Research on the magnetic resonance imaging brain tumor segmentation algorithm based on DO-UNet

With the social and economic development and the improvement of people's living standards, smart medical care is booming, and medical image processing is becoming more and more popular in research, of which brain tumor segmentation is an important branch of medical image processing. However, the manual segmentation method of brain tumors requires a lot of time and effort from the doctor and has a great impact on the treatment of patients. In order to solve this problem, we propose a DO-UNet model for magnetic resonance imaging brain tumor image segmentation based on attention mechanism and multi-scale feature fusion to realize fully automatic segmentation of brain tumors. Firstly, we replace the convolution blocks in the original U-Net model with the residual modules to prevent the gradient disappearing. Secondly, the multi-scale feature fusion is added to the skip connection of U-Net to fuse the low-level features and high-level features more effectively. In addition, in the decoding stage, we add an attention mechanism to increase the weight of effective information and avoid information redundancy. Finally, we replace the traditional convolution in the model with DO-Conv to speed up the network training and improve the segmentation accuracy. In order to evaluate the model, we used the BraTS2018, BraTS2019, and BraTS2020 datasets to train the improved model and validate it online, respectively. Experimental results show that the DO-UNet model can effectively improve the accuracy of brain tumor segmentation and has good segmentation performance.     

Efficient image segmentation based on deep learning for mineral image classification

Mineral image segmentation plays a vital role in the realization of machine vision based intelligent ore sorting equipment. However, the existing image segmentation methods still cannot effectively solve the problem of adhesion and overlap between mineral particles, and the segmentation performance of small and irregular particles still needs to be improved. To overcome these bottlenecks, we propose a deep learning based image segmentation method to segment the key areas in mineral images using morphological transformation to process mineral image masks. This investigation explores four aspects of the deep learning-based mineral image segmentation model, including backbone selection, module configuration, loss function construction, and its application in mineral image classification. Specifically, referring to the designs of U-Net, FCN, Seg Net, PSP Net, and DeepLab Net, this experiment uses different backbones as Encoder to building ten mineral image segmentation models with different layers, structures, and sampling methods. Simultaneously, we propose a new loss function suitable for mineral image segmentation and compare CNNs-based segmentation models' training performance under different loss functions. The experiment results show that the proposed mineral image segmentation has excellent segmentation performance, effectively solves adhesion and overlap between adjacent particles without affecting the classification accuracy. By using the Mobile Net as backbone, the PSP Net and DeepLab can achieve a high segmentation performance in mineral image segmentation tasks, and the 15 × 15 is the most suitable size for erosion element structure to process the mask images of the segmentation models.     

Empirical Comparisons of Deep Learning Networks on Liver Segmentation

Accurate segmentation of CT images of liver tumors is an important adjunct for the liver diagnosis and treatment of liver diseases. In recent years, due to the great improvement of hard device, many deep learning based methods have been proposed for automatic liver segmentation. Among them, there are the plain neural network headed by FCN and the residual neural network headed by Resnet, both of which have many variations. They have achieved certain achievements in medical image segmentation. In this paper, we firstly select five representative structures, i.e., FCN, U-Net, Segnet, Resnet and Densenet, to investigate their performance on liver segmentation. Since original Resnet and Densenet could not perform image segmentation directly, we make some adjustments for them to perform live segmentation. Our experimental results show that Densenet performs the best on liver segmentation, followed by Resnet. Both perform much better than Segnet, U-Net, and FCN. Among Segnet, U-Net, and FCN, U-Net performs the best, followed by Segnet. FCN performs the worst.     

Semantic Segmentation by Using Down-Sampling and Subpixel Convolution: DSSC-UNet

Recently, semantic segmentation has been widely applied to image processing, scene understanding, and many others. Especially, in deep learning-based semantic segmentation, the U-Net with convolutional encoder-decoder architecture is a representative model which is proposed for image segmentation in the biomedical field. It used max pooling operation for reducing the size of image and making noise robust. However, instead of reducing the complexity of the model, max pooling has the disadvantage of omitting some information about the image in reducing it. So, this paper used two diagonal elements of down-sampling operation instead of it. We think that the down-sampling feature maps have more information intrinsically than max pooling feature maps because of keeping the Nyquist theorem and extracting the latent information from them. In addition, this paper used two other diagonal elements for the skip connection. In decoding, we used Subpixel Convolution rather than transposed convolution to efficiently decode the encoded feature maps. Including all the ideas, this paper proposed the new encoder-decoder model called Down-Sampling and Subpixel Convolution U-Net (DSSC-UNet). To prove the better performance of the proposed model, this paper measured the performance of the U-Net and DSSC-UNet on the Cityscapes. As a result, DSSC-UNet achieved 89.6% Mean Intersection Over Union (Mean-IoU) and U-Net achieved 85.6% Mean-IoU, confirming that DSSC-UNet achieved better performance.     

