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1.
目的 评价计算机辅助检测系统(CAD)在64层CT低剂量肺癌筛查肺结节检出中的应用价值及其对放射科医师的辅助作用.方法 从2007年6月至2008年6月肺癌低剂量筛查数据库共578例中运用纯随机抽样方法抽取100例.低剂量CT扫描参数管电压120 kV,管电流30及40 mA或管电流调制技术,层厚1.25或1.00 mm.由2名胸部影像医师首先阅读胸部CT图像,再应用CAD系统按结节所在位置分为肺外野、肺内野两部分,并将结节检出阈值分别设定为3.0、4.0、5.0 mm进行分析.所有结节以2人达成一致作为真结节.分别分析医师双阅片及CAD系统检出结节的能力,并进行McNemar-Bowker检验.结果 在100例胸部低剂量CT中,共检出真结节257枚,直径为1.7 ~18.5 mm;分布在双肺外野191枚,双肺内野66枚.CAD系统肺结节检出率为91.1%( 234/257),漏诊率为8.9%( 23/257),在漏诊的23枚结节中,10枚为实性结节,直径为2.4~6.0 mm;13枚为非实性结节,直径为2.1~8.6 mm;分布在肺外野17枚,肺内野6枚.放射科医师阅片结节检出率为59.1%(152/257),漏诊率为40.9%( 105/257),漏诊结节中94枚为实性结节,10枚为部分实性结节,1枚为非实性结节,结节大小2.4~11.8 mm;分布在肺外野69枚,肺内野36枚.结论 低剂量螺旋CT肺癌筛查中CAD检出肺结节的能力明显高于医师双阅片,尤其是对肺内野病灶的检出.使用CAD作为辅助诊断时,对非实性结节漏诊率高.  相似文献   

2.
RATIONALE AND OBJECTIVES: A computerized scheme for automated detection of lung nodules in low-dose computed tomography images for lung cancer screening was developed. MATERIALS AND METHODS: Our scheme is based on a difference-image technique for enhancing the lung nodules and suppressing the majority of background normal structures. The difference image for each computed tomography image was obtained by subtracting the nodule-suppressed image processed with a ring average filter from the nodule-enhanced image with a matched filter. The initial nodule candidates were identified by applying a multiple-gray level thresholding technique to the difference image, where most nodules were well enhanced. A number of false-positives were removed first in entire lung regions and second in divided lung regions by use of the two rule-based schemes on the localized image features related to morphology and gray levels. Some of the remaining false-positives were eliminated by use of a multiple massive training artificial neural network trained for reduction of various types of false-positives. This computerized scheme was applied to a confirmed cancer database of 106 low-dose computed tomography scans with 109 cancer lesions for 73 patients obtained from a lung cancer screening program in Nagano, Japan. RESULTS: This computed-aided diagnosis scheme provided a sensitivity of 83% (91/109) for all cancers with 5.8 false-positives per scan, which included 84% (32/38) for missed cancers with 5.9 false-positives per scan. CONCLUSION: This computerized scheme may be useful for assisting radiologists in detecting lung cancers on low-dose computed tomography images for lung cancer screening.  相似文献   

3.
4.
Purpose  We have been developing a computer-aided detection (CAD) system for lung nodules on multidetector row computed tomography (MDCT). The scheme for nodule detection in this system is featured by three-dimensional analysis for nodule detection in nodules and their surroundings, which is designed to discriminate nodules from blood vessels. The purpose of this study was to evaluate the CAD system. Materials and methods  MDCT images from 30 patients with lung nodules were read twice, 3 weeks apart by a chest radiologist to detect noncalcified nodules of ≥4 mm. The first reading was without CAD, and the second reading was with CAD. Based on the reference standard later determined by another chest radiologist, the sensitivity of the former chest radiologist without or with CAD was obtained; the sensitivity and false-positive rate of the system alone were also obtained. Results  The reference standard consisted of 66 nodules. The sensitivity of the chest radiologist was 77% (51/66) without CAD and 91% (60/66) with CAD, showing a significant improvement. The CAD system alone showed a sensitivity of 79% (52/66) with the false-positive rate of 4.5 per patient. Conclusion  Although preliminary, these results indicate the efficacy of the CAD system.  相似文献   

