Optimal Fuzzy Rule based Pulmonary Nodule Detection

School of Information and Mechatronics

Signal and Image Processing Laboratory

Wook-Jin Choi

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• Introduction• Lung Segmentation• Nodule Candidates Detection• Optimal Fuzzy Rule-based Pruning• Experimental Results• Conclusions

Contents

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• Lung cancer is the leading cause of cancer deaths.

• Most patients diagnosed with lung cancer already 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 early stage– The survival rate can be increased

Introduction

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• Early detection of lung nodules is ex-tremely important for the diagnosis and clinical management of lung cancer

• Lung cancer had been commonly de-tected and diagnosed on chest radiogra-phy

• Since the early 1990s CT has been re-ported to improve detection and charac-terization of pulmonary nodules

Introduction

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• CT was introduced in 1971– Sir Godfrey Hounsfield, United Kingdom

• CT utilize computer-processed X-rays– to produce tomographic images or 'slices' of spe-

cific areas of the body

• The Hounsfield unit (HU) scale is a linear transformation of the original linear attenua-tion coefficient measurement into one in which the radio density of distilled water

Computed Tomography

water

waterx1000

HU

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Computed Tomography

The HU of common substances

Substance HU

Air −1000

Lung −500

Fat −84

Water 0

Cerebrospinal Fluid 15

Blood +30 to +45

Muscle +40

Soft Tissue, Contrast Agent +100 to +300

Bone +700(cancellous bone)to +3000 (dense bone)

Nodule

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• Lung cancer screening is currently implemented using low-dose CT examinations

• Advanced in CT technology– Rapid image acquisition with thinner image sections– Reduced motion artifacts and improved spatial reso-

lution

• The typical examination generates large-vol-ume data sets

• These large data sets must be evaluated by a radiologist– A fatiguing process

Computed Tomography

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• The use of pulmonary nodule detection CAD sys-tem can provide an effective solution

• CAD system can assist radiologists by increasing efficiency and potentially improving nodule de-tection

Pulmonary Nodule Detection CAD system

General structure of pulmonary nodule detection system

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CAD systems Lung segmentationNodule Candidate De-

tectionFalse Positive Reduction

Suzuki et al.(2003)[26] Thresholding Multiple thresholding MTANN

Rubin et al.(2005)[27] Thresholding Surface normal overlapLantern transform and rule-based classifier

Dehmeshki et al.(2007)[28]

Adaptive thresholding Shape-based GATM Rule-based filtering

Suarez-Cuenca et al.(2009)[29]

Thresholding and 3-D connected component la-beling

3-D iris filteringMultiple rule-based LDA classifier

Golosio et al.(2009)[30] Isosurface-triangulation Multiple thresholding Neural network

Ye et al.(2009)[31]3-D adaptive fuzzy seg-mentation

Shape based detectionRule-based filtering and weighted SVM classifier

Sousa et al.(2010)[32] Region growing Structure extraction SVM classifier

Messay et al.(2010)[33]Thresholding and 3-D connected component la-beling

Multiple thresholding and morphological opening

Fisher linear discriminant and quadratic classifier

Riccardi et al.(2011)[34] Iterative thresholding3-D fast radial filtering and scale space analysis

Zernike MIP classification based on SVM

Cascio et al.(2012)[35] Region growing Mass-spring modelDouble-threshold cut and neural network

Pulmonary Nodule Detection CAD system

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Lung Volume Segmentation

• Thresholding– Fixed threshold– Optimal threshold– 3-D adaptive fuzzy thresholding

• Lung region extraction– 3-D connectivity with seed

point– 3-D connected component

labeling

• Contour correction– Morphological dilation– Rolling ball algorithm– Chain code representation

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• A fixed threshold is applicable to segment lung area– The intensity ranges of images are varied by different

acquisition protocols

• To obtain optimal threshold– Iterative approach continues until the threshold con-

verges– The initial threshold : – is i th threshold and new threshold as

Optimal Threshold

(0) 500T HU

( 1)

2i o bT

( )iT

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Optimal Threshold

Input CT images, their intensity histograms, and thresh-olded images

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Lung Region Extraction

• White areas– non-body voxels – including lung cavity

• Black areas– body voxels– excluding lung region

• Lung regions are ex-tracted from the non-body voxels by using 3-D connected com-ponent labeling18-connectivity voxels

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Lung Region Extraction

Labeled images after applying 3-D connected component la-beling

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• To extract lung volume– Remove rim attached to boundaries of image– The first and the second largest volumes are

selected as the lung region

• The lung region contains small holes– To remove these holes– Morphological hole filling operations are ap-

plied

Lung Region Extraction

|lung first secondS l l

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Lung Region Extraction

Binary images of the selected lung region

Lung mask images after hole filling

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• The contour of the lung volume is needed to correct– To include wall side nodule (juxta-pleural nodule)