Tissue Segmentation in Nasopharyngeal CT Images Using TwoStage Learning

Tissue segmentation is a fundamental and important task in nasopharyngeal images analysis. However, it is a challenging task to accurately and quickly segment various tissues in the nasopharynx region due to the small difference in gray value between tissues in the nasopharyngeal image and the complexity of the tissue structure. In this paper, we propose a novel tissue segmentation approach based on a two-stage learning framework and U-Net. In the proposed methodology, the network consists of two segmentation modules. The first module performs rough segmentation and the second module performs accurate segmentation. Considering the training time and the limitation of computing resources, the structure of the second module is simpler and the number of network layers is less. In addition, our segmentation module is based on U-Net and incorporates a skip structure, which can make full use of the original features of the data and avoid feature loss. We evaluated our proposed method on the nasopharyngeal dataset provided by West China Hospital of Sichuan University. The experimental results show that the proposed method is superior to many standard segmentation structures and the recently proposed nasopharyngeal tissue segmentation method, and can be easily generalized across different tissue types in various organs.     

基于超像素的联合能量主动轮廓CT图像分割方法

为解决医学CT图像主动轮廓分割方法中对初始轮廓敏感的问题,提出一种基于超像素和卷积神经网络的人体器官CT图像联合能量函数主动轮廓分割方法。该方法首先基于超像素分割对CT图像进行超像素网格化,并通过卷积神经网络进行超像素分类确定边缘超像素;然后提取边缘超像素的种子点组成初始轮廓;最后在提取的初始轮廓基础上,通过求解本文提出的综合能量函数最小值实现人体器官分割。实验结果表明,本文方法与先进的U-Net方法相比平均Dice系数提高5%,为临床CT图像病变诊断提供理论基础和新的解决方案。     

Automatic vision-based grain optimization and analysis of multi-crystalline solar wafers using hierarchical region growing

Solar power has become an attractive alternative source of energy. The multi-crystalline solar cell has been widely accepted in the market because it has a relatively low manufacturing cost. Multi-crystalline solar wafers with larger grain sizes and fewer grain boundaries are higher quality and convert energy more efficiently than mono-crystalline solar cells. In this article, a new image processing method is proposed for assessing the wafer quality. An adaptive segmentation algorithm based on region growing is developed to separate the closed regions of individual grains. Using the proposed method, the shape and size of each grain in the wafer image can be precisely evaluated. Two measures of average grain size are taken from the literature and modified to estimate the average grain size. The resulting average grain size estimate dictates the quality of the crystalline solar wafers and can be considered a viable quantitative indicator of conversion efficiency.     

基于图像监测的微量移液技术的研究

为了提高生化分析仪微量移液的可靠性,提出了一种基于图像分割法的移液故障判断和移液量检测系统.基于STM32微控制器,利用速度位置双闭环PID(proportion-integral-derivative,比例-积分-微分)算法控制微量移液系统的步进电机,设计了生化分析仪的自动移液控制系统.采集移液过程图像作为样本图像,...     

High-speed MRF-based segmentation algorithm using pixonal images

Abstract

Segmentation is one of the most complicated procedures in the image processing that has important role in the image analysis. In this paper, an improved pixon-based method for image segmentation is proposed. In proposed algorithm, complex partial differential equations (PDEs) is used as a kernel function to make pixonal image. Using this kernel function causes noise on images to reduce and an image not to be over-segment when the pixon-based method is used. Utilising the PDE-based method leads to elimination of some unnecessary details and results in a fewer pixon number, faster performance and more robustness against unwanted environmental noises. As the next step, the appropriate pixons are extracted and eventually, we segment the image with the use of a Markov random field. The experimental results indicate that the proposed pixon-based approach has a reduced computational load and a better accuracy compared to the other existing pixon-image segmentation techniques. To evaluate the proposed algorithm and compare it with the last best algorithms, many experiments on standard images were performed. The results indicate that the proposed algorithm is faster than other methods, with the most segmentation accuracy.     

MRI brain segmentation in combination of clustering methods with Markov random field

Medical image segmentation is a preliminary stage of inclusion in identification tools. The correct segmentation of brain Magnetic Resonance Imaging (MRI) images is crucial for an accurate detection of the disease diagnosis. Due to in‐homogeneity, low distinction and noise the segmentation of the brain MRI images is treated as the most challenging task. In this article, we proposed hybrid segmentation, by combining the clustering methods with Hidden Markov Random Field (HMRF) technique. This aims to decrease the computational load and improves the runtime of segmentation method, as MRF methodology is used in post‐processing the images. Its evaluation has performed on real imaging data, resulting in the classification of brain tissues with dice similarity metric. These results indicate the improvement in performance of the proposed method with various noise levels, compared with existing algorithms. In implementation, selection of clustering method provides better results in the segmentation of MRI brain images.     

