흉부 CT영상에서 유전자 프로그래밍을 이용한 폐 결절 자동 검출
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흉부 CT영상에서 유전자 프로그래밍을 이용한 폐 결절 자동검출
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흉부 CT영상에서 유전자 프로그래밍을 이용한 폐 결절 자동 검출
- 1. 흉부 CT영상에서 유전자 프로그래밍 을 이용한 폐 결절 자동검출 광주과학기술원 정보기전공학부 신호 및 영상처리 연구실 최욱진 최태선
- 2. 목차 • 서론 • 3D기반 폐 영역 분리 • 폐 결절 후보 검출 및 특징 값 추출 • 유전자 프로그래밍 기반 폐 결절 검출기 • 실험결과 • 결론 • 참고자료 2
- 3. 서론 • 폐 암은 치사율이 높지만 조기에 발견하여 치료할 경우 5년 생 존율이 50%까지 상승하는것으로 알려져 있다. • 폐 암의 주요 소견인 폐 결절의 검출이 중요하다. • 폐 결절의 조기검출을 위해 CT 영상을 사용하여 폐 결절을 검 출 하는 방법이 제안되었다. • 폐 CT의 경우 환자당 100장 이상의 영상이 생성됨 – 분석하는데 많은 시간이 소요되며 피로 누적으로 인한 오류발생이 우려 된다. • 폐 결절 검출을 자동화하기 위한 Computer-Aided Detection (CAD) 시스템의 연구가 활발하다. 3
- 4. 관련 연구 • Template matching methods – Genetic Algorithm Template Matching [10] – 3D Template Matching [11] • Model based methods – Patient-specific models [5] – Surface normal overlap model [7] • Machine learning techniques – Neural network [6] – Fuzzy c-means clustering [9] • Digital filtering – Quantized convergence index filter [8] – Iris filter [13] • Statistical analysis [12] 4
- 5. 제안 알고리즘 폐 결절 검출 CAD 시스템 흐름도 5 흉부 CT 3D기반 폐 영역 분리 폐 결절 후보 검출 및 특징 값 추출 유전자 프로그래밍 기반 폐 결절 검출기 검출된 폐 결절
- 6. 3D기반 폐 영역 분리 6 흉부 CT 3D기반 폐 영역 분리 폐 결절 후보 검출 및 특징 값 추출 유전자 프로그래밍 기반 폐 결절 검출기 검출된 폐 결절
- 7. 3D기반 폐 영역 분리 • 폐 영역을 분리하기 위해 폐 영상의 대각선 픽셀에 대한 히스토그램 에서 adaptive threshold [4] 값 T를 구한다. • T값을 이용하여 폐 영역의 밝은 부분(몸통 부분 - 검은색)과 어두운 부분(폐 내부 및 외부 - 흰색)으로 이진화 한다. 7 원본 CT 영상과 T값을 이용하여 이진화된 영상
- 8. 3D기반 폐 영역 분리 8 • 이진화 된 영상을 3D Connected Component Labeling을 한다. • 분리된 volume 중에서 영상 외곽과 맞닿아 있는 부분을 제거 한다. • 추출된 폐 영역의 외곽선을 정리한다. 3D connected component labeling을 이용하여 추출한 폐 영역과 외곽선 정 리가 끝난 후의 폐 영역
- 9. 폐 결절 후보 검출 및 특징 값 추출 9 흉부 CT 3D기반 폐 영역 분리 폐 결절 후보 검출 및 특징 값 추출 유전자 프로그래밍 기반 폐 결절 검출기 검출된 폐 결절
- 10. ROI 추출 10 6단계의 값으로 추출된 ROI • Multiple adaptive threshold 를 이용한 ROI 추출 – 각 슬라이스 별로 adaptive threshold값 Tbase 를 구한 후 아래와 같이 추 가적인 Threshold를 구한 후 6단계의 ROI를 추출한 다. • T1 = Tbase + 50 • T2 = Tbase - 50 • T3 = Tbase - 100 • T4 = Tbase - 150 • T5 = Tbase - 200.
