一种改进的自适应谱聚类图像分割算法

摘要：目的提出一种改进的自适应谱聚类图像分割算法,该算法能自动选择出最优尺度参数从而提高谱聚类算法分割的准确
率。方法利用约束条件优化相关准则函数,对相似度量函数自动学习迭代并得到最优尺度参数,再运用基于Nyström 估计的
谱聚类算法得到最后的图像分割结果。选择对不同性质的纹理图像采用适合的相似度量函数并应用本文的算法进行图像分
割,最后与k-均值算法和预分割后再使用人工调整到最优参数的谱聚类算法的分割结果进行了比较。结果这种改进的自动选
择最优尺度参数的谱聚类算法在分割效果上较其它两种聚类算法能得到更好的分割结果。结论本文提出的改进方法,能使谱
聚类算法的图像分割效果更理想。
     

1种视网膜眼底图像增强的新方法

目的：提高视网膜眼底图像中血管和病灶部位的可视性,便于临床诊断。方法：首先,对采集到的视网膜眼底图像进行数学形态学处理,以弱化背景,使图像得到初步增强;然后,运用有限对比度自适应直方图均衡化算法改善光照不均的现象;最后,利用二维匹配滤波算法进一步强化血管和病灶部位信息,提高血管与背景的对比度。结果：该方法能很好地弱化背景,增强对比度,凸显感兴趣区域,也对视盘区域血管的分割和病灶部位的识别有极大地帮助。结论：该方法可有效地增强正常和病变的眼底图像,辅助医师诊断。     

磁共振图像局部增强技术临床实验研究

目的 对基于数学形态学的磁共振图像局部对比度增强算法进行临床 MR图像测试,为完善算法提供依据.方法 运用来自不同磁共振设备的 DICOM图像数据对其算法进行测试,并与同类基于多尺度形态学方法进行比较.结果 实际图像测试与模拟测试结果基本一致,并且获得了在精度、敏感性和稳定性方面完善算法的依据.结论 本研究算法的临床应用还有待于进一步的研究.     

MRI胸腔图像中肺组织的自动分割

目的探讨实现MRI胸腔图像中肺组织自动分割的方法。方法基于MRI胸腔图像,首先对整套图像数据集进行预处理,以增强图像中肺组织区域和周围组织的对比度。采用由粗到细的肺组织自动分割算法。该算法先采用阈值法分割出粗略的肺实质,然后通过标记连通区域统一背景,最后利用形态学的方法平滑肺边缘、填补肺区内的细小缺失并去掉黏连的气管及主支气管。结果自动分割的肺组织边缘轮廓清晰,与原图基本吻合,而且与手工分割结果的一致性较好。各时相点的平均DSI系数均超过89%,可视为有效分割。结论该算法能准确有效的自动分割出一个呼吸周期中不同时相点的MRI胸腔图像序列中的肺组织。     

利用Gibbs距离图Snake模型分割医学图像

目的 提出利用Gibbs距离图Snake模型分割医学图像的算法.该方法能克服医学图像周有的噪声和伪边缘干扰,收敛到正确的目标轮廓.方法 首先推导Gibbs形态学梯度,然后提出基于Gibbs形态学梯度的距离图Snake模型的医学图像分割方法.结果 本文所提出的算法克服了传统距离图Snake模型的上述缺点.结论 本文所提出的方法分割结果鲁棒性好,分割过程无须人工干预,适合应用于临床医学图像分割.     

各向异性PDE改进分水岭脑MRI图像分割算法

详细论述了各向异性扩散的PDE(偏微分方程)降噪算法原理,提出一种各向异性扩散的PDE降噪和分水岭算法相结合的脑MRI医学图像分割算法。采用各向异性扩散的PDE降低原始图像噪声,然后利用形态学算法对降噪后的图像进行形态学处理,通过形态学知识提取图像边界。利用图像的几何特征,去除非目标区域,再采用分水岭变换进行图像分割,并通过脑MRI图像验证了此方法的优势。实验结果进一步验证了其可行性。     

