基于HSI修正空间信息融合的彩色白细胞图像分割方法

本文提出一种基于HSI修正空间信息融合的白细胞自动分割方法。首先将细胞原图转换至HSI彩色空间,由于H分量分段函数变换公式的不连续,导致原图中视觉均匀的细胞浆区域在此通道中均匀性变差。对色调计算公式进行了修改,然后依据白细胞核、浆在H、S、I通道分布特点提取核、浆、红细胞和背景区域信息,利用信息融合理论和方法构造融合图像Ⅰ和只存在细胞浆和少量干扰的融合图像Ⅱ,分别提取细胞核和细胞浆。最后标记细胞核、浆,得到分割结果。实验结果表明:该算法对白细胞图像分割准确性高、鲁棒性强且具有普适性。     

免疫组化彩色细胞图像自动分割的研究

目的：免疫组化彩色细胞图像中阳性细胞的自动分割提取有着重要的临床意义。本文结合三种分割算法的特点，研究实现免疫组化彩色细胞图像的自动分割，提取图像中的阳性细胞。方法：（1）采用OTSU方法在灰度的基础上对免疫组化彩色细胞进行预分割，去除无关背景。（2）使用K-聚类算法，对彩色细胞图像进行彩色分类筛选出阳性细胞和阴性细胞，并对所得阳性细胞图像进行腐蚀，以获取阳性细胞图像的种子。（3）使用区域生长算法对阳性细胞种子进行区域增长。获取完整的阳性细胞图。结果：准确分割出图像中的阳性细胞。结论：该自动分割方法可用于后续的阳性细胞自动计数，辅助医生诊断疾病。     

白细胞图像自动识别系统的研究

我们所用的图像分割方法是在对图像距离变换的基础上,综合区域和边界方法,充分利用图像中包含的信息,实现白细胞图像的分割。根据细胞的形状、纹理、颜色等的特点选取并测定22个特征值,用统计分类的方法设计分类器。通过对560幅图像共831个细胞的测试表明,此系统的识别正确率为96%,经临床专家评估,本系统运用模式识别技术对外周血中的白细胞图像实现自动识别,具有较好的实用价值。     

彩色血液细胞图像的自动分割方法研究

提出基于自适应多尺度阈值和种子点增长的混合方法自动分割彩色血液细胞图像。首先对原始图像直方图进行多尺度滤波，根据它的尺度空间图特性，确定合理的阈值，完成对胞核的分割和白细胞的检出。其次，利用局部颜色特征及全局形态特性控制种子点增长，完成对白细胞浆区域的分割。该方法对白细胞的检出率为98％，分割效果主观评价为好的占93％，它能有效地分割白细胞区域。     

基于最小方差Snake模型的医学图像分割

传统的参数活动轮廓模型（Snake模型）难于处理自动分割医学图像的弱边界。我们在分析参数活动轮廓和几何活动轮廓模型的基础上，提出最小方差参数活动轮廓模型，并成功应用于医学图像自动分割。该方法将气球力Snake模型中的恒定气球力修改为包含区域信息的变力，以目标和背景两区域具有最小方差为准则，引导轮廓线演化。实验结果表明，该模型对初始轮廓位置不敏感，能实现自动分割。对于带噪声的医学图像，先进行保边界特性的曲率流滤波，然后应用该模型也能取得满意的分割效果。     

病理切片图像分割技术的研究

介绍了用图像处理及模式识别技术对显微细胞图像的自动分析和分类的方法,并针对医学图像分析中的难点,提出了基于归一化彩色空间和RGB,HSV彩色模型的分割方法:利用模式识别技术中关于特征向量空间聚类的方法实施真彩色分割.这种方式有效地利用了多维特征空间对于分割目标所提供的信息,使分割的准确性有了较大的提高,解决了图像分割过程中的单个细胞检出问题.     