- 11. 폐 결절 후보 검출 • 추출된 ROI에서 각각의 단계 별로 3D구조를 분석하여 폐 혈관과 노 이즈를 제거한다. • 폐 혈관 제거 폐 혈관은 폐 영역 전체에 나뭇가지 형태로 퍼져 있다. 또한 연결되지 않 은 폐 혈관은 긴 막대 형태이다. – 부피가 결절에 비하여 아주 큰 것 제거 – 막대 형태 제거 – 둥근 형태가 아닌 것 제거 • 노이즈 제거 – 반지름이 3mm 보다 작거나 30mm 보다 큰 것 제거 • 추출된 ROI에서 폐 혈관과 노이즈를 제거하여 폐 결절 후보를 검출한다. 11 검출된 폐 결절 후보
- 12. 특징 값 추출 • 3D 기하학적 특징 • 부피, 최장축의 길이 • 추정 반지름, 좌표 • Bounding Box의 크기 • 2D 기하학적 특징 • 전체 폐 결절 후보의 슬라이스 중에서 가장 면적이 큰 슬라이스 사용 • 면적, 둘레, 반지름, Compactness • 2D 명암도 기반 특징 • 전체 폐 결절 후보의 슬라이스 중에서 가장 면적이 큰 슬라이스 사용 • 폐 결절 후보 영상의 크기를 세가지로 정규화 • < 5mm : the size of image matrix is 8x8. • 5mm ~10mm : the size of image matrix is 16x16. • > 10 mm : the size of image matrix is 32x32 • 영상에서 아래와 같은 명암도 기반 특징 값 추출 • 평균, 분산, skewness, kurtosis, 8 biggest eigenvalues. 12
- 13. 특징 값 추출 13 Index Feature 1 KVP 2 평균 3 분산 4 Skewness 5 Kurtosis 6 면적 7 2D 반지름 8 둘레 9 Compactness 10~17 Largest Eigen value 1~8 18 X 중심좌표 19 Y 중심좌표 20 Z 중심좌표 21 Width 22 Height 23 Depth 24 3D 반지름
- 14. 유전자 프로그래밍 기반 폐 결절 검출기 14 흉부 CT 3D기반 폐 영역 분리 폐 결절 후보 검출 및 특징 값 추출 유전자 프로그래밍 기반 폐 결절 검출기 검출된 폐 결절
- 15. 유전자 프로그래밍 기반 폐 결절 검출기 • 유전자 프로그래밍 (Genetic Programming : GP) – 진화론을 이용한 최적화 기법 [14]. • 기본 구조는 유전자 알고리즘 (Genetic Algorithm : GA)과 흡사하다. • GP와 GA의 염색체 – GP : 프로그램 (트리 또는 그래프로 나타냄) – GA : 숫자 또는 문자열 (이진수, 염색체 염기 문자열) 15
- 16. 유전자 프로그래밍 기반 폐 결절 검출기 16 트리로 나타낸 수식
- 17. 유전자 프로그래밍 기반 폐 결절 검출기 • 제안된 GP 기반 검출기는 낮은 false positive (FP) 와 반 면에 높은 true positive (TP) 값을 가지는 검출기를 생성 하는 것을 목표로 한다. • 제안된 GP 기반 검출기는 폐 결절 후보에서 추출된 특징 값에서 적절한 특징 값들을 선택하고 수학 연산자를 이 용하여 선택된 특징 값을 조합하여 폐 결절을 검출할 수 있는 검출식을 생성한다. • GP는 생성된 각각의 검출식을 본 논문에서 제안하는 titness function을 이용하여 검증하고 최적의 검출 식을 찾는다. • GP를 이용하여 생성된 검출식은 복잡한 특징 벡터를 간 단한 실수로 변환하여 폐 결절을 검출 할 수 있다. 17
- 18. 유전자 프로그래밍 기반 폐 결절 검출기 • GP 염색체 – The terminal set – 입력된 특징 값 벡터의 요소와 랜덤하게 생성된 0~1 사이의 실수를 이용한다. – The function set – 기본 사칙연산과 log, exp, abs, sin, cos 과 같은 연 산을 사용하며 각각의 연산은 모두 오류를 피할 수 있도록 구현하였다. • GP 는 the terminal set 과 function set의 조합을 진화 시켜 최적화된 검출식을 생성한다. 18
- 19. 유전자 프로그래밍 기반 폐 결절 검출기 • Fitness Function – GP를 통하여 생성된 검출식 검증 – True positive rate (TPR) – Specificity (SPC) • SPC 는 FPR 에서 1을 뺀 값으로 true negative rate(TNR) 이라고도 부른다. TN FP SPC FPR – Area under the ROC curve (Az) • ROC 커브 는 TP 와 FP간의 관계를 다양한 Threshold에 대하여 나타낸 그래프이다. • Az 는 ROC 커브 아래의 면적을 나타내며 검출기나 분류기의 성능을 나타내는 중요한 지표이다. – Fitness Function 19 TP TPR TP FN 1 1 TN FP FP TN f TPR*SPC* Az
- 20. 유전자 프로그래밍 기반 폐 결절 검출기 Objective To evolve maximum fitness Selection Generational Population Size 300 Generation Size 80 Initial Tree Depth Limit 6 Initial population Ramped half and half GP Operators prob Variable ratio of crossover mutation is used Sampling Tournament Survival mechanism Keep the best individuals Real max. tree level 30 유전자 프로그래밍 parameter 20
- 21. 유전자 프로그래밍 기반 폐 결절 검출기 특징 값 벡터 입력 Initial population 생성 Fitness 검증 종료조건 New generation · Crossover · Mutation · Repication GP 기반 검출기 21 GP 기반 검출기 학습