一种快速自动分割视网膜血管的方法

背景 眼底视网膜血管自动分割是基于眼底视网膜照片的计算机辅助诊断的首要课题。针对血管具有一定宽度的形态学特征,本文提出一种基于Sobel算子的眼底视网膜血管自动分割算法。 方法 在经过Sobel算子处理的眼底视网膜灰度梯度图像上,血管两侧边缘总是对应着沿水平方向或竖直方向相继出现的一对数值相当,符号相反的梯度谷-峰,这对梯度谷-峰之间的区域则被判断为血管像素。 结果 本文提出的方法采用公共DRIVE数据库中的眼底图像进行检测,检测结果显示平均灵敏度为 72.12% ,平均正确率为89.00%. 结论 本文提出的算法简单易实现,计算量小,能够提取视网膜上绝大部分血管。     

基于眼底图像视网膜血管分割、分类方法的中医目诊研究

中医目诊是通过观察眼部的神、色、形、态来诊断全身疾病的一种方法。对眼底血管的观察是目诊的主要内容。视网膜血管结构的变化与许多慢性疾病密切相关。图像处理技术在眼底图像研究的应用可以为系统性疾病提供早期诊断。眼底图像血管分割是眼底血管研究的基础,目前大体可分为基于匹配滤波、血管跟踪、形态学处理、形变模型以及机器学习的分割算法5类。眼底血管分类,如动静脉分类,为临床诊断提供重要价值。在此类研究的基础上,提出了眼底血管分割、分类方法在中医目诊中的实际应用方法。     

基于U型全卷积神经网络的腹部动脉CT图像分割算法研究

目的为了精确分割腹部动脉血管,提出一种基于深度学习的全自动腹部动脉CT图像分割算法。方法采用区域不平衡块生成方法提取CT血管横断面、冠状面和矢状面图像特征,接着采用U型全卷积神经网络对块特征进行训练与分割,最后采用最大体素保留法获得三维血管分割图像。选用120例患者腹部CT血管图像进行网络训练和分割实验,分割结果评价指标采用精确率、召回率和Dice系数。结果基于U型全卷积神经网络能分割全部腹部CT图像大血管和绝大多数小血管。全卷积神经网络中块尺寸s=32所得平均Dice系数、精确率和召回率分别达87.2%、85.9%和88.5%,且与块尺寸s=48和s=64大致相等。基于U型全卷积神经网络所得平均Dice系数、精确率和召回率均优于其他血管分割算法。结论基于U型全卷积神经网络算法的图像分割精度高,是一种可行的腹部CT血管分割算法。     

基于多尺度三维卷积神经网络的头颈部危及器官分割方法

目的 研究一种基于三维卷积神经网络的CT图像头颈部危及器官分割算法。方法 本文构建了一个基于V-Net模型的头颈部危及器官自动分割算法。为了增强分割模型的特征表达能力,将SE（Squeeze-and-Excitation）模块与V-Net模型中残差卷 积模块相结合,提高与分割任务相关性更大的特征通道权重;采用多尺度策略,使用粗定位和精分割两个级联模型完成器官分割,其中输入图像在预处理时重采样为不同分辨率,使得模型分别专注于全局位置信息和局部细节特征的提取。结果 我们在头颈部22个危及器官的分割实验表明,相比于已有方法,本文提出的方法分割平均精度提升了9%,同时平均测试时间从33.82 s降低至2.79 s。结论 基于多尺度策略的三维卷积神经网络达到了更好的分割精度,且耗时极短,有望在临床应用中提高医生的工作效率。     

Recent Advancements in Retinal Vessel Segmentation

Retinal vessel segmentation is a key step towards the accurate visualization, diagnosis, early treatment and surgery planning of ocular diseases. For the last two decades, a tremendous amount of research has been dedicated in developing automated methods for segmentation of blood vessels from retinal fundus images. Despite the fact, segmentation of retinal vessels still remains a challenging task due to the presence of abnormalities, varying size and shape of the vessels, non-uniform illumination and anatomical variability between subjects. In this paper, we carry out a systematic review of the most recent advancements in retinal vessel segmentation methods published in last five years. The objectives of this study are as follows: first, we discuss the most crucial preprocessing steps that are involved in accurate segmentation of vessels. Second, we review most recent state-of-the-art retinal vessel segmentation techniques which are classified into different categories based on their main principle. Third, we quantitatively analyse these methods in terms of its sensitivity, specificity, accuracy, area under the curve and discuss newly introduced performance metrics in current literature. Fourth, we discuss the advantages and limitations of the existing segmentation techniques. Finally, we provide an insight into active problems and possible future directions towards building successful computer-aided diagnostic system.     

