股骨CT图像的边缘提取

为重建和测量股骨的解剖结构,需要大量地读取CT图像的信息,以获得股骨轮廓的坐标值。本研究采用直方图阈值图像分割、Kirsh边缘提取方法获得股骨的二值化轮廓图像。轮廓的提取应用了“迷宫”边缘跟踪算法。本方法可大量、快捷、正确地提取图像轮廓信息。     

运动红细胞形变结构的图象自动定量分析的研究

建立了运动目标图象自动分析ＭＣＥＬＬ应用软件和方法,并用于微血管中运动红细胞变形参数的定量分析。该图象处理主要特征功能包括压缩数字图象的灰度窗口设置,运动目标的抽取,形态相似性判决和运动目标的跟踪。同时给出运动图象处理的主要算式、动态曲线和应用结果。此外,还抽提和录取在微血管中红细胞快速运动情况下,所呈现拖鞋形的二值图象。研究结果证明红细胞具有可变性生理功能,这对于心脑血管的发病机理、早期病变的预测和有关药物疗效的研究有着十分重要的意义。     

基于多尺度曲率植物叶片特征提取

基于B-样条小波计算边缘曲线多尺度曲率函数,根据多尺度信息筛选和定位超过一定曲率闲值的精角点,这样的点代表了边缘曲线的主要信息．文中使用了Canny边缘检测算子和数学形态学方法进行图像预处理,B-样条小波降低对噪声及扰动的灵敏性,以提高真实精角点定位水平．综合[1,2]给出新的角特征矢量,并生成角点特征序列CS和弧段特征序列SS．特征序列可作为自适应-时滞单元混合神经网络的输入,通过学习完成图像分类与识别,对基于植物叶片形状识别种类提供辅助。     

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。     

鼻咽癌细胞双光子显微图像处理

鼻咽细胞的双光子显微图像中含有着丰富信息,借助计算机和图像处理算法可进行分析处理。图象分割是双光子显微图象处理中的一项重要技术,至今为止尚未形成一个最佳通用方法,也没有定义出双光子显微图象分割的统一标准。本文首先采用噪声干扰法进行去噪,采用低帽的变换等的数学形态学来增强鼻咽癌细胞图像,使细胞更加容易分辨,接着对几种经典边缘检测算法进行讨论比较,紧接着根据鼻咽双光子显微图像的实际特征,采取腐蚀算法求出鼻咽癌细胞边缘。然后进行区域生长定位细胞,并采用一些改进的判别分析算法和区域面积算法对鼻咽癌细胞进行阈值分割,获得较好结果。     

骨小梁二维结构的自动分析

本文介绍一种以计算机图像处理为手段,对骨小梁的二维结构进行形态计量学分析的方法。该方法在二值图像上,用改进的HILDITCH算法细化二值图,计算分析骨小梁的结节、游离点。并用人工方法及计算机方法对骨小梁的结节、游离点作统计比较,结果显示两种方法呈正相关(r=0.95,p<0.01)。该方法具有自动、快速和准确性较高等优点。     

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。     

基于多尺度植物种子图像边缘检测算法的设计与实现

小波变换,由于其具有时频局部化的特性及多尺度特性,能敏感地反映突变信号,是一种理想的边缘提取方法．本文系统地介绍了作者在图像边缘检测方面所做的理论探讨、算法及应用研究工作．目前的边缘提取方法有多种,本文将重点集中于基于小波变换的图像边缘检测方法的理论推导和算法实现．     

三江源大型食草动物数量无人机自动监测方法

应用无人机进行动物调查的研究越来越多,但是缺乏有效的自动处理图像方法,使得目视解释的工作量较大。为研究快速与准确估算三江源区域大型食草动物数量的方法,本文使用两种无人机进行7个站点的遥感影像采集。首先对无人机影像进行灰度化,使矩阵维数下降但梯度信息仍然保留,使运算速度大幅度提高;其次对影像开展高斯滤波,高斯滤波将数据进行能量转化,排除掉属于低能量部分的噪声;第三开展阈值处理得到二值化图像;采用样本中动物形态学特征开展形态运算,先用开运算消除小物体尽可能排除干扰,同时不误删牲畜,再用闭运算排除小型黑洞将同一对象连通不重复计数;最后从二值图像中检索轮廓,并返回检测到的轮廓的个数;从而自动获得主要大型食草动物物种数量与空间分布。精度检验方法为手工计数与自动计数结果比较,相对误差3.1%~6.5%,大多数情况均可达到此水平。采用计算机自动处理图像后,每张图像处理和计数的平均时间小于3 s。无人机影像的自动处理方法可为今后大规模进行藏羊、牦牛、西藏野驴和藏原羚等动物调查提供一种有效、可靠的技术途径。     

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。     

An edge relocation segmentation algorithm

An automated procedure that refines the nuclear contour of a previously segmented nucleus is described. The algorithm makes use of intensity information, edge magnitude information and both object and edge connectivity information. This automated procedure generates a closed contour precisely along the edge of the nucleus. The procedure was tested on a database of 3,680 red-green-blue images of thionin-SO2 and orange II-stained cervical cells obtained from normal and dysplastic samples. When used in conjunction with a simple threshold selection algorithm and an artifact removal routine, this edge relocation algorithm resulted in the correct segmentation of over 98% of the nuclei. Only 63 (1.7%) of all nuclei were incorrectly segmented.     