- 22. 유전자 프로그래밍 기반 폐 결절 검출기 • GP로 생성된 검출식 – minus(minus(F_21,exp(F_23)),minus(mypower(mylog(plus(times(F_14,mi nus(F_23,mypower(mylog(plus(times(F_12,minus(F_11,mypower(F_13,F_ 13))),F_22)),F_13))),minus(F_20,cos(exp(F_7))))),mypower(exp(F_7),F_7)),ti mes(minus(minus(mypower(exp(F_23),F_7),F_11),times(exp(F_23),F_12)), F_11))) – minus(minus(plus(F_4,F_7),sin(minus(F_7,mypower(F_24,plus(minus(F_7, F_11),mypower(F_15,F_7)))))),mypower(mypower(mypower(F_24,exp(F_1 1)),minus(plus(F_10,mypower(plus(minus(F_13,sin(exp(F_12))),mypower( F_24,plus(F_13,F_4))),plus(plus(minus(0.35089,0.35089),F_3),F_7))),F_11)), minus(plus(F_10,minus(F_4,plus(F_10,F_7))),mypower(minus(plus(F_4,ex p(F_2)),minus(F_9,F_4)),F_11)))) 22
- 23. 실험결과 • Lung Image Database Consortium (LIDC) 데이터베이스[15] – 제안된 알고리즘의 검증과 평가를 위하여 LIDC 데이터베이스를 사용하였다. – LIDC는 미국 NIH에서 CT를 이용한 폐 결절 검출 CAD 시스템의 발전을 촉진하기 위하여 개발된 공개 폐 결절 데이터베이스이다. – 본 데이터베이스는 총 84개의 CT scan으로 구성되어 있으며 각각의 CT scan은 100-400개의 Digital Imaging and Communication (DICOM) 영상과 전문의들의 폐 결절 검출 의견이 저장되어 있는 XML파일로 이루어져 있다. • 본 논문에서는 153개의 결절이 포함된 32개의 영상을 사용하였다. 사용된 영상은 총 7528개의 영상으로 구성되어 있으며 영상의 pixel 크기는 0.65 ~ 0.75 mm 이다. • 사용된 32개의 scan중 16개는 제안된 GP기반 검출기의 학습에 사용 되었으며 나머지 16개의 scan은 검증에 사용되었다. 23
- 24. Experimental Results (a) (b) 폐 결절 검출결과 : (a) 43번째 slice, (b) 3D volume rendering 영상 24
- 25. Experimental Results 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 25 폐 결절 검출 결과
- 26. Experimental Results 세가지 데이터에 대한 ROC 커브 26
- 27. Conclusion • 본 논문에서는 흉부 CT 영상에서 폐 결절을 검출하는 새로운 CAD시스템을 제안하였다. • Adaptive threshold와 3D Connected Component Labeling을 이용하여 폐 영역을 분할하였으며 • 폐 영역 내에서 multiple adaptive threshold를 사용하여 ROI를 추출 후 3D 형태 분석을 이용하여 폐 결절 후보를 검출하였다. • 또한 폐 결절 후보에서 2D 및 3D 특징 값을 추출하였으며 • 추출된 특징 값을 GP기반 폐 결절 검출기를 이용하여 폐 결절을 검출 하였 다. • 제안된 알고리즘은 미국 NIH의 LIDC 데이터베이스를 이용하여 검증하였으 며 FP를 효과적으로 제거하였다. • 제안된 CAD시스템은 Scan별로 6.5개의 FP가 발생할 때 90%이상의 검출률 을 보였다. • 이러한 실험 결과는 기존의 방법에 비하여 월등히 좋은 성능을 나타낸다. 27
- 28. References • [1] Ahmedin Jemal, Rebecca Siegel, ElizabethWard, Yongping Hao, Jiaquan Xu, and Michael J Thun, “Cancerstatistics, 2009,” CA Cancer J Clin, vol. 59, no. 4, pp. 225–49, Jan 2009. • [2] 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. • [3] Qiang Li, “Recent progress in computer-aided diagnosis of lung nodules on thin-section ct.,” Comput Med Imaging Graph, vol. 31, no. 4-5, pp. 248–257, 2007. • [4] 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. • [5] M Brown, M McNitt-Gray, J Goldin, R Suh, J Sayre, and D Aberle, “Patient-specific models for lung nodule detection and surveillance in ct images,” IEEE TMI, vol. 20, no. 12, pp. 1242 – 1250, Dec 2001. • [6] K Suzuki, SG Armato III, F Li, S Sone, and K Doi, “Massive training artificial neural network (mtann) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography,” Medical physics, vol. 30, pp. 1602, 2003. • [7] D Paik, C Beaulieu, G Rubin, B Acar, R Jeffrey, J Yee, J Dey, and S Napel, “Surface normal overlap: a computer-aided detection algorithm with application to colonic polyps and lung nodules in helical ct,” IEEE TMI, vol. 23, no. 6, pp. 661 – 675, Jun 2004. • [8] Sumiaki Matsumoto, Harold L Kundel, James C Gee, Warren B Gefter, and Hiroto Hatabu, “Pulmonary nodule detection in ct images with quantized convergence index filter.,” Med Image Anal, vol. 10, no. 3, pp. 343–352, Jun 2006. 28
