Edge detection in medical images using a genetic algorithm

An algorithm is developed that detects well-localized, unfragmented, thin edges in medical images based on optimization of edge configurations using a genetic algorithm (GA). Several enhancements were added to improve the performance of the algorithm over a traditional GA. The edge map is split into connected subregions to reduce the solution space and simplify the problem. The edge-map is then optimized in parallel using incorporated genetic operators that perform transforms on edge structures. Adaptation is used to control operator probabilities based on their participation. The GA was compared to the simulated annealing (SA) approach using ideal and actual medical images from different modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound. Quantitative comparisons were provided based on the Pratt figure of merit and on the cost-function minimization. The detected edges were thin, continuous, and well localized. Most of the basic edge features were detected, Results for different medical image modalities are promising and encourage further investigation to improve the accuracy and experiment with different cost functions and genetic operators     

Compact storage of medical images with patient information

Digital watermarking is a technique of hiding specific identification data for copyright authentication. This technique is adapted here for interleaving patient information with medical images to reduce storage and transmission overheads. The text data are encrypted before interleaving with images to ensure greater security. The graphical signals are compressed and subsequently interleaved with the image. Differential pulse-code-modulation and adaptive-delta-modulation techniques are employed for data compression, and encryption and results are tabulated for a specific example     

Segmentation of medical images using LEGION

Advances in visualization technology and specialized graphic workstations allow clinicians to virtually interact with anatomical structures contained within sampled medical-image datasets. A hindrance to the effective use of this technology is the difficult problem of image segmentation. In this paper, we utilize a recently proposed oscillator network called the locally excitatory globally inhibitory oscillator network (LEGION) whose ability to achieve fast synchrony with local excitation and desynchrony with global inhibition makes it an effective computational framework for grouping similar features and segregating dissimilar ones in an image. We extract an algorithm from LEGION dynamics and propose an adaptive scheme for grouping. We show results of the algorithm to two-dimensional (2-D) and three-dimensional (3-D) (volume) computerized topography (CT) and magnetic resonance imaging (MRI) medical-image datasets. In addition, we compare our algorithm with other algorithms for medical-image segmentation, as well as with manual segmentation. LEGION's computational and architectural properties make it a promising approach for real-time medical-image segmentation.     

基于隔行扫描奇偶场图像的动态物体三维面形测量

存基于傅里叶变换轮廓术(FTP)的动态物体三维面形测量中,选用隔行扫描CCD相机记录动态图像,由于其一帧中两场图像时间上的分离性,导致帧图像错位模糊,使测量误差增大甚至产生错误.针对这一问题,提出利用隔行扫描CCD获取动态物体的错位模糊帧图像分成两个单场图像,各自直接利用FTP重建物体面形的新方法.理论分析得出单场图像与准确的满帧图像重建物体三维面形是完全相同的结论;仿真及实验结果都表明该方法的有效性.该方法既保持了原有准确帧图像的重建精度,又使整个测量系统的时间分辨率提高了1倍;实现简单快捷.     

心脏图像三维运动与材料信息的提取和重建

针对传统心脏图像分析方法将运动分析和材料分析作为两个独立过程带来的弊端,本文基于心脏连续生物力学模型,利用有限元方法和扩展卡尔曼滤波器(EKF),将运动分析和材料分析作为一个问题来解决,取得了较好的结果。仿真实验验证了本文方法的有效性,给出了利用真实病人心脏图像实现三维运动和材料信息的同时重建的结果。     

基于邻域信息的SAR图像变化检测

为了减少合成孔径雷达(SAR)图像中乘性斑点噪声对变化检测结果的影响,充分地利用了像素的邻域信息。首先使用邻域比值(NR)方法构造差异图像,然后提出基于邻域信息的模糊C均值聚类(FCM)算法。NR算子在构造差异图像时能够较好地保留图像信息并抑制噪声的干扰。同时将邻域信息引入到FCM算法的目标函数,以邻域加权距离改进了FCM算法在欧式距离计算中的不足,并约束了隶属度函数,减少了噪声对邻域中心像素的干扰。通过以上考虑像素邻域信息的算法,得到了差异图像的聚类结果,从而实现了SAR图像的变化检测。实验结果表明,所提算法较传统的FCM和K-means聚类算法,可以较好地保留图像变化区域的信息,同时提高了SAR图像变化检测的准确度。     

Estimation of 3-D left ventricular deformation from medical images using biomechanical models

The quantitative estimation of regional cardiac deformation from three-dimensional (3-D) image sequences has important clinical implications for the assessment of viability in the heart wall. We present here a generic methodology for estimating soft tissue deformation which integrates image-derived information with biomechanical models, and apply it to the problem of cardiac deformation estimation. The method is image modality independent. The images are segmented interactively and then initial correspondence is established using a shape-tracking approach. A dense motion field is then estimated using a transversely isotropic, linear-elastic model, which accounts for the muscle fiber directions in the left ventricle. The dense motion field is in turn used to calculate the deformation of the heart wall in terms of strain in cardiac specific directions. The strains obtained using this approach in open-chest dogs before and after coronary occlusion, exhibit a high correlation with strains produced in the same animals using implanted markers. Further, they show good agreement with previously published results in the literature. This proposed method provides quantitative regional 3-D estimates of heart deformation.     

