双能量CT心肌灌注碘图图像质量与重组时相的关系

目的 探讨第2代双源CT（DSCT）心肌灌注图像质量与重组时相的关系。方法 收集接受DSCT双能量成像方法扫描的患者,选取其中冠状动脉正常者28例。采用回顾性心电门控间隔5%重建出30%~80% R-R时相的图像,采用280 ms全期相时间窗,定量测量各R-R间期的每段心肌灌注碘图图像的伪影面积,计算心底部、中部、心尖部、心尖层面平均各段的伪影面积,并进行统计学分析。结果 心底部、中部、心尖部、心脏整体平均各段伪影面积差异有统计学意义（P均<0.01）,均以60% R-R间期时相最小[（0.31±0.28）cm²、（0.18±0.23）cm²、（0.13±0.13）cm²）、（0.22±0.18）cm²]。心尖伪影面积不同R-R间期时相差异无统计学意义（P=0.634）。60% R-R间期心底部、中部、心尖部、心尖各层面平均各段碘图伪影面积差异有统计学意义（F=3.701,P=0.014）,心尖部伪影面积最小,与中部差异无统计学意义（P>0.05）,但明显低于其他部位的伪影面积（P<0.05）。结论 第2代DSCT采用280 ms全期相时间窗可提供优良的心肌灌注碘图,其中以60%左右R-R间期图像质量最佳。     

一种用于肺部图像引导放射治疗的双能X射线透视成像方法

目的 研究一种双能X线透视成像方法，采集呼吸周期高低能X线图像序列，通过改进双能减影算法获取软组织减影图像，以提高在图像引导放射治疗中无标记肺部肿瘤运动跟踪的肿瘤可视度。方法 采用具有C臂旋转结构和高低能快速切换采图机制的双能X线透视成像系统，分别在4个投影方向实时采集呼吸周期9或10个时相的高低能图像对序列。通过优化加权对数减影算法，对去除同一时相高低能图像对中的骨骼，得到软组织减影图。双能减影算法采用CNR作为图像质量评价参数，自动获取最佳软组织减影图像。采集和分析20例患者数据，评价软组织减影图像中肿瘤可视度的提高程度。结果 分别在0°、45°、90°和135°投影方向采集198、196、198、和198个高低能图像对，肿瘤可视图像分别为198、38、69和49对。所获软组织减影图像中，肿瘤可视图像分别为198、108、149和159幅。结论 本研究提出的双能X线透视成像方法可显著提高肺部肿瘤的可视度。     

全模型迭代重建算法评价125I粒子植入术后CT图像

目的 探讨全模型迭代重建（IMR）算法评价¹²⁵I粒子植入术后图像的应用价值。方法 收集接受¹²⁵I粒子植入术及术后CT随访的16例腹部肿瘤患者,对扫描原始数据分别以滤波反投影法（FBP）、IMR和高级重建迭代（iDose⁴）算法进行重建,比较3种重建方法图像的噪声、伪影指数（AI）、CNR和主观评分。结果 FBP重建图像的噪声、CNR及AI分别为（58.65±4.03） HU、1.09±0.43和51.60±9.23,iDose⁴图像分别为（48.38±5.34） HU、1.29±0.48和43.77±4.91,IMR图像分别为（41.46±3.44） HU、1.58±0.56和38.51±4.64,3种重建方法图像的噪声、CNR及AI两两比较差异均有统计学意义（P均<0.05）。IMR图像的主观图像质量评分显著高于FBP和iDose⁴算法图像（调整后P<0.001,P=0.011）。结论 IMR算法获得的图像质量较高,可有效减少¹²⁵I粒子伪影,为¹²⁵I粒子植入术后随访与疗效评估提供了更佳方法。     

实时剪切波弹性成像测量胸锁乳突肌杨氏模量时声束平面与肌束不同角度对测量值的影响

目的 探讨应用实时剪切波成像测量胸锁乳突肌杨氏模量值时声束平面与肌束不同角度对测量值的影响。方法 选取210名健康志愿者,按年龄分为青年组（20~39岁）、中年组（40~59岁）和老年组（≥60）3组;同时按性别分为男性组和女性组。采用Supersonic Imagine AixPlorer型Shear Wave^TM实时剪切波弹性成像超声诊断仪,测量声束平面与肌束成不同角度（0°、30°、60°、90°）时的杨氏模量值;对不同组别杨氏模量值进行统计学分析。结果 0°、30°、60°、90°时的杨氏模量值分别为（27.54±1.46）kPa、（14.87±3.05）kPa、（11.27±2.12）kPa、（9.33±1.94）kPa,两两比较差异均有统计学意义（P均<0.05）;不同性别和年龄组间杨氏模量值差异均无统计学意义。结论 胸锁乳突肌杨氏模量值受声束平面与肌束角度的影响,但不受性别和年龄的影响。     