- 29. References • [9] N Memarian, J Alirezaie, and P Babyn, “Computerized detection of lung nodules with an enhanced false positive reduction scheme,” IEEE ICIP 2006, pp. 1921 –1924, Sep 2006. • [10] Jamshid Dehmeshki, Xujiong Ye, Xinyu Lin, Manlio Valdivieso, and Hamdan Amin, “Automated detection of lung nodules in ct images using shape-based genetic algorithm.,” Comput Med Imaging Graph, vol. 31, no. 6, pp. 408–417, Sep 2007. • [11] Onur Osman, Serhat Ozekes, and Osman N Ucan, “Lung nodule diagnosis using 3d template matching.,” Comput Biol Med, vol. 37, no. 8, pp. 1167–1172, Aug 2007. • [12] A El-Baz, G Gimel’farb, R Falk, and M Abo El-Ghar, “Automatic analysis of 3d low dose ct images for early diagnosis of lung cancer,” Pattern Recognition, vol. 42, no. 6, pp. 1041–1051, Jan 2009. • [13] JJ Su´arez-Cuenca, PG Tahoces, M Souto, MJ Lado, M Remy-Jardin, J Remy, and J Jos´e Vidal, “Application of the iris filter for automatic detection of pulmonary nodules on computed tomography images,” Computers in Biology and Medicine, 2009. • [14] J Koza, “Genetic programming: On the programming of computers by means of natural selection,” The MIT Press, Jan 1992. • [15] S G Armato, G McLennan, M F McNitt-Gray, C R Meyer, D Yankelevitz, D R Aberle, C I Henschke, E A Hoffman, E A Kazerooni, H MacMahon, A P Reeves, B Y Croft, L P Clarke, and Lung Image Database Consortium Research Group, “Lung image database consortium: developing a resource for the medical imaging research community.,” Radiology, vol. 232, no. 3, pp. 739–748, Sep 2004. 29
- 30. 감사합니다. 30
Editor's Notes
- Pulmonary nodule detection is attractive applications of computer-aided detection (CAD) because lung cancer is the leading cause of cancer deaths in Korea. According to the statistics, the total number of deaths caused by lung cancer is greater than other cancers[1]. The pulmonary nodule detection and diagnosis of lesion in computed tomography (CT) images are important in treatment of lung cancer. If lung cancer detected in early phase, the 3-year survival rate is more than 80%. Recently, researchers have developed a number of CAD methods for lung nodules to aid radiologists in identifying nodule candidates from CT images. Current CT technology allows for near isotropic, submillimeter resolution acquisition of the complete chest in a single breath hold. These thin-slice chest scans have become indispensable in thoracic radiology, but have also substantially increased the data load for radiologists. Automating the analysis of such data is, therefore, a necessity and this has created a rapidly developing research area in medical imaging. In literature, several nodule detecting methods has been proposed. Multiple gray level thresholding, genetic algorithm template matching (GATM), rule-based linear discriminant analysis, massive training artificial neural network based method, shape-based GATM, and 3D template matching(3DTM) based algorithm are famous among them[2–5].
