一种新的基于蒙特卡罗模拟和光子传输模型的图像分割算法

本文以蒙特卡罗模拟方法为基础,结合组织光学的光子传输模型,提出了一种新的图像分割算法,该算法将复杂的图像分割问题简化为大量简单的光子传输随机实验,通过分析传输规律来获取目标区域.在随后的实验中,结合细胞核提取这一问题建立了一个简单的光学传输模型,并依据此模型分别对人造图和实际图进行了分割.人造图的分割结果表明了该算法的可行性,说明了该算法的一些优点;而实际图的分割结果则反映了该算法的不足之处,文章针对其中存在的问题和算法待改进之处进行了分析.     

基于最大熵和改进PCNN的医学图像自动分割算法研究与实现

本文提出了基于最大熵和改进的PCNN(Pulse Coupled Neural Network)相结合的新方法,采用最大熵确定PCNN网络的循环迭代次数.提出的方法无需考虑PCNN参数的选择,可有效的自动分割各种医学图像,同时利用最大熵得到最优分割结果.该方法对于PCNN理论在医学图像分割领域的应用有着重要的意义.     

基于Snake模型的医学图像分割

基于Snake模型的图像分割技术是近年来图像处理领域的研究热点之一。Snake模型承载上层先验知识并融合了图像的底层特征,针对医学图像的特殊性,能有效地应用于医学图像的分割中。本文对各种基于Snake模型的改进算法和进化模型进行了研究,并重点梳理了最新的研究成果,以利于把握基于Snake模型的医学图像分割方法的脉络和发展方向。     

Fundus Retinal Vessels Image Segmentation Method Based on Improved U-Net

ObjectivesAlthough the segmentation of retinal vessels in the fundus is of great significance for screening and diagnosing retinal vascular diseases, it remains difficult to detect the low contrast and the information around the lesions provided by retinal vessels in the fundus and to locate and segment micro-vessels in the fine-grained area. To overcome this problem, we propose herein an improved U-Net segmentation method NoL-UNet.Material and methodsThis work introduces NoL-UNet. First of all, the ordinary convolution block of the U-Net network is changed to random dropout convolution blocks, which can better extract the relevant features of the image and effectively alleviate the network overfitting. Next, a NoL-Block attention mechanism added to the bottom of the encoding-decoding structure expands the receptive field and enhances the correlation of pixel information without increasing the number of parameters.ResultsThe proposed method is verified by applying it to the fundus image datasets DRIVE, CHASE_DB1, and HRF. The AUC for DRIVE, CHASE_DB1 and HRF is 0.9861, 0.9891 and 0.9893, Se for DRIVE, CHASE_DB1 and HRF is 0.8489, 0.8809 and 0.8476, and the Acc for DRIVE, CHASE_DB1 and HRF is 0.9697, 0.9826 and 0.9732, respectively. The total number of parameters is 1.70M, and for DRIVE, it takes 0.050s to segment an image.ConclusionOur method is statistically significantly different from the U-Net method, and the improved method shows superior performance with better accuracy and robustness of the model, which has good practical application in auxiliary diagnosis.     

An Improved Model for the hTERT Promoter Quadruplex

Mutations occur at four specific sites in the hTERT promoter in >75% of glioblastomas and melanomas, but the mechanism by which the mutations affect gene expression remains unexplained. We report biophysical computational studies that show that the hTERT promoter sequence forms a novel G-quadruplex structure consisting of three contiguous, stacked parallel quadruplexes. The reported hTERT mutations map to the central quadruplex within this structure, and lead to an alteration of its hydrodynamic properties and stability.     

An Algorithm to Automate Yeast Segmentation and Tracking

Our understanding of dynamic cellular processes has been greatly enhanced by rapid advances in quantitative fluorescence microscopy. Imaging single cells has emphasized the prevalence of phenomena that can be difficult to infer from population measurements, such as all-or-none cellular decisions, cell-to-cell variability, and oscillations. Examination of these phenomena requires segmenting and tracking individual cells over long periods of time. However, accurate segmentation and tracking of cells is difficult and is often the rate-limiting step in an experimental pipeline. Here, we present an algorithm that accomplishes fully automated segmentation and tracking of budding yeast cells within growing colonies. The algorithm incorporates prior information of yeast-specific traits, such as immobility and growth rate, to segment an image using a set of threshold values rather than one specific optimized threshold. Results from the entire set of thresholds are then used to perform a robust final segmentation.     