Modeling real-time 3-d lung deformations for medical visualization.

In this paper, we propose a physics-based and physiology-based approach for modeling real-time deformations of 3-D high-resolution polygonal lung models obtained from high-resolution computed tomography (HRCT) images of normal human subjects. The physics-based deformation operator is nonsymmetric, which accounts for the heterogeneous elastic properties of the lung tissue and spatial-dynamic flow properties of the air. An iterative approach is used to estimate the deformation with the deformation operator initialized based on the regional alveolar expandability, a key physiology-based parameter. The force applied on each surface node is based on the airflow pattern inside the lungs, which is known to be based on the orientation of the human subject. The validation of lung dynamics is done by resimulating the lung deformation and comparing it with HRCT data and computing force applied on each node derived from a 4-D HRCT dataset of a normal human subject using the proposed deformation operator and verifying its gradient with the orientation.     

Automated segmentation of acetabulum and femoral head from 3-d CT images

This paper describes several new methods and software for automatic segmentation of the pelvis and the femur, based on clinically obtained multislice computed tomography (CT) data. The hip joint is composed of the acetabulum, cavity of the pelvic bone, and the femoral head. In vivo CT data sets of 60 actual patients were used in the study. The 120 (60 /spl times/ 2) hip joints in the data sets were divided into four groups according to several key features for segmentation. Conventional techniques for classification of bony tissues were first employed to distinguish the pelvis and the femur from other CT tissue images in the hip joint. Automatic techniques were developed to extract the boundary between the acetabulum and the femoral head. An automatic method was built up to manage the segmentation task according to image intensity of bone tissues, size, center, shape of the femoral heads, and other characters. The processing scheme consisted of the following five steps: 1) preprocessing, including resampling 3-D CT data by a modified Sine interpolation to create isotropic volume and to avoid Gibbs ringing, and smoothing the resulting images by a 3-D Gaussian filter; 2) detecting bone tissues from CT images by conventional techniques including histogram-based thresholding and binary morphological operations; 3) estimating initial boundary of the femoral head and the joint space between the acetabulum and the femoral head by a new approach utilizing the constraints of the greater trochanter and the shapes of the femoral head; 4) enhancing the joint space by a Hessian filter; and 5) refining the rough boundary obtained in step 3) by a moving disk technique and the filtered images obtained in step 4). The above method was implemented in a Microsoft Windows software package and the resulting software is freely available on the Internet. The feasibility of this method was tested on the data sets of 60 clinical cases (5000 CT images).     

改进自组织特征网络的医学图像分割算法

为了提高医学图像分割精度,提出一种改进自组织特征网络的医学图像分割算法。首先采用小波包分解提取医学图像的特征,然后改进自组织特征网络建立医学图像分类器,实现医学图像分割,最后采用仿真实验测试算法的性能。仿真结果表明,本文算法不仅解决了传统医学图像分割算法存在的缺陷,提高医学图像分割的精度,具有较好的实际应用价值。     

Nonrigid registration of 3-d multichannel microscopy images of cell nuclei.

We present an intensity-based nonrigid registration approach for the normalization of 3-D multichannel microscopy images of cell nuclei. A main problem with cell nuclei images is that the intensity structure of different nuclei differs very much; thus, an intensity-based registration scheme cannot be used directly. Instead, we first perform a segmentation of the images from the cell nucleus channel, smooth the resulting images by a Gaussian filter, and then apply an intensity-based registration algorithm. The obtained transformation is applied to the images from the nucleus channel as well as to the images from the other channels. To improve the convergence rate of the algorithm, we propose an adaptive step length optimization scheme and also employ a multiresolution scheme. Our approach has been successfully applied using 2-D cell-like synthetic images, 3-D phantom images as well as 3-D multichannel microscopy images representing different chromosome territories and gene regions. We also describe an extension of our approach, which is applied for the registration of 3D + t (4-D) image series of moving cell nuclei.     

Managing medical images and clinical information: InCor's experience.

Patients usually get medical assistance in several clinics and hospitals during their lifetime, archiving vital information in a dispersed way. Clearly, a proper patient care should take into account that information in order to check for incompatibilities, avoid unnecessary exams, and get relevant clinical history. The Heart Institute (InCor) of S?o Paulo, Brazil, has been committed to the goal of integrating all exams and clinical information within the institution and other hospitals. Since InCor is one of the six institutes of the University of S?o Paulo Medical School and each institute has its own information system, exchanging information among the institutes is also a very important aspect that has been considered. In the last few years, a system for transmission, archiving, retrieval, processing, and visualization of medical images integrated with a hospital information system has been successfully created and constitutes the InCor's electronic patient record (EPR). This work describes the experience in the effort to develop a functional and comprehensive EPR, which includes laboratory exams, images (static, dynamic, and three dimensional), clinical reports, documents, and even real-time vital signals. A security policy based on a contextual role-based access control model was implemented to regulate user's access to EPR. Currently, more than 10 TB of digital imaging and communications in medicine (DICOM) images have been stored using the proposed architecture and the EPR stores daily more than 11 GB of integrated data. The proposed storage subsystem allows 6 months of visibility for rapid retrieval and more than two years for automatic retrieval using a jukebox. This paper addresses also a prototype for the integration of distributed and heterogeneous EPR.     

