Exploring feature-based approaches in PET images for predicting cancer treatment outcomes

Accumulating evidence suggests that characteristics of pre-treatment FDG-PET could be used as prognostic factors to predict outcomes in different cancer sites. Current risk analyses are limited to visual assessment or direct uptake value measurements. We are investigating intensity-volume histogram metrics and shape and texture features extracted from PET images to predict patient's response to treatment. These approaches were demonstrated using datasets from cervix and head and neck cancers, where AUC of 0.76 and 1.0 were achieved, respectively. The preliminary results suggest that the proposed approaches could potentially provide better tools and discriminant power for utilizing functional imaging in clinical prognosis.     

Personalized identification of abdominal wall hernia meshes on computed tomography

An abdominal wall hernia is a protrusion of the intestine through an opening or area of weakness in the abdominal wall. Correct pre-operative identification of abdominal wall hernia meshes could help surgeons adjust the surgical plan to meet the expected difficulty and morbidity of operating through or removing the previous mesh. First, we present herein for the first time the application of image analysis for automated identification of hernia meshes. Second, we discuss the novel development of a new entropy-based image texture feature using geostatistics and indicator kriging. Third, we seek to enhance the hernia mesh identification by combining the new texture feature with the gray-level co-occurrence matrix feature of the image. The two features can characterize complementary information of anatomic details of the abdominal hernia wall and its mesh on computed tomography. Experimental results have demonstrated the effectiveness of the proposed study. The new computational tool has potential for personalized mesh identification which can assist surgeons in the diagnosis and repair of complex abdominal wall hernias.     

基于共生矩阵和颜色特征的墙地砖缺陷自动检测的研究

该文提出了一种用于墙地砖自动缺陷检测的新算法,该算法综合了颜色的空间分布信息和比例分布信息,应用共生矩阵纹理特征与颜色统计特征构造一个判断矢量,能对复杂纹理的多色墙地砖进行各种缺陷检测,实验证明该算法是有效的。     

薄膜褶皱的纹理特征分析

用薄膜进行了褶皱实验,选取规则区域,利用灰度共生矩阵提取的纹理特征进行了褶皱的纹理分析.结果表明,利用灰度共生矩阵的纹理特征可以准确的判断褶皱的疏密程度和方向,实验结果与理论预测吻合.     

基于小波分解和灰度共生矩阵的纹理图像检索

采用Daubechies4小波对图像进行三层Mallat塔式分解,取每个分解层次上的每个子带图像的能量,加入在低频子带上提取的灰度共生矩阵统计量,来形成最终的特征向量以提高检索精度.     

一种基于FCM的医学图像检索方法与实现

针对医学内窥镜图像,提出两种基于模糊C-均值聚类(FCM)的特征融合算法:融合颜色相关图和图像纹理特征算法以及融合颜色直方图和颜色相关图算法。据此,实现了一个图像检索的原型系统,依据所设计的评价实验,并对实验结果进行了比较和分析。实验表明,基于FCM的融合颜色相关图和纹理特征的特征融合算法,在基于内容的医学内窥镜图像检索中,具有相对较好的检索效果。     

面向XML文档的概念检索技术

面向XML文档的信息检索是一个重要的研究课题，文中介绍了结构化文档的结构索引以及语义检索中的“上下文共现分析”技术，并在此基础上提出了一个面向XML文档的概念检索原型系统，并对系统设计及实现中应注意考虑的几个主要问题进行了分析。     

基于多特征融合的图像检索算法

为有效提取和描述图像特征,提高图像检索性能,提出一种基于纹理、颜色和形状多特征融合的图像检索算法。检测彩色图像的边缘,对其进行变换得到基元图像。遍历基元图像得到基元共生矩阵,对每个基元求梯度值得到基元梯度直方图。将彩色图像量化到64色颜色空间,得到对应的颜色直方图。利用上述3个特征量描述图像特征,并用于图像检索。实验结果表明,与BCTF和MCM算法相比,该算法的查全率和查准率较高,计算复杂度较低。     

基于灰度共生矩阵的图像纹理特征提取分析

图像纹理作为一种重要的视觉手段,是图像中普遍存在而又难以描述的特征。目前常用的纹理特征提取的方法主要有统计方法、模型方法、信号处理方法和结构方法。灰度共生矩阵即为灰度级的空间相关矩阵,以其为基础的统计方法通过对矩阵统计量的求取较好地提取到了纹理特征,通过选取关键参数编程并进行仿真实现,分别求取了四个方向的灰度共生矩阵及其特征量来分析图像的纹理特征。