An Improved Pigeon-Inspired Optimization for Multi-focus Noisy Image Fusion

Image fusion technology is the basis of computer vision task,but information is easily affected by noise during transmission.In this paper,an Improved Pigeon-Inspired Optimization(IPIO)is proposed,and used for multi-focus noisy image fusion by combining with the boundary handling of the convolutional sparse representation.By two-scale image decomposition,the input image is decomposed into base layer and detail layer.For the base layer,IPIO algorithm is used to obtain the optimized weights for fusion,whose value range is gained by fusing the edge information.Besides,the global information entropy is used as the fitness index of the IPIO,which has high efficiency especially for discrete optimization problems.For the detail layer,the fusion of its coefficients is completed by performing boundary processing when solving the convolution sparse representation in the frequency domain.The sum of the above base and detail layers is as the final fused image.Experimental results show that the proposed algorithm has a better fusion effect compared with the recent algorithms.     

Adaptive Mesh Expansion Model (AMEM) for Liver Segmentation from CT Image

This study proposes a novel adaptive mesh expansion model (AMEM) for liver segmentation from computed tomography images. The virtual deformable simplex model (DSM) is introduced to represent the mesh, in which the motion of each vertex can be easily manipulated. The balloon, edge, and gradient forces are combined with the binary image to construct the external force of the deformable model, which can rapidly drive the DSM to approach the target liver boundaries. Moreover, tangential and normal forces are combined with the gradient image to control the internal force, such that the DSM degree of smoothness can be precisely controlled. The triangular facet of the DSM is adaptively decomposed into smaller triangular components, which can significantly improve the segmentation accuracy of the irregularly sharp corners of the liver. The proposed method is evaluated on the basis of different criteria applied to 10 clinical data sets. Experiments demonstrate that the proposed AMEM algorithm is effective and robust and thus outperforms six other up-to-date algorithms. Moreover, AMEM can achieve a mean overlap error of 6.8% and a mean volume difference of 2.7%, whereas the average symmetric surface distance and the root mean square symmetric surface distance can reach 1.3 mm and 2.7 mm, respectively.     

An Improved Approach of Mesh Segmentation to Extract Feature Regions

The objective of this paper is to extract concave and convex feature regions via segmenting surface mesh of a mechanical part whose surface geometry exhibits drastic variations and concave-convex features are equally important when modeling. Referring to the original approach based on the minima rule (MR) in cognitive science, we have created a revised minima rule (RMR) and presented an improved approach based on RMR in the paper. Using the logarithmic function in terms of the minimum curvatures that are normalized by the expectation and the standard deviation on the vertices of the mesh, we determined the solution formulas for the feature vertices according to RMR. Because only a small range of the threshold parameters was selected from in the determined formulas, an iterative process was implemented to realize the automatic selection of thresholds. Finally according to the obtained feature vertices, the feature edges and facets were obtained by growing neighbors. The improved approach overcomes the inherent inadequacies of the original approach for our objective in the paper, realizes full automation without setting parameters, and obtains better results compared with the latest conventional approaches. We demonstrated the feasibility and superiority of our approach by performing certain experimental comparisons.     

Intravascular Optical Coherence Tomography Image Segmentation Based on Support Vector Machine Algorithm

Intravascular optical coherence tomography (IVOCT) is becoming more and more popular in clinical diagnosis of coronary atherosclerotic. However, reading IVOCT images is of large amount of work. This article describes a method based on image feature extraction and support vector machine (SVM) to achieve semi-automatic segmentation of IVOCT images. The image features utilized in this work including light attenuation coefficients and image textures based on gray level co-occurrence matrix. Different sets of hyper-parameters and image features were tested. This method achieved an accuracy of 83% on the test images. Single class accuracy of 89% for fibrous, 79.3% for calcification and 86.5% lipid tissue. The results show that this method can be a considerable way for semi-automatic segmentation of atherosclerotic plaque components in clinical IVOCT images.     

An Improved Whale Algorithm and Its Application in Truss Optimization

The current Whale Optimization Algorithm(WOA)has several drawbacks,such as slow convergence,low solution accuracy and easy to fall into the local optimal soluti...     

An Active Contour Model for the Segmentation of Images with Intensity Inhomogeneities and Bias Field Estimation

Intensity inhomogeneity causes many difficulties in image segmentation and the understanding of magnetic resonance (MR) images. Bias correction is an important method for addressing the intensity inhomogeneity of MR images before quantitative analysis. In this paper, a modified model is developed for segmenting images with intensity inhomogeneity and estimating the bias field simultaneously. In the modified model, a clustering criterion energy function is defined by considering the difference between the measured image and estimated image in local region. By using this difference in local region, the modified method can obtain accurate segmentation results and an accurate estimation of the bias field. The energy function is incorporated into a level set formulation with a level set regularization term, and the energy minimization is conducted by a level set evolution process. The proposed model first appeared as a two-phase model and then extended to a multi-phase one. The experimental results demonstrate the advantages of our model in terms of accuracy and insensitivity to the location of the initial contours. In particular, our method has been applied to various synthetic and real images with desirable results.     

