box Dimensions9 Elongation 26. 26Rule DescriptionR1 Small noiseR2 VesselR3 Large noiseR4 NoduleNot preciseNot optimalPruning rules for nodule candidate detection 27. Input Fuzzy layer Rule layer Output Y27R1R2R3GA basedFuzzy RuleInducerX1X2X3X4X5F1F2F3F4F5Optimal fuzzy rules are induced by using GA-Fuzzy Inference System 28. A fuzzy inference system (FIS) is a systemthat uses fuzzy set theory to map inputs(features in the case of fuzzy classification)to outputs (classes in the case of fuzzyclassification). Two FISs will be discussed here, theMamdani and the Sugeno.28 29. 29 30. 30 31. (a) A fuzzy inference system and (b) a fuzzy inference system as neural network.31 32. Input Features extracted fromROIs Fuzzy layer Input features are fuzzified Fuzzy membership function is optimized by GA Rule layer Fuzzified features are combined as a optimal fuzzyrule Weight matrix for linear combination is optimized byGA Output Defuzzifipication of optimal fuzzy rules32 33. Chromosome Fuzzy membership function selection Sigmoidal membership function Negative sigmoidal membership function Product of two sigmoidal membership functions Gaussian membership function Parameters of the selected fuzzy membership function Fitness function Subtraction between average membership degree oftrue and false data33d t f 34. Chromosome Weight matrix for linear combinations offuzzified features Fitness function Balanced accuracy of classification results34TPR FPR(1 )2BACC 35. 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 (up to 2009) 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 3mm35 36. 36False positives in ROIs: 72466SensitivityFalse positiverateAccuracyBalancedAccuracy0.9800 0.6068 0.3965 0.6866Performance of conventional rule-based pruningFalse positives: 43970False positives per scan : 523.4524 37. 37Elongation Circularity 38. 38AUROC = 0.9711 39. 39Fitness SensitivityFalse positiverateAccuracyBalancedAccuracyAUROC1 0.8883 0.9825 0.2302 0.7709 0.8761 0.97112 0.8892 0.9775 0.2148 0.7862 0.8813 0.97083 0.8863 0.9900 0.2732 0.7282 0.8584 0.96994 0.8874 0.9875 0.2515 0.7498 0.8680 0.96925 0.8865 0.9900 0.2676 0.7338 0.8612 0.97376 0.8871 0.9875 0.2562 0.7452 0.8657 0.97117 0.8871 0.9900 0.2565 0.7449 0.8668 0.97458 0.8882 0.9800 0.2332 0.7679 0.8734 0.96929 0.8882 0.9875 0.2341 0.7672 0.8767 0.970810 0.8885 0.9875 0.2291 0.7720 0.8792 0.9709mean 0.8877 0.9860 0.2446 0.7566 0.8707 0.9711std 0.0009 0.0044 0.0190 0.0189 0.0078 0.0017Performance of optimal fuzzy rule-based pruningFalse positives: 17728False positives per scan: 211.04 40. Automated pulmonary nodule detectionsystem is studied Pulmonary nodule detection CAD systemis an effective solution for early detectionof lung cancer The proposed method are based onoptimal fuzzy rule The optimal fuzzy rule pruned unwantedROIs with higher sensitivity40 41. 41 42. 42