Abstract
The spiculation sign is one of the main signs to distinguish benign and malignant pulmonary nodules. In order to effectively extract the image feature of a pulmonary nodule for the spiculation sign distinguishment, a new spiculation sign recognition model is proposed based on the doctors' diagnosis process of pulmonary nodules. A maximum density projection model is established to fuse the local three-dimensional information into the two-dimensional image. The complete boundary of a pulmonary nodule is extracted by the improved Snake model, which can take full advantage of the parallel calculation of the Spike Neural P Systems to build a new neural network structure. In this paper, our experiments show that the proposed algorithm can accurately extract the boundary of a pulmonary nodule and effectively improve the recognition rate of the spiculation sign.
Highlights
A pulmonary nodule is an early pattern of lung cancer
The diagnosis of benign and malignant pulmonary nodules can be divided into imaging detection and “biopsy.” The most accurate detection method is “biopsy,” but it cannot predict the development trend of pulmonary nodules
All the experimental data are from the database of the International Early Lung Cancer Action Project and the American Association of Lung Imaging Databases, as shown in Figure 4. 514 pulmonary nodules with spiculation signs and 501 pulmonary nodules without spiculation signs are labeled by two professional doctors as the detection basis
Summary
A pulmonary nodule is an early pattern of lung cancer. Malignant lesions might occur in some pulmonary nodules and even threaten patients’ lives seriously [1]. The spiculation sign is the feature of a pulmonary nodule. It is a main method to predict the development trend of benign and malignant pulmonary nodules from the perspective of imaging [5, 6]. CT cannot accurately locate the signs of pulmonary nodules and make accurate judgment Aiming at this problem, a density projection algorithm is proposed to integrate local 3D information into two-dimensional images for accurate diagnosis. Brandman and Ko [9] establish a complete process including the detection of pulmonary nodules and the distinguishment and management of signs. In this paper, a spiculation sign recognition algorithm is proposed after studying the doctors’ diagnosis process of pulmonary nodules. (3) A neural network framework based on the Spike Neural P Systems is constructed through focusing on boundary features of pulmonary nodules In this paper, a spiculation sign recognition algorithm is proposed after studying the doctors’ diagnosis process of pulmonary nodules. (1) A maximum intensity projection model is established to fuse the threedimensional information into the two-dimensional image to reduce the missed rate of spiculation signs. (2) The accurate extraction of pulmonary nodules can be realized by the improved Snake model to strengthen the boundary effect. (3) A neural network framework based on the Spike Neural P Systems is constructed through focusing on boundary features of pulmonary nodules
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