Implementation of CT Image Segmentation Based on an Image Segmentation Algorithm.

The objective of this research was to explore the effect of the treatment regimen of Spiriva combined with Symbicort on the immune function of non-small-cell lung cancer (NSCLC) based on computed tomography (CT) imaging features. An automatic CT image segmentation algorithm (RW-CT) was constructed based on random walk (RW) and image segmentation technology. The image segmentation algorithm based on the Toboggan method (C-CT) was introduced to compare with the traditional RW algorithm. 60 subjects were divided into four groups: a Chinese combined with Western medicine group (treated with Spiriva combined with Symbicort, group C+W), a Chinese medicine group (treated with Spiriva, group C), a Western medicine group (treated with Symbicort, group W), and a model group for control (group M). The results show that the Dice coefficient of the RW-CT algorithm was obviously larger than that of the C-CT algorithm and the RW algorithm, while the Hausdorff distance (HD) of the RW-CT algorithm was much smaller than that of the other two algorithms (P < 0.05). The proportion of positive cells of hypoxia-inducible factor-1α (HIF-1α) in group C+W was the least (15%-23%), followed by the group W (21%-29%) and the group C (28%-37%), and that in the group M was the highest (39%-49%). There was a remarkable difference in the immunohistochemical scores (HIS) of vascular endothelial growth factor (VEGF) in the tumor tissues between group C+W and the group M (H = 14.200, P = 0.001), but there was no great difference from the group C and the group W (P > 0.05). There was a notable difference in the IHS of vascular endothelial factor recepto-2 (VEGFR-2) between the group C+W medication group and the group M (H = 12.800, P = 0.002), and there was no statistical difference between the group C and W (P > 0.05). In short, the RW-CT constructed based on RW was better than the traditional algorithms for CT image segmentation. The Spiriva combined with Symbicort could effectively inhibit the expression of VEGF, VEGFR-2, and HIF-1α in NSCLC and promote the immunologic function of the body.

Computed tomography (CT) is an important imaging technique used in medical analysis of the internal structure of the human body. Previously, image segmentation methods were required after acquiring reconstructed CT images to obtain segmented CT images which made it susceptible to errors from both reconstruction and segmentation algorithms. However, this paper introduces a new approach using an advanced quantum optimization algorithm called quadratic unconstrained binary optimization (QUBO) for CT image segmentation. This algorithm allows CT image reconstruction and segmentation to be performed simultaneously. This algorithm segments CT images by minimizing the difference between a sinogram in a superposition state with qubits, obtained using the mathematical projection including the Radon transform, and the experimentally acquired sinogram from X-ray images for various angles. Furthermore, we leveraged X-ray mass attenuation coefficients to reduce the number of logical qubits required for our quantum optimization algorithm, and we employed D-Wave’s hybrid solver to solve the optimization problem. We compared the segmentation results of our algorithm with those of classical algorithms using X-ray images of actual tooth samples to validate the results of our algorithm. The comparison revealed that, after undergoing appropriate image post-processing, our algorithm’s segmentation results matched those of classical algorithms that perform segmentation after reconstruction, except for some pixels at the boundary. We expect that the new quantum optimization CT algorithm will bring about great advancements in medical imaging.

In order to solve the problem of foreground and background segmentation of soil images under complex backgrounds, this paper proposes an image segmentation algorithm that combines colour features and local blur characteristics. First, the collected field soil images are converted from the RED colour space to the HSL colour space and the H component features are extracted. The H component is then converted to the CIELAB colour space. The b* component image is selected as a binary value based on the soil colour characteristics Pre-processing of H component. Then, the image fusion is performed according to the H component features, the b* binarization result, and the H-component local fuzzy variance pre-processing result to obtain a fusion result. Finally, in order to solve the hole problem in the segmentation result, the fusion result is subjected to boundary recognition and maximum boundary extraction, and the obtained maximum boundary region is filled to obtain the final image segmentation result. In order to test the segmentation effect of the proposed algorithm, Otsu is used to perform threshold segmentation on the soil image. The average time consumption of the two algorithms is 2.360 and 4.48. Comparing the time consumption of the two segmentation algorithms and the segmentation effect of the soil image, the experimental results of the proposed algorithm are significantly better than the Otsu segmentation algorithm.