An Image Analysis Algorithm for Malaria Parasite Stage Classification and Viability Quantification

With more than 40% of the world’s population at risk, 200–300 million infections each year, and an estimated 1.2 million deaths annually, malaria remains one of the most important public health problems of mankind today. With the propensity of malaria parasites to rapidly develop resistance to newly developed therapies, and the recent failures of artemisinin-based drugs in Southeast Asia, there is an urgent need for new antimalarial compounds with novel mechanisms of action to be developed against multidrug resistant malaria. We present here a novel image analysis algorithm for the quantitative detection and classification of Plasmodium lifecycle stages in culture as well as discriminating between viable and dead parasites in drug-treated samples. This new algorithm reliably estimates the number of red blood cells (isolated or clustered) per fluorescence image field, and accurately identifies parasitized erythrocytes on the basis of high intensity DAPI-stained parasite nuclei spots and Mitotracker-stained mitochondrial in viable parasites. We validated the performance of the algorithm by manual counting of the infected and non-infected red blood cells in multiple image fields, and the quantitative analyses of the different parasite stages (early rings, rings, trophozoites, schizonts) at various time-point post-merozoite invasion, in tightly synchronized cultures. Additionally, the developed algorithm provided parasitological effective concentration 50 (EC50) values for both chloroquine and artemisinin, that were similar to known growth inhibitory EC50 values for these compounds as determined using conventional SYBR Green I and lactate dehydrogenase-based assays.     

Medical Image Segmentation using PCNN based on Multi-feature Grey Wolf Optimizer Bionic Algorithm

Medical image segmentation is a challenging task especially in multimodality medical image analysis.In this paper,an improved pulse coupled neural network based...     

一种改进的图象模糊增强技术

本文简要地讨论了传统模糊增强算法的原理,并详细讨论了这种算法所存在的缺陷。针对传统模糊增强算法的缺陷,本文提出了一种改进的模糊增强算法。实验证明,改进的的算法在图象的处理质量上得到了提高。     

An Improved Model for Nucleation-Limited Ice Formation in Living Cells during Freezing

Ice formation in living cells is a lethal event during freezing and its characterization is important to the development of optimal protocols for not only cryopreservation but also cryotherapy applications. Although the model for probability of ice formation (PIF) in cells developed by Toner et al. has been widely used to predict nucleation-limited intracellular ice formation (IIF), our data of freezing Hela cells suggest that this model could give misleading prediction of PIF when the maximum PIF in cells during freezing is less than 1 (PIF ranges from 0 to 1). We introduce a new model to overcome this problem by incorporating a critical cell volume to modify the Toner''s original model. We further reveal that this critical cell volume is dependent on the mechanisms of ice nucleation in cells during freezing, i.e., surface-catalyzed nucleation (SCN) and volume-catalyzed nucleation (VCN). Taken together, the improved PIF model may be valuable for better understanding of the mechanisms of ice nucleation in cells during freezing and more accurate prediction of PIF for cryopreservation and cryotherapy applications.     

An Improved Population Migration Algorithm Introducing the Local Search Mechanism of the Leap-Frog Algorithm and Crossover Operator

The population migration algorithm (PMA) is a simulation of a population of the intelligent algorithm. Given the prematurity and low precision of PMA, this paper introduces a local search mechanism of the leap-frog algorithm and crossover operator to improve the PMA search speed and global convergence properties. The typical test function verifies the improved algorithm through its performance. Compared with the improved population migration and other intelligential algorithms, the result shows that the convergence rate of the improved PMA is very high and its convergence is proved.     

白血病图像分割方法的适应性分析与改进算法

系统介绍了几种对白血病骨髓图像进行分割的算法,比较各类算法的优缺点,分析各自的适用范围,并在此基础上根据所处理图像的实际情况对传统算法做出一些改进,综合应用上述技术实现对图片更准确地分割。     

An Image Encryption Algorithm Utilizing Julia Sets and Hilbert Curves

Image encryption is an important and effective technique to protect image security. In this paper, a novel image encryption algorithm combining Julia sets and Hilbert curves is proposed. The algorithm utilizes Julia sets’ parameters to generate a random sequence as the initial keys and gets the final encryption keys by scrambling the initial keys through the Hilbert curve. The final cipher image is obtained by modulo arithmetic and diffuse operation. In this method, it needs only a few parameters for the key generation, which greatly reduces the storage space. Moreover, because of the Julia sets’ properties, such as infiniteness and chaotic characteristics, the keys have high sensitivity even to a tiny perturbation. The experimental results indicate that the algorithm has large key space, good statistical property, high sensitivity for the keys, and effective resistance to the chosen-plaintext attack.