In this paper, we propose a novel classification method for the four types of lung nodules, i.e., well-circumscribed, vascularized, juxta-pleural, and pleural-tail, in low dose computed tomography scans. The proposed method is based on contextual analysis by combining the lung nodule and surrounding anatomical structures, and has three main stages: an adaptive patch-based division is used to construct concentric multilevel partition; then, a new feature set is designed to incorporate intensity, texture, and gradient information for image patch feature description, and then a contextual latent semantic analysis-based classifier is designed to calculate the probabilistic estimations for the relevant images. Our proposed method was evaluated on a publicly available dataset and clearly demonstrated promising classification performance.

Distinguishing malignant lung nodules from benign nodules is an important aspect of lung cancer diagnosis. In this paper, we propose an automatic method to classify lung nodules into four different types, i.e. well-circumscribed, juxta-vascular, juxta-pleural and pleural-tail. Additionally, since the morphology of lung nodules forms a continuum between the different types, our proposed method is superior to previous methods that classify single nodules into a single type. First, a weighted similarity network is constructed based on the SVM with probability estimates, turning the 128-length SIFT descriptor to a 4-length probability vector against the four types. Then, the classification of nodules while identifying those with overlapping types is made using the weighed Clique Percolation Method (CPMw). We evaluate the proposed method on low-dose CT images from ELCAP. Our results show that there is more overlap between well-circumscribed and juxta-vascular, and between juxta-pleural and pleural tail. Also, quantitative comparisons among various methods demonstrate highly effective nodule classification results by identifying the overlapping nodule types.

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In this paper, a feature-based imaging classification method is presented to classify the lung nodules in low dose computed tomography (LDCT) slides into four categories: wellcircumscribed, vascularized, juxta-pleural and pleural-tail. The proposed method focuses on the feature design, which describes both lung nodule and surrounding context information, and contains two main stages: (1) superpixel labeling, which labels the pixels into foreground and background based on an image patch division approach, (2) context curve calculation, which transfers the superpixel labeling result into feature vector. While the first stage preprocesses the image, extracting the major context anatomical structures for each type of nodules, the context curve provides a discriminative description for intra-and inter-type nodules. The evaluation is conducted on a publicly available dataset and the results indicate the promising performance of the proposed method on lung nodule classification.

We present herein a high-level ab initio study on the electronic excited states of CHCl using the internally contracted multireference configuration interaction method including Davidson correction (icMRCI+Q). A total of 13 electronic states with energy of up to 7 eV have been investigated. The vertical transition energies, oscillator strengths, electron configurations, and transitions of the electronic states of CHCl have been calculated at the icMRCI+Q/aug-cc-pv(5+d)Z level. The potential energy curves of the electronic states have been studied along the H-C-Cl angle, the C-H bond length, and the C-Cl bond length, respectively. Our theoretical study has provided comprehensive information for understanding the interaction and the behavior of the electronic excited states of CHCl. In particular, the excited state involved in the 193 nm photodissociation as well as the corresponding dissociation dynamics have been discussed on the basis of our calculation results. The present study should shed more light on the photochemistry of CHCl in the ultraviolet region.