Clustering-based segmentation algorithm is one of the common methods in image segmentation. However, how to avoid the influence of the initialization and the singular value is a problem we have to face. For overcoming this problem, we proposed a new image segmentation technology which is based on the AIC criterion and not affected by the singular values. The simulation results show that the method has good performance on stability and accuracy. Image segmentation is the key step in image analysis of computer vision and image processing field. It has been widely applied in various areas such as in the process of automating production, sensing images and biomedical images. The main goal of image segmentation is to simplify an image into segments that have a strong correlation with objects in the real world. The existing segmentation algorithm can be generally divided into three classes: edge detection, region- based segmentation and data clustering. Edge detection method relies on the edges of the object with background and the other objects. For the images with noise, it is difficult to separate the boundary and the target background (1). Region- based segmentation method segments an image into several meaningful sub-domains that are non-overlapping and same- nature. But it may lead to the over-segmentation, and has to combine with other methods to use (2). Data clustering method is based on the whole image and considers the distance between each data. Advantages of data clustering method are low complexity and easy to implement. Disadvantages are sensitive to the noise and selection of the initial centroids (3). Data clustering is one of the common techniques in image segmentation. Its purpose is to cluster pixels into several parts (regions) according to the feature of image. Based on the properties of clustering algorithm, researchers have proposed various image segmentation algorithm, such as based on hierarchical clustering, based on K-means clustering, based on fuzzy clustering, based on mean shift, and so on (3-5). Clustering analysis is an unsupervised process of partitioning a data set into subsets of similar data objects. The elementary principle is as far as possible to increase the difference between categories and reduce the difference within the category. In the process of image segmentation based on data clustering, thus the objective is that the similar pixels are divided into the same regions and the non-similar pixels are divided into different regions. However, Two difficulties we must face are that how to determine the cluster centers and avoid influence of noise. In this paper, a new image segmentation algorithm is proposed based on the AIC criterion. The new segmentation technology can achieve an ideal performance and not affected by the initialization of centroids and the singular values. The simulation results verify the efficiency of proposed algorithm.

PurposeCorona virus disease 2019 (COVID‐19) is threatening the health of the global people and bringing great losses to our economy and society. However, computed tomography (CT) image segmentation can make clinicians quickly identify the COVID‐19‐infected regions. Accurate segmentation infection area of COVID‐19 can contribute screen confirmed cases.MethodsWe designed a segmentation network for COVID‐19‐infected regions in CT images. To begin with, multilayered features were extracted by the backbone network of Res2Net. Subsequently, edge features of the infected regions in the low‐level feature f 2 were extracted by the edge attention module. Second, we carefully designed the structure of the attention position module (APM) to extract high‐level feature f 5 and detect infected regions. Finally, we proposed a context exploration module consisting of two parallel explore blocks, which can remove some false positives and false negatives to reach more accurate segmentation results.ResultsExperimental results show that, on the public COVID‐19 dataset, the Dice, sensitivity, specificity, Sα, E∅mean, and mean absolute error (MAE) of our method are 0.755, 0.751, 0.959, 0.795, 0.919, and 0.060, respectively. Compared with the latest COVID‐19 segmentation model Inf‐Net, the Dice similarity coefficient of our model has increased by 7.3%; the sensitivity (Sen) has increased by 5.9%. On contrary, the MAE has dropped by 2.2%.ConclusionsOur method performs well on COVID‐19 CT image segmentation. We also find that our method is so portable that can be suitable for various current popular networks. In a word, our method can help screen people infected with COVID‐19 effectively and save the labor power of clinicians and radiologists.

Diffusion coupling model based on neural oscillation synchronization theory, which can extract different targets with different gray levels from one, has been an important way for realizing image segmentation. This paper describes the diffusion coupling model and its algorithm and process for image segmentation. Simulation programs for supplementing this algorithm were compiled based on MATLAB platform. Experimental results on synthetic images, infrared images and brain magnetic resonance images show that, by setting reasonable algorithm parameters, the target image extraction and segmentation of different elements can be achieved. This image segmentation algorithm exhibits wide applications in medical image diagnosis, satellite image processing, traffic control, biometrics and others.