Contour Correction

Extracted lung region using 3D connected component labeling and contour corrected lung region (containing wall side nodule)

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Contour Correction

Contour correction using chain-code representation

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Segmented Lung Volume

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Results

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Nodule Candidate Detec-tion

• Detection of nodule candi-dates is important

• The performance of nodule detection system relies on the accuracy of candidate detection

• ROI extraction– Optimal multi-thresholding

• Nodule candidates detec-tion and segmentation– Rule-based pruning

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• The traditional multi-thresholding method needs many steps of grey levels

• An iterative approach is applied to se-lect the threshold value

• The optimal threshold value is calcu-lated on median slice of lung CT scan

Optimal Multi-thresholding

( 1)

2i o bT

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• The optimal threshold value– A base threshold for multi-thresholding

• Additional six threshold values are ob-tained– Base threshold + 400,+ 300,+ 200,+ 100, -

100, and - 200

Optimal Multi-thresholding

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Nodule Candidtes Detection

Optimal Fuzzy Rulebased on GA

ROIs Nodule Candidates

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• 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 candidates

Rule-based Pruning

Index Feature

1 Area

2 Diameter

3 Circularity

4 Volume

5-8 Bounding Box Dimensions

9 Elongation

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Conventional Rule-based Pruning

Rule

Description

R1 Small noise

R2 Vessel

R3 Large noise

R4 Nodule

Pruning rules for nodule candidate detection

Not preciseNot optimal

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Optimal Fuzzy Rule-based Pruning

Σ Y

R1

R2

R3

GA basedFuzzy Rule

Inducer

X1

X2

X3

X4

X5

F1

F2

F3

F4

F5

Input Fuzzy layer Rule layer Output

Optimal fuzzy rules are induced by using GA-Fuzzy Inference System

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• A fuzzy inference system (FIS) is a system that uses fuzzy set theory to map inputs (features in the case of fuzzy classification) to outputs (classes in the case of fuzzy classifi-cation).

• Two FIS’s will be discussed here, the Mamdani and the Sugeno.

Fuzzy Inference Systems

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Fuzzy Inference Systems (Mam-dani)

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Fuzzy Inference System (Sugeno)

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Fuzzy Inference Systems

 (a) A fuzzy inference system and (b) a fuzzy inference system as neu-ral network.

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• 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 fuzzy

rule– Weight matrix for linear combination is optimized by

GA

• Output– Defuzzifipication of optimal fuzzy rules

GA-Fuzzy Inference System

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• 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 of

true and false data

Fuzzy Membership Function Op-timization

t fd

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• Chromosome–Weight matrix for linear combinations of

fuzzified features

• Fitness function– Balanced accuracy of classification re-

sults

GA basedFuzzy Rule Inducer

(1 )

2

TPR FPRBACC

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• To evaluate the performance of the proposed method, Lung Image Database Consortium (LIDC) database is applied

• LIDC database, National Cancer Institute (NCI), United States– The 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 pul-monary 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 3mm

Experimental Data Set

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Experimental Results

Sensitivity False posi-tive rate Accuracy Balanced

Accuracy

0.9800 0.6068 0.3965 0.6866

Performance of conventional rule-based pruning

False positives: 43970False positives per scan : 523.4524

False positives in ROIs: 72466

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Experimental Results

Elongation Circularity

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Experimental Results

AUROC = 0.9711

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Experimental Results

Fitness SensitivityFalse posi-tive rate

AccuracyBalancedAccuracy

AUROC

1 0.8883 0.9825 0.2302 0.7709 0.8761 0.9711 2 0.8892 0.9775 0.2148 0.7862 0.8813 0.9708 3 0.8863 0.9900 0.2732 0.7282 0.8584 0.9699 4 0.8874 0.9875 0.2515 0.7498 0.8680 0.9692 5 0.8865 0.9900 0.2676 0.7338 0.8612 0.9737 6 0.8871 0.9875 0.2562 0.7452 0.8657 0.9711 7 0.8871 0.9900 0.2565 0.7449 0.8668 0.9745 8 0.8882 0.9800 0.2332 0.7679 0.8734 0.9692 9 0.8882 0.9875 0.2341 0.7672 0.8767 0.9708

10 0.8885 0.9875 0.2291 0.7720 0.8792 0.9709 mean 0.8877 0.9860 0.2446 0.7566 0.8707 0.9711 std 0.0009 0.0044 0.0190 0.0189 0.0078 0.0017

Performance of optimal fuzzy rule-based pruning

False positives: 17728False positives per scan: 211.04

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• Automated pulmonary nodule detec-tion system is studied

• Pulmonary nodule detection CAD sys-tem is an effective solution for early detection of lung cancer

• The proposed method are based on optimal fuzzy rule

• The optimal fuzzy rule pruned un-wanted ROIs with higher sensitivity

Conclusions

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Q & A

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THANK YOU