交互式GACV模型的肿瘤图像分割

目的:提取医学图像中肿瘤区域,用以测量肿瘤体积问题。方法:提出一种基于GACV(Geodesic-Aided C-Vmethod)的交互式模型。该模型首先人工选取感兴趣区域,并在区域内设定初始水平集与肿瘤内部种子点,然后在感兴趣区域上应用将图像梯度边缘信息与图像区域灰度特性统一到同一分割中的GACV模型,得到肿瘤的粗分割结果。最后为去除目标内外孔洞,提出一种无损边缘的膨胀搜索算法,作为细分割。结果:将该模型应用于不同形状的肿瘤图像中,能成功检测肿瘤轮廓。通过实验与其它活动轮廓分割方法结果对比,结果显示该模型在准确分割肿瘤边界与分割算法耗时方面均具有良好表现。结论:本文提出的分割方法能高效率、准确识别肿瘤区域。     

启发式牙颌CT影像自动分割

准确快速地分割CT切片特征轮廓是医学图像三维重建的重要环节。现有的轮廓分割方法必须通过手动层层交互操作,不仅耗时而且分割精度不高。针对这种局限性,提出一种基于启发式牙颌CT影像自动分割方法。首先用拉普拉斯算子对CT图像序列进行边缘增强,其次用轮廓匹配映射技术实现轮廓启发式传递,最后基于收缩包围算法自动分割牙颌序列。以14例完整牙（每例28～32颗牙数据样本）锥束CT断层扫描图像序列进行实验,在相同条件下分别用所提出的轮廓自动提取方法和其他提取方法,对实验样本进行轮廓提取,得到单颗牙轮廓提取的平均用时和提取轮廓与真实轮廓之间的距离差平均值。实验结果显示,轮廓自动分割算法提取单颗牙轮廓的用时约为其他手工分割法提取单颗牙轮廓用时的23%,同时提取的轮廓质量和用传统方法提取的轮廓质量相当。该方法为CT数据特征区自动化分割提供一种可行且高效的方法,为进一步改进现有的CT影像分割和三维重建算法提供了新的思路。     

结合贝叶斯最优分类器的GACV模型对医学彩色细胞图像进行分割

目的:本文对GACV模型进行改进,并用改进的模型对医学彩色细胞图像进行分割。方法:本文在GACV模型基础上加入了贝叶斯最优分类器,得到了结合贝叶斯最优分类器的GACV模型,并用该模型对医学彩色细胞图像进行分割。结果:应用本文提出的模型分割3组不同特点的医学彩色细胞图像,分割结果显示,该模型能正确将细胞从不同噪声环境中分割出来。结论:结合贝叶斯最优分类器的GACV模型对弱边界,噪声以及复杂背景有很强的鲁棒性可以有效、准确的分割医学彩色细胞图像。     

基于几何活动轮廓的神经元干细胞序列图像的自动分割

一个快速稳定的分割系统是研究神经元干细胞变化的基础，为完善此系统，针对多连接边缘模糊的细胞分割提取问题，根据曲线进化原理，我们提出了一种基于水平集方法的改进的几何活动轮廓算法。此算法能自动解决图像的拓扑变化，并能获得更加真实的细胞轮廓边缘。将此方法应用于神经元干细胞的序列图像分割，实验结果证明了此算法的有效性与准确性。     

Detection of the intima and media layer thickness of ultrasound common carotid artery image using efficient active contour segmentation technique

An active contour segmentation technique for extracting the intima–media layer of the common carotid artery (CCA) ultrasound images employing semiautomatic region of interest identification and speckle reduction techniques is presented in this paper. An attempt has been made to test the ultrasound images of the carotid artery of different subjects with this contour segmentation based on improved dynamic programming method. It is found that the preprocessing of ultrasound images of the CCA with region identification and despeckleing followed by active contour segmentation algorithm can be successfully used in evaluating the intima–media thickness (IMT) of the normal and abnormal subjects. It is also estimated that the segmentation used in this paper results an intermethod error of 0.09 mm and a coefficient of variation of 18.9%, for the despeckled images. The magnitudes of the IMT values have been used to explore the rate of prediction of blockage existing in the cerebrovascular and cardiovascular pathologies and also hypertension and atherosclerosis.     

基于单水平集函数的细胞分割算法

目的：由于细胞图像十分复杂,传统的基于像素或者边界的图像分割方法难以精确的实现细胞分割。因此,需要设计一种可以实现细胞图像精确分割的方法。方法：结合大津分割算法和主动轮廓模型的优点,设计出一种基于单水平集函数的细胞分割算法,首先对细胞图像大津分割,其结果作为水平集函数的初始值,然后使用迭代法对水平集函数演化。采用MATLAB对显微镜下获取的细胞图像进行试验,将本文改进后的算法与常规的算法进行了对比。结果：与传统的水平集分割算法相比,本文方法对细胞图像分割结果更加准确,迭代次数减少一半左右,因此分割时间也减少了一半左右。结论：结合细胞图像的结构特点,利用大津分割结果作为主动轮廓模型的初始值,可有效解决主动轮廓模型因为初始值设置不当导致的分割缺陷问题,水平集函数能够跟踪拓扑结构变化,具有计算精度高、算法稳定、优化边界清晰光滑等优点,在本文中得到了充分的应用。因此本文所提出的算法能够高效地实现细胞图像的分割。     