This study investigates the impact of image complexity on the accuracy of interactive image segmentation algorithms. Image complexity plays a crucial role in segmentation performance, yet previous studies have primarily relied on subjective methods, leaving a gap in understanding how objective measures impact accuracy. The purpose of this research is to explore the relationship between image complexity and segmentation performance and to propose an adaptive approach for improving accuracy based on complexity measures. The study utilizes objective measures, namely entropy and fractal dimension, to quantify image complexity. An interactive image segmentation algorithm is employed, with a bounding box as the background and strokes as the foreground annotations. The number of strokes is dynamically adjusted based on complexity measures, ensuring a tailored segmentation approach. Comparative evaluations are conducted to assess the effectiveness of dynamic and fixed stroke allocation strategies. The principal results reveal a significant influence of image complexity on segmentation accuracy. The dynamic stroke allocation strategy outperforms fixed allocation, highlighting the importance of adapting to complexity. Moreover, the optimal combination of strokes and superpixel sizes is explored, providing valuable insights for practitioners. The findings emphasize the need to consider image complexity in segmentation algorithms to achieve accurate results. In conclusion, this study contributes to the understanding of the relationship between image complexity and interactive image segmentation. By introducing a dynamic stroke allocation approach and evaluating different configurations, the research provides insights into optimizing accuracy based on image complexity. The adaptive strategy improves segmentation performance and guides the development of robust algorithms. Future research can further refine the adaptive approach, explore additional complexity measures, and incorporate advanced machine learning techniques to enhance interactive image segmentation. Overall, this study advances the field by highlighting the importance of image complexity, providing guidance for practitioners, and paving the way for more efficient segmentation algorithms.

The purpose of the research is to develop a universal algorithm for partial texture segmentation of any visual images. The main peculiarity of the proposed segmentation procedure is the extraction of only homogeneous fine-grained texture segments present in the images. At first, an initial seed point is found for the largest and most homogeneous segment of the image. This initial seed point of the segment is expanded using a region growing method. Other texture segments of the image are extracted analogously in turn. At the second stage, the procedure of merging the extracted segments belonging to the same texture class is performed. Then, the detected texture segments are input to a neural network with competitive layers which accomplishes more accurate delineation of the shapes of the extracted texture segments. The proposed segmentation procedure is fully unsupervised, i.e., it does not use any a priori knowledge on either the type of textures or the number of texture segments in the image. The research results in development of the segmentation algorithm realized as a computer program tested in a series of experiments that demonstrate its efficiency on grayscale natural scenes.

In mineral processing, froth characteristics are important for flotation results. To monitor the variation of froth characteristics, an optical system has been developed. In the flotation system, one important procedure for image processing and analysis is image segmentation. This paper presents four newly developed segmentation algorithms for froth images. Results of image segmentation were compared to manual segmentation results, and comparisons show that the newly developed algorithms work well for the tested froth images.

Images acquired from actual manufacturing practice are difficult to segment for uncertainty surface quality of work-piece and unstable imaging environment, which may result in limited robustness for traditional image segmentation algorithms. Nevertheless, the result of image segmentation will affect the precision of subsequent feature extraction, image analysis and robot positioning. To address the above issues, an adaptive multi-threshold segmentation algorithm for complex images under unstable imaging environment is proposed to further improve the stability of an industrial vision system in a given inspection scheme. The proposed approach consists of three basic parts and each of them is indispensable for achieving high accuracy. Firstly, curve fitting with cubic spline interpolation to determine peaks and troughs of grey histogram by calculating function extremums, and then the higher greyscale between top-peak and sub-peak is selected as the initial lower threshold. Secondly, the initial upper threshold is constrained by calculating the probability density distribution of ROI. Thirdly, the upper threshold and lower threshold are iteratively calculated until the threshold range of ROI is achieved. The proposed algorithm was compared to state-of-the-art segmentation approaches in both synthetic and real images to demonstrate its superior performance.