基于改进MultiResUNet网络的甲状腺超声图像分割

甲状腺超声图像分割在临床超声图像研究中有很重要的意义。针对甲状腺超声图像信噪比低,斑点噪声多,且甲状腺形态不确定等问题,提出了一种改进的MultiResUNet分割网络(称为Oct-MRU-Net网络)。该方法在MultiResUNet网络的基本结构的基础上引入Octave卷积,并采用改进的Inception模块学习不同空间尺度的特征,将训练过程中的特征图按通道方向分为高低频特征。其中,高频特征描述图像细节和边缘信息,低频特征描述图像整体轮廓信息。在甲状腺超声图像分割过程中可以重点关注高频信息,减少空间冗余,从而实现对边缘更加精细的分割。实验结果表明,Oct-MRU-Net网络的性能相较于U-Net网络和MultiResUNet网络都有较大的提升,说明该网络对甲状腺超声图像的分割效果较好。     

Hybrid Segmentation Scheme for Skin Features Extraction Using Dermoscopy Images

Objective and quantitative assessment of skin conditions is essential for cosmeceutical studies and research on skin aging and skin regeneration. Various handcraft-based image processing methods have been proposed to evaluate skin conditions objectively, but they have unavoidable disadvantages when used to analyze skin features accurately. This study proposes a hybrid segmentation scheme consisting of Deeplab v3+ with an Inception-ResNet-v2 backbone, LightGBM, and morphological processing (MP) to overcome the shortcomings of handcraft-based approaches. First, we apply Deeplab v3+ with an Inception-ResNet-v2 backbone for pixel segmentation of skin wrinkles and cells. Then, LightGBM and MP are used to enhance the pixel segmentation quality. Finally, we determine several skin features based on the results of wrinkle and cell segmentation. Our proposed segmentation scheme achieved a mean accuracy of 0.854, mean of intersection over union of 0.749, and mean boundary F1 score of 0.852, which achieved 1.1%, 6.7%, and 14.8% improvement over the panoptic-based semantic segmentation method, respectively.     

Block-based tri-channel hybrid segmentation of images for foreground extraction

Image segmentation is crucial in image analysis, object representation, visualization and other image processing tasks. An image can be distinguished in terms of the foreground and the background. A new hybrid segmentation of images for foreground extraction is proposed, based on Interval Neutrosophic Set (INS) and Sparse Field Active Contour. In this method, an image is represented in three channels using a Gaussian filter bank and each channel is split into blocks to which the INS is applied. The resultant neutrosophic image for three channels undergoes isodata thresholding to obtain the tri-channel edge image, which is segmented using the Sparse Field Active Contour. The proposed method is evaluated by conducting three different experiments in natural image datasets like the Semantic Dataset100, Weizmann_Seg_DB_1obj, BSR and standard MATLAB test images. Finally, it is compared to other existing segmentation methods, which shows promising achievement in terms of their evaluation metrics like overlap-based metrics, pair-counting-based method and distance measures.     

An algorithm for finger-vein segmentation based on modified repeated line tracking

Abstract

Image segmentation is an important step for finger-vein identification technique. However, it is difficult to extract precise details of the image because of the irregular noise and shades around the finger-vein. The repeated line tracking algorithm achieves good segmentation performance for low quality images of finger-vein, but it has some drawbacks such as low robustness and efficiency. In this paper, a modified repeated line tracking algorithm is proposed for image segmentation of finger-vein. Firstly, we propose a segmentation method called threshold image to execute rough segmentation and obtain binary and skeleton image of finger-vein. Secondly, the width of finger-vein is estimated based on the binary and skeleton image. The parameters are revised according to the width. Then, the modified repeated line tracking algorithm is executed to figure out the locus space of finger-vein based on the revised parameters. Finally, processing results are obtained by using Otsu algorithm which executes exact segmentation on the locus space. Experiments show that the proposed algorithm is more robust and efficient than traditional repeated line tracking algorithm.     

基于嵌入式系统和图像识别的拉索表面缺陷检测技术

以关系桥梁安全健康的拉索表面缺陷检测作为研究内容,针对国内对拉索表面保护材料层的两种检测方法的不足,设计了以嵌入式DSP为核心的扫描采集和缺陷检测系统,包括图像采集机构,DSP硬件平台、爬行运动机构以及损伤定位机构;提出了基于图像的快速识别拉索表面缺陷的检测识别方法,包括图像截取、滤波降噪.图像分割、缺陷识别,并加以算法实现.实验结果表明,该方法不仅实现了拉索表面缺陷的实时检测,同时系统指标达到:最小识别面积为1cm2,图像实时处理速度为10cm/3s.     

