Relevance Vector Machine Based Pulmonary Nodule Classification

Xia

et al. 2020

IET signal process.

Self Cite

The traditional robust principal component analysis (RPCA) model is based on the nuclear norm, which usually underestimates the singular values of the low-rank matrix. As a consequence, the restoration image experiences serious interference by Gaussian noise, and the image quality degenerates during the denoising process. Therefore, an improved RPCA method via non-convex regularisation terms is proposed to remedy the above shortcomings. First, in order to estimate the singular value of the low-rank matrix more accurately, the authors employ the non-convex penalty function and add a weight vector to it. Then, the regularisation with non-convex penalty function and its weighted version are used to replace the nuclear norm and entry-wise l 1 norm in original RPCA, respectively, to establish an improved model. Finally, an optimal solution algorithm is derived by developing the alternating direction multiplier method. Experimental results show that the proposed method has better performance in terms of both quantitative measurement and visual perception quality than other several state-of-the-art image denoising methods.

“…The LIDC database contains a total of 1018 patient cases, each of which stores the lung CT images of each patient (image size is

512 \times 512

Section: Resultsmentioning

confidence: 99%

Improved RPCA method via non‐convex regularisation for image denoising

Xia

et al. 2020

IET signal process.

Self Cite

Computer Methods and Programs in Biomedicine

“…After a series of medical image processing steps presented in our previous work [29], 204 candidate nodules of size > 3mm were extracted, consisting of 64 nodules and 140 non-nodules marked by at least one radiologist.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Correlation coefficient based supervised locally linear embedding for pulmonary nodule recognition

Panpan

Xia

2016

Self Cite

Background and Objective Dimensionality reduction techniques are developed to suppress the negative effects of high dimensional feature space of lung CT images on classification performance in computer aided detection (CAD) systems for pulmonary nodule detection. Methods An improved supervised locally linear embedding (SLLE) algorithm is proposed based on the concept of correlation coefficient. The Spearman’s rank correlation coefficient is introduced to adjust the distance metric in the SLLE algorithm to ensure that more suitable neighborhood points could be identified, and thus to enhance the discriminating power of embedded data. The proposed Spearman’s rank correlation coefficient based SLLE (SC2SLLE) is implemented and validated in our pilot CAD system using a clinical dataset collected from the publicly available lung image database consortium and image database resource initiative (LICD-IDRI). Particularly, a representative CAD system for solitary pulmonary nodule detection is designed and implemented. After a sequential medical image processing steps, 64 nodules and 140 non-nodules are extracted, and 34 representative features are calculated. The SC2SLLE, as well as SLLE and LLE algorithm are applied to reduce the dimensionality. Several quantitative measurements are also used to evaluate and compare the performance. Results Using a 5-fold cross-validation methodology, the proposed algorithm achieves 87.65% accuracy, 79.23% sensitivity, 91.43% specificity, and 8.57% false positive rate, on average. Experimental results indicate that the proposed algorithm outperforms the original locally linear embedding and SLLE coupled with the support vector machine (SVM) classifier. Conclusions Based on the preliminary results from a limited number of nodules in our dataset, this study demonstrates the great potential to improve the performance of a CAD system for nodule detection using the proposed SC2SLLE.

“…The AUC achieved by their approach is better than ours, but the accuracy is lower than ours. The works in both (Wu et al 2016, Li et al 2018 used texture, shape and intensity features to characterize nodules. An AUC of 0.95 was achieved in Li et al (2018) but not described in Wu et al (2016).…”

Section: Comparison Of the Proposed With Other Methods Based On Tradi...mentioning

confidence: 99%

“…The SVM classifier achieved the best accuracy of 91.81%. Wu et al (2016) extracted gray, five geometric features and grey-level co-occurrence matrix (GLCM)-based texture features from nodules and used relevance vector machine (RVM) to improve the classification task. They achieved an accuracy of 79.4%.…”

Section: Traditional Featuresmentioning

confidence: 99%

Classification of benign and malignant lung nodules from CT images based on hybrid features

Zhang

Yang²,

Gong³

et al. 2019

Phys. Med. Biol.

The classification of benign and malignant lung nodules has great significance for the early detection of lung cancer, since early diagnosis of nodules can greatly increase patient survival. In this paper, we propose a novel classification method for lung nodules based on hybrid features from computed tomography (CT) images. The method fused 3D deep dual path network (DPN) features, local binary pattern (LBP)-based texture features and histogram of oriented gradients (HOG)-based shape features to characterize lung nodules. DPN is a convolutional neural network which integrates the advantages of aggregated residual transformations (ResNeXt) for feature reuse and a densely convolutional network (DenseNet) for exploring new features. LBP is a prominent feature descriptor for texture classification, when combining with the HOG descriptor, it can improve the classification performance considerably. To differentiate malignant nodules from benign ones, a gradient boosting machine (GBM) algorithm is employed. We evaluated the proposed method on the publicly available LUng Nodule Analysis 2016 (LUNA16) dataset with 1004 nodules, achieving an area under the receiver operating characteristic curve (AUC) of 0.9687 and accuracy of 93.78%. The promising results demonstrate that our method has strong robustness on the classification of nodule patterns by virtue of the joint use of texture features, shape features and 3D deep DPN features. The method has the potential to help radiologists to interpret diagnostic data and make decisions in clinical practice.