Multiscale active contour model for vessel segmentation

This paper presents a novel multiscale active contour model for vessel segmentation. The model is based on accurate analysis of the vessel structure in the image. According to different scale response of the eigenvalues of local second order derivative (Hessian matrix), a new vessel region information function, which shows a valid estimation of the vesselness measure, is defined. We introduce the posteriori probability estimation into the active contours framework and design a new objective function. The defined objective function is minimized using the variational method, and a new region-based external force is obtained, which is more accurate to the vessel structure and not sensitive to the initial condition. This active contour model combines the obtained region-based and conventional boundary-based force, which aims at finding more accurate vessel edges even when the vessel branches are low contrast or blurry. Furthermore, the proposed model is implemented by an implicit method of level set framework, the solution of which is steady and suitable for various topology changes. Moreover, two new speed functions for vessel segmentation in the level set method are presented, one for fast marching and the other for a narrow-band algorithm. The vessel segmentation experiments compared with previous geometric active contour models are shown on several medical images. The experimental results demonstrate the performance of our approach.     

A dual-stage method for lesion segmentation on digital mammograms

Mass lesion segmentation on mammograms is a challenging task since mass lesions are usually embedded and hidden in varying densities of parenchymal tissue structures. In this article, we present a method for automatic delineation of lesion boundaries on digital mammograms. This method utilizes a geometric active contour model that minimizes an energy function based on the homogeneities inside and outside of the evolving contour. Prior to the application of the active contour model, a radial gradient index (RGI)-based segmentation method is applied to yield an initial contour closer to the lesion boundary location in a computationally efficient manner. Based on the initial segmentation, an automatic background estimation method is applied to identify the effective circumstance of the lesion, and a dynamic stopping criterion is implemented to terminate the contour evolution when it reaches the lesion boundary. By using a full-field digital mammography database with 739 images, we quantitatively compare the proposed algorithm with a conventional region-growing method and an RGI-based algorithm by use of the area overlap ratio between computer segmentation and manual segmentation by an expert radiologist. At an overlap threshold of 0.4, 85% of the images are correctly segmented with the proposed method, while only 69% and 73% of the images are correctly delineated by our previous developed region-growing and RGI methods, respectively. This resulting improvement in segmentation is statistically significant.     

Cell contour extraction on multi-threshold images.

A procedure for the extraction of cell and nuclear contours from digital images is presented. The procedure is simple and suitable for efficient implementation in an interactive environment where the user selects a region of interest containing the cell to be segmented. Segmentation is facilitated by multi-thresholding where the threshold values are determined by an iterative algorithm using the mean square error criteria to cluster the image pixel values. Edges are detected on the threshold images by employing a binary morphological filter and the desired contours are selected by considering closed contours in the edge image. The cell region is first extracted by performing segmentation, edge detection and contour extraction on the region of interest. The nuclear region is subsequently extracted by reapplication of the algorithms within the extracted cell region. Segmentation examples are presented.     

Level Set Segmentation of Breast Masses in Contrast-Enhanced Dedicated Breast CT and Evaluation of Stopping Criteria

Dedicated breast CT (bCT) produces high-resolution 3D tomographic images of the breast, fully resolving fibroglandular tissue structures within the breast and allowing for breast lesion detection and assessment in 3D. In order to enable quantitative analysis, such as volumetrics, automated lesion segmentation on bCT is highly desirable. In addition, accurate output from CAD (computer-aided detection/diagnosis) methods depends on sufficient segmentation of lesions. Thus, in this study, we present a 3D lesion segmentation method for breast masses in contrast-enhanced bCT images. The segmentation algorithm follows a two-step approach. First, 3D radial-gradient index segmentation is used to obtain a crude initial contour, which is then refined by a 3D level set-based active contour algorithm. The data set included contrast-enhanced bCT images from 33 patients containing 38 masses (25 malignant, 13 benign). The mass centers served as input to the algorithm. In this study, three criteria for stopping the contour evolution were compared, based on (1) the change of region volume, (2) the average intensity in the segmented region increase at each iteration, and (3) the rate of change of the average intensity inside and outside the segmented region. Lesion segmentation was evaluated by computing the overlap ratio between computer segmentations and manually drawn lesion outlines. For each lesion, the overlap ratio was averaged across coronal, sagittal, and axial planes. The average overlap ratios for the three stopping criteria ranged from 0.66 to 0.68 (dice coefficient of 0.80 to 0.81), indicating that the proposed segmentation procedure is promising for use in quantitative dedicated bCT analyses.     