胸部双能X线摄影中运动伪影的影响因素

目的 观察胸部DR双能减影图像运动伪影的好发部位，分析其影响因素。方法 分析128例胸部双能减影图像，观察运动伪影出现的位置，测量并比较伪影长度和宽度，分析心率与不同位置运动伪影的相关性。结果 128例胸部双能减影图像中，115例出现黑白条运动伪影，分别位于左心室段（87例，75.65%）、主动脉弓处（82例，71.30%）、右心缘（60例，52.17%）、心膈缘（42例，36.52%）、左心房耳肺动脉段（30例，26.09%）及上腔静脉处（27例，23.48%），在骨组织图像中更为明显；其中15例膈肌伪影明显，经呼吸训练后行第2次摄片，膈肌伪影减小或消失。其中左心室段、主动脉弓段、右心缘处运动伪影的平均长度及宽度差异均有统计学意义（F=4.59、3.46，P均<0.05）。心率与左心室段、主动脉弓段运动伪影均呈正相关（r=1.00、0.99，P均<0.05）。结论 心脏搏动及呼吸运动可致胸部DR双能减影图像出现运动伪影，多位于左心室段及主动脉弓段，且心率越快越易出现运动伪影。     

一体化PET/MR检查中“热气管征”伪影探讨

目的 探讨在一体化全身PET/MR检查中"热气管征"伪影具体的发生部位、概率，以及飞行时间（TOF）技术在减轻该伪影方面的应用价值。方法 回顾性分析了105例受检者进行一体化全身PET/MR检查后的"热气管征"伪影出现部位及发生概率情况，同时评价非TOF技术下的一体化全身PET/MR和一体化全身TOF-PET/MR检查图像上"热气管征"出现情况。结果 一体化PET/MR检查中出现"热气管征"伪影的部位分布在鼻窦、气管、胃窦、结肠和直肠，发生率分别为60.00%（63/105）、68.57%（72/105）、8.57%（9/105）、20.00%（21/105）和16.19%（17/105）。非TOF技术一体化PET/MR图像上不同部位的"热气管征"伪影的SUV_max分别为4.09±2.17，1.77±0.81，1.75±0.85，3.73±0.51和11.77±8.39；SUV_mean分别为3.19±1.87，1.38±0.70，1.44±0.85，2.68±0.46和6.78±4.19。非TOF技术的一体化全身PET/MR图像上的"热气管征"伪影部位的SUV_max和SUV_mean值均高于一体化TOF-PET/MR全身图像上相应部位（P均<0.01）。结论 一体化全身PET/MR检查中"热气管征"伪影主要存在于鼻窦、气管和消化道，其中气管最常见。TOF技术有助于明显减轻一体化PET/MR图像上的"热气管征"伪影，从而显著提高一体化PET/MR检查图像的质量。     

基于MRI图像的羊水体积的测量

目的 探讨基于MRI图像,通过算法计算测量羊水量的可行性。方法 利用MatLab图像处理技术,对胎儿磁共振图像进行分割,提取目标区域并计算目标区域的面积,乘以层厚得出一层的体积,再将每层体积相加计算得到羊水总体积。结果 通过该算法最终计算得出羊水的体积为495.10 ml。定性分析显示,图像羊水分割边缘与原图目标区域的边缘拟合较好。定量分析显示,手动金标准分割的体积为458.20 ml,本研究算法与手动分割结果的误差率为8.06%。脂肪抑制序列图像分割效果定性评价亦显示羊水分割边缘与原图目标区域的边缘拟合较好;定量分析显示,金标准手动计算得到的羊水量为557.34 ml,本研究算法计算得到的羊水量为604.50 ml,与手动分割的误差率为8.46%。结论 采用本研究算法测量羊水量切实可行。     