- GATM based algorithm performed quite good results. Lee et al.[2] proposed a template-matching technique based on GATM for detecting nodules existing within the lung area. Seventy-one nodules out of 98 were correctly etected with the number of FPs at approximately 1.1 per sectional image.
- First of all, the lung region extraction should be performed before any other part of nodule detection. To extract lung region, we propose a segmentation method based on adaptive thresholding and voxel labelling. Because lung region is dark, we convert the image to a binary with less than the selected threshold as foreground.
- After that, we remove the rim from the binary image at every slice of CT images. We segment lung region and remove the rim which is outer part of the body. However, there are many noisy parts likes gas in the intestine. So, we applied 18-connectedness voxel labelling(3D connected component labelling). After labelled, we calculate the volumes of the every connected components then select the two largest volumes as lung volume. In the end, we correct the contour of the lung volume because there may some nodules in wall side of the lung. To correct this problem, the rolling ball algorithm [4] is applied on every slice of lung volumes. The red circles in Fig. 1 are wall side nodule and corrected wall side nodule.
- The nodule candidates detection and feature extraction are important in nodule detecting scheme. It consists of the extracting the region of interest(ROI), detecting the nodule candidates and extracting of 3D and 2D features of nodule candidates. These features are provided as input for GP module.
- To extract ROI for nodule candidates detection, we propose a adaptive multiple thresholding method. In the literature, the multiple thresholding method is commonly used in applications. However, it is not adaptive and makes many steps of grey levels, we calculate the adaptive threshold value using diagonal histogram at every slice of lung volume. This value is base threshold value for multiple thresholding and we make additional five threshold values which are base threshold + 50, -50, -100, -150 and -200. A thresholded lung volume has 6 steps of grey level. Fig. 2a shows a slice of extracted ROI.
- In this part, We can remove the vessels and noise in the lung volume using rule based classifier. We extracted 3D geometric features from every ROI. The features are volume, elongation factor, compactness and approximated radius. The vessel is classified by volume elongation factor and compactness. The vessel is connected every slice so its volume is extremely bigger than nodule. Moreover, it is longer than nodule and not compact object. The noise is removed if radius of ROI is smaller than 3mm or bigger than 30mm. Fig. 3b shows a detected nodule candidates.
- Nodule candidates are detected from segmented lung region. We extracte 3D geometric features and 2D pixel based features. Four 3D geometric features are already extracted in nodule candidates detection. These are volume, elongation factor, compactness and approximated radius. The 2D features are extracted from median slice of nodule candidates because area of the median slice is the largest in the nodule candidate volume. To extract 2D texture feature, we normalize the image size of nodule candidates. So, we divide into 3 types of nodule sizes and then extract the features. If the radius of nodule candidate is less than 5mm, the size of image matrix is 8x8. If the radius of nodule candidate is varied form 5mm to 10mm then the size of image matrix is 16x16. The largest size of image matrix is 32x32 that is greater than 10mm and less than 20mm. We extract 14 features from the image matrix. Those are mean, variance, skewness, kurtosis, area, radius and 8 biggest eigenvalues. These features are provided as input for GP module
- Nodule candidates are detected from segmented lung region. We extract 3D geometric features and 2D pixel based features. Four 3D geometric features are already extracted in nodule candidates detection. These are volume, elongation factor, compactness and approximated radius. The 2D features are extracted from median slice of nodule candidates because area of the median slice is the largest in the nodule candidate volume. To extract 2D texture feature, we normalize the image size of nodule candidates. So, we divide into 3 types of nodule sizes and then extract the features. If the radius of nodule candidate is less than 5mm, the size of image matrix is 8x8. If the radius of nodule candidate is varied form 5mm to 10mm then the size of image matrix is 16x16. The largest size of image matrix is 32x32 that is greater than 10mm and less than 20mm. We extract 14 features from the image matrix. Those are mean, variance, skewness, kurtosis, area, radius and 8 biggest eigenvalues. These features are provided as input for GP module
- The nodule candidates detection and feature extraction are important in nodule detecting scheme. It consists of the extracting the region of interest(ROI), detecting the nodule candidates and extracting of 3D and 2D features of nodule candidates. These features are provided as input for GP module.