Diabetic Retinopathy: A Quadtree Based Blood Vessel Detection Algorithm Using RGB Components in Fundus Images

Blood vessel detection in retinal images is a fundamental step for feature extraction and interpretation of image content. This paper proposes a novel computational paradigm for detection of blood vessels in fundus images based on RGB components and quadtree decomposition. The proposed algorithm employs median filtering, quadtree decomposition, post filtration of detected edges, and morphological reconstruction on retinal images. The application of preprocessing algorithm helps in enhancing the image to make it better fit for the subsequent analysis and it is a vital phase before decomposing the image. Quadtree decomposition provides information on the different types of blocks and intensities of the pixels within the blocks. The post filtration and morphological reconstruction assist in filling the edges of the blood vessels and removing the false alarms and unwanted objects from the background, while restoring the original shape of the connected vessels. The proposed method which makes use of the three color components (RGB) is tested on various images of publicly available database. The results are compared with those obtained by other known methods as well as with the results obtained by using the proposed method with the green color component only. It is shown that the proposed method can yield true positive fraction values as high as 0.77, which are comparable to or somewhat higher than the results obtained by other known methods. It is also shown that the effect of noise can be reduced if the proposed method is implemented using only the green color component.     

An Approach to Identify Optic Disc in Human Retinal Images Using Ant Colony Optimization Method

In this work, an attempt has been made to identify optic disc in retinal images using digital image processing and optimization based edge detection algorithm. The edge detection was carried out using Ant Colony Optimization (ACO) technique with and without pre-processing and was correlated with morphological operations based method. The performance of the pre-processed ACO algorithm was analysed based on visual quality, computation time and its ability to preserve useful edges. The results demonstrate that the ACO method with pre-processing provides high visual quality output with better optic disc identification. Computation time taken for the process was also found to be less. This method preserves nearly 50% more edge pixel distribution when compared to morphological operations based method. In addition to improve optic disc identification, the proposed algorithm also distinctly differentiates between blood vessels and macula in the image. These studies appear to be clinically relevant because automated analyses of retinal images are important for ophthalmological interventions.     

Unsupervised Fuzzy Based Vessel Segmentation In Pathological Digital Fundus Images

Performing the segmentation of vasculature in the retinal images having pathology is a challenging problem. This paper presents a novel approach for automated segmentation of the vasculature in retinal images. The approach uses the intensity information from red and green channels of the same retinal image to correct non-uniform illumination in color fundus images. Matched filtering is utilized to enhance the contrast of blood vessels against the background. The enhanced blood vessels are then segmented by employing spatially weighted fuzzy c-means clustering based thresholding which can well maintain the spatial structure of the vascular tree segments. The proposed method’s performance is evaluated on publicly available DRIVE and STARE databases of manually labeled images. On the DRIVE and STARE databases, it achieves an area under the receiver operating characteristic curve of 0.9518 and 0.9602 respectively, being superior to those presented by state-of-the-art unsupervised approaches and comparable to those obtained with the supervised methods.     

基于区域增长的肺结节自适应形态分割

目的 提高计算机辅助诊断系统对胸部CT图像中肺结节,特别是与胸膜相连和与血管相连的肺结节分割的准确性.方法 首先提出了肺结节图像的自动分割过程.然后,应用基于对比度和梯度的区域增长方法 .获得肺结节的分割图像.最后,针对区域增长法不能成功分割的特殊情况,提出了自适应形态分割算法.结果 对临床2D肺部CT图像的初步实验结果 表明,应用本算法能够成功的将孤立性肺结节和与胸膜或血管相连的肺结节分割出来.结论 这是一种实用的肺结节自动分割算法.     

A fast method to detect blood vessel in retinal image

Due to the increasing number of diabetic patients,the number of people affected by diabetic retinopathy is expected to increase.Diabetic retinopathy is a complication of diabetes and the most serious frequent eye disease in the world.Large-scale retinal screening for diabetic patients is necessary to prevent visual loss and blindness.The analysis of digital retinal images,obtained by the fundus camera,is viewed as a feasible approach because retinal blood vessels have been shown to change in diameter,branching angles,or tortuosity as a result of diabetic retinopathy.The morphological change can help identify the different stages of diabetic retinopathy.In addition,the acquisition of retinal image is nonintrusive and low cost.Automatic segmentation of the retinal blood vessel is a prerequisite for this analysis.1-3 This article presents a method to detect blood vessel based on sobel operators.4 Small and fast computation is the outstanding merit of this method.