Automatic segmentation of corpus collasum using Gaussian mixture modeling and Fuzzy C means methods

This paper presents a comparative study of the success and performance of the Gaussian mixture modeling and Fuzzy C means methods to determine the volume and cross-sectionals areas of the corpus callosum (CC) using simulated and real MR brain images. The Gaussian mixture model (GMM) utilizes weighted sum of Gaussian distributions by applying statistical decision procedures to define image classes. In the Fuzzy C means (FCM), the image classes are represented by certain membership function according to fuzziness information expressing the distance from the cluster centers. In this study, automatic segmentation for midsagittal section of the CC was achieved from simulated and real brain images. The volume of CC was obtained using sagittal sections areas. To compare the success of the methods, segmentation accuracy, Jaccard similarity and time consuming for segmentation were calculated. The results show that the GMM method resulted by a small margin in more accurate segmentation (midsagittal section segmentation accuracy 98.3% and 97.01% for GMM and FCM); however the FCM method resulted in faster segmentation than GMM. With this study, an accurate and automatic segmentation system that allows opportunity for quantitative comparison to doctors in the planning of treatment and the diagnosis of diseases affecting the size of the CC was developed. This study can be adapted to perform segmentation on other regions of the brain, thus, it can be operated as practical use in the clinic.     

TWO-LAYER STABILIZATION OF CONTINUOUS NEURAL NETWORKS WITH FEEDBACKS

Methods of stabilization as applied to Hopfield-type continuous neural networks with a unique equilibrium point are considered. These methods permit the design of stable networks where the elements of the interconnection matrix and nonlinear activation functions of separate neurons vary with time. For stabilization with a variable interconnection matrix it is suggested that a new second layer of neurons be introduced to the initial single-layer network and some additional connections be added between the new and old layers. This approach gives us a system with a unique equilibrium point that is globally asymptotically stable, i.e. the entire space serves as the domain of attraction of this point, and the stability does not depend on the interconnection matrix of the system. In the case of the variable activation functions, some results from a recent investigation of the absolute stability problem for neural networks are presented, along with some recommendations.     

Motion-based segmentation of objects using overlapping temporal windows

Motion segmentation refers to the problem of separating the objects in a video sequence according to their motion. It is a fundamental problem of computer vision, since various systems focusing on the analysis of dynamic scenes include motion segmentation algorithms. In this paper we present a novel approach, where a video shot is temporally divided in successive and overlapping windows and motion segmentation is performed on each window respectively. This attribute renders the algorithm suitable even for long video sequences. In the last stage of the algorithm the segmentation results for every window are aggregated into a final segmentation. The presented algorithm can handle effectively asynchronous trajectories on each window even when they have no temporal intersection. The evaluation of the proposed algorithm on the Berkeley motion segmentation benchmark demonstrates its scalability and accuracy compared to the state of the art.     

Generic fitted shapes (GFS): Volumetric object segmentation in service robotics

In this paper, a simultaneous 3D volumetric segmentation and reconstruction method, based on the so-called Generic Fitted Shapes (GFS) is proposed. The aim of this work is to cope with the lack of volumetric information encountered in visually controlled mobile manipulation systems equipped with stereo or RGB-D cameras. Instead of using primitive volumes, such as cuboids or cylinders, for approximating objects in point clouds, their volumetric structure has been estimated based on fitted generic shapes. The proposed GFSs can capture the shapes of a broad range of object classes without the need of large a-priori shape databases. The fitting algorithm, which aims at determining the particular geometry of each object of interest, is based on a modified version of the active contours approach extended to the 3D Cartesian space. The proposed volumetric segmentation system produces comprehensive closed object surfaces which can be further used in mobile manipulation scenarios. Within the experimental setup, the proposed technique has been evaluated against two state-of-the-art methods, namely superquadrics and 3D Object Retrieval (3DOR) engines.     

A novel automatic algorithm for the segmentation of the lumen of the carotid artery in ultrasound B-mode images

A novel algorithm is proposed for the segmentation of the lumen and bifurcation boundaries of the carotid artery in B-mode ultrasound images. It uses the image contrast characteristics of the lumen and bifurcation of the carotid artery in relation to other tissues and structures for their identification. The relevant ultrasound data regarding the artery presented in the input image is identified using morphologic operators and processed by an anisotropic diffusion filter for speckle noise removal. The information obtained is then used to define two initial contours, one corresponding to the lumen and the other one regarding the bifurcation boundaries, for the application of the Chan-Vese level set segmentation model. A set of longitudinal ultrasound B-mode grayscale images of the common carotid artery was acquired using a GE Healthcare Vivid-e ultrasound system. The results reveal that the new algorithm is effective and robust, and that its main advantage relies on the automatic identification of the carotid lumen, which overcomes the known limitations of the traditional algorithms.     

基于GeoEye-1影像的城区建筑物提取方法研究

在传统信息提取方法的基础上,提出监督分类和规则分类相结合的方法以及基于知识规则的多尺度分割方法进行建筑物的提取,旨在简单高效地从高分辨率影像数据中准确提取出城区建筑物,并通过北京市区的GeoEye-1影像进行方法验证.结果表明:提出的两种方法的Kappa系数均达到了87％,相比传统的监督分类方法提高了22％,其中生产者精度提高了30％以上,精度较高,而且这两种方法原理简单,适于流程化操作,具有明显的优越性.     

基于高斯模型的T-LoCoH候鸟家域估计算法研究及应用

近年来,随着GPS定位技术的发展,动物轨迹数据成为了当前的研究热点,其中动物家域估计是轨迹数据研究的重要部分。已有的T-LoCoH家域估计算法没有考虑候鸟迁徙行为在速度上的显著差异性,不适合候鸟家域估计。针对以上问题,本文提出将候鸟运动轨迹数据按其活动的周期性分段,并使用高斯模型估算其各阶段的最大运动速度,再结合T-LoCoH方法估计出候鸟在各个运动阶段的家域。实验表明,这种基于高斯模型的T-LoCoH候鸟家域估计算法能更精确地划分候鸟在各阶段的活动范围。     

改进FCM和LFP相结合的白细胞图像分类

研究白细胞图像分类识别中有效的图像分割与特征提取方法,以提高白细胞图像的正确识别率.由于某些白细胞(粒细胞)中颗粒的存在,严重影响细胞核与细胞质区域的正确分割,通过将空间信息与核函数融入模糊C-均值聚类(FCM)算法,提出一种改进的FCM算法.应用该算法对白细胞图像进行分割,并采用数学形态学方法对分割后的图像进行处理,获得了很好的分割效果,解决了粒细胞的质核分割难题.对于细胞的纹理特征提取,通过对局部二值模式(LBP)中阈值参数的模糊化,建立了基于局部模糊模式(LFP)的纹理特征提取算法.运用本文方法进行图像分割和纹理提取,以支持向量机作为分类器,对CellAtlas的100幅白细胞图像进行了分类识别的实验,结果表明白细胞的正确识别率达到93％.     

融合边缘测度的自然场景彩色图像区域分割

针对传统分割方法基于单个视觉线索的不足,提出一种结合两种局部边缘测度的自然场景彩色图像区域分割方法.首先,采用logistic回归模型对200幅彩色图像进行训练,建立边缘测度与对象边界的回归模型;然后,采用该模型预测图像中每个像素的边界置信度;最后,将置信度的函数以自适应权重的形式整合到均值漂移分割算法中,实现图像区域分割.近20幅图像的定量和目视对比实验结果表明,本文方法能有效地控制过分割和欠分割的产生,且具有更好的区域边界定位效果.