基于改进C-V模型的超声图像分割方法

图像分割是超声医学图像学中的难题之一。改进的Chan-Vese（C-V）法加入了约束符号距离函数的能量项，避免了演化时候的重新初始化。在改进C-V模型的基础上，首先借用分水岭中的思想，找到分割目标的近似轮廓，并以此轮廓生成符号距离函数，然后采用改进的C-V法进行超声图像分割。实验表明，改进的方法有更高的精准度和对多目标分割的能力。     

基于多阈值单水平集方法的医学图像分割

针对Chan-Vese的无边界主动轮廓模型(CV模型)只能区分前景与背景的缺点,提出了一种基于多阈值单水平集的医学图像分割方法,并将此方法应用于微创手术的预处理中.由于医学图像结构复杂,具有器官轮廓多连接等特点,因此使用常规的水平集方法进行分割往往不能取得理想的效果,而该方法采用修改目标泛函的方式引入多类分割,具有多区域分割的特点,只需经过一次单水平集的迭代循环,即可将图像根据灰度不同划分为多个区域,具有精确、快速等优点.对不同的合成图像和医学图像的实验结果表明,该方法实现了快速精确的多区域分割,能很好地提取到医学图像中的骨骼轮廓,分割效果达到了预期水平.     

结合数学形态学和Level Set超声图像的分割方法

针对噪声严重的超声图像,提出了一种结合数学形态学和Level Set的分割方法。首先采用全变差模型进行图像滤波,再通过交互式区域选择和数学形态学方法获得感兴趣目标的二值化图像,并把该二值化图像轮廓作为水平集方法的初始曲线。改进隐式测地活动轮廓模型(GAC)中的边缘检测函数,增强了处理弱边缘的能力。分割结果表明,该方法能够准确地提取出目标轮廓,同时减少了迭代次数和运算时间。     

一种改进的C-V主动轮廓模型

本文对C-V主动轮廓模型进行改进.依据曲线演化理论对C-V模型中的图像数据力驱动项进行简化,提出一种常微分方程(ODE)类型的模型.理论分析验证了该模型的水平集函数可初始化为零.与传统C-V模型相比,不但具有其特点,如可以自动检测带孔目标的内轮廓等,而且具有以下优点:抗噪性能较优;水平集函数无需重新初始化,可快速计算出全局最优分割;远离轮廓的边界可以被准确检测;时间迭代步长不受限制.对合成和真实图像的分割结果证明了本文模型具有稳健、快速的优点.     

基于snake模型的IVUS图像序列三维分割方法

针对血管内超声（Intravascular Ultrasound,IVUS）图像序列中血管壁内外膜轮廓的提取问题,提出一种基于snake模型的三维并行分割方法。首先,对原始图像进行滤除噪声和抑制环晕伪像等预处理。然后,获取IVUS图像序列的四个纵向视图,并从中提取出内腔边界和中-外膜边界。通过将这些边界曲线映射到各帧IVUS图像中,得到横向视图中的初始轮廓。最后,将该初始轮廓作为snake模型的初始形状,通过使snake能量函数最小,模型不断变形,最终得到各帧IVUS图像中的内腔和中-外膜边界。该方法可实现对IVUS图像序列的并行分割,与二维串行分割方法相比,可大大提高处理效率。采用大量临床图像数据的实验结果证明该方法可自动、快速、可靠的完成IVUS图像序列的分割。     

应用于灰度图像分割的基于流体静力学原理活动轮廓模型

提出一种新的活动轮廓模型,应用于灰度图像分割。此模型建立在流体静力学理论之上,运用流体静力学理论直接驱动连续曲线,逼近被包围的目标。该模型能够分割多重目标、能够分割嵌套的目标、能够有效地控制过分割现象。     

小波多尺度改进Chan-Vese模型的红外图像分割

针对红外图像特点,提出了一种小波多尺度改进Chan-Vese模型的红外图像分割算法.首先,利用小波多尺度分析的良好信噪分离性能提取红外图像的有效边缘信息,将边缘信息添加到Chan.Vese模型的能量函数中从而提高模型的局部控制能力.同时增加内部变形能量项,约束水平集函数逼近符号距离函数,避免了水平集函数的重新初始化过程,改进了Chan-Vese模型.然后,从小波变换的顶层低频子带图像开始逐层采用改进的Chan-Vese模型分割图像,并将分割结果通过插值方式传递至下一层作为分割的初始轮廓,最终实现红外图像的分割,具有抗噪性能强和运算速度快的特点.     

改进测地线活动轮廓模型的肝脏超声图像分割

针对超声医学图像的特点,系统地发展和应用测地线活动轮廓模型技术进行肝脏超声图像的边缘提取,提出一种改进的测地线活动轮廓模型.首先把总变分和各向异性扩散方程的有机结合构造出新的边缘停止函数;其次,提出三种边缘停止函数,通过实验比较得出最佳模型.新模型能有效地保留目标信息,消除噪音,增大图像梯度值,具有更强的收敛效果.实验表明,该方法能更加准确地提取弱边缘,达到更理想的分割效果.     

