一种眼底视盘图像的自动分割方法

本文研究了基于计算机图像处理技术的眼底视盘图像的自动分割方法。首先获取视盘图像的红基色图像,然后根据红基色图像中视盘的明显的边沿特征实现视盘的自动分割。一幅具体的眼底图像实验证明了本方法的可行性。     

一种利用直方图特征峰的肾小球图像分割方法

根据肾小球医学图像的特点,提出了一种新的基于直方图特征峰的图像分割方法。通过定位图像的特征峰,从而有效地坚小球医学图像进行阈值化。经实验证明,本提出的算法能快速、准确地分割肾小球图像。     

磁共振图像的分割

近年来，磁共振图像在临床上的应用越来越广泛和深入，但是，制约磁共振图像在临床上广泛应用和研究的一个瓶颈问题是图像分割。自从８０年代末磁共振图像应用于临床检查以来，人们提出了众多的磁共振图像的分方法。这些方法中有经典的方法，如阈值法、基于边界的方法和基于区域的方法；有现代的方法，如概率统计的方法、基于知识的方法、模糊方法和人工神经网络的方法等。本文对这些方法进行了综述和讨论。     

磁共振图像的分割

近年来,磁共振图像在临床上的应用越来越广泛和深入,但是,制约磁共振图像在临床上广泛应用和研究的一个瓶颈问题是图像分割。自从７０ 年代末磁共振图像应用于临床检查以来,人们提出了众多的磁共振图像的分割方法。这些方法中有经典的方法,如阈值法、基于边界的方法和基于区域的方法;有现代的方法,如概率统计的方法、基于知识的方法、模糊方法和人工神经网络的方法等。本文对这些方法进行了综述和讨论。     

基于人工生命的医学图像分割

医学图像中解剖结构和相关诊断信息的提取有着极为重要的意义，但目前的分割算法大都需要借助专家的干预和监控，寻求一种全自动分割的方法变得日益重要。人工生命的方法有助于人们了解生物学规律，并且在机器人、计算机图形学等方面得到了成功应用。主要介绍基于人工生命的方法在医学图像自动分割领域的初步应用。     

医学图像分割技术

图像分割是制约医学图像在临床上广泛应用的关键性问题。医学图像分割则是图像分割的一个重要应用领域。本文讨论了医学图像分割的目的和意义，简述了医学图像分割技术的进展，对近年来医学图像分割技术进行了综述。     

一种新的舌癌图像快速自动分割方法

目的:研究一种新的舌癌图像自动分割算法以实现对舌癌肿瘤的快速准确分割。方法:通过引入一种基于局部均方差的自适应尺度算子实现演化曲线在演化过程中的自动调整,从而更高效率地向真实目标边界运动,并且克服舌癌肿瘤图像中目标边界不清和图像灰度不均匀等不良因素带来的影响。此外,为加快曲线的收敛速度,本文提出了一种新的能量项评估演化曲线轮廓内部和轮廓外部区域灰度的分布差异,以此引导曲线自适应地调整演化速度,减少完成分割任务所需的迭代次数。结果:使用本方法对22幅舌癌肿瘤MRI图像进行分割,分割结果与真实结果之间的重叠率Dice值为0.82,豪斯多夫距离HD值为1.732 mm。结论:将本文算法与其它现有的几种活动轮廓模型进行定性和定量对比分析,实验结果表明本文算法在对细节及弱边缘灰度的处理上表现更加优异,可用于舌癌肿瘤的精确分割,为临床分析提供辅助信息。     

一种用于医学图像分割算法验证的模拟图像生成方法

目的 医学图像分割是计算机辅助诊断与治疗的基础技术,对各种自动分割算法性能的验证极为重要;但是临床图像无法直接提供“金标准”,亟需解决导致验证所需的测试数据难以量化评估的问题.方法 使用傅里叶描述子作为描述临床图像中待分割区域轮廓的函数,通过对傅里叶描述子的抽样生成新的轮廓,最后使用纹理匹配技术计算新图像中像素点的灰度值.结果 针对颅内出血的图像,以生成的模拟图像作为测试图像,对多阈值分割和水平集分割算法的精确性和准确性进行了定量的验证.结论 实验表明本方法能够快速地生成逼真的模拟医学图像,对分割算法的验证具有很强的实用性.     