Automated extraction of canopy shadow fraction using unmanned helicopter-based color vegetation indices

The canopy shadow fraction (CSF) is composed of the fractional area covered by shadowed tree crowns and shadowed backgrounds for a given illumination and view geometry. Since the CSF is related to the canopy biological and structural features, an accurate estimation of the CSF is expected for better understanding of the canopy characteristics. This study explores an algorithm for an automated extraction of the CSF using near-surface remote sensing method. The high-spatial resolution true-color images over different forested canopies were acquired using an unmanned helicopter. For each site, the images of the same target canopy from multiple view zenith angles (VZA) were taken at the principal plane. The digital images were processed to extract the CSF using the color vegetation indices (CVI) combined with an image threshold algorithm. The CSF was measured based on visual interpretation of the grayscale images. For an automated extraction of the CSF, different CVI related to CSF were assessed with Otsu threshold algorithm. A new CVI called the blue deficient index (BDI) was proposed as an indicator of the CSF exploiting the canopy spectral properties. The performance of each automated extraction method was evaluated with comparison to measured CSF. Among the methods assessed under the study, the CSF estimated by the BDI with the Otsu algorithm was found to be most closely related to the measured CSF. After a successful extraction of the CSF, the effect of VZA on CSF was analyzed. The substantial variation of the CSF with respect to the VZA in the principal plane was confirmed for a given solar position.     

Automated Assessment of Upper Extremity Movement Impairment due to Stroke

Current diagnosis and treatment of movement impairment post-stroke is based on the subjective assessment of select movements by a trained clinical specialist. However, modern low-cost motion capture technology allows for the development of automated quantitative assessment of motor impairment. Such outcome measures are crucial for advancing post-stroke treatment methods. We sought to develop an automated method of measuring the quality of movement in clinically-relevant terms from low-cost motion capture. Unconstrained movements of upper extremity were performed by people with chronic hemiparesis and recorded by standard and low-cost motion capture systems. Quantitative scores derived from motion capture were compared to qualitative clinical scores produced by trained human raters. A strong linear relationship was found between qualitative scores and quantitative scores derived from both standard and low-cost motion capture. Performance of the automated scoring algorithm was matched by averaged qualitative scores of three human raters. We conclude that low-cost motion capture combined with an automated scoring algorithm is a feasible method to assess objectively upper-arm impairment post stroke. The application of this technology may not only reduce the cost of assessment of post-stroke movement impairment, but also promote the acceptance of objective impairment measures into routine medical practice.     

A three-dimensional model of the human airway tree.

A three-dimensional (3D) model of the human airway tree is proposed using a deterministic algorithm that can generate a branching duct system in an organ. The algorithm is based on two principles: 1) the amount of fluid delivery through a branch is proportional to the volume of the region it supplies; and 2) the terminal branches are arranged homogeneously within the organ. These principles define the basic process of branching: generation of the dimensions and directionality of two daughter branches is governed by the properties of the parent branch and the region the parent supplies. The algorithm is composed of nine basic rules and four complementary rules. When the contour of an organ and the position of the trunk are specified, branches are successively generated by the algorithm. Applied to the human lung, the algorithm generates an airway tree that consists of approximately 54,000 branches. Its morphometric characteristics are in good agreement with those reported in the literature. The algorithm and the 3D airway model are useful for studying the structure-function relationship in the lung.     

Interactive measurement and characterization of DNA molecules by analysis of AFM images.

BACKGROUND: In the past few years, computer-based analysis of atomic-force microscopic images has acquired increasing importance for studying biomolecules such as DNA. On the one hand, fully automated methods do not allow analysis of complex shapes; on the other hand, manual methods are usually time consuming and inaccurate. The semiautomated approach presented in this report overcomes the drawbacks of both methods. METHODS: Two kinds of images were analyzed: computer-generated filaments that modeled circular DNA molecules on a surface and real atomic-force microscopic images of DNA molecules adsorbed on an appropriate substrate surface. RESULTS: The algorithm was tested on a group of 140 simulated and 189 real plasmids with a nominal length of 913 nm. The accuracy of the length measurement was statistically evaluated on the ensemble of molecules, with particular attention to the influence of the noise. Mean contour lengths of 912 +/- 5 nm and 910 +/- 47 nm were found for simulated and real plasmids, respectively. The measured end-to-end distance of lambda-DNA molecules as a function of their contour length is reported, from which it is possible to estimate the stiffness of the DNA molecules adsorbed onto a surface; the value obtained for the DNA persistence length (42 +/- 5 nm) is consistent with values measured by other imaging techniques. CONCLUSIONS: An interactive algorithm for DNA molecule measurements based on the detection of the filament ridge line in a digitized image is presented. The simulation of artificial filaments combined with the experimental data demonstrates that the proposed method can be a valuable tool for the DNA contour length evaluation, especially in the case of complex shapes where the use of automatic methods is not possible.     

