Context Curves for Classification of Lung Nodule Images

Abstract

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.

Medical image classification with convolutional neural network

“…The feature vectors extracted are normally used to train a classification model, e.g. the support vector machine (SVM) [4], [11], [12], [17], [23], [25], [26] and sparse representation [10], [15], [16], [18], [20], [24], to obtain the image label.…”

Automatic Lung Nodule Detection Based on Statistical Region Merging and Support Vector Machines

“…Along with the proposed method, Table 1 contains the results of applying nine other nodule detection methods to similar datasets. The competing methods are based on Pixon-based segmentation (Hassanpour et al, 2011), template matching and neural classifier (Hasanabadi et al, 2014), hybrid features (Akram et al, 2016), Level-Set method (Silveira et al, 2007), a method based on genetic algorithm (GA) and SRM (Zehtabian and Ghassemian, 2016b), context curve calculation (Zhang et al, 2013), circular features based method (Mousa and Khan, 2002), a method based on threshold clustering and GA (de Carvalho et al, 2014), and a method based on artificial crawlers feature extraction and SVM (Froz et al, 2017). Among the competing methods, the first five methods have been implemented again by the authors of the present article on the new dataset(s), but under similar circumstances and conditions.…”

Lung cancer detection and classification on CT scan images using enhanced artificial bee colony optimization

“…Fan Zhang et.al. (2013) [15] suggested Support Vector Machine (SVM) based classifier by means of feature based imaging classification method, to categorize the lung nodules in Low Dose. Computed Tomography glides into four groups that are, well constrained, vascularised, juxtapleural and pleural-tail.…”