Two-dimensional reciprocal cross entropy multi-threshold combined with improved firefly algorithm for lung parenchyma segmentation of COVID-19 CT image. (September 2022) - Wang, Guowei; Guo, Shuli; Han, Lina; Cekderi, Anil Baris
Abstract: The lesions of COVID-19 CT image show various kinds of ground-glass opacity and consolidation, which are distributed in left lung, right lung or both lungs. The lung lobes are uneven and it have similar gray value to the surrounding arteries, veins, and bronchi. The lesions of COVID-19 have different sizes and shapes in different periods. Accurate segmentation of lung parenchyma in CT image is a key step in COVID-19 detection and diagnosis. Aiming at the unideal effect of traditional image segmentation methods on lung parenchyma segmentation in CT images, a lung parenchyma segmentation method based on two-dimensional reciprocal cross entropy multi-threshold combined with improved firefly algorithm is proposed. Firstly, the optimal threshold method is used to realize the initial segmentation of the lung, so that the segmentation threshold can change adaptively according to the detailed information of lung lobes, trachea, bronchi and ground-glass opacity. Then the lung parenchyma is further processed to obtain the lung parenchyma template, and then the defective template is repaired combined with the improved Freeman chain code and Bezier curve. Finally, the lung parenchyma is extracted by multiplying the template with the lung CT image. The accuracy of lung parenchyma segmentation has been improved in the contrast clarity of CT image and the consistency of lung parenchyma regional features, with an average segmentation accuracy rate of 97.4%. The experimental results show that for COVID-19 and suspected cases, the method has an ideal segmentation effect, and it has good accuracy and robustness. Highlights: A multi-threshold segmentation method based on two-dimensional reciprocal cross entropy is proposed to make up for the problem of undefined and zero value of Shannon cross entropy due to logarithm operation. Aiming at the problem the traditional histogram only considers the background and the target area, the edge and the noise information are ignored. This paper improves the segmentation of the binary histogram that is divided the four-region histogram into two-region of the target and the background, and the gray information is fully considered Traditional threshold segmentation algorithms only use the value of gray level in the image without considering the spatial neighborhood information of pixels, which greatly increases the ratio of misclassification between the target and the background of the gray image. Aiming at this problem, a weighted fuzzy threshold segmentation based on the spatial neighborhood information is proposed. Aiming at the problem that the standard firefly algorithm is easy to fall into local optimization, this paper improves the attraction term by using exponential distribution in the position movement and the exponential decreasing inertia weight to enhance the global detection ability. The step size monotone decreasing mode is used to improve the random item to enhance the local mining ability in the later stage of the optimization. The lung parenchyma template is repaired by the proposed eight-direction Freeman chain code combined with quadratic Bezier curve can make up for the under-segmentation problem when repairing large-area defects with straight lines. It has flexible adjustment to better meet the characteristics of lung parenchyma edge, which can achieve a good repair effect.