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。     

集成模糊特征的Snakes模型分割图像纹理

对纹理图像进行局部一维离散傅里叶变换,把傅里叶变换能反映图像纹理的高频部分的振幅谱作为纹理的特征向量.利用模糊概念定义模糊特征向量,把模糊特征向量集成到Snakes模型.改进的Snakes模型使活动轮廓曲线在曲线内部力场、图像梯度力场和特征向量对区域的模糊隶属度确定大小的曲线法向力场的共同作用下运动.对纹理实验图像和真实图像的进行了分割实验,实验结果说明新的模型能对图像纹理进行有效的分割,模糊特征的融入扩大了Snakes模型分割图像的范围.     

基于HSV空间的岩心图像处理

针对石油勘探中的岩心图像在分割过程中难以有效识别出灰度值相近的颗粒与颗粒、颗粒与背景的边缘信息,且容易造成过度分割或欠分割的问题,提出一种彩色图像分割并获取颗粒粒度分布的算法,并将结果与灰度迭代算法、最大熵法和分水岭算法分割结果进行比较。结果表明,该算法以彩色图像HSV模型的V分量为依据进行图像分割,可以在较为复杂的岩心背景下校正分割结果,更准确地分割出目标和背景颜色相近的边缘,获得砾石、泥沙颗粒的粒度分布。     

The foreground detection algorithm combined the temporal–spatial information and adaptive visual background extraction

Visual background extraction algorithm, which utilises a global threshold to complete the foreground segmentation, cannot adapt to illumination change well. It will easily choose the wrong pixels to initialise the background model, resulting in the emergence of the ghost in the beginning of detection. In order to address these problems, this article proposes an improved algorithm based on pixel’s temporal–spatial information to initialise the background model. First of all, the pixels in video image sequences and their neighbourhood pixels are used to complete background model initialisation in the first five frames. Second, the segmentation threshold is adaptively obtained by the complexity of background that uses the spatial neighbourhood pixels. Finally, the background model of the neighbourhood pixels is updated by a dynamic update rate which is gained by calculating the Euclidean distance between pixels. Experimental results and comparative study illustrate that the improved method can not only increase the accuracy of target detection by reducing the impact of illumination change effectively but also eliminate the ghost quickly.     

Automated image registration by maximization of a region similarity metric

This article presents a robust algorithm for automated registration of images related by rigid-body transformations. This algorithm uses a new region-based similarity metric, which enables accurate registration of images of large contrast differences. Region segmentation required by the metric is accomplished using a multiscale segmentation algorithm, and minimization of this metric is done using the Powell direction set method. Experimental results are presented to demonstrate that the algorithm is effective for aligning images from single or multiple imaging modalities without the use of any fiducial markers. © 1997 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 8, 513–518, 1997     

Morphologic field morphing: Contour model-guided image interpolation

An interpolation method using contours of organs as the control parameters is proposed to recover the intensity information in the physical gaps of serial cross-sectional images. In our method, contour models are used to generate the control lines required for the warping algorithm. Contour information derived from this contour model-based segmentation process is processed and used as the control parameters to warp the corresponding regions in both input images into compatible shapes. In this way, the reliability of establishing the correspondence among different segments of the same organs is improved and the intensity information for the interpolated intermediate slices can be derived more faithfully. To improve the efficiency for calculating the image warp in the field morphing process, a hierarchic decomposition process is proposed to localize the influence of each control line segment. In comparison with the existing intensity interpolation algorithms that only search for corresponding points in a small physical neighborhood, this method provides more meaningful correspondence relationships by warping regions in images into similar shapes before resampling to account for significant shape differences. Several sets of experimental result are presented to show that this method generates more realistic and less blurred interpolated images, especially when the shape difference of corresponding contours is significant. © 1997 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 8, 480–490, 1997     

预扫描算法求解图像分割的Chan-Vese模型(英文)

本文提出了一种新的有效的算法来求解图像分割中的Chan-Vese模型。新算法避免了求解PDE的过程,极大地提高了图像分割的运算速度。这种算法保持了C-V模型和水平集方法的优点,能够自动处理图像分割过程中边缘的拓扑变形,保持边缘的尖角以及对于非凸边缘的有效的检测等等。这种算法思路简单,很容易推广到任意有限维的图像分割问题的求解中。     

Automatic diagnosis of diabetic retinopathy with the aid of adaptive average filtering with optimized deep convolutional neural network

