Computer Aided Detection of

Pulmonary Nodules in CT Scans

Wookjin Choi, PhD

Introduction

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

(a) male (b) female

Trends in death rates for selected cancers, United States, 1930-2008 [1]

Pulmonary Nodule Detection CAD system

The use of pulmonary nodule detection CAD system can

provide an effective solution

CAD system can assist radiologists by increasing efficiency

and potentially improving nodule detection

General structure of pulmonary nodule detection system

Pulmonary Nodule Detection CAD system

CAD systems Lung segmentation Nodule Candidate Detection False Positive Reduction

Suzuki et al.(2003)[3] Thresholding Multiple thresholding MTANN

Rubin et al.(2005)[4] Thresholding Surface normal overlap

Lantern transform and rule-

based classifier

Dehmeshki et al.(2007)[5] Adaptive thresholding Shape-based GATM Rule-based filtering

Suarez-Cuenca et al.(2009)[6] Thresholding and 3-D

connected component

labeling

3-D iris filtering Multiple rule-based LDA

classifier

Golosio et al.(2009)[7] Isosurface-triangulation Multiple thresholding Neural network

Ye et al.(2009)[8] 3-D adaptive fuzzy

segmentation Shape based detection

Rule-based filtering and

weighted SVM classifier

Sousa et al.(2010)[9] Region growing Structure extraction SVM classifier

Messay et al.(2010)[10] Thresholding and 3-D

connected component

labeling

Multiple thresholding and

morphological opening

Fisher linear discriminant and

quadratic classifier

Riccardi et al.(2011)[11] Iterative thresholding

3-D fast radial filtering and

scale space analysis

Zernike MIP classification

based on SVM

Cascio et al.(2012)[12] Region growing Mass-spring model

Double-threshold cut and

neural network

Experimental Data Set

Lung Image Database Consortium (LIDC) database [2] is applied to

evaluate the performance of the proposed method.

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 pulmonary

nodules.

The database consists of 84 CT scans (up to 2007) [2]

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

Genetic Programming based Classifier

for Detection of Pulmonary nodules

Wook-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.2012.05.008

Feature spaces for four types of features

2-D geometric feature 3-D geometric feature

2-D intensity-based statistical feature 3-D intensity-based statistical feature

Genetic programming classifier learning

Classification space

GP based classification expression in tree shape

Hierarchical Block-image Analysis for

Pulmonary Nodule Detection

Wook-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/e15020507

ROC 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

θ φ

θ φ

Pulmonary Nodule Detection using Three-dimensional Shape-

based Feature Descriptor

Wook-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. 37–54, doi: http://dx.doi.org/10.1016/j.cmpb.2013.08.015

Surface saliency weighted surface

normal vectors

Two angular histograms of the

surface normal vectors

θ φ

ROC 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 dimensions of AHSN

features

θ φ

θ φ

Feature optimization with wall detection a

nd elimination algorithm

3D shape-based feature descriptor

Comparative Analysis

CAD systems Nodule size FPs per case Sensitivity

Suzuki et al.(2003)[3] 8 - 20 mm 16.1 80.3%

Rubin et al.(2005)[4] >3 mm 3 76%

Dehmeshki et al.(2007)[5] 3 - 20 mm 14.6 90%

Suarez-Cuenca et al.(2009)[6] 4 - 27 mm 7.7 80%

Golosio et al.(2009)[7] 3 - 30 mm 4.0 79%

Ye et al.(2009)[8] 3 - 20 mm 8.2 90.2%

Sousa et al.(2010)[9] 3 - 40.93 mm - 84.84%

Messay et al.(2010)[10] 3-30 mm 3 82.66%

Riccardi et al.(2011)[11] >3 mm 6.5 71.%

Cascio et al.(2012)[12] 3-30 mm 6.1 97.66%

Genetic Programming 3-30 mm 5.45 90.9%

Hierarchical Block Analysis 3-30 mm 2.27 95.2%

Shape-based Feature 3-30 mm 2.43 95.4%

Conclusions

Automated pulmonary nodule detection system is studied

Pulmonary nodule detection CAD system is an effective solution for early detection of lung cancer

The proposed systems are based on

Genetic programming based classifier

• Feature transform to classification space

Hierarchical block-image analysis

• Locally optimized nodule segmentation

3-D shape-based feature descriptor

• Shape feature without nodule segmentation

The performance of the proposed CAD systems is evaluated on the LIDC database of NCI

The proposed methods have significantly reduced the false positives in nodule candidates

Future work

Clinically applicable computer aided diagnosis and image guided radiation therapy system for lung cancer (long term goal)

Multi-modal images

Clinical and gene information

Quantitative analysis of lung images based on image processing techniques

Improved segmentation, registration, classification, and etc.

Lung cancer, COPD and other lung diseases

CT, Dual-energy CT, PET/CT, 4DCT

References

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