基于二进小波变换和快速主动轮廓模型的医学图像边缘提取

医学图像的病灶边缘一般呈弱边缘特性,噪声干扰使得提取病灶边缘更加困难,传统的分割方法不能取得令人满意的效果.我们提出了一种基于二进小波变换和主动轮廓模型的病灶边缘提取方法.该方法采用二进小波检测出真正的边缘点,将其作为初始轮廓,再利用改进的快速主动轮廓模型算法连接边缘点,得到病灶的边缘.将该算法用于脑部MRI的肿瘤边缘提取的实验结果表明这种方法可以有效减少噪声的影响,能够准确地提取出复杂的病灶边缘.     

Prostate segmentation by feature enhancement using domain knowledge and adaptive region based operations

Estimation of prostate location and volume is essential in determining a dose plan for ultrasound-guided brachytherapy, a common prostate cancer treatment. However, manual segmentation is difficult, time consuming and prone to variability. In this paper, we present a semi-automatic discrete dynamic contour (DDC) model based image segmentation algorithm, which effectively combines a multi-resolution model refinement procedure together with the domain knowledge of the image class. The segmentation begins on a low-resolution image by defining a closed DDC model by the user. This contour model is then deformed progressively towards higher resolution images. We use a combination of a domain knowledge based fuzzy inference system (FIS) and a set of adaptive region based operators to enhance the edges of interest and to govern the model refinement using a DDC model. The automatic vertex relocation process, embedded into the algorithm, relocates deviated contour points back onto the actual prostate boundary, eliminating the need of user interaction after initialization. The accuracy of the prostate boundary produced by the proposed algorithm was evaluated by comparing it with a manually outlined contour by an expert observer. We used this algorithm to segment the prostate boundary in 114 2D transrectal ultrasound (TRUS) images of six patients scheduled for brachytherapy. The mean distance between the contours produced by the proposed algorithm and the manual outlines was 2.70 +/- 0.51 pixels (0.54 +/- 0.10 mm). We also showed that the algorithm is insensitive to variations of the initial model and parameter values, thus increasing the accuracy and reproducibility of the resulting boundaries in the presence of noise and artefacts.     

图像分割技术在中医舌诊客观化研究中的应用

舌诊是中医四诊的主要内容,是辨证论治的主要依据。客观化研究对中医辨证规范化及中医临床、教学和科研手段的现代化具有重要意义。对舌诊客观化研究中涉及的图像预处理的重要内容——舌体分割提取和舌苔舌质同类区域划分——进行了深入研究,提出了相应算法,通过实验充分证明了算法具有很好的鲁棒性。这给进一步的自动特征提取提供了保障和重要信息。     

基于卷积神经网络和图像显著性的心脏CT图像分割

目的心脏医学影像中,感兴趣部分的提取与分割是诊断心脏病变部位的关键。由于心脏舒张、收缩以及血液的流动,心脏CT图像易出现弱边界、伪影,传统分割算法易产生过度分割的情况。为此,提出一种基于卷积神经网络和图像显著性的心脏CT图像分割方法。方法采用卷积神经网络对目标区域进行定位,滤除肋骨、肌肉等造影对比不明显部分,截取出感兴趣区域,结合感兴趣区域的对比度计算并提高感兴趣区域的心脏组织的显著值。通过获得的显著值图像截取心脏图像,并与区域生长算法的分割结果进行对比。最后使用泰州人民医院11例患者的影像数据对算法模型进行训练和测试,随机选择9例用于训练,剩余2例用于测试。结果所提算法模型在心底、心中、心尖3个心脏分段的分割正确率分别达到了92.79%、92.79%、94.11%,均优于基于区域生长的分割方法。结论基于卷积神经网络和图像显著性的分割方法能够准确获取心脏的外围轮廓,轮廓边缘更加平滑,完全能够满足CT图像序列的心脏全自动分割任务需求,分割后的图像更有利于医生对患者心脏健康状况和病变部位的观察。