The mesh-LBP: a framework for extracting local binary patterns from discrete manifolds

Automatic diagnosis of coronavirus (COVID-19) using shape and texture characteristics extracted from X-Ray and CT-Scan images

Automatic diagnosis of coronavirus (COVID-19) is studied in this research. Deep learning methods especially convolutional neural networks (CNNs) have shown great success in COVID-19 diagnosis in recent works. But they are efficient when the depth of network is high enough. However, the use of a deep network requires a sufficiently large training set, which is not available in practice. From the other hand, the use of a shallow CNN may not provide superior results because it is not able to rich feature extraction due to lacking enough convolutional layers. To deal with this difficulty, the contextual features reduced by convolutional filters (CFRCF) is proposed in this work. CFRCF extracts shape and textural features as contextual feature maps from the chest X-ray radiographs and abdominal computed tomography (CT) images. Morphological operators, Gabor filter banks and attribute filters are used for contextual feature extraction. Then, two convolutional filters are applied to the contextual feature cube to extract the nonlinear sub-features and hidden relationships among the contextual features. Finally, a fully connected layer is used to produce a reduced feature vector which is fed to a classifier. Support vector machine and random forest are used as classifier. The experimental results show the superior performance of the proposed method from the recognition accuracy and running time point of view using limited training samples. More than 76% and 94% overall classification accuracy is obtained by the proposed method in CT scan and X-ray images datasets, respectively.

MR-Forest: A Deep Decision Framework for False Positive Reduction in Pulmonary Nodule Detection

With the development of deep learning methods such as convolutional neural network (CNN), the accuracy of automated pulmonary nodule detection has been greatly improved. However, the high computational and storage costs of the large-scale network have been a potential concern for the future widespread clinical application. In this paper, an alternative Multi-ringed (MR)-Forest framework, against the resource-consuming neural networks (NN)-based architectures, has been proposed for false positive reduction in pulmonary nodule detection, which consists of three steps. First, a novel multi-ringed scanning method is used to extract the order ring facets (ORFs) from the surface voxels of the volumetric nodule models; Second, Mesh-LBP and mapping deformation are employed to estimate the texture and shape features. By sliding and resampling the multi-ringed ORFs, feature volumes with different lengths are generated. Finally, the outputs of multi-level are cascaded to predict the candidate class. On 1034 scans merging the dataset from the Affiliated Hospital of Liaoning University of Traditional Chinese Medicine (AH-LUTCM) and the LUNA16 Challenge dataset, our framework performs enough competitiveness than state-of-the-art in false positive reduction task (CPM score of 0.865). Experimental results demonstrate that MR-Forest is a successful solution to satisfy both resource-consuming and effectiveness for automated pulmonary nodule detection. The proposed MR-forest is a general architecture for 3D target detection, it can be easily extended in many other medical imaging analysis tasks, where the growth trend of the targeting object is approximated as a spheroidal expansion.

The Framework of Passable Region Recognition Based on Vanish-Line

For the defect of the traditional vanishing point detection algorithm that is invalid in unstructured environment, a novel vanishing detection algorithm based on Dynamic Template Matching (DTM) is proposed. And a framework of access area recognition is put forward according to the vanishing point line. First, a series of lines are selected from the image in the form of the scanning at the same interval and then calculate the between each line and the previous one. The horizontal position of vanish point is that of the line with the minimum normalized correlation value in all scanning line. Second, a new image is constructed by getting rid of the part above of the viewpoint line, and be divided into several subimages without overlap to extract the multi features. The end, a train set is constructed based on the assumption of no deviation of the vehicle and the test set is classified by multi-kernel learning (MKL) method to obtain passable area. In addition, according to the need of intelligent vehicles during working, a weight-accuracy is delimited by assigning the different weights to the near areas and far areas. This kind of accuracy is more significative than the original one. In the experiments on various environments image sets, the proposed method exhibits favorable performances compared to the other methods.

Training-Based Gradient LBP Feature Models for Multiresolution Texture Classification

Local binary pattern (LBP) is a simple, yet efficient coding model for extracting texture features. To improve texture classification, this paper designs a median sampling regulation, defines a group of gradient LBP (gLBP) descriptors, proposes a training-based feature model mapping method, and then develops a texture classification frame using the multiresolution feature fusion of four gLBP descriptors. Cooperated by median sampling, four descriptors encode a pixel respectively by central gradient, radial gradient, magnitude gradient and tangent gradient to generate initial gLBP patterns. The feature mapping models of gLBP descriptors are constructed by the maximal relative-variation rate (mr2) of rotation-invariant patterns, and then prestored as mapping lookup files. By mapping, initial patterns can be transformed into low-dimensional ones. And then it generates multiresolution texture features via the joint and concatenation of gLBP descriptors on different sampling parameters. A trained nearest neighbor classifier with chi-square distance is applied to classify textures by feature histograms. The experimental results of simulation on five public texture databases show that the proposed method is reliable and efficient in texture classification. In comparison with nine other similar approaches, including two state-of-the-art ones, the proposed method runs faster than most of them and also outperforms all of them in terms of classification accuracy and noise robustness. It achieves higher accuracy and has also better robustness to the Salt&Pepper and Gaussian noise added artificially into texture images.