Boundary detection in medical images using edge following algorithm based on intensity gradient and texture gradient features

Finding the correct boundary in noisy images is still a difficult task. This paper introduces a new edge following technique for boundary detection in noisy images. Utilization of the proposed technique is exhibited via its application to various types of medical images. Our proposed technique can detect the boundaries of objects in noisy images using the information from the intensity gradient via the vector image model and the texture gradient via the edge map. The performance and robustness of the technique have been tested to segment objects in synthetic noisy images and medical images including prostates in ultrasound images, left ventricles in cardiac magnetic resonance (MR) images, aortas in cardiovascular MR images, and knee joints in computerized tomography images. We compare the proposed segmentation technique with the active contour models (ACM), geodesic active contour models, active contours without edges, gradient vector flow snake models, and ACMs based on vector field convolution, by using the skilled doctors' opinions as the ground truths. The results show that our technique performs very well and yields better performance than the classical contour models. The proposed method is robust and applicable on various kinds of noisy images without prior knowledge of noise properties.     

Reversible 3-D decorrelation of medical images

Two methods, namely, differential pulse code modulation (DPCM) and hierarchical interpolation (HINT), are considered. It is shown that HINT cannot be extended straightforwardly to 3-D images as contrasted with DPCM. A 3-D HINT is therefore proposed which is based on a combination of 2-D and 3-D filters. Both decorrelation methods were applied to three-dimensional computed tomography (CT), magnetic resonance (MR), and single-photon-emission CT (SPECT) images. It was found that a 3-D approach is optimal for some studies, while for other studies 2-D or even 1-D decorrelation performs better. The optimal dimensionality of DPCM is related to the magnitudes of the local correlation coefficients (CCs). However, the nonlocal nature of HINT makes the local correlation coefficients useless as indicators of the dimensionality; a better candidate is the image voxel size. For images with cubic or nearly cubic voxels 3-D HINT is generally optimal. For images in which the slice thickness is large compared to the pixel size a 2-D (intraslice) HINT is best. In general, the increase in efficiency obtained by extending 2-D decorrelation method to 3-D is small.     

The use of contextual information in the reversible compression of medical images

The authors investigate the use of conditioning events (or contexts) in improving the performances of known compression methods by building a source model with multiple contexts to code the decorrelated pixels. Three methods for reversible compression, namely DPCM (differential pulse code modulation), WHT (Walsh-Hadamard transform), and HINT (hierarchical interpolation), employing, respectively, predictive decorrelation, transform decorrelation, and multiresolution decorrelation, are considered. It is shown that the performance of these methods can be enhanced significantly, sometimes even up to 40%, by using contexts. The enhanced DPCM method is found to perform the best for MR and UT (ultrasound) medical images; the enhanced WHT method is found to be the best for X-ray images. The source models used in the enhanced models employ several hundred contexts.     

Deformable boundary finding in medical images by integrating gradient and region information

Accurately segmenting and quantifying structures is a key issue in biomedical image analysis. The two conventional methods of image segmentation, region-based segmentation, and boundary finding, often suffer from a variety of limitations. Here the authors propose a method which endeavors to integrate the two approaches in an effort to form a unified approach that is robust to noise and poor initialization. The authors' approach uses Green's theorem to derive the boundary of a homogeneous region-classified area in the image and integrates this with a gray level gradient-based boundary finder. This combines the perceptual notions of edge/shape information with gray level homogeneity. A number of experiments were performed both on synthetic and real medical images of the brain and heart to evaluate the new approach, and it is shown that the integrated method typically performs better when compared to conventional gradient-based deformable boundary finding. Further, this method yields these improvements with little increase in computational overhead, an advantage derived from the application of the Green's theorem.     

Fast 3-d tomographic microwave imaging for breast cancer detection

Microwave breast imaging (using electromagnetic waves of frequencies around 1 GHz) has mostly remained at the research level for the past decade, gaining little clinical acceptance. The major hurdles limiting patient use are both at the hardware level (challenges in collecting accurate and noncorrupted data) and software level (often plagued by unrealistic reconstruction times in the tens of hours). In this paper we report improvements that address both issues. First, the hardware is able to measure signals down to levels compatible with sub-centimeter image resolution while keeping an exam time under 2 min. Second, the software overcomes the enormous time burden and produces similarly accurate images in less than 20 min. The combination of the new hardware