基于超声图像处理的HIFU所致组织损伤自动检测方法:实验研究

目的 探讨基于超声图像处理的HIFU所致组织损伤的自动检测方法。方法 针对HIFU辐照后新鲜离体猪肉声像图中的ROI,通过搜索灰度极大区域自动定位图像中的所有亮斑,结合数学形态学、连通域标记和Canny边缘检测算法提取测试对象的边缘轮廓;根据亮斑中心至边缘轮廓的欧式距离去除边缘附近的亮斑噪声,获取HIFU损伤候选区;而后提取候选区特征参数,并结合支持向量机（SVM）识别HIFU损伤。结果 最大灰度值和矩形度两个特征参数的识别率分别为86.25%和93.33%。选用识别率更高的矩形度,可正确识别单处、多处HIFU损伤或无HIFU损伤的图像。结论 采用此法可直接分析HIFU辐照后超声声像图而自动检测HIFU损伤,无需依靠图像配准技术,可减少手动定位带来的误差。     

自适应非局域均值滤波的超声图像去噪

目的 提出一种去除超声图像噪声的新方法。方法 对超声图像进行非局域搜索,找到相似的图像块进行加权平均,降低噪声。通过定义一个特征强度,区分斑点噪声和图像边界;然后将特征强度引入非局域滤波方法中,对平坦区域和边界进行自适应滤波。结果 本方法可有效去除斑点噪声,提高噪声图像的峰值信噪比(PSNR)和结构相似度指数(SSIM),优于常规方法。结论 自适应非局域均值滤波可有效去噪,并保护超声图像特征。     

循环生成对抗网络基于颅脑MR图生成伪CT图模型

目的 采用改进循环生成对抗网络（UCycleGAN）基于颅脑MR图映射模型生成伪CT图。方法 对50例鼻咽癌颅脑MR图与CT图进行配准及预处理；以U-net网络并添加L1距离函数替换原始循环GAN （CycleGAN）模型生成器的深度残差网络。随机选取40例图像作为训练数据对UCycleGAN模型进行训练，将剩余10例用于测试；比较生成伪CT图与原始图像质量的差异，并与以ResNet、U-net的CycleGAN以及Pix2Pix生成的图像进行对比。结果 相比其他模型，以UCycleGAN模型生成的伪CT图与原始CT图更为接近，体素平均绝对误差（MAE）为（81.45±3.87） HU，峰值信噪比（PSNR）为（34.13±3.28） dB，结构相似性（SSIM）为0.87±0.03。采用UCycleGAN模型生成的伪CT图的MAE小于、而SSIM明显大于其他3种模型（P均<0.05）；UCycleGAN伪CT图的PSNR大于CycleGAN_ResNet图像（P<0.05）。结论 利用UCycleGAN可基于颅脑MR图生成伪CT图；改良后CycleGAN模型的准确性更高。     

CT图像中肺实质的自动分割

目的 为解决肺实质分割中肺部结节及高密度血管易遗漏的问题,提出一种自动肺实质分割方法.方法 首先利用二维区域生长反操作、连通区域判别等方法提取肺实质区域;然后利用行扫描法定位肺区边界点;最后通过对边界点参数分析,定位受肿瘤侵占的边界点,利用曲线拟合修复受损边界.结果 通过对多组胸部CT图像的分割,验证了算法的有效性;与几种常见边界修复算法对比,验证了行扫描边界修复算法的优越性.结论 本文提出的算法能将肿瘤包含到肺实质区域,确保分割的完整性、准确性、实时性.     

Boundary-rendering network for breast lesion segmentation in ultrasound images

Breast Ultrasound (BUS) has proven to be an effective tool for the early detection of cancer in the breast. A lesion segmentation provides identification of the boundary, shape, and location of the target, and serves as a crucial step toward accurate diagnosis. Despite recent efforts in developing machine learning algorithms to automate this process, problems remain due to the blurry or occluded edges and highly irregular nodule shapes. Existing methods often produce over-smooth or inaccurate results, failing the need of identifying detailed boundary structures which are of clinical interest. To overcome these challenges, we propose a novel boundary-rendering framework that explicitly highlights the importance of boundary for automated nodule segmentation in BUS images. It utilizes a boundary selection module to automatically focuses on the ambiguous boundary region and a graph convolutional-based boundary rendering module to exploit global contour information. Furthermore, the proposed framework embeds nodule classification via semantic segmentation and encourages co-learning across tasks. Validation experiments were performed on different BUS datasets to verify the robustness of the proposed method. Results show that the proposed method outperforms states-of-art segmentation approaches (Dice=0.854, IOU=0.919, HD=17.8) in nodule delineation, as well as obtains a higher classification accuracy than classical classification models.     

