基于局部调整动态轮廓模型提取超声图像乳腺肿瘤边缘

提出一种基于局部调整动态轮廓模型提取超声图像乳腺肿瘤边缘的算法.该算法在Chan-Vese (CV)模型基础上,定义了一个局部调整项,采用基于水平集的动态轮廓模型提取超声图像乳腺肿瘤边缘.将该算法应用于89例临床超声图像乳腺肿瘤的边缘提取实验,结果表明:该算法比CV模型更适用于具有区域非同质性的超声图像的分割,可有效实现超声图像乳腺肿瘤边缘的提取.     

乳腺肿瘤超声图像特征参数量化研究进展

乳腺肿瘤超声图像的特征量化分析对判别肿瘤的良、恶性具有重要价值。本文总结了良性和恶性乳腺肿瘤在超声图像上的特点，将乳腺良性肿瘤和恶性肿瘤鉴别特征在形状、边缘、边界、朝向、回声特点几个方面的量化方法和量化参数进行了较为全面的梳理，并对量化特征与肿瘤良、恶性之间的关系进行了分析。     

乳腺肿瘤超声图像中感兴趣区域的自动检测

由于斑点噪声、伪影以及病灶形状多变的影响,乳腺肿瘤超声图像中肿瘤区域的自动检测以及病灶的边缘提取比较困难,已有的方法主要是由医生先手工提取感兴趣区域(ROI)。本研究提出一种乳腺肿瘤超声图像中感兴趣区域自动检测的方法,选用超声图像的局部纹理、局部灰度共生矩阵以及位置信息作为特征,采用自组织映射神经网络进行分类,自动识别乳腺肿瘤区域。对包含168幅乳腺肿瘤超声图像的数据库进行识别的结果表明:该方法自动识别ROI的准确率达到86.9%,可辅助医生提取肿瘤的实际边缘以及进一步诊断。     

超声图像处理中Snake模型研究

Snake模型是一种基于高层信息的有效目标轮廓提取算法,其优点是作用过程及最后结果的目标轮廓是一条完整的曲线,从而引起广泛的关注.鉴于医学超声图像的信噪比较低,用经典的边缘提取算法无法得到较好的结果,因此人们将Snake模型进行了各种各样的改进,并且越来越多地将它运用到医学超声图像处理中来.本文对乳腺超声图像进行阈值分割、形态滤波等一系列预处理后,将改进的Snake模型对乳腺超声图像进行肿瘤的边缘提取,得到了比较好的结果.     

基于边界特征的乳腺肿瘤超声图像识别

通过对乳腺肿瘤边界特征的分析，得到边界的特征量似圆度，面积比率，长宽比组成的特征矢量，最后用反向传播人工神经网络（BP）的算法对经病理证实的119幅乳腺良、恶性肿块超声图像进行分类识别。BP神经网络对良、恶性肿瘤正确识别率分别为89．7％、73．5N。量化后的乳腺超声图像肿瘤轮廓特征结合BP神经网络可以比较有效的用于肿瘤的良、恶性识别。     

基于直方图的纹理度量在超声乳腺肿瘤识别中的应用

探讨纹理特征在超声乳腺肿瘤诊断中的价值。提取超声图像中乳腺肿瘤的纹理度量,得到由均值、标准差、平滑度、三阶矩、一致性和熵组成的特征矢量,最后用反向传播人工神经网络（BP）对96幅乳腺肿瘤的良恶性进行分类识别。BP 神经网络对良、恶性肿瘤的正确识别率分别为88．4％和78．6％。基于乳腺肿瘤超声图像的纹理特征建立的神经网络系统对肿瘤的良恶性具有较好的识别能力。     

基于改进型组织特异纹理滤波器的乳腺肿瘤超声图像处理

基于乳腺超声图像的多参数纹理分类实验,改进了Gjenna Stippel等的自适应纹理滤波器,通过引入模糊函数、增加重叠区域和迭代次数的措施,在减少图像噪声的同时,增强肿瘤与周围正常组织的视觉差别.量化比较乳腺超声图像经该滤波算法和几种常用滤波算法处理前后的的统计特征参量和肿瘤边缘检测的精确率,验证了该算法的有效性和优越性.     

用于乳腺肿瘤分级诊断超声图像增强的算法

为提高乳腺肿瘤分级诊断的能力,提出一种基于超声信号用于乳腺肿瘤分级诊断的图像增强算法。通过分析良性和不同恶性程度肿瘤的超声图像的特征差异,提出了一种将灰度的动态变换方法和利用局部标准差及熵特征相结合的办法,对图像对比度进行增强处理,增强了乳腺超声图像的细节,提高了图像质量。该算法可对良性、恶性肿瘤等不同超声图像进行增强处理,使得图像之间差异更加明显,为临床医生分级诊断提供更加清晰的图像,具有一定的实际应用价值。     

基于启发式A*算法的超声图像颈动脉内膜提取

从超声图像准确提取颈动脉内膜,为基于颈动脉超声图像判断动脉粥样硬化服务.方法 提出一种基于启发式A*算法从超声图像中提取颈动脉内膜边缘的方法.先使用图像分割法区分血管腔和血管壁,再采用结合图像灰度值特点的A*算法准确地提取颈动脉内膜边缘.结果 通过对临床采集的32幅颈动脉超声图像的分析研究,表明本方法自动提取的结果与医生手工描绘的结果基本吻合.结论 本方法有望应用于超声图像颈动脉内膜的自动提取.     

