本研究通过135例临床乳腺肿瘤的灰阶超声和应变弹性超声的双模态图像研究,并结合肿瘤感兴趣区域(region of interest,ROI)与瘤周组织超声信息进行乳腺肿瘤的良恶性分类.首先,分别提取肿瘤ROI区域的常规灰阶超声和应变弹性超声的影像组学特征:形态学特征(14个)、强度特征(18个)和纹理特征(75个),并... 相似文献
乳腺X线摄影技术是早期发现和诊断乳腺肿瘤的首选方法。提取乳腺钼靶图像的感兴趣区域(region of interest,ROI)并利用人工智能算法对其进行模式识别,可有效提高乳腺肿瘤筛查工作的效率。试验图像均来自DDSM乳腺X线钼靶图像公开数据库,以其中BI-RADS分类为第4类(BI-RADS4)的簇状分布多形性钙化钼靶图像为研究对象,探求在设计乳腺钼靶图像分类器过程中提取ROI的新方法。结果显示,设计出优化的分类器后,可高效地识别试验对象,其测试集上的分类准确率最高可达99.3%。因此,本研究可为医生的临床研判提供辅助信息,并为细分BI-RADS4、进一步精准诊断奠定技术基础。 相似文献
This work aims at investigating texture parameters in distinguishing malign and benign breast tumors on ultrasound images. A rectangular region of interest (ROI) containing the tumor and its neighboring was defined for each image. Five parameters were extracted from the complexity curve (CC) of the ROI. Another five parameters were calculated from the grey-level co-occurrence matrix (GLCM) also for the ROI. The same was carried out for internal tumor region, hence, totaling 20 parameters. The linear discriminant analysis was applied to sets of up to five parameters and then the performances were assessed. The most relevant individual parameters were the contrast (con) (from the GLCM over the ROI) and the maximum value (mvi) from the CC just for the tumor internal region). When they were taken together, a correct classification slightly over 80% of the breast tumors was achieved. The highest performance (accuracy=84.2%, sensitivity=87.0%, and specificity=78.8%) was obtained with mvi, con, the standard deviation of the pixel pairs and the entropy, both for GLCM, and the internal region contrast also from GLCM. Parameters extracted from the internal region generally performed better and were more significant than those from the ROI. Moreover, parameters calculated only from CC or GLCM resulted in no statistically significant performance difference. These findings suggest that the texture parameters can be useful to help radiologist in distinguishing between benign or malign breast tumors on ultrasound images. 相似文献
Breast ultrasound (BUS) image segmentation is a very difficult task due to poor image quality and speckle noise. In this paper, local features extracted from roughly segmented regions of interest (ROIs) are used to describe breast tumors. The roughly segmented ROI is viewed as a bag. And subregions of the ROI are considered as the instances of the bag. Multiple-instance learning (MIL) method is more suitable for classifying breast tumors using BUS images. However, due to the complexity of BUS images, traditional MIL method is not applicable. In this paper, a novel MIL method is proposed for solving such task. First, a self-organizing map is used to map the instance space to the concept space. Then, we use the distribution of the instances of each bag in the concept space to construct the bag feature vector. Finally, a support vector machine is employed for classifying the tumors. The experimental results show that the proposed method can achieve better performance: the accuracy is 0.9107 and the area under receiver operator characteristic curve is 0.96 (p < 0.005). 相似文献
The echogenicity, echotexture, shape, and contour of a lesion are revealed to be effective sonographic features for physicians
to identify a tumor as either benign or malignant. Automatic contouring for breast tumors in sonography may assist physicians
without relevant experience, in making correct diagnoses. This study develops an efficient method for automatically detecting
contours of breast tumors in sonography. First, a sophisticated preprocessing filter reduces the noise, but preserves the
shape and contrast of the breast tumor. An adaptive initial contouring method is then performed to obtain an approximate circular
contour of the tumor. Finally, the deformation-based level set segmentation automatically extracts the precise contours of
breast tumors from ultrasound (US) images. The proposed contouring method evaluates US images from 118 patients with breast
tumors. The contouring results, obtained with computer simulation, reveal that the proposed method always identifies similar
contours to those obtained with manual sketching. The proposed method provides robust and fast automatic contouring for breast
US images. The potential role of this approach might save much of the time required to sketch a precise contour with very
high stability. 相似文献
Due to the low contrast and ambiguous boundaries of the tumors in breast ultrasound (BUS) images, it is still a challenging task to automatically segment the breast tumors from the ultrasound. In this paper, we proposed a novel computational framework that can detect and segment breast lesions fully automatic in the whole ultrasound images. This framework includes several key components: pre-processing, contour initialization, and tumor segmentation. In the pre-processing step, we applied non-local low-rank (NLLR) filter to reduce the speckle noise. In contour initialization step, we cascaded a two-step Otsu-based adaptive thresholding (OBAT) algorithm with morphologic operations to effectively locate the tumor regions and initialize the tumor contours. Finally, given the initial tumor contours, the improved Chan-Vese model based on the ratio of exponentially weighted averages (CV-ROEWA) method was utilized. This pipeline was tested on a set of 61 breast ultrasound (BUS) images with diagnosed tumors. The experimental results in clinical ultrasound images prove the high accuracy and robustness of the proposed framework, indicating its potential applications in clinical practice.
Microwave imaging promises high contrast between tumor and normal breast tissues, but its spatial resolution is limited. Here, we present a multimodality approach for high-resolution microwave imaging, where microwave image reconstruction is structurally guided by ultrasound imaging. The combined imaging concept is demonstrated using tissue phantom measurements obtained from a 16 x 15 transmitter/receiver microwave imaging system and a modified B-mode ultrasound system. With the geometry of the target and background known a priori from ultrasound, successful dielectric property images are recovered using a finite element-based reconstruction algorithm. We show that a target as small as 1.2 mm in diameter can be imaged with the multimodality approach, whereas it is impossible to detect such a small-size object using microwave imaging alone. The pilot clinical studies on two cases suggest that breast tumors can be much more accurately detected by the multimodality method. 相似文献
Ultrasound breast images have been used to improve diagnostics and decrease the number of unneeded biopsies. Malignant breast tumors tend to present irregular and blurred contours while benign ones are usually round, smooth and well-defined. Accordingly, investigating the tumor contour may help in establishing diagnosis. Herein, Mutual Information and Linear Discriminant Analysis were implemented to rank morphometric features in discriminating breast tumors in ultrasound images. Seven features were extracted from Convex Polygon and the Normalized Radial Length techniques. By applying a Mutual Information based approach, it was possible to identity features with possibly non-linear contributions to the outcome. 相似文献