5.
RATIONALE AND OBJECTIVES: To assess the effect of three-dimensional (3D) lossy image compression of multidetector computed tomography chest scans on computer-aided detection (CAD) of solid lung nodules greater than 4 mm in size. MATERIALS AND METHODS: A total of 120 cases, acquired with 1.25-mm collimation, were collected from 5 different sites, of which 66/120 were low-dose cases. Two chest radiologists established that 37 cases had no actionable lung nodules; the remaining 83 cases contained 169 nodules (range 3.8-35.0 mm, mean 5.8 mm +/- 3.0 [SD]). All cases were compressed using the 3D Set Partitioning in Hierarchical Trees algorithm to 24:1, 48:1, and 96:1 levels. A study of the effect of compression on computer-aided detection (CAD) sensitivity was performed at operating points of 2.5 false marks (FM), 5 FM, and 10 FM per case using McNemar's test. Logistic regression models were used to evaluate the impact on CAD sensitivity by compression level on nodule and image characteristics. RESULTS: Compared with no compression, there was no significant degradation in CAD sensitivity found at any of the studied compression levels and operating points. However, between compression levels, there was marginal association with sensitivity. Specifically, 24:1 level was significantly better than 96:1 at all operating points, and occasionally better than no compression at 10 FM/case. Based on multivariate analysis, nodule location was found to be a significant predictor (P = .01) with a lower sensitivity associated with juxtapleural nodules. Nodule size, dose, reconstruction filter, and contrast medium were not significant predictors. CONCLUSION: CAD detection performance of solid lung nodules did not suffer until 48:1 compression.  相似文献   

6.
RATIONALE AND OBJECTIVES: The purpose of this study was to evaluate the performance of a fully automated lung nodule detection method in a large database of low-dose computed tomography (CT) scans from a lung cancer screening program. Because nodules demonstrate a spectrum of radiologic appearances, the performance of the automated method was evaluated on the basis of nodule malignancy status, size, subtlety, and radiographic opacity. MATERIALS AND METHODS: A database of 393 thick-section (10 mm) low-dose CT scans was collected. Automated lung nodule detection proceeds in two phases: gray-level thresholding for the initial identification of nodule candidates, followed by the application of a rule-based classifier and linear discriminant analysis to distinguish between candidates that correspond to actual lung nodules and candidates that correspond to non-nodules. Free-response receiver operating characteristic analysis was used to evaluate the performance of the method based on a jackknife training/testing approach. RESULTS: An overall nodule detection sensitivity of 70% (330 of 470) was attained with an average of 1.6 false-positive detections per section. At the same false-positive rate, 83% (57 of 69) of the malignant lung nodules in the database were detected. When the method was trained specifically for malignant nodules, a sensitivity of 80% (55 of 69) was attained with 0.85 false-positives per section. CONCLUSION: We have evaluated an automated lung nodule detection method with a large number of low-dose CT scans from a lung cancer screening program. An overall sensitivity of 80% for malignant nodules was achieved with 0.85 false-positive detections per section. Such a computerized lung nodule detection method is expected to become an important part of CT-based lung cancer screening programs.  相似文献   

7.
Computer-aided detection and automated CT volumetry of pulmonary nodules   总被引:5,自引:5,他引:0  
With use of multislice computed tomography (MSCT), small pulmonary nodules are being detected in vast numbers, constituting the majority of all noncalcified lung nodules. Although the prevalence of lung cancers among such lesions in lung cancer screening populations is low, their isolation may contribute to increased patient survival. Computer-aided diagnosis (CAD) has emerged as a diverse set of diagnostic tools to handle the large number of images in MSCT datasets and most importantly, includes automated detection and volumetry of pulmonary nodules. Current CAD systems can significantly enhance experienced radiologists’ performance and outweigh human limitations in identifying small lesions and manually measuring their diameters, augment observer consistency in the interpretation of such examinations and may thus help to detect significantly higher rates of early malignomas and give more precise estimates on chemotherapy response than can radiologists alone. In this review, we give an overview of current CAD in lung nodule detection and volumetry and discuss their relative merits and limitations.  相似文献   