Strain-Initialized Robust Bone Surface Detection in 3-D Ultrasound

Three-dimensional ultrasound has been increasingly considered as a safe radiation-free alternative to radiation-based fluoroscopic imaging for surgical guidance during computer-assisted orthopedic interventions, but because ultrasound images contain significant artifacts, it is challenging to automatically extract bone surfaces from these images. We propose an effective way to extract 3-D bone surfaces using a surface growing approach that is seeded from 2-D bone contours. The initial 2-D bone contours are estimated from a combination of ultrasound strain images and envelope power images. Novel features of the proposed method include: (i) improvement of a previously reported 2-D strain imaging-based bone segmentation method by incorporation of a depth-dependent cumulative power of the envelope into the elastographic data; (ii) incorporation of an echo decorrelation measure-based weight to fuse the strain and envelope maps; (iii) use of local statistics of the bone surface candidate points to detect the presence of any bone discontinuity; and (iv) an extension of our 2-D bone contour into a 3-D bone surface by use of an effective surface growing approach. Our new method produced average improvements in the mean absolute error of 18% and 23%, respectively, on 2-D and 3-D experimental phantom data, compared with those of two state-of-the-art bone segmentation methods. Validation on 2-D and 3-D clinical in vivo data also reveals, respectively, an average improvement in the mean absolute fitting error of 55% and an 18-fold improvement in the computation time.     

Automatic left ventricle segmentation in volumetric SPECT data set by variational level set

Introduction

Left ventricle (LV) quantification in nuclear medicine images is a challenging task for myocardial perfusion scintigraphy. A hybrid method for left ventricle myocardial border extraction in SPECT datasets was developed and tested to automate LV ventriculography.

Methods

Automatic segmentation of the LV in volumetric SPECT data was implemented using a variational level set?algorithm. The method consists of two steps: (1) initialization and (2) segmentation. Initially, we estimate the initial closed curves in SPECT images using adaptive thresholding and morphological operations. Next, we employ the initial closed curves to estimate the final contour by variational level set. The performance of the proposed approach was evaluated by comparing manually obtained boundaries with automated segmentation contours in 10 SPECT data sets obtained from adult patients. Segmented images by proposed methods were visually compared with manually outlined contours and the performance was evaluated using ROC analysis.

Results

The proposed method and a traditional level set method were compared by computing the sensitivity and specificity of ventricular outlines as well as ROC analysis. The results show that the proposed method can effectively segment LV regions with a sensitivity and specificity of 88.9 and 96.8%, respectively. Experimental results demonstrate the effectiveness and reasonable robustness of the automatic method.

Conclusion

A new variational level set technique was able to automatically trace the LV contour in cardiac SPECT data sets, based on the characteristics of the overall region of LV images. Smooth and accurate LV contours were extracted using this new method, reducing the influence of nearby interfering structures including a hypertrophied right ventricle, hepatic or intestinal activity, and pulmonary or intramammary activity.     

Segmentation of breast ultrasound images based on active contours using neutrosophic theory

Purpose

Ultrasound imaging is an effective approach for diagnosing breast cancer, but it is highly operator-dependent. Recent advances in computer-aided diagnosis have suggested that it can assist physicians in diagnosis. Definition of the region of interest before computer analysis is still needed. Since manual outlining of the tumor contour is tedious and time-consuming for a physician, developing an automatic segmentation method is important for clinical application.

Methods

The present paper represents a novel method to segment breast ultrasound images. It utilizes a combination of region-based active contour and neutrosophic theory to overcome the natural properties of ultrasound images including speckle noise and tissue-related textures. First, due to inherent speckle noise and low contrast of these images, we have utilized a non-local means filter and fuzzy logic method for denoising and image enhancement, respectively. This paper presents an improved weighted region-scalable active contour to segment breast ultrasound images using a new feature derived from neutrosophic theory.

Results

This method has been applied to 36 breast ultrasound images. It generates true-positive and false-positive results, and similarity of 95%, 6%, and 90%, respectively.

Conclusion

The purposed method indicates clear advantages over other conventional methods of active contour segmentation, i.e., region-scalable fitting energy and weighted region-scalable fitting energy.