Adaptive real-time motion segmentation technique based on statistical background model

Motion segmentation is a crucial step for video analysis and has many applications. This paper proposes a method for motion segmentation, which is based on construction of statistical background model. Variance and Covariance of pixels are computed to construct the model for scene background. We perform average frame differencing with this model to extract the objects of interest from the video frames. Morphological operations are used to smooth the object segmentation results. The proposed technique is adaptive to the dynamically changing background because of change in the lighting conditions and in scene background. The method has the capability to relearn the background to adapt these variations. The immediate advantage of the proposed method is its high processing speed of 30 frames per second on large sized (high resolution) videos. We compared the proposed method with other five popular methods of object segmentation in order to prove the effectiveness of the proposed technique. Experimental results demonstrate the novelty of the proposed method in terms of various performance parameters. The method can segment the video stream in real-time, when background changes, lighting conditions vary, and even in the presence of clutter and occlusion     

Deep Learning in DXA Image Segmentation

Many existing techniques to acquire dual-energy X-ray absorptiometry (DXA) images are unable to accurately distinguish between bone and soft tissue. For the most part, this failure stems from bone shape variability, noise and low contrast in DXA images, inconsistent X-ray beam penetration producing shadowing effects, and person-to-person variations. This work explores the feasibility of using state-of-the-art deep learning semantic segmentation models, fully convolutional networks (FCNs), SegNet, and U-Net to distinguish femur bone from soft tissue. We investigated the performance of deep learning algorithms with reference to some of our previously applied conventional image segmentation techniques (i.e., a decision-tree-based method using a pixel label decision tree [PLDT] and another method using Otsu’s thresholding) for femur DXA images, and we measured accuracy based on the average Jaccard index, sensitivity, and specificity. Deep learning models using SegNet, U-Net, and an FCN achieved average segmentation accuracies of 95.8%, 95.1%, and 97.6%, respectively, compared to PLDT (91.4%) and Otsu’s thresholding (72.6%). Thus we conclude that an FCN outperforms other deep learning and conventional techniques when segmenting femur bone from soft tissue in DXA images. Accurate femur segmentation improves bone mineral density computation, which in turn enhances the diagnosing of osteoporosis.     

利用空间填充曲线和灰度分布估计的声图分割

针对声图中的目标分割问题,提出了一种利用空间填充曲线和灰度分布估计的声图目标分割方法。该方法首先利用空间填充曲线将声图由二维矩阵变换为一维向量;其次在一维空间下进行滤波、灰度分布估计、阈值计算、分割处理;最后将分割后的一维向量逆变换为二维矩阵,得到目标分割结果。实际的声图处理验证了方法的有效性。     

Automatic segmentation system for liver tumors based on the multilevel thresholding and electromagnetism optimization algorithm

In this article, we propose an automated segmentation system for liver tumors using magnetic resonance imaging and computed tomography. The proposed system is based on the algorithm of multilevel thresholding with electromagnetism optimization (EMO). The system starts with visualizing a patient's digital communication in medicine (DICOM) abdominal data set in three views. Two-stage active contour segmentation methods that integrate region-based local and global techniques using the active geodesic contour technique are proposed to segment the liver. To increase the accuracy and speed of segmentation for liver images, we identify the optimal threshold of the image segmentation method based on EMO with Otsu and Kapur algorithms. EMO offers interesting search capabilities while keeping a low computational cost. The proposed system was tested using a set of five DICOM data sets. All images were of the same size and stored in JPEG format (512 × 512 pixels). Experimental results illustrate that the proposed system outperforms state-of-the-art methods such as the watershed algorithm. The average sensitivity, specificity, and accuracy of the segmented liver using the active contour model were 97.05%, 99.88%, and 98.47%, respectively. Moreover, the average sensitivity, specificity, and accuracy of the segmented liver tumor results were 94.15%, 99.57%, and 96.86%, respectively.     

Double regularization control based on level set evolution for tablet packaging image segmentation

This paper proposes a novel double regularization control(DRC) method which is used for tablet packaging image segmentation.Since the intensities of tablet packaging images are inhomogenous,it is difficult to make image segmentation.Compared to methods based on level set,the proposed DRC method has some advantages for tablet packaging image segmentation.The local regional control term and the rectangle initialization contour are first employed in this method to quickly segment uneven grayscale images and accelerate the curve evolution rate.Gaussian filter operator and the convolution calculation are then adopted to remove the effects of texture noises in image segmentation.The developed penalty energy function,as regularization term,increases the constrained conditions based on the gradient flow conditions.Since the potential function is embedded into the level set of evolution equations and the image contour evolutions are bilaterally extended,the proposed method further improves the accuracy of image contours.Experimental studies show that the DRC method greatly improves the computational efficiency and numerical accuracy,and achieves better results for image contour segmentation compared to other level set methods.