The topic of colour image segmentation has been and still is a hot issue in areas such as computer vision and image processing because of its wide range of practical applications. The urge has led to the development of numerous colour image segmentation algorithms to extract salient objects from colour images. However, because of the diverse imaging conditions in varying application domains, accuracy and robustness of several state-of-the-art colour image segmentation algorithms still leave room for further improvement. This dissertation reports on the development of a new image segmentation algorithm based on perceptual colour difference saliency along with binary morphological operations. The algorithm consists of four essential processing stages which are colour image transformation, luminance image enhancement, salient pixel computation and image artefact filtering. The input RGB colour image is first transformed into the CIE L*a*b colour image to achieve perceptual saliency and obtain the best possible calibration of the transformation model. The luminance channel of the transformed colour image is then enhanced using an adaptive gamma correction function to alleviate the adverse effects of illumination variation, low contrast and improve the image quality significantly. The salient objects in the input colour image are then determined by calculating saliency at each pixel in order to preserve spatial information. The computed saliency map is then filtered using the morphological operations to eliminate undesired factors that are likely present in the colour image. A series of experiments was performed to evaluate the effectiveness of the new perceptual colour difference saliency algorithm for colour image segmentation. This was accomplished by testing the algorithm on a large set of a hundred and ninety images acquired from four distinct publicly available benchmarks corporal. The accuracy of the developed colour image segmentation algorithm was quantified using four widely used statistical evaluation metrics in terms of precision, F-measure, error and Dice. Promising results were obtained despite the fact that the experimental images were selected from four different corporal and in varying imaging conditions. The results have indeed demonstrated that the performance of the newly developed colour image segmentation algorithm is consistent with an improved performance compared to a number of other saliency and non- saliency state-of-the-art image segmentation algorithms.

Due to the need of correct diseases analysis, MR image segmentation remains till now a challenging problem, especially in the presence of random noise. This paper proposes a new meta-heuristic algorithm for MR brain image segmentation, named Modified Shuffled Frog Leaping Algorithm (MSFLA), based on the technique of Shuffled Frog Leaping Algorithm (SFLA). In this new paradigm, there is no need to filter the original image. The new fitness function proposed in our algorithm helps to evaluate quickly the particle frogs in order to arrange them in descending order. The proposed approach has been compared with other meta-heuristics such as 3D-Otsu thresholding with SFLA and Genetic Algorithm (GA) and also with the algorithm of segmentation using the Rician Classifier (RiCE). Experimental results show that the proposed MSFLA is able to achieve better segmentation quality and execution time than the latest methods.

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Summary form only given, as follows. Building on our image restoration and segmentation algorithms developed in Pollak et al. (2000), we present a very simple nonlinear diffusion equation and show its utility both for image segmentation and for the detection of abrupt changes in 1-D. We show that it may be interpreted as a variant of the Perona-Malik (1990) equation, as the steepest descent equation for the total variation (Bouman and Sauer 1991, Rudin et al. 1992), and-in 1-D-as a solver of a simple version of the Mumford-Shah (1985) problem. The analysis of our equation in 1-D reveals it to be an exact solver of certain maximum likelihood detection/estimation problems. The major advantage over other methods to solve these problems is O(N log N) computational complexity in one spatial dimension. Finally, we show our method to be a robust estimator (in the spirit of H-infinity estimation (Nagpal and Khargonekar 1991)) for a restricted class of 1-D problems. Experiments suggest that the 2-D version of our algorithm retains robustness properties of the 1-D version. Moreover, if only a binary segmentation is required, the computational complexity of our 2-D algorithm is still O(N log N). A remaining challenge is to extend our probabilistic analysis of the 1-D algorithm to 2-D, designing fast and optimal image segmentation algorithms.

The concept of the shortest (or minimum) spanning tree (SST)and recursive SST (RSST) of an undirected weighted graph has been successfully applied in image segmentation and edge detection. This paper presents a divide-and-conquer approach for (R)SST based image segmentation in order to overcome the problem of high computational complexity associated with conventional graph algorithms. In the simplest form, the proposed approach, block-based RSST (BRSST), first divides the image into rectangular blocks, finds the (R)SST of each block individually using conventional graph algorithms and, then, merges the (R)SSTs of all image blocks to form an (R)SST of the entire image. Efficient merging algorithms are presented in this paper. We proved a theorem showing that the (R)SST obtained by the merging algorithms is one of the (R)SST that would be found by applying to the entire image the same algorithm used for finding the (R)SST of each image block. Theoretical analysis and experimental results have shown that BRSST has significantly reduced the computational cost. In addition, an incremental BRSST is proposed for video segmentation and results are presented