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Computer-Aided Detection of Pulmonary 
Nodules using Genetic Programming 
Wook-Jin Choi and Tae-Sun Choi 
Signal and Image Processing Laboratory, 
Gwangju Institute of Science and Technology (GIST), Korea 
Introduction 
• Lung cancer is leading cause of death (Five year relative survival rate is 14%) 
• The early detection of pulmonary nodule is important (Five year relative survival rate is increased to 50%) 
• CT is widely used to detect pulmonary nodules. 
• It is difficult to analyze pulmonary nodules since complex structure of lung and many slices in CT scan. 
• Computer aided detection is helpful to detect pulmonary nodules in early stage. 
• Genetic Programming can generate accurate classifier for detecting pulmonary nodule. 
Proposed Method 
Original CT image Thresholded Image Extracted ROIs 
1. Lung Segmentation 2. Nodule candidates detection and Feature Extraction 
• Adaptive thresholding 
Results & Discussion 
Acknowledgements: 
The works was supported by the Bio Imaging Research Center at GIST 
Corresponding Author: 
Tae-Sun Choi, tschoi@gist.ac.kr 
Data set TPR FPR Az 
learn 93.33% 0.127 0.934 
test 91.67% 0.138 0.897 
all 92.31% 0.133 0.912 
The results of GP based classifier 
After removing noise 
Fitness function 
•To evaluate individual classifier, we use 
three indicators(True Positive Rate (TPR), 
Specificity (SPC), Area under ROC curve 
(Az)) 
•Every indicators should have same portion. 
•So, we use production of three indicators 
(TPR: True Positive Rate, FPR: False Positive Rate, Az: Area under 
ROC curve) 
= = - = - 
ROC curves of GP based classifier with respect to 
three datasets 
TPR TP 
TP FN 
= 
+ 
SPC TN 1 FPR 1 FP 
TN + FP FP + 
TN 
f = TPR*SPC * Az 
on histogram of diagonal 
pixels 
• Hole filling and remove 
rim using 3D 
morphology and 3D 
connected component 
labeling (18- 
connectedness) 
• Contour correction using 
rolling ball algorithm 
1) ROI extraction - Multiple Adaptive thresholding 
• Base threshold – adaptive threshold on histogram of diagonal pixels 
• Additional threshold – base threshold +50, -50, -100, -150 and -200 
2) Nodule candidates detection – Rule based classifier 
• Remove noise (vessel-huge volume, long object, small or big 
object) 
1) Feature extraction 
• 2D texture features – (use center slice of nodule candidate) 
mean, variance, standard deviation, skewness, kurtosis, 8 
largest eigen valules 
• 3D geometric features 
Volume, elongation factor, compactness, approximated radius 
3. Genetic Programming based Classifier 
• Genetic Programming (GP) is evolutionary optimization technique. 
• GP and Genetic Algorithm(GA) have similar structure. 
• The chromosome 
– GP: Program (Tree or Graph) 
– GA: Value (Binary digit, String) 
• GP evolves combination of the terminal set and function set for 
higher sensitivity and specificity. 
- Terminal set: The elements of feature vector and randomly 
generated constants 
- Function set: Plus, minus, multiply, division, log, exp, abs, sin and 
cos. (All operators in the function set are protected to avoid 
exception) 
• Lung Image Database Consortium (LIDC) database 
32 scans consisting of 153 nodules and 7528 slices 
16 scans is for training data 
Another 16 scans is for testing the classifier 
• Detection accuracy: 
Sensitivity: 92%, Specificity:86%, FPs per scan: 6.5 
• Contributions: Genetic Programming classifiers for Pulmonary 
nodule detection 
• Future work: Compare on different types of nodules and another 
database; refine fitness function and input features. 
References: 
•K-W Jung, Y-J Won, S Park, H-J Kong, J Sung, H-R Shin, E-Cl Park, and J S Lee, “Cancer statistics in korea: 
incidence, mortality and survival in 2005,” J Korean Med Sci, vol. 24, no. 6, pp. 995–1003, Dec 2009. 
•S G Armato, M L Giger, C J Moran, J T Blackburn, K Doi, and H MacMahon, “Computerized detection of 
pulmonary nodules on ct scans,” Radiographics, vol. 19, no. 5, pp. 1303–11, Jan 1999. 