     

Advanced contour detection for three-dimensional intracoronary ultrasound: a validation--in vitro and in vivo

Intracoronary ultrasound (ICUS) provides high-resolution transmural images of the arterial wall. By performing a pullback of the ICUS transducer and three-dimensional reconstruction of the images, an advanced assessment of the lumen and vessel wall morphology can be obtained. To reduce the analysis time and the subjectivity of boundary tracing, automated segmentation of the image sequence must be performed. The Quantitative Coronary Ultrasound – Clinical Measurement Solutions (QCU-CMS) (semi)automated analytical software package uses a combination of transversal and longitudinal model and knowledge-guided contour detection techniques. On multiple longitudinal sections through the pullback stack, the external vessel contours are detected simultaneously, allowing mutual guidance of the detection in difficult areas. Subsequently, luminal contours are detected on these longitudinal sections. Vessel and luminal contour points are transformed to the individual cross-sections, where they guide the vessel and lumen contour detection on these transversal images. The performance of the software was validated stepwise. A set of phantoms was used to determine the systematic and random errors of the contour detection of external vessel and lumen boundaries. Subsequently, the results of the contour detection as obtained in in vivo image sets were compared with expert manual tracing, and finally the contour detection in in vivo image sequences was compared with results obtained from another previously validated ICUS quantification system. The phantom lumen diameters were underestimated by 0.1 mm, equally by the QCU-CMS software and by manual tracing. Comparison of automatically detected contours and expert manual contours, showed that lumen contours correspond very well (systematic and random radius difference: –0.025 ± 0.067 mm), while automatically detected vessel contours slightly overestimated the expert manual contours (radius difference: 0.061 ± 0.037 mm). The cross-sectional vessel and lumen areas as detected with our system and with the second computerized system showed a high correlation (r = 0.995 and 0.978, respectively). Thus, use of the new QCU-CMS analytical software is feasible and the validation data suggest its application for the analysis of clinical research.     

The predictive value of segmentation metrics on dosimetry in organs at risk of the brain

BackgroundFully automatic medical image segmentation has been a long pursuit in radiotherapy (RT). Recent developments involving deep learning show promising results yielding consistent and time efficient contours. In order to train and validate these systems, several geometric based metrics, such as Dice Similarity Coefficient (DSC), Hausdorff, and other related metrics are currently the standard in automated medical image segmentation challenges. However, the relevance of these metrics in RT is questionable. The quality of automated segmentation results needs to reflect clinical relevant treatment outcomes, such as dosimetry and related tumor control and toxicity. In this study, we present results investigating the correlation between popular geometric segmentation metrics and dose parameters for Organs-At-Risk (OAR) in brain tumor patients, and investigate properties that might be predictive for dose changes in brain radiotherapy.MethodsA retrospective database of glioblastoma multiforme patients was stratified for planning difficulty, from which 12 cases were selected and reference sets of OARs and radiation targets were defined. In order to assess the relation between segmentation quality -as measured by standard segmentation assessment metrics- and quality of RT plans, clinically realistic, yet alternative contours for each OAR of the selected cases were obtained through three methods: (i) Manual contours by two additional human raters. (ii) Realistic manual manipulations of reference contours. (iii) Through deep learning based segmentation results. On the reference structure set a reference plan was generated that was re-optimized for each corresponding alternative contour set. The correlation between segmentation metrics, and dosimetric changes was obtained and analyzed for each OAR, by means of the mean dose and maximum dose to 1% of the volume (Dmax 1%). Furthermore, we conducted specific experiments to investigate the dosimetric effect of alternative OAR contours with respect to the proximity to the target, size, particular shape and relative location to the target.ResultsWe found a low correlation between the DSC, reflecting the alternative OAR contours, and dosimetric changes. The Pearson correlation coefficient between the mean OAR dose effect and the Dice was -0.11. For Dmax 1%, we found a correlation of -0.13. Similar low correlations were found for 22 other segmentation metrics. The organ based analysis showed that there is a better correlation for the larger OARs (i.e. brainstem and eyes) as for the smaller OARs (i.e. optic nerves and chiasm). Furthermore, we found that proximity to the target does not make contour variations more susceptible to the dose effect. However, the direction of the contour variation with respect to the relative location of the target seems to have a strong correlation with the dose effect.ConclusionsThis study shows a low correlation between segmentation metrics and dosimetric changes for OARs in brain tumor patients. Results suggest that the current metrics for image segmentation in RT, as well as deep learning systems employing such metrics, need to be revisited towards clinically oriented metrics that better reflect how segmentation quality affects dose distribution and related tumor control and toxicity.     