- The pulmonary nodule detection is a binary classification problem. In a binary classification problem, the outputs are labelled as positive or negative. The positive is nodule and the negative is non-nodule. In pulmonary nodule detection, the almost nodule candidates are truly negative. So, It makes many false positives. The goal of GP evolution is to reduce false positive(FP) and increase true positive(TP). In the proposed scheme, an optimized classifier is carried out using combination of features and random constant values. It reduced FP while higher TP rate.
- The goal of GP evolution is to reduce false positive (FP) and increase true positive (TP). In the proposed scheme, an optimized classifier is carried out using combination of features and random constant values. GP optimally selects adequate features from all extracted features and combines the selected features with mathematical operators. The GP generates individual classifiers and those are evaluated by fitness function. The result of GP can convert complex input features to simple value. This value is easily classified into nodule and non-nodule.
- ROC curve is plotted between TP and FP for different threshold values. Az is area under the ROC curve and a good measure of classifier performance in different condition. If we use only Az as a fitness function, we also achieve good TP and FP. However, GP can not produce the proper classification threshold. Therefore, we also use true positive rate (TPR) and specificity (SPC) as parts of fitness function. We used specificity (SPC) instead of false positive rate(FPR) because it is good at low value but other indicators are good at high value. SPC is the value subtracted from 1 to FPR and also called true negative rate(TNR). The fitness function is defined as the product of three indicators. In GP cycle, the fitness function evaluates the quality of each individual(classifier). In this work, we used three indicators as a fitness function. These are area under receiver operating characteristic (ROC) curve (Az), sensitivity and specificity.
- GP evolution is controlled by parameters as shown in Table.1. All parameters are set to the general values. We used ramped half and half method to generate initial population. The output of GP is real value. We need wrapper to simplify the output of GP. If output of GP is positive, the nodule candidates is classified as nodule otherwise non-nodule. Finally, if number of generations reaches the maximum limit, GP run is stopped. The best individual is obtained at the end of GP run.
- GP evolution is controlled by parameters as shown in Table.1. All parameters are set to the general values. We used ramped half and half method to generate initial population. The output of GP is real value. We need wrapper to simplify the output of GP. If output of GP is positive, the nodule candidates is classified as nodule otherwise non-nodule. Finally, if number of generations reaches the maximum limit, GP run is stopped. The best individual is obtained at the end of GP run.
- The sensitivity of nodule candidate detection is about 100% and its FP rate is 0.9. The nodule candidates have many FPs. The results in Table. 2 show the nodule detection rate(TPR), FPR and Az with respect to three types of datasets. FP rates of three datasets are about 10% of FP rate without GP.
- The ROC curves of the datasets are shown in Fig. 4. The proposed method achieved 92% detection rate with 6.5 FPs per scan.
- We have proposed a novel pulmonary nodule detection algorithm in CT images. Lung region is segmented using adaptive thresholding and voxel labelling based method. Then nodule candidates are detected using adaptive multiple thresholding and rule based classifier with 3D geometric features. Next, 3D and 2D features are extracted from the detected nodule Candidates. Finally, the extracted features are optimized and then classified into nodule and non-nodule using GP. We applied proposed algorithm to the LIDC database of NCI. This method extremely reduced FP rate. The FPs per scan is only 6.5 with more than 90% sensitivity. The results show the superiority of the proposed method.