8.
OBJECTIVE: The purpose of our study was to assess relative intra- and interobserver agreement in detecting pulmonary nodules when interpreting low-dose chest CT screening examinations. MATERIALS AND METHODS: Two hundred ninety-three selected low-dose CT examinations of the lung were independently interpreted by three radiologists to detect and classify pulmonary nodules. The data set selected was enriched with examinations depicting pulmonary nodules. A subset of 30 examinations was interpreted twice. All pulmonary nodules greater than 1.0 mm were marked. All nodules greater than 3.0 mm were marked, measured, and scored as to their probability of being benign or malignant. Nodule-based and examination-based relative reviewer agreements were evaluated using percentage of agreement and kappa statistics. Similar assessments were performed on the subset of examinations interpreted twice. RESULTS: The three radiologists identified a total of 470, 729, and 876 pulmonary nodules of which 395, 641, and 778 were rated as noncalcified with some level of suspicion for being malignant. Nodule-based interobserver agreement among the radiologists was poor (highest kappa value in a paired comparison, 0.120). Examination-based agreement was higher (highest kappa value in a paired comparison, 0.458). Intraobserver agreement was higher than interobserver agreement for examination-based agreement (highest kappa = 0.889) but lower for nodule-based agreement (highest kappa = -0.035). Agreement improved as the suspicion of malignancy increased. CONCLUSION: Unaided intra- and interobserver agreement in detecting pulmonary nodules in low-dose CT of the lung is relatively low. Computer-assisted detection may provide the consistency that is needed for this purpose.  相似文献   

9.

Purpose

To compare the lung nodules’ detection of digital tomosynthesis (DTS) and computed tomography (CT) in the context of the SOS (Studio OSservazionale) prospective screening program for lung cancer detection.

Materials and methods

One hundred and thirty-two of the 1843 subjects enrolled in the SOS study underwent CT because non-calcified nodules with diameters larger than 5 mm and/or multiple nodules were present in DTS. Two expert radiologists reviewed the exams classifying the nodules based on their radiological appearance and their dimension. LUNG-RADS classification was applied to compare receiver operator characteristics curve between CT and DTS with respect to final diagnosis. CT was used as gold standard.

Results

DTS and CT detected 208 and 179 nodules in the 132 subjects, respectively. Of these 208 nodules, 189 (91%) were solid, partially solid, and ground glass opacity. CT confirmed 140/189 (74%) of these nodules but found 4 nodules that were not detected by DTS. DTS and CT were concordant in 62% of the cases applying the 5-point LUNG-RADS scale. The concordance rose to 86% on a suspicious/non-suspicious binary scale. The areas under the curve in receiver operator characteristics were 0.89 (95% CI 0.83–0.94) and 0.80 (95% CI 0.72–0.89) for CT and DTS, respectively. The mean effective dose was 0.09 ± 0.04 mSv for DTS and 4.90 ± 1.20 mSv for CT.

Conclusions

The use of a common classification for nodule detection in DTS and CT helps in comparing the two technologies. DTS detected and correctly classified 74% of the nodules seen by CT but lost 4 nodules identified by CT. Concordance between DTS and CT rose to 86% of the nodules when considering LUNG-RADS on a binary scale.
  相似文献   

10.
PURPOSE: To evaluate diagnostic sensitivity of the pulmonary nodules computer-aided detection (CAD) in computed tomography. To analyze parameters that modify CAD performance. We made a critical analysis of the literature, and we described CAD sensitivity. Moreover, we compared CAD and CAD plus radiologist sensitivity in detection of pulmonary nodules, and we compared different acquisition techniques (thin slice vs thick slice and low dose vs normal dose). MATERIALS AND METHODS: We used as major data sources the medical literature database of PubMed and MEDLINE, where we searched for articles in English language published from January 2001 to November 2006. We included studies that used spiral or multidetector row CT for CAD. RESULTS: Twenty studies met the inclusion criteria containing a total of more than 827 patients and 2717 pulmonary nodules detected by CAD. We observed an overall sensitivity of 79% for the CAD and of 92% for CAD plus radiologist; CAD sensitivity was 80% and 74% for thin slice and thick slice protocols, respectively. CONCLUSIONS: Results of our study suggest that CAD technique is an accurate tool in detection of pulmonary nodules, by working as useful second look for the physician. Sensitivity becomes higher by using it together with radiologist. Actually, the main limitation about the use of CAD to be solved is represented by the persistent high false-positive rate.  相似文献   

