Abstract
The lung organ of human anatomy captured by a medical device reveals inhalation and exhalation information for treatment and monitoring. Given a large number of slices covering an area of the lung, we have a set of three-dimensional lung data. And then, by combining additionally with breath-hold measurements, we have a dataset of multigroup CT images (called 4DCT image set) that could show the lung motion and deformation over time. Up to now, it has still been a challenging problem to model a respiratory signal representing patients' breathing motion as well as simulating inhalation and exhalation process from 4DCT lung images because of its complexity. In this paper, we propose a promising hybrid approach incorporating the local binary pattern (LBP) histogram with entropy comparison to register the lung images. The segmentation process of the left and right lung is completely overcome by the minimum variance quantization and within class variance techniques which help the registration stage. The experiments are conducted on the 4DCT deformable image registration (DIR) public database giving us the overall evaluation on each stage: segmentation, registration, and modeling, to validate the effectiveness of the approach.
Highlights
Nowadays, diseases of the respiratory system have been increasing because of more and more pollution in many cities
We suggest an approach using local binary pattern (LBP) and entropy error evaluation (EEE) for registration and modeling 4DCT images into a breathing signal without using any landmark
Minimum variance quantization (MVQ) and within class variance (WCV) methods are applied for segmentation effectively and precisely
Summary
Diseases of the respiratory system have been increasing because of more and more pollution in many cities. An approach to model or visualize a respiratory cycling process from 4DCT images for diagnosis is highly encouraged but still has a lot of challenges. Researchers in this field try to investigate and solve the problem, the results are still rather limited and unsatisfied. In the paper [2], in 2020, Peng et al applied two processes to extract coarse lung contours first and refine the segmentation depending on the basis of the principal curve model. We suggest an approach using local binary pattern (LBP) and entropy error evaluation (EEE) for registration and modeling 4DCT images into a breathing signal without using any landmark. It will help a doctor track or monitor a patient respiratory process for an accurate treatment plan
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