Pulmonary nodule classification aided by clustering

Lung nodules can be detected through examining CT scans. An automated lung nodule classification system is presented in this paper. The system employs random forests as it base classifier. A unique architecture for classification-aided-by-clustering is presented. Four experiments are conducted to study the performance of the developed system. 5721 CT lung image slices from the LIDC database are employed in the experiments. According to the experimental results, the highest sensitivity of 97.92%, and specificty of 96.28% are achieved by the system. The results demonstrate that the system has improved the performances of its tested counterparts

폐결절 용적측정의 측정 오차 범위 모델링: 전이성 폐결절의 당일 반복 전산화 단층 촬영을 통한 전향적 연구

학위논문 (석사)-- 서울대학교 대학원 : 의학과, 2014. 8. 구진모.Introduction: Semi-automated lung nodule volumetry is a promising tool for early detection of volume change, which might led to early diagnosis of malignancy and treatment failure. Measurement variability in volumetry is known to be affected by various factors. The purpose of our study was to model the range of variability in lung nodule volumetry, in patient with metastatic lung nodules by same day repeat computed tomography (CT) scans Methods: The present prospective study included 50 patients with known pulmonary metastatic nodules between November 2013 and April 2014, with written informed consents. Two consecutive noncontrast chest CT scans were performed within 10 minutes of time interval. Non-calcified nodules with diameter between 4mm and 15mm were segmented using in-house software for each CT scans. After calculation of mean segmented nodule volume (Vm), surface voxel proportion (SVP) was defined as the proportion of surface voxels in total segmented voxels, while attachment proportion (APN) was defined as the proportion of voxels with greater attenuation than N in total voxels just outside of surface of nodule. Absolute percentage error (APE) and relative percentage error (RPE) were calculated from segmented nodule volume on each CT scans. Univariate and multivariate quantile regression analyses were performed for estimation of 95% upper limit of APE. Results: The 95% limits of variability of RPE was from -20.81% to 20.62%. In univariate quantile analyses, Vm, Sphericity, SVP, AP-700 and AP-600 were significant variables for estimation of APE. In multivariate analysis, SVP and AP-700 were proven to be independently significant variable and final model for 95% limit of APE was as follows: APE = 46.01• SVP + 36.32 • AP-700 -12.94. Conclusions: In conclusion, SVP and AP were independent factors for variability in lung nodule volumetry. With those two parameters, 95% limit of absolute percentage error in lung nodule volumetry could be estimated with linear model.CONTENTS Abstract i Contents iii List of tables and figures iv List of Abbreviations v Introduction 1 Materials and Methods 3 Results 11 Discussion 29 References 37 Abstract in Korean 43Maste

Automatic Detection of 2D and 3D Lung Nodules in Chest Spiral CT Scans

Automatic detection of lung nodules is an important problem in computer analysis of chest radiographs. In this paper, we propose a novel algorithm for isolating lung abnormalities (nodules) from spiral chest low-dose CT (LDCT) scans. The proposed algorithm consists of three main steps. The first step isolates the lung nodules, arteries, veins, bronchi, and bronchioles from the surrounding anatomical structures. The second step detects lung nodules using deformable 3D and 2D templates describing typical geometry and gray-level distribution within the nodules of the same type. The detection combines the normalized cross-correlation template matching and a genetic optimization algorithm. The final step eliminates the false positive nodules (FPNs) using three features that robustly define the true lung nodules. Experiments with 200 CT data sets show that the proposed approach provided comparable results with respect to the experts

Computerâ aided diagnosis of pulmonary nodules on CT scans: Segmentation and classification using 3D active contours

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135040/1/mp7129.pd