11.
Purpose  The ground-glass opacity (GGO) of lung cancer is identified only subjectively on computed tomography (CT) images as no quantitative characteristic has been defined for GGOs. We sought to define GGOs quantitatively and to differentiate between GGOs and solid-type lung cancers semiautomatically with a computer-aided diagnosis (CAD). Methods and materials  High-resolution CT images of 100 pulmonary nodules (all peripheral lung cancers) were collected from our clinical records. Two radiologists traced the contours of nodules and distinguished GGOs from solid areas. The CT attenuation value of each area was measured. Differentiation between cancer types was assessed by a receiver-operating characteristic (ROC) analysis. Results  The mean CT attenuation of the GGO areas was −618.4 ± 212.2 HU, whereas that of solid areas was −68.1 ± 230.3 HU. CAD differentiated between solidand GGO-type lung cancers with a sensitivity of 86.0% and specificity of 96.5% when the threshold value was −370 HU. Four nodules of mixed GGOs were incorrectly classified as the solid type. CAD detected 96.3% of GGO areas when the threshold between GGO and solid areas was 194 HU. Conclusion  Objective definition of GGO area by CT attenuation is feasible. This method is useful for semiautomatic differentiation between GGOs and solid types of lung cancer.  相似文献   

12.

Objective

To investigate the potential of MRI for lung nodule detection in a high-risk population in comparison to low-dose CT.

Methods

49 participants (31 men, 18 women, 51–71 years) of the German Lung Cancer Screening and Intervention Trial (LUSI) with a cancer-suspicious lung lesion in CT were examined with non-contrast-enhanced MRI of the lung at 1.5 T. Data were pseudonymized and presented at random order together with 30 datasets (23 in men, 7 in women, 18–64 years) from healthy volunteers. Two radiologists read the data for the presence of nodules. Sensitivity and specificity were calculated. Gold standard was either histology or long-term follow-up. Contrast-to-Noise-Ratio (CNR) was measured for all detected lesions in all MRI sequences.

Results

Average maximum diameter of the lesions was 15 mm. Overall sensitivity and specificity of MRI were 48% (26/54) and 88% (29/33) compared to low-dose CT. Sensitivity of MRI was significantly higher for malignant nodules (78% (12.5/16)) than for benign ones (36% (13.5/38); P = 0.007). There was no statistically significant difference in sensitivity between nodules (benign and malignant) larger or smaller than 10 mm (P = 0.7). Inter observer agreement was 84% (κ = 0.65). Lesion-to-background CNR of T2-weighted single-shot turbo-spin-echo was significantly higher for malignant nodules (89 ± 27) than for benign ones (56 ± 23; P = 0.002).

Conclusion

The sensitivity of MRI for detection of malignant pulmonary nodules in a high-risk population is 78%. Due to its inherent soft tissue contrast, MRI is more sensitive to malignant nodules than to benign ones. MRI may therefore represent a useful test for early detection of lung cancer.  相似文献   

13.
PURPOSE: The purpose of this study was to determine the frequency of coronary artery calcification (CAC) in high-risk people undergoing computed tomography (CT) screening for lung cancer. METHODS: Between 1999 and 2004, we performed CT screening for lung cancer on 4250 participants, all without documented prior cardiovascular disease, using multidetector-row (MD) CT. Of the patients, 1102 underwent imaging with a four-detector-row CT at 120 kVp and 40 mA, with pitch 1.5 and collimation of 2.5 mm in a single breath hold of 15-20 seconds, and 3148 did with an eight-detector-row CT at the same kVp, mA, and pitch settings but with collimation of 1.25 mm. Visualized CACs in each coronary artery (main, left anterior descending, circumflex, and right) were scored separately as 0 (absent), 1 (mild), 2 (moderate), or 3 (severe), yielding a possible score of 0-12 for each person. Frequency distributions by gender, age, and pack-years of smoking were determined. Odds ratios (ORs) were calculated using logistic regression analysis of the prevalence of CAC as a joint function of gender, age, pack-years of smoking, and presence of diabetes. RESULTS: Among the subjects younger than 50 years, positive CAC scores were three times more frequent for men than for women (22% vs. 7%); among those older than 50 years, the frequency increased for both men and women but the increase for women was greater than that for men. The frequency of positive CAC scores increased with increasing pack-years of smoking; it was always higher for men than for women. The ORs were 2.6 for male gender (P<.0001), 3.7 and 9.6 for ages 60-69 years and 70 years or older, respectively, for increasing age (P<.0001 for both), 1.6 and 2.3 for 30-59 pack-years and 60 pack-years or longer, respectively, for increasing pack-years of smoking (P<.0001 for both), and 1.6 for having diabetes (P=.016). CONCLUSION: The CAC score can be derived from ungated low-dose MDCT images. This information can contribute to risk stratification and management of coronary artery disease.  相似文献   