•J Koza, “Genetic programming: On the programming of computers by means of natural selection,” The MIT 
Press, Jan 1992 
Corrected contour After hole filling and 
removing rim 
Result of computer aided nodule detection using Genetic Programming 
(red – nodule, white-non-nodule) 
Generate GP based classifier

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Computer-aided Detection of Pulmonary Nodules using Genetic Programming

  • 1. Computer-Aided Detection of Pulmonary Nodules using Genetic Programming Wook-Jin Choi and Tae-Sun Choi Signal and Image Processing Laboratory, Gwangju Institute of Science and Technology (GIST), Korea Introduction • Lung cancer is leading cause of death (Five year relative survival rate is 14%) • The early detection of pulmonary nodule is important (Five year relative survival rate is increased to 50%) • CT is widely used to detect pulmonary nodules. • It is difficult to analyze pulmonary nodules since complex structure of lung and many slices in CT scan. • Computer aided detection is helpful to detect pulmonary nodules in early stage. • Genetic Programming can generate accurate classifier for detecting pulmonary nodule. Proposed Method Original CT image Thresholded Image Extracted ROIs 1. Lung Segmentation 2. Nodule candidates detection and Feature Extraction • Adaptive thresholding Results & Discussion Acknowledgements: The works was supported by the Bio Imaging Research Center at GIST Corresponding Author: Tae-Sun Choi, tschoi@gist.ac.kr Data set TPR FPR Az learn 93.33% 0.127 0.934 test 91.67% 0.138 0.897 all 92.31% 0.133 0.912 The results of GP based classifier After removing noise Fitness function •To evaluate individual classifier, we use three indicators(True Positive Rate (TPR), Specificity (SPC), Area under ROC curve (Az)) •Every indicators should have same portion. •So, we use production of three indicators (TPR: True Positive Rate, FPR: False Positive Rate, Az: Area under ROC curve) = = - = - ROC curves of GP based classifier with respect to three datasets TPR TP TP FN = + SPC TN 1 FPR 1 FP TN + FP FP + TN f = TPR*SPC * Az on histogram of diagonal pixels • Hole filling and remove rim using 3D morphology and 3D connected component labeling (18- connectedness) • Contour correction using rolling ball algorithm 1) ROI extraction - Multiple Adaptive thresholding • Base threshold – adaptive threshold on histogram of diagonal pixels • Additional threshold – base threshold +50, -50, -100, -150 and -200 2) Nodule candidates detection – Rule based classifier • Remove noise (vessel-huge volume, long object, small or big object) 1) Feature extraction • 2D texture features – (use center slice of nodule candidate) mean, variance, standard deviation, skewness, kurtosis, 8 largest eigen valules • 3D geometric features Volume, elongation factor, compactness, approximated radius 3. Genetic Programming based Classifier • Genetic Programming (GP) is evolutionary optimization technique. • GP and Genetic Algorithm(GA) have similar structure. • The chromosome – GP: Program (Tree or Graph) – GA: Value (Binary digit, String) • GP evolves combination of the terminal set and function set for higher sensitivity and specificity. - Terminal set: The elements of feature vector and randomly generated constants - Function set: Plus, minus, multiply, division, log, exp, abs, sin and cos. (All operators in the function set are protected to avoid exception) • Lung Image Database Consortium (LIDC) database 32 scans consisting of 153 nodules and 7528 slices 16 scans is for training data Another 16 scans is for testing the classifier • Detection accuracy: Sensitivity: 92%, Specificity:86%, FPs per scan: 6.5 • Contributions: Genetic Programming classifiers for Pulmonary nodule detection • Future work: Compare on different types of nodules and another database; refine fitness function and input features. References: •K-W Jung, Y-J Won, S Park, H-J Kong, J Sung, H-R Shin, E-Cl Park, and J S Lee, “Cancer statistics in korea: incidence, mortality and survival in 2005,” J Korean Med Sci, vol. 24, no. 6, pp. 995–1003, Dec 2009. •S G Armato, M L Giger, C J Moran, J T Blackburn, K Doi, and H MacMahon, “Computerized detection of pulmonary nodules on ct scans,” Radiographics, vol. 19, no. 5, pp. 1303–11, Jan 1999. •J Koza, “Genetic programming: On the programming of computers by means of natural selection,” The MIT Press, Jan 1992 Corrected contour After hole filling and removing rim Result of computer aided nodule detection using Genetic Programming (red – nodule, white-non-nodule) Generate GP based classifier