A technique for semiautomatic segmentation of echogenic structures in 3D ultrasound,applied to infant hip dysplasia

Purpose

Automatic segmentation of anatomical structures and lesions from medical ultrasound images is a formidable challenge in medical imaging due to image noise, blur and artifacts. In this paper we present a segmentation technique with features highly suited to use in noisy 3D ultrasound volumes and demonstrate its use in modeling bone, specifically the acetabulum in infant hips. Quantification of the acetabular shape is crucial in diagnosing developmental dysplasia of the hip (DDH), a common condition associated with hip dislocation and premature osteoarthritis if not treated. The well-established Graf technique for DDH diagnosis has been criticized for high inter-observer and inter-scan variability. In our earlier work we have introduced a more reliable instability metric based on 3D ultrasound data. Visualizing and interpreting the acetabular shape from noisy 3D ultrasound volumes has been one of the major roadblocks in using 3D ultrasound as diagnostic tool for DDH. For this study we developed a semiautomated segmentation technique to rapidly generate 3D acetabular surface models and classified the acetabulum based on acetabular contact angle (ACA) derived from the models. We tested the feasibility and reliability of the technique compared with manual segmentation.

Methods

The proposed segmentation algorithm is based on graph search. We formulate segmentation of the acetabulum as an optimal path finding problem on an undirected weighted graph. Slice contours are defined as the optimal path passing through a set of user-defined seed points in the graph, and it can be found using dynamic programming techniques (in this case Dijkstra’s algorithm). Slice contours are then interpolated over the 3D volume to generate the surface model. A three-dimensional ACA was calculated using normal vectors of the surface model.

Results

The algorithm was tested over an extensive dataset of 51 infant ultrasound hip volumes obtained from 42 subjects with normal to dysplastic hips. The contours generated by the segmentation algorithm closely matched with those obtained from manual segmentation. The average RMS errors between the semiautomated and manual segmentation for the 51 volumes were 0.28 mm/1.1 voxel (with 2 node points) and 0.24 mm/0.9 voxel (with 3 node points). The semiautomatic algorithm gave visually acceptable results on images with moderate levels of noise and was able to trace the boundary of the acetabulum even in the presence of significant shadowing. Semiautomatic contouring was also faster than manual segmentation at 37 versus 56 s per scan. It also improved the repeatability of the ACA calculation with inter-observer and intra-observer variability of \(1.4 \pm 0.9\) degree and \(1.4 \pm 1.0\) degree.

Conclusion

The semiautomatic segmentation technique proposed in this work offers a fast and reliable method to delineate the contours of the acetabulum from 3D ultrasound volumes of the hip. Since the technique does not rely upon contour evolution, it is less susceptible than other methods to the frequent missing or incomplete boundaries and noise artifacts common in ultrasound images. ACA derived from the segmented 3D surface was able to accurately classify the acetabulum under the categories normal, borderline and dysplastic. The semiautomatic technique makes it easier to segment the volume and reduces the inter-observer and intra-observer variation in ACA calculation compared with manual segmentation. The method can be applied to any structure with an echogenic boundary on ultrasound (such as a ventricle, blood vessel, organ or tumor), or even to structures with a bright border on computed tomography or magnetic resonance imaging.

     

Segmentation of medical images using mean value guided contour

Partial differential equation-based (PDE-based) methods are extensively used in image segmentation, especially in contour models. Difficulties associated with the boundaries, namely troubles with developing initialization, inadequate convergence to boundary concavities, and difficulties connected to saddle points and stationary points of active contours make the contour models suffer from a feeble performance of referring to complex geometries. The present paper is designed to take advantage of mean value theorem rather than minimizing energy function for contours. It is efficiently capable of resolving above-mentioned problems by applying this theorem to the edge map gradient vectors, which is calculated from the image. Since the contour is computed in a straightforward manner from an edge map instead of force balance equation, it varies from other contour-based image segmentation methods. To illustrate the ability of the proposed model in complex geometries and ruptures, several experiments were also provided to validate the model. The experiments’ results demonstrated that the proposed method, which is called mean value guided contour (MVGC), is capable of repositioning contours into boundary concavities and has suitable forcefulness in complex geometries.     

一种半自动分割脑部MRI中胼胝体的方法

针对胼胝体的图像特点以及实际应用要求,采用半自动方法对MRI中的胼胝体进行分割。首先采用基于Live-Wire的算法对胼胝体影像的起始层和终止层进行初始分割,然后利用基于距离变换的形状插值算法获取中间层的初始轮廓信息,对插值获得的初始轮廓采用Snake模型进行局部收缩,获得真实的胼胝体边界。对序列MRI脑影像中的胼胝体进行分割、重建、标定。实验结果与临床医师的使用反馈证明,本文提出的算法具有较高的灵活性与可信度,对胼胝体的分割精度与解剖统计信息相符,分割结果可满足临床需求。