CT Image Segmentation Method of Composite Material Based on Improved Watershed Algorithm and U-Net Neural Network Model

In the study of the composite materials performance,X-ray computed tomography(XCT)scanning has always been one of the important measures to detect the internal structures.CT image segmentation technology will effectively improve the accuracy of the subsequent material feature extraction process,which is of great significance to the study of material performance.This study focuses on the low accuracy problem of image segmentation caused by fiber cross-section adhesion in composite CT images.In the core layer area,area validity is evaluated by morphological indicator and an iterative segmentation strategy is proposed based on the watershed algorithm.In the transition layer area,a U-net neural network model trained by using artificial labels is applied to the prediction of segmentation result.Furthermore,a CT image segmentation method for fiber composite materials based on the improved watershed algorithm and the U-net model is proposed.It is verified by experiments that the method has good adaptability and effectiveness to the CT image segmentation problem of composite materials,and the accuracy of segmentation is significantly improved in comparison with the original method,which ensures the accuracy and robustness of the subsequent fiber feature extraction process.

Ore Image Segmentation Method Based on U-Net and Watershed

Ore image segmentation is a key step in an ore grain size analysis based on image processing.The traditional segmentation methods do not deal with ore textures and shadows in ore images well Those methods often suffer from under-segmentation and over-segmentation.In this article,in order to solve the problem,an ore image segmentation method based on U-Net is proposed.We adjust the structure of U-Net to speed up the processing,and we modify the loss function to enhance the generalization of the model.After the collection of the ore image,we design the annotation standard and train the network with the annotated image.Finally,the marked watershed algorithm is used to segment the adhesion area.The experimental results show that the proposed method has the characteristics of fast speed,strong robustness and high precision.It has great practical value to the actual ore grain statistical task.

3D reconstruction method based on contour features

To guarantee the accuracy and real-time of the 3D reconstruction method for outdoor scene,an algorithm based on region segmentation and matching was proposed.Firstly,on the basis of morphological gradient information,obtained by comparing color weight gradient images and proposing a multi-threshold segmentation,scene contour features were extracted by a watershed algorithm and a fuzzy c-means clustering algorithm.Secondly,to reduce the search area,increase the correct matching ratio and accelerate the matching speed,the region constraint was established according to a region＇s local position,area and gray characteristics,the edge pixel constraint was established according to the epipolar constraint and the continuity constraint.Finally,by using the stereo matching edge pixel pairs,their 3D coordinates were estimated according to the binocular stereo vision imaging model.Experimental results show that the proposed method can yield a high stereo matching ratio and reconstruct a 3D scene quickly and efficiently.

A method of automatic image segmentation for gas bubbles in water

This paper presents an algorithm of automatic bubble image segmentation using the improved ant colony optimization methodology. The ant colony optimization method is a metaheuristic algorithm, and has been applied in many fields. To reveal the versatility and appropriateness of automatic bubble image segmentation, the fuzzy clustering analysis method is employed in ant colony optimization algorithm. Compared with the well-known image feature extraction operators such as SUSAN and Canny, the proposed method can comparatively suitable to extract the gas bubbles image edge features. The experimental results show that the proposed method is effective and reliable, and can achieve satisfactory image edge extraction effect.

Interactive Liver Segmentation Algorithm Based on Geodesic Distance and V-Net

Convolutional neural networks(CNNs)are prone to mis-segmenting image data of the liver when the background is complicated,which results in low segmentation accuracy and unsuitable results for clinical use.To address this shortcoming,an interactive liver segmentation algorithm based on geodesic distance and V-net is proposed.The three-dimensional segmentation network V-net adequately considers the characteristics of the spatial context information to segment liver medical images and obtain preliminary segmentation results.An artificial algorithm based on geodesic distance is used to form artificial hard constraints to modify the image,and the superpixel piece created by the watershed algorithm is introduced as a sample point for operation,which significantly improves the efficiency of segmentation.Results from simulation of the liver tumor segmentation challenge(LiTS)dataset show that this algorithm can effectively refine the results of automatic liver segmentation,reduce user intervention,and enable a fast,interactive liver image segmentation that is convenient for doctors.

Image segmentation method for coal particle size distribution analysis

Particle size distribution is extremely important in the coal preparation industry.It is traditionally analysed by a manual screening method,which is relatively time-consuming and cannot immediately guide production.In this paper,an image segmentation method for images of coal particles is proposed.It employs the watershed algorithm,k-nearest neighbour algorithm,and convex shell method to achieve preliminary segmentation,merge small pieces with large pieces,and split adhered particles,respectively.Comparing the automated segmentation using this method with manual segmentation,it is found that the results are comparable.The size distributions obtained by the automated and manual segmentation methods are nearly identical,and the standard deviation is less than 3%,indicating good reliability.This automated image segmentation method provides a new approach for rapidly analysing the size distribution of coal particles with size fractions defined according to consumer requirements.