Using anatomical knowledge expressed as fuzzy constraints to segment the heart in CT images

Segmenting the heart in medical images is a challenging and important task for many applications. In particular, segmenting the heart in CT images is very useful for cardiology and oncological applications such as radiotherapy. Although the majority of methods in the literature are designed for ventricle segmentation, there is a real interest in segmenting the heart as a whole in this modality. In this paper, we address this problem and propose an automatic and robust method, based on anatomical knowledge about the heart, in particular its position with respect to the lungs. This knowledge is represented in a fuzzy formalism and it is used both to define a region of interest and to drive the evolution of a deformable model in order to segment the heart inside this region. The proposed method has been applied on non-contrast CT images and the obtained results have been compared to manual segmentations of the heart, showing the good accuracy and high robustness of our approach.     

Object detection by global contour shape

We present a method for object class detection in images based on global shape. A distance measure for elastic shape matching is derived, which is invariant to scale and rotation, and robust against non-parametric deformations. Starting from an over-segmentation of the image, the space of potential object boundaries is explored to find boundaries, which have high similarity with the shape template of the object class to be detected. An extensive experimental evaluation is presented. The approach achieves a remarkable detection rate of 83-91% at 0.2 false positives per image on three challenging data sets.     

Color Subspaces as Photometric Invariants

Complex reflectance phenomena such as specular reflections confound many vision problems since they produce image ‘features’ that do not correspond directly to intrinsic surface properties such as shape and spectral reflectance. A common approach to mitigate these effects is to explore functions of an image that are invariant to these photometric events. In this paper we describe a class of such invariants that result from exploiting color information in images of dichromatic surfaces. These invariants are derived from illuminant-dependent ‘subspaces’ of RGB color space, and they enable the application of Lambertian-based vision techniques to a broad class of specular, non-Lambertian scenes. Using implementations of recent algorithms taken from the literature, we demonstrate the practical utility of these invariants for a wide variety of applications, including stereo, shape from shading, photometric stereo, material-based segmentation, and motion estimation.     

Nonparametric Bayesian Image Segmentation

Image segmentation algorithms partition the set of pixels of an image into a specific number of different, spatially homogeneous groups. We propose a nonparametric Bayesian model for histogram clustering which automatically determines the number of segments when spatial smoothness constraints on the class assignments are enforced by a Markov Random Field. A Dirichlet process prior controls the level of resolution which corresponds to the number of clusters in data with a unique cluster structure. The resulting posterior is efficiently sampled by a variant of a conjugate-case sampling algorithm for Dirichlet process mixture models. Experimental results are provided for real-world gray value images, synthetic aperture radar images and magnetic resonance imaging data.     

Partial Partitions,Partial Connections and Connective Segmentation

In connective segmentation (Serra in J. Math. Imaging Vis. 24(1):83–130, [2006]), each image determines subsets of the space on which it is “homogeneous”, in such a way that this family of subsets always constitutes a connection (connectivity class); then the segmentation of the image is the partition of space into its connected components according to that connection. Several concrete examples of connective segmentations or of connections on sets, indicate that the space covering requirement of the partition should be relaxed. Furthermore, morphological operations on partitions require the consideration of wider framework. We study thus partial partitions (families of mutually disjoint non-void subsets of the space) and partial connections (where connected components of a set are mutually disjoint but do not necessarily cover the set). We describe some methods for generating partial connections. We investigate the links between the two lattices of partial connections and of partial partitions. We generalize Serra’s characterization of connective segmentation and discuss its relevance. Finally we give some ideas on how the theory of partial connections could lead to improved segmentation algorithms.

A new graph cut-based multiple active contour algorithm without initial contours and seed points

This paper presents a new graph cut-based multiple active contour algorithm to detect optimal boundaries and regions in images without initial contours and seed points. The task of multiple active contours is framed as a partitioning problem by assuming that image data are generated from a finite mixture model with unknown number of components. Then, the partitioning problem is solved within a divisive graph cut framework where multi-way minimum cuts for multiple contours are efficiently computed in a top-down way through a swap move of binary labels. A split move is integrated into the swap move within that framework to estimate the model parameters associated with regions without the use of initial contours and seed points. The number of regions is also estimated as a part of the algorithm. Experimental results of boundary and region detection of natural images are presented and analyzed with precision and recall measures to demonstrate the effectiveness of the proposed algorithm.     

遥感图像分形特征提取与分割

分形理论由B.B.Mandelbrot于20世纪70年代中期创立,现已被广泛地应用于自然科学和社会科学的几乎所有领域.本文在前人研究的基础上,利用双毯法(Double Blanket Method)提取出图像的分形特征并用于图像分割,进一步证实了分形在此领域的可行性和有效性.首先,通过比较局部分形维数偏移全局分形维数的标准差来确定适合该方法的最优滑动窗口.其次,考虑到单尺度分维特征的局限性,提取出多尺度的特征值并建立分形维数谱.然后,以模拟图像为例,分析图像中各区域的分维谱,选择适当尺度的分形特征,利用最大似然法对图像进行分割.最后,将分形理论应用于遥感图像中,与传统的基于灰度值特征的图像分割方法相比,加入图像的空间分形纹理特征后分割精度明显提高.研究结果表明:分维值的大小和变化趋势可以表示不同地物的空间复杂度,结合地物的光谱以及灰度信息能有效地识别目标地物.     

一种基于遗传算法的彩色图像分割改进算法

图像分割是进行图像理解的基础,也是图像工程技术中一个重要的问题.近几年来关于图像的分割方法层出不穷,但随着多媒体技术和Internet技术的发展,彩色图像分割处理的准确性和实时性要求也越来越高.为此提出了一种基于遗传算法的彩色图像分割改进算法.经过大量的对比实验表明,用这种方法分割目标和背景区域差别较大的彩色图像具有分割效果好、实时性、鲁棒性强的特点,是一种较为理想的分割方法.     

基于改进型最大匹配法的教学资源自然语义查询

中文智能信息检索中的关键是自然语言处理技术,汉语自动分词技术是中文自然语言处理的基础.基于机械匹配的分词方法简洁、实用,但该方法切分精度不高且无法有效克服切分歧义.针对机械匹配最大匹配算法的应用进行了改进,并将相似串应用于教学资源自然语义查询.     

B样条函数的图像分割分水岭算法研究

针对传统分水岭算法存在的过分割和对噪声敏感问题,提出了一种能够很好地抑制噪声,剔除图像的伪边缘、准确定位图像边缘信息的方法.采用高频强调滤波对梯度图像进行增强,利用B样条函数对增强后的图像进行多次拟合,最后对拟合的曲面进行分水岭分割.实验结果表明,通过该方法处理的梯度图像再进行分水岭变换,有效避免了过分割问题,同时准确定位了图像边缘信息,提高了分割精度.