14.
RATIONALE AND OBJECTIVES: In this study, we developed a prototype model-based computer aided detection (CAD) system designed to automatically detect both solid and subsolid pulmonary nodules in computed tomography (CT) images. By using this CAD algorithm, along with the radiologist's initial interpretation, we aim to improve the sensitivity of radiologic readings of CT lung exams. MATERIALS AND METHODS: We have developed a model-based CAD algorithm through the use of precise mathematic models that capture scanner physics and anatomic information. Our model-based CAD algorithm uses multiple segmentation algorithms to extract noteworthy structures in the lungs and a Bayesian statistical model selection framework to determine the probability of various anatomical events throughout the lung. We tested this algorithm on 50 low-dose CT lung cancer screening cases in which ground truth was produced through readings by three expert chest radiologists. RESULTS: Using this model-based CAD algorithm on 50 low-dose CT cases, we measured potential sensitivity improvements of 7% and 5% in two radiologists with respect to all noncalcified nodules, solid and subsolid, greater than 5 mm in diameter. The third radiologist did not miss any nodules in the ground truth set. The CAD algorithm produced 8.3 false positives per case. CONCLUSION: Our prototype CAD system demonstrates promising results as a tool to improve the quality of radiologic readings by increasing radiologist sensitivity. A significant advantage of this model-based approach is that it can be easily extended to support additional anatomic models as clinical understanding and scanning practices improve.  相似文献   

15.
Objectives The objective of this study was to compare the sensitivity of detection of lung nodules on low-dose screening CT images between radiologists and technologists. Methods 11 radiologists and 10 technologists read the low-dose screening CT images of 78 subjects. On images with a slice thickness of 5 mm, there were 60 lung nodules that were ≥5 mm in diameter: 26 nodules with pure ground-glass opacity (GGO), 7 nodules with mixed ground-glass opacity (GGO with a solid component) and 27 solid nodules. On images with a slice thickness of 2 mm, 69 lung nodules were ≥5 mm in diameter: 35 pure GGOs, 7 mixed GGOs and 27 solid nodules. The 21 observers read screening CT images of 5-mm slice thickness at first; then, 6 months later, they read screening CT images of 2-mm slice thickness from the 78 subjects. Results The differences in the mean sensitivities of detection of the pure GGOs, mixed GGOs and solid nodules between radiologists and technologists were not statistically significant, except for the case of solid nodules; the p-values of the differences for pure GGOs, mixed GGOs and solid nodules on the CT images with 5-mm slice thickness were 0.095, 0.461 and 0.005, respectively, and the corresponding p-values on CT images of 2-mm slice thickness were 0.971, 0.722 and 0.0037, respectively. Conclusion Well-trained technologists may contribute to the detection of pure and mixed GGOs ≥5 mm in diameter on low-dose screening CT images.  相似文献   

16.
目的 评价计算机辅助检测(CAD)肺结节系统在数字化X线胸片上肺癌筛查中的应用价值.方法 由1名放射科医师和CAD肺结节检测系统独立阅读100例连续的数字摄影(DR)X线胸片,CAD系统可以检出最长直径在5~15 mm的肺结节.由2名放射科专家(有15年胸部影像诊断经验)进行回顾性阅读,参照相应的CT图像,两人意见达成一致后标记真结节的个数和位置并保存标记结果,将标记结果作为金标准来比较放射科医师和CAD系统的肺结节检测敏感性和假阳性率.结果 放射科医师共检测到95个结节,CAD系统共检测到304个结节.在回顾性检查中2名放射科专家共标记134个真结节,其中放射科医师检测到82个(61.2%),CAD检测到105个(78.4%),CAD系统检测到而被放射科医师漏诊的结节35个,放射科医师检测到而CAD系统漏诊的结节10个.放射科医师应用CAD系统后检测到112个真结节,检测率提高到83.6%.放射科专家意见一致后认为CAD系统检出199个假阳性结节,平均每张胸片约2.0个.结论 在肺癌筛查中放射科医师和CAD系统必须联合应用才可以识别X线胸片中所有的结节.  相似文献   

