###### Abstract  
In order to detect the size and shape of defects inside wood, an image reconstruction method based on segmented propagation rays of stress waves is proposed. The method uses sensors to obtain the stress wave velocity data by hanging around the timber equally, visualizes those data, and reconstructs the image of internal defects with the visualized propagation rays. The basis of the algorithm is precisely segmenting the rays to benefit the spatial interpolation. First, a ray segmentation algorithm using the elliptical neighborhood technique is proposed, which can be used to segment the rays and estimate the velocity values of segmented rays by the nearby original rays using elliptical zones. Second, a spatial interpolation algorithm utilizing a segmented ellipse according to the segmented rays is also proposed, which can be used to estimate the velocity value of a grid cell by the segmented ellipses corresponding to the nearby segmented rays. Then, the image of the internal defect inside the wood is reconstructed. Both simulation and experimental data were used to evaluate the proposed method, and the area and shape of the imaging results were analyzed. The comparison results show that the proposed method can produce high quality reconstructions with clear edges and high accuracy. image reconstruction; wood internal defects; stress wave; segmented propagation rays; spatial interpolation  
around hollows, increasing the transmission time between two testing points [13; 15; 16; 17]. Based on this fundamental conclusion and signal acquisition of stress wave propagation velocity in wood cross sections, the horizontal distribution of the stress wave velocity in wood can be analyzed and images of wood internal defects can also be reconstructed [18; 19; 20]. The image reconstruction algorithm is the key step of stress wave nondestructive testing technology. [PERSON] et al. [21] proposed an image reconstruction method for detecting wood internal decay. The method uses the Kriging interpolation algorithm, which estimates the velocity values of the unknown grid cells based on the surrounding information. [PERSON] et al. [22] visualized the elastic waves that propagate in a trunk during a tomographic measurement by numerical simulations. The numerical model enabled investigation of the influence of decay on tomographic measurements while neglecting the heterogeneity of wood. [PERSON] et al. [23] analyzed the envelope peak value of the first arrival wave in the stress wave signal, determined the location of the defect, drew the general outline of the cavity, and verified the reliability of the method by experiments on camphor trees. [PERSON] et al. [24] collected stress wave velocity signals of tree cross-sections using professional equipment, and proposed a stress wave imaging algorithm named IABLE, which calculated the grid distribution of wave velocity in tree faults by an iterative inversion method. [PERSON] et al. [25] proposed a stress save tomography method of wood internal defects using ellipse-based spatial interpolation and velocity compensation. The algorithm is good at processing the regions near the sensors and can resist signal interference. Image reconstruction for stress wave tomography is a nonlinear problem, and the signals for imaging algorithms are usually sparse. Therefore, the solutions for stress wave tomography are not unique [26; 27]. Although several image reconstruction methods for stress wave tomography have been proposed, the accuracy of imaging is still not satisfactory. For example, when an internal defect is present in the tree trunk, the existing stress wave tomography methods tend to overestimate its size [28; 29]. In addition, the quality of images taken near the sensor is significantly lower than those taken in the middle part of a trunk using stress wave velocity and the tomography method [30]. The objective of this study is to improve the quality of reconstructed imaging of internal defects in wood using stress wave technology.