图像分割在医学图像处理中的应用

目的讨论了医学图像处理中图像分割的几种算法。方法讲述了几种图像分割的算法，并应用于实际的医学图像处理中。结果每种图像分割算法与图像处理都有各自不同的处理效果，各有优、缺点。结论在具体实际情况的使用中，根据不同的情况采用不同的分割算法，以达到更好的效果。     

基于人工生命的医学图像分割

医学图像中解剖结构和相关诊断信息的提取有着极为重要的意义,但目前的分割算法大都需要借助专家的干预和监控,寻求一种全自动分割的方法变得日益重要。人工生命的方法有助于人们了解生物学规律,并且在机器人、计算机图形学等方面得到了成功应用。主要介绍基于人工生命的方法在医学图像自动分割领域的初步应用。     

Study of Automatic Enhancement for Chest Radiograph

Because of the large difference of the densities in the lung and other structures, the chest x-ray image behaves as a wide-range intensity distribution, which brings on a bit of difficulty to investigate the focus. In the paper, according to the intensity properties of the chest radiograph, the chest radiographic image is divided into three subregions, and a piecewise linear transformation model is established. An approach of automatic enhancement is presented, based on the gray-level normalization. The average enhanced ratios of three subregions of the normal and severe acute respiratory syndrome image are increased by 10.70% and 25.55%, respectively. The technique is proved to be effective through the evaluation of the improved images.     

Lung Field Segmenting in Dual-Energy Subtraction Chest X-ray Images

The purpose of this study was to develop and test a method to delineate lung field boundaries in dual-energy chest x-ray images. The segmenting method uses soft-tissue images and spatial frequency–dependent, background-subtracted images. Large-scale chest anatomy features are located and used to select the lung apices, the lateral lung boundaries, and the lung–mediastinum and lung–diaphragm boundaries. Extraneous parts of the contours are removed and they are joined to form complete lung boundaries. The reliability measure uses a statistical shape model to estimate the probability of occurrence of a contour. The method was experimentally tested with 30 human subject images. It has higher accuracy and specificity and a sensitivity parameter equal to the best previously reported method. The reliability measure is able to detect contours with unusual lung outlines or errors in the processing. The method exploits the characteristics of dual-energy subtraction images to improve lung field segmenting performance.     

一种基于参数自适应的CT图像肺气道树分割方法

肺CT图像气道树分割对于肺呼吸功能测定和疾病诊断具有重要意义,但图像噪声和部分容积效应的影响会造成气管分割的泄漏,难于分割出微小的气管。本文利用肺气道树的解剖结构信息,对肺气道树进行分段处理,并提出一种分割参数自适应的方法,动态调整各气管段的分割参数。实验表明,该方法能提高分割的速度和精度,并有效防止泄漏。     

X光胸片中肋骨框架自动检测方法的综述

实现对X光胸片中肋骨框架的自动精确定位在计算机辅助诊断分析中是具有意义的,然而它至今仍是一个没有完全解决难题.本文针对如今国外现有的一些算法进行了分析总结,并对现存的一些问题进行了相关讨论.     

冠脉数字造影图像血管分割方法研究

为了从冠脉数字造影图中提取具有复杂形态结构的血管 ,以便于血管临床心血管疾病的定量分析与诊断 ,我们对造影图像设计了一种有效的血管分割算法 ,然而为了获得更加准确的血管形态 ,我们对造影图像和掩膜图像进行匹配减影 ,然后再从减影图像中分割血管 ,实验结果表明这样分割得到的血管较直接从造影图像分割得到的血管更加准确     

An Automatic Computer-Aided Detection Scheme for Pneumoconiosis on Digital Chest Radiographs

