Shu Ling Alycia Lee

Random forest based lung nodule classification aided by clustering

An automated lung nodule detection system can help spot lung abnormalities in CT lung images. Lung nodule detection can be achieved using template-based, segmentation-based, and classification-based methods. The existing systems that include a classification component in their structures have demonstrated better performances than their counterparts. Ensemble learners combine decisions of multiple classifiers to form an integrated output. To improve the performance of automated lung nodule detection, an ensemble classification aided by clustering (CAC) method is proposed. The method takes advantage of the random forest algorithm and offers a structure for a hybrid random forest based lung nodule classification aided by clustering. Several experiments are carried out involving the proposed method as well as two other existing methods. The parameters of the classifiers are varied to identify the best performing classifiers. The experiments are conducted using lung scans of 32 patients including 5721 images within which nodule locations are marked by expert radiologists. Overall, the best sensitivity of 98.33% and specificity of 97.11% have been recorded for proposed system. Also, a high receiver operating characteristic (ROC) A(z) of 0.9786 has been achieved.

Automated detection of lung nodules in computed tomography images: a review

Lung nodules refer to a range of lung abnormalities the detection of which can facilitate early treatment for lung patients. Lung nodules can be detected by radiologists through examining lung images. Automated detection systems that locate nodules of various sizes within lung images can assist radiologists in their decision making. This paper presents a study of the existing methods on automated lung nodule detection. It introduces a generic structure for lung nodule detection that can be used to represent and describe the existing methods. The structure consists of a number of components including: acquisition, pre-processing, lung segmentation, nodule detection, and false positives reduction. The paper describes the algorithms used to realise each component in different systems. It also provides a comparison of the performance of the existing approaches.

Automated identification of lung nodules

A system that can automatically detect nodules within lung images may assist expert radiologists in interpreting the abnormal patterns as nodules in 2D CT lung images. A system is presented that can automatically identify nodules of various sizes within lung images. The pattern classification method is employed to develop the proposed system. A random forest ensemble classifier is formed consisting of many weak learners that can grow decision trees. The forest selects the decision that has the most votes. The developed system consists of two random forest classifiers connected in a series fashion. A subset of CT lung images from the LIDC database is employed. It consists of 5721 images to train and test the system. There are 411 images that contained expert- radiologists identified nodules. Training sets consisting of nodule, non-nodule, and false-detection patterns are constructed. A collection of test images are also built. The first classifier is developed to detect all nodules. The second classifier is developed to eliminate the false detections produced by the first classifier. According to the experimental results, a true positive rate of 100%, and false positive rate of 1.4 per lung image are achieved.

Lung nodules detection by ensemble classification

A method is presented that achieves lung nodule detection by classification of nodule and non-nodule patterns. It is based on random forests which are ensemble learners that grow classification trees. Each tree produces a classification decision, and an integrated output is calculated. The performance of the developed method is compared against that of the support vector machine and the decision tree methods. Three experiments are performed using lung scans of 32 patients including thousands of images within which nodule locations are marked by expert radiologists. The classification errors and execution times are presented and discussed. The lowest classification error (2.4%) has been produced by the developed method.

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 its 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 specificity of 96.28% are achieved by the system. The results demonstrate that the system has improved the performances of its tested counterparts.

From lung images to lung models: A review

Automated 3D lung modeling involves analyzing 2D lung images and reconstructing a realistic 3D model of the lung. This paper presents a review of the existing works on automatic formation of 3D lung models from 2D lung images. A common framework for 3D lung modeling is proposed. It consists of eight components: image acquisition, image pre-processing, image segmentation, boundary creation, image recognition, image registration, 3D surface reconstruction, and 3D rendering and visualization. The algorithms used by the existing systems to implement these components are also reviewed.

A random forest for lung nodule identification

A method is presented for identification of lung nodules. It includes three stages: image acquisition, background removal, and nodule detection. The first stage improves image quality. The second stage extracts long lobe regions. The third stage detects lung nodules. The method is based on the random forest learner. Training set contains nodule, non-nodule, and false-positive patterns. Test set contains randomly selected images. The developed method is compared against the support vector machine. True-positives of 100% and 85.9%, and false-positives of 1.27 and 1.33 per image were achieved by the developed method and the support vector machine, respectively.

Empirical evaluation of segmentation algorithms for lung modelling

Lung modelling has emerged as a useful method for diagnosing lung diseases. Image segmentation is an important part of lung modelling systems. The ill-defined nature of image segmentation makes automated lung modelling difficult. Also, low resolution of lung images further increases the difficulty of the lung image segmentation. It is therefore important to identify a suitable segmentation algorithm that can enhance lung modelling accuracies. This paper investigates six image segmentation algorithms, used in medical imaging, and also their application to lung modelling. The algorithms are: normalised cuts, graph, region growing, watershed, Markov random field, and mean shift. The performance of the six segmentation algorithms is determined through a set of experiments on realistic 2D CT lung images. An experimental procedure is devised to measure the performance of the tested algorithms. The measured segmentation accuracies as well as execution times of the six algorithms are then compared and discussed.

Hybrid Classification of Pulmonary Nodules

Automated classification of lung nodules is challenging because of the variation in shape and size of lung nodules, as well as their associated differences in their images. Ensemble based learners have demonstrated the potentialof good performance. Random forests are employed for pulmonary nodule classification where each tree in the forest produces a classification decision, and an integrated output is calculated. A classification aided by clustering approach is proposed to improve the lung nodule classification performance. Three experiments are performed using the LIDC lung image database of 32 cases. The classification performance and execution times are presented and discussed.