Segmentation of the Clustered Cells with Optimized Boundary Detection in Negative Phase Contrast Images

Cell image segmentation plays a central role in numerous biology studies and clinical applications. As a result, the development of cell image segmentation algorithms with high robustness and accuracy is attracting more and more attention. In this study, an automated cell image segmentation algorithm is developed to get improved cell image segmentation with respect to cell boundary detection and segmentation of the clustered cells for all cells in the field of view in negative phase contrast images. A new method which combines the thresholding method and edge based active contour method was proposed to optimize cell boundary detection. In order to segment clustered cells, the geographic peaks of cell light intensity were utilized to detect numbers and locations of the clustered cells. In this paper, the working principles of the algorithms are described. The influence of parameters in cell boundary detection and the selection of the threshold value on the final segmentation results are investigated. At last, the proposed algorithm is applied to the negative phase contrast images from different experiments. The performance of the proposed method is evaluated. Results show that the proposed method can achieve optimized cell boundary detection and highly accurate segmentation for clustered cells.     

Automated measurements on cell nuclei in tissue sections. Validation procedures

A methodology was developed for fully automated measurements of nuclear features in Feulgen-stained tissue sections by means of videomicroscopy and image analysis. Segmentation is performed within one minute on 512 X 512 optical density (OD) images covering about 75 nuclei, resulting in a graphic contour overlay. The corresponding image subset is scanned by an object data extraction program, producing the raw figures for statistical interpretation. The segmentation software was evaluated by three tests, involving comparison with manual delineation and assessment of the influence of OD. Two case studies (ACTH-stimulated adrenal cortex and pancreatic carcinoma) illustrate the biologic accuracy and medical significance of the described methodology.     

Peak picking multidimensional NMR spectra with the contour geometry based algorithm CYPICK

The automated identification of signals in multidimensional NMR spectra is a challenging task, complicated by signal overlap, noise, and spectral artifacts, for which no universally accepted method is available. Here, we present a new peak picking algorithm, CYPICK, that follows, as far as possible, the manual approach taken by a spectroscopist who analyzes peak patterns in contour plots of the spectrum, but is fully automated. Human visual inspection is replaced by the evaluation of geometric criteria applied to contour lines, such as local extremality, approximate circularity (after appropriate scaling of the spectrum axes), and convexity. The performance of CYPICK was evaluated for a variety of spectra from different proteins by systematic comparison with peak lists obtained by other, manual or automated, peak picking methods, as well as by analyzing the results of automated chemical shift assignment and structure calculation based on input peak lists from CYPICK. The results show that CYPICK yielded peak lists that compare in most cases favorably to those obtained by other automated peak pickers with respect to the criteria of finding a maximal number of real signals, a minimal number of artifact peaks, and maximal correctness of the chemical shift assignments and the three-dimensional structure obtained by fully automated assignment and structure calculation.     

Moment Feature Based Fast Feature Extraction Algorithm for Moving Object Detection Using Aerial Images

Fast and computationally less complex feature extraction for moving object detection using aerial images from unmanned aerial vehicles (UAVs) remains as an elusive goal in the field of computer vision research. The types of features used in current studies concerningmoving object detection are typically chosen based on improving detection rate rather than on providing fast and computationally less complex feature extraction methods. Because moving object detection using aerial images from UAVs involves motion as seen from a certain altitude, effective and fast feature extraction is a vital issue for optimum detection performance. This research proposes a two-layer bucket approach based on a new feature extraction algorithm referred to as the moment-based feature extraction algorithm (MFEA). Because a moment represents thecoherent intensity of pixels and motion estimation is a motion pixel intensity measurement, this research used this relation to develop the proposed algorithm. The experimental results reveal the successful performance of the proposed MFEA algorithm and the proposed methodology.     

Efficient automatic noise reduction of electron tomographic reconstructions based on iterative median filtering

A simple, fast and efficient noise-reduction protocol for three-dimensional electron tomographic reconstructions of biological material is presented. The approach is based on iterative application of median filtering and shows promise for automatic noise reduction as a pre-processor for automated data analysis tools which aim at segmentation, feature extraction and pattern recognition. The application of this algorithm produces encouraging results for a wide variety of experimental and synthetic electron tomographic reconstructions.