多尺度MRI断层图像特征提取新技术研究

由于医学图像本身信号噪声大，边缘呈弱信号特征，用传统的图像边缘检测算法提取图像特征常会将图像中的噪声作为边缘提取，不能准确地反映医学图像中有价值的信息(如病灶大小等)。现提出一种改进算法，此法以现有的小波模极大值特征提取算法为基础，利用模糊理论确定隶属函数，提取弱信号边缘并用多尺度融合理论边缘点合成。结果表明，此方法在提取MRI图像特征的同时，可有效地抑制噪声，有助于剔除图像的伪边缘，准确定位图像边缘信息，有利于图像分割重建，便于医生根据图像确定病灶或组织的位置大小等。     

A methodology for evaluation of boundary detection algorithms on breast ultrasound images

Image segmentation is the partition of an image into a set of non-overlapping regions that comprise the entire image. The image is decomposed into meaningful parts, which are uniform with respect to certain characteristics, such as grey level or texture. This study presents a novel methodology to evaluate ultrasound image segmentation algorithms. The sonographic features can differentiate between various sized malignant and benign breast tumours. The clinical experiment can determine whether a tumour is benign or not, based on contour, shape, echogenicity and echo texture. Further study of the standardized sonographic features, especially the tumour contour and shape, will improve the positive predictive value and accuracy rate in breast tumour detection. The effectiveness of using this methodology is illustrated by evaluating image segmentation on breast ultrasound images. Via definite segmentation, the appreciated tumour shape and contour can be ascertained. Furthermore, this method can enhance the ability of ultrasound to distinguish between benign and malignant breast lesions.     

高频超声、钼靶X射线在乳腺BI-RADSⅣ～Ⅴ级诊断中的应用对比研究

目的 探讨高频超声、钼靶X射线单独与联合应用在乳腺影像报告与数据系统(BI - RADS)Ⅳ～Ⅴ级诊断中的应用价值及对比研究.方法 136个病灶经病理证实为乳腺恶性肿瘤,回顾性分析高频超声、钼靶X射线影像表现,并对诊断的准确性进行统计学分析.结果 高频超声、钼靶X射线及两者联合应用对乳腺BI- RADSⅣ-Ⅴ级诊断的正确率、误诊率比较,差异有统计学意义,P＜0.01.结论 高频超声在乳腺肿块(BI - RADS Ⅳ～Ⅴ级)诊断正确率方面优于钼靶X射线,特别是肿瘤直径＜1.0cm,且不伴有钙化时,两者联合应用较单一方法更能提高乳腺肿块(BI - RADS Ⅳ～Ⅴ级)的检出率及良、恶性鉴别诊断.     

Shape symmetry analysis of breast tumors on ultrasound images

Shape characteristics of malignant and benign breast tumors are significantly different. In this paper, the reflective symmetry of breast tumor shapes on ultrasound images was investigated. A new reflective symmetry measure (RSML) derived from multiscale local area integral invariant was proposed to quantify the shape symmetry of breast tumor, which could be computed directly from the binary mask image without the shape parameterization in terms of arc length. The performance of several symmetry measures for differentiating malignant and benign breast tumors at varying scales was evaluated and compared by receiver operating characteristic (ROC) analysis. RSML with Gaussian kernel at scale 0.04 (related to the maximal diameter) achieved the highest area under the ROC curve (0.85) on the image data of 168 tumors (104 benign and 64 malignant). The experimental results showed that the reflective symmetry of breast tumor shape was capable of providing potential diagnostic information, which could be characterized quantitatively by RSML with the appropriate scale parameter.     

基于空间FCM与MRF方法的乳腺MRI序列三维病灶分割研究

针对乳腺DCE-MRI病灶分割,提出一种空间FCM聚类与MRF随机场相结合的三维分割方法。首先,对MRI图像进行空间FCM粗分割,提取病灶粗轮廓。然后,在其基础上进行MRF精分割,并结合病灶三维信息：用相邻切片分割结果对应标号矩阵初始化MRF精分割标号场,同时用该张切片粗分割所得隶属度矩阵对MRF精分割进行参数自适应调整。用该方法与空间FCM、水平集、模糊MRF方法对50例MRI数据进行分割对比实验,得到良、恶性病灶分割重叠率分别为76.4、75.5;相比于空间FCM的68.%、69.5水平集的70.8、72.6以及模糊MRF的72.9、73.6有明显提升。对所有175例MRI数据分割结果进行非监督评价,得到良、恶性病灶区域均匀性均大于0.92;区域内差异性良性病灶92%小于150、恶性病灶98%小于150;区域间差异性良性病灶87%大于0.25、恶性病灶90%大于0.3综上表明,该方法具有较高的分割精度。     