17.
RATIONALE AND OBJECTIVES: We sought to evaluate the potential benefits of a computer-aided detection (CAD) system for detecting lung nodules in multidetector row CT (MDCT) scans. METHODS: A CAD system was developed for detecting lung nodules on MDCT scans and was applied to the data obtained from 15 patients. Two chest radiologists in consensus established the reference standard. The nodules were categorized according to their size and their relationship to the surrounding structures (nodule type). The differences in the sensitivities between an experienced chest radiologist and a CAD system without user interaction were evaluated using a chi2 analysis. The differences in the sensitivities also were compared in terms of the nodule size and the nodule type. RESULTS: A total of 309 nodules were identified as the reference standard. The sensitivity of a CAD system (81%) was not significantly different from that of a radiologist (85%; P > 0.05). The sensitivities of the CAD system for detecting nodules < or = 5 mm in diameter as well as detecting isolated nodules were higher than those of a radiologist (83% vs. 75%, P > 0.05; 93% vs. 76%, P < 0.001). The sensitivities of a radiologist for detecting nodules >5 mm and the nodules attached to other structures were higher than those of a CAD system (98% vs. 79%, P < 0.001; 91% vs. 71%, P < 0.001). There were 28.8 false-positive results of CAD per CT study. CONCLUSION: The CAD system developed in this study performed the nodule detection task in different ways to that of a radiologist in terms of the nodule size and the nodule type, which suggests that the CAD system can play a complementary role to a radiologist in detecting nodules from large CT data sets.  相似文献   

18.
OBJECTIVE: We sought to assess the reproducibility of size measurements of small lung nodules examined with low-dose thin-section computed tomography (LDTSCT). MATERIALS AND METHODS: Three radiologists measured volume with a semiautomatic tool and diameters manually of 20 (equivalent diameter range, 5.3-11 mm) phantom nodules and 37 (mean diameter range, 5-8.5 mm) lung nodules in subjects undergoing LDTSCT. RESULTS: In phantoms, the worst 95% limits of agreement (95% LA) for volume were -3.0% and 3.0% within operator and -3.1% and 2.8% between operators. The coefficient of repeatability (CR) for diameter ranged between 0.51 and 0.67 mm within operator and the 95% LA were from -0.71 to 0.71 mm between operators. In nodules, the worst intraoperator 95% LA for volume were -14.4% and 17.6% within operator and -13.1% and 14.2% between operators. The CR for diameter ranged between 0.48 and 0.73 mm within operator and the 95% LA were from -1.16 to 1.16 mm between operators. CONCLUSION: Operator-dependent variability of size measurements of small nodules examined with LDTSCT is not negligible and should be considered in lung cancer-screening studies.  相似文献   

19.
OBJECTIVE: To test the hypothesis that a calibration phantom would improve interpatient and interscan variability in coronary artery calcium (CAC) studies. METHODS: We scanned 144 patients twice with or without the calibration phantom and then scanned 93 patients with a single calcific lesion twice and, finally, scanned a cork heart with calcific foci. RESULTS: There were no linear correlations in computed tomography Hounsfield unit (CT HU) and CT HU interscan variation between blood pool and phantom plugs at any slice level in patient groups (p > 0.05). The CT HU interscan variation in phantom plugs (2.11 HU) was less than that of the blood pool (3.47 HU; p < 0.05) and CAC lesion (20.39; p < 0.001). Comparing images with and without a calibration phantom, there was a significant decrease in CT HU as well as an increase in noise and peak values in patient studies and the cork phantom study. CONCLUSION: The CT HU attenuation variations of the interpatient and interscan blood pool, calibration phantom plug, and cork coronary arteries were not parallel. Therefore, the ability to adjust the CT HU variation of calcific lesions by a calibration phantom is problematic and may worsen the problem.  相似文献   

20.
We have developed an automated computerized method for the detection of lung nodules in three-dimensional (3D) computed tomography (CT) images obtained by helical CT. In this scheme, a lung segmentation technique for the determination of the nodule search area is performed based on a gray-level thresholding technique. To enhance lung nodules, we employed the 3D cross-correlation method by using a 3D Gaussian template with zero-surrounding as a model of lung nodule. False positives are then eliminated by using a rule-base with 53 features. For further reduction of false positives, we performed linear discriminant analysis using these 53 features. The average number of false positives was 6.7 per case at a percent sensitivity of 85.0%. This computerized scheme will be useful to radiologists by providing a "second opinion" in case of possible early lung cancer.  相似文献   

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