The most effective treatment for diabetic retinopathy (DR) is the early detection through regular screening, which is critical for a better prognosis. Automatic screening of the images would assist the physicians in diagnosing the condition of patients easily and accurately. This condition searches out for special importance of image processing technology in the way of processing the retinal fundus images. Accordingly, this article plans to develop an automatic DR detection model with the aid of three main stages like (a) image preprocessing, (b) blood vessel segmentation, and (c) classification. The preprocessing phase includes two steps: conversion of RGB to Lab, and contrast enhancement. The Histogram equalization process is done using the contrast enhancement of an image. To the next of preprocessing, the segmentation phase starts with a valuable procedure. It includes (a), thresholding the contrast-enhanced and filtered images, (b) thresholding the keypoints of contrast-enhanced and filtered images, and (c) adding both thresholded binary images. Here, the filtering process is performed by proposed adaptive average filtering, where the filter coefficients are tuned or optimized by an improved meta-heuristic algorithm called fitness probability-based CSO (FP-CSO). Finally, the classification part uses Deep CNN, where the improvement is exploited on the convolutional layer, which is optimized by the same improved FP-CSO. Since the conventional CSO depends on a fitness probability in the improved algorithm, the proposed algorithm termed as FP-CSO. Finally, valuable comparative and performance analysis has confirmed the effectiveness of the proposed model.     

Proposed Framework for Detection of Breast Tumors

Computer vision is one of the significant trends in computer science. It plays as a vital role in many applications, especially in the medical field. Early detection and segmentation of different tumors is a big challenge in the medical world. The proposed framework uses ultrasound images from Kaggle, applying five diverse models to denoise the images, using the best possible noise-free image as input to the U-Net model for segmentation of the tumor, and then using the Convolution Neural Network (CNN) model to classify whether the tumor is benign, malignant, or normal. The main challenge faced by the framework in the segmentation is the speckle noise. It’s is a multiplicative and negative issue in breast ultrasound imaging, because of this noise, the image resolution and contrast become reduced, which affects the diagnostic value of this imaging modality. As result, speckle noise reduction is very vital for the segmentation process. The framework uses five models such as Generative Adversarial Denoising Network (DGAN-Net), Denoising U-Shaped Net (D-U-NET), Batch Renormalization U-Net (Br-U-NET), Generative Adversarial Network (GAN), and Nonlocal Neutrosophic of Wiener Filtering (NLNWF) for reducing the speckle noise from the breast ultrasound images then choose the best image according to peak signal to noise ratio (PSNR) for each level of speckle-noise. The five used methods have been compared with classical filters such as Bilateral, Frost, Kuan, and Lee and they proved their efficiency according to PSNR in different levels of noise. The five diverse models are achieved PSNR results for speckle noise at level (0.1, 0.25, 0.5, 0.75), (33.354, 29.415, 27.218, 24.115), (31.424, 28.353, 27.246, 24.244), (32.243, 28.42, 27.744, 24.893), (31.234, 28.212, 26.983, 23.234) and (33.013, 29.491, 28.556, 25.011) for DGAN, Br-U-NET, D-U-NET, GAN and NLNWF respectively. According to the value of PSNR and level of speckle noise, the best image passed for segmentation using U-Net and classification using CNN to detect tumor type. The experiments proved the quality of U-Net and CNN in segmentation and classification respectively, since they achieved 95.11 and 95.13 in segmentation and 95.55 and 95.67 in classification as dice score and accuracy respectively.     

Feature classification techniques in model-based object recognition

We describe a system that performs model-based recognition of the projections of generalized cylinders, and present new results on the final classification of the feature data. Two classification methods are proposed and compared. The first is a Bayesian technique that ranks the object space according to estimated conditional probability distributions. The second technique is a new feed-forward “neural” implementation that utilizes the back-propagation learning algorithm. The neural approach yields a 31.8% reduction in classification error for a database of twenty models relative to the Bayesian approach, although it does not provide an ordered ranking of the object space. The accuracy results of the neural approach represent a significant performance advance in feature-based recognition by perceptual organization without the use of depth information. Examples are provided using the results of a simple segmentation system applied to real image data.     

A model-based clustering approach to the recognition of the spatial defect patterns produced during semiconductor fabrication

Defects on semiconductor wafers tend to cluster and the spatial defect patterns of these defect clusters contain valuable information about potential problems in the manufacturing processes. This study proposes a model-based clustering algorithm for automatic spatial defect recognition on semiconductor wafers. A mixture model is proposed to model the distributions of defects on wafer surfaces. The proposed algorithm can find the number of defect clusters and identify the pattern of each cluster automatically. It is capable of detecting defect clusters with linear patterns, curvilinear patterns and ellipsoidal patterns. Promising results have been obtained from simulation studies.     

快速成型技术中分段算法的研究综述

在快速成型技术中,模型分段处理是分层处理的前一步骤,对分段算法进行研究十分有必要。为此,对快速成型技术中的分段算法进行总结,主要从人机交互分段和自动分段两方面展开。对于人机交互分段算法,主要从根据不同成型要求进行分段方面展开叙述;对于自动分段算法,主要介绍了体素化分段算法、特征识别式分段算法、多目标优化分段算法和聚类式分段算法。分析结果表明,现有分段算法仍存在适用范围受限、应用快速成型工艺单一、未考虑后续拼接环节以及测试模型多为非工程用零件等问题。因此,开展关于分段算法效果评价准则的建立、结合多种快速成型工艺的应用以及考虑模型拼接后的性能分析等方面的相关研究,可有效提高分段算法的实用价值,为后续分层处理奠定基础。