Abstract
Accurate malignancy level classification of lung nodules can reduce lung cancer mortality rate effectively. In this study, we present a fast and efficient CAD system to improve the performance of nodules malignancy level classification. Firstly, to reduce false positives (FPs), we propose a novel vessel segmentation method which measures vessel likelihood by tubular-like structures discriminating from multiple views. The method can recognize irregular vascular structures robustly and sensitively, and achieve fast vessel segmentation. In addition, a mathematical description for 3D pulmonary entities using neighbor centroids clustering is provided as a fundamental condition for spatial feature extraction. To optimize features extraction, we formulate a gray values cumulative function and a patches selection function based on the mathematical description, to generate axial spatial outline and spatial density distribution samples of the entities, respectively. Then, we use Edge Orientation Histogram (EOH) to extract edge features from the spatial outline and propose a multi-scale path LBP (MSPLBP) to extract the texture feature of the density distribution samples. Finally, the fused EOH and MSPLBP are classified into 6 malignancy levels by three state-of-the-art classifiers. The experimental results show that the vessel segmentation method achieves an average $F1\_Score$ of 78.14% and AUC value under $PR$ curves of 0.8149. Moreover, our system reaches an average $accuracy$ of 95.88% and consumes average 176.26 seconds for evaluating a CT set on malignancy level classification. These results indicate that the system can segment vessels exactly, and classify the malignancy level of nodules efficiently. Our system is the potential to be a powerful tool for early diagnosis of lung cancer.
Highlights
Cancer is defined as a relentless growth of abnormal cells in a specific tissue that can spread from the primary neoplasm to distant organs [1]
To optimize the texture feature extraction from the selected patches, we propose multi-scale path LBP (MSPLBP) based on path integral Local Binary Patterns (pi-LBP)
The experiment consists of two parts: (i) the evaluation of the proposed vessel segmentation method, and (ii) the comparative experiment among different malignancy level classification methods
Summary
Cancer is defined as a relentless growth of abnormal cells in a specific tissue that can spread from the primary neoplasm to distant organs [1]. Lung cancer causes 1.3 million deaths annually and has the highest mortality rate among all cancer-related diseases [2], [3]. Of 234,030 new cases of lung cancer occurred in 2018 and 66% of these patients died [3]. 80% of lung cancer patients are diagnosed in the advanced stages which results in the 5-year survival rate falling from 70% to 18% [4]–[6]. To solve this problem, classifying the malignancy level of lung nodules is clinically important because they are the crucial manifestations of lung cancer at an early stage. A diversity of radiographic characteristics of lung nodule e.g., size, margin, nodular
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