Computerized analysis of shadowing on breast ultrasound for improved lesion detection

Sonography is being considered for the screening of women at high risk for breast cancer. We are developing computerized detection methods to aid in the localization of lesions on breast ultrasound images. The detection scheme presented here is based on the analysis of posterior acoustic shadowing, since posterior acoustic shadowing is observed for many malignant lesions. The method uses a nonlinear filtering technique based on the skewness of the gray level distribution within a kernel of image data. The database used in this study included 400 breast ultrasound cases (757 images) consisting of complicated cysts, solid benign lesions, and malignant lesions. At a false-positive rate of 0.25 false positives per image, a detection sensitivity of 80% by case (66% by image) was achieved for malignant lesions. The performance for the overall database (at 0.25 false positives per image) was less at 42% sensitivity by case (30% by image) due to the more limited presence of posterior acoustic shadowing for benign solid lesions and the presence of posterior acoustic enhancement for cysts. Our computerized method for the detection of lesion shadows alerts radiologists to lesions that exhibit posterior acoustic shadowing. While this is not a characterization method, its performance is best for lesions that exhibit posterior acoustic shadowing such as malignant and, to a lesser extent, benign solid lesions. This method, in combination with other computerized sonographic detection methods, may ultimately help facilitate the use of ultrasound for breast cancer screening.     

Computerized-aid diagnosis of breast mass using ultrasound image

This paper presents a comparative study of the diagnostic results of the ultrasologists with/without using a novel enhancement algorithm for breast ultrasonic images based on fuzzy entropy principle and textural information. Totally, 350 ultrasound images of 115 cases were analyzed including 59 benign and 56 malignant lesions. The original breast images were fuzzified, the edge and textural information were extracted, and the images were enhanced. The original and enhanced images were assessed and evaluated by ultrasologists using double blind method before and after enhancement. The diagnostic sensitivity and specificity were calculated by the areas (Az) under the receiver operating characteristic (ROC) curves. And the two diagnostic results before and after enhancement were compared by Chi-square test in a 2 x 2 table. The results demonstrated that the discrimination rate of breast masses had been highly improved after employing the novel enhancement algorithm. The result indicates the sensitivity could be raised from 74.3% to 89.3% with the false-positive rate 14.3%, and the area (Az) under the ROC curve of diagnosis also increased from 0.84 to 0.93. The novel enhancement algorithm can increase the classification accuracy and decrease the rate of missing and misdiagnosis, and it is useful for breast cancer control.     

高频超声结合BI-RADS分级在乳腺良恶性病变中的应用价值

目的 探讨高频超声结合BI-RADS分级在乳腺良恶性病变诊断的应用价值。方法 选取2016年3月~2017年10月东莞市石碣镇参与“两癌”筛查乳腺体检者2874例,应用超声检查双乳情况。对2874例乳腺体检者进行随访,其中经穿刺检查或手术切除肿物组织病理检查发现,恶性肿瘤17例。以病理诊断结果为对照,评估高频超声结合BI-RADS分级在乳腺良恶性病变诊断的应用价值。结果 乳腺恶性病变的高频超声高评分的例数高于良性病变,差异有统计学意义（P＜0.05）;乳腺恶性病变BI-RADS评分Ⅳ级例数高于良性病变,差异有统计学意义（P＜0.05）;高频超声评分与BI-RADS分级诊断乳腺病变的灵敏度、特异度、准确率、阳性预测值及阴性预测值之间比较,差异无统计学意义（P＞0.05）;两种方法联合诊断时,其灵敏度、特异度、准确率、阳性预测值及阴性预测值均较单独高频超声评分诊断、单独BI-RADS分级诊断有不同程度的提高,其中两种方法联合诊断时灵敏度和阳性预测值与两种方法单独诊断比较,差异有统计学意义（P＜0.05）。结论 高频超声检查与BI-RADS评分分级在鉴别诊断乳腺肿块良恶性上具有较高的临床价值,但仍存在一定局限性,两者联合应用在一定程度上能提高乳腺良恶性病变的诊断准确率,是较为理想的检查手段。     

基于模糊贝叶斯网络的星形细胞瘤智能分级模型

本研究提出一种新的融合影像低层视觉特征和语义的模糊贝叶斯网络模型。使用了高斯混合模型（GMM）对连续的视觉特征模糊化处理，解决了传统贝叶斯网络小能操作连续输入的问题，更合理地表达了具有模糊性、不确定性的专业领域的结构性知识。为了验证它的有效性，将它应用于星形细胞瘤恶性程度的分级。建立了一个概率模型。实验结果得出83．33％的正确识别率。该模型为星形细胞瘤恶性程度预测提供了新的定量而客观的辅助手段。