Abstract
Pulmonary nodules are the main pathological changes of the lung. Malignant pulmonary nodules will be transformed into lung cancer, which is a serious threat to human health and life. Therefore, the detection of pulmonary nodules is of great significance to save lives. However, in the face of a large number of lung CT image sequences, doctors need to spend a lot of time and energy, and in the detection process will inevitably produce the problem of false detection and missed detection. Therefore, it is very necessary for computer-aided doctors to detect pulmonary nodules. It is difficult to segment pulmonary nodules accurately and recognize the characteristics of pulmonary nodules in CT images. A complete set of semi-automatic lung nodule extraction and feature identification system is established, which is in line with the doctor’s diagnosis process. A segmentation algorithm of pulmonary nodules based on regional statistical information is proposed to extract pulmonary nodules accurately. This is the first time that dynamic time warping algorithm is applied in the field of image processing, focusing on the lung nodule boundary. On this basis, the recursive graph visualization model is established to realize the visualization of boundary similarity. Finally, in order to accurately identify the characteristics of pulmonary nodules, a video similarity distance discrimination system is introduced to quantify the similarity between the nodules to be examined and the pulmonary nodules in the database. The experimental results show that the algorithm can accurately identify the normal shape, lobulated shape and lobulated shape of pulmonary nodules. The average processing speed is 0.58s/nodule. To some extent, it can reduce the misdiagnosis caused by experience and fatigue.
Highlights
Lung cancer is a common malignant tumor with high morbidity, rapid growth and other features, and it has huge obstacle and hidden danger to human life and health [1], [2]
For the shape recognition of pulmonary nodules, the edge of the pulmonary nodules is unfolding from different angles, which will produce different time series, but the dynamic time warping (DTW) algorithm can only search for similar areas nearby
We propose to use the recurrence plot (RP) [34] method to study the boundary sequence of pulmonary nodules, so as to make full use of the chaotic, non-stationary and periodicity of the boundary, and better represent the internal structure of the sequence
Summary
Lung cancer is a common malignant tumor with high morbidity, rapid growth and other features, and it has huge obstacle and hidden danger to human life and health [1], [2]. The isolated pulmonary nodule which divided into benign and malignant [3] is the most common manifestations of lung cancer in the early stages. The detection of pulmonary nodules through observing CT images, and the discrimination of pulmonary nodules characteristics is the main and effective method to determine benign and malignant pulmonary nodules [4]. Liu et al [13] constructed classifiers based on 24 pulmonary nodule image features to distinguish pulmonary nodules characteristics. 2) The more image features used, the importance of the boundary information in distinguishing pulmonary nodule characteristics are weakening. We conducted a study based on the physical’s process of distinguishing pulmonary nodules characteristics, focusing on the boundary information, and using the principle of similarity to intuitively display and distinguish pulmonary nodules characteristics
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