This paper presents an automatic computer-aided detection scheme on digital chest radiographs to detect pneumoconiosis. Firstly, the lung fields are segmented from a digital chest X-ray image by using the active shape model method. Then, the lung fields are subdivided into six non-overlapping regions, according to Chinese diagnosis criteria of pneumoconiosis. The multi-scale difference filter bank is applied to the chest image to enhance the details of the small opacities, and the texture features are calculated from each region of the original and the processed images, respectively. After extracting the most relevant ones from the feature sets, support vector machine classifiers are utilized to separate the samples into the normal and the abnormal sets. Finally, the final classification is performed by the chest-based report-out and the classification probability values of six regions. Experiments are conducted on randomly selected images from our chest database. Both the training and the testing sets have 300 normal and 125 pneumoconiosis cases. In the training phase, training models and weighting factors for each region are derived. We evaluate the scheme using the full feature vectors or the selected feature vectors of the testing set. The results show that the classification performances are high. Compared with the previous methods, our fully automated scheme has a higher accuracy and a more convenient interaction. The scheme is very helpful to mass screening of pneumoconiosis in clinic.     

Automatic Detection of the Nipple in Screen-Film and Full-field Digital Mammograms Using a Novel Hessian-Based Method

Automatic detection of the nipple in mammograms is an important step in computerized systems that combine multiview information for accurate detection and diagnosis of breast cancer. Locating the nipple is a difficult task owing to variations in image quality, presence of noise, and distortion and displacement of the breast tissue due to compression. In this work, we propose a novel Hessian-based method to locate automatically the nipple in screen-film and full-field digital mammograms (FFDMs). The method includes detection of a plausible nipple/retroareolar area in a mammogram using geometrical constraints, analysis of the gradient vector field by mean and Gaussian curvature measurements, and local shape-based conditions. The proposed procedure was tested on 566 mammographic images consisting of 372 randomly selected scanned films from two public databases (mini-MIAS and DDSM), and 194 digital mammograms acquired with a GE Senographe 2000D FFDM system. A radiologist independently marked the centers of the nipples for evaluation of the results. The average error obtained was 6.7 mm (22 pixels) with reference to the center of the nipple as identified by the radiologist. Only two out of the 566 detected nipples (0.35 %) had an error larger than 50 mm. The method was also directly compared with two other techniques for the detection of the nipple. The results indicate that the proposed method outperforms other algorithms presented in the literature and can be used to identify accurately the nipple on various types of mammographic images.     

Automatic Segmentation of Ground-Glass Opacities in Lung CT Images by Using Markov Random Field-Based Algorithms

Chest radiologists rely on the segmentation and quantificational analysis of ground-glass opacities (GGO) to perform imaging diagnoses that evaluate the disease severity or recovery stages of diffuse parenchymal lung diseases. However, it is computationally difficult to segment and analyze patterns of GGO while compared with other lung diseases, since GGO usually do not have clear boundaries. In this paper, we present a new approach which automatically segments GGO in lung computed tomography (CT) images using algorithms derived from Markov random field theory. Further, we systematically evaluate the performance of the algorithms in segmenting GGO in lung CT images under different situations. CT image studies from 41 patients with diffuse lung diseases were enrolled in this research. The local distributions were modeled with both simple and adaptive (AMAP) models of maximum a posteriori (MAP). For best segmentation, we used the simulated annealing algorithm with a Gibbs sampler to solve the combinatorial optimization problem of MAP estimators, and we applied a knowledge-guided strategy to reduce false positive regions. We achieved AMAP-based GGO segmentation results of 86.94%, 94.33%, and 94.06% in average sensitivity, specificity, and accuracy, respectively, and we evaluated the performance using radiologists' subjective evaluation and quantificational analysis and diagnosis. We also compared the results of AMAP-based GGO segmentation with those of support vector machine-based methods, and we discuss the reliability and other issues of AMAP-based GGO segmentation. Our research results demonstrate the acceptability and usefulness of AMAP-based GGO segmentation for assisting radiologists in detecting GGO in high-resolution CT diagnostic procedures.     

基于最大熵的超声图像中胎儿股骨分割方法的研究

提出一种基于最大熵分割的胎儿股骨自动测量方法.首先,用中值滤波器对原始图像进行去噪,并用最大熵分割方法对去噪后的图像进行分割,得到股骨候选区域;其次,利用股骨区域位置、形状等特征信息对股骨候选区域进行筛选,得到最终的股骨区域;最后,通过股骨区域的外接矩形斜边长,计算股骨长度;与医生手动测量结果对比,70幅超声图像的自动测量结果平均相对误差为1.42±4.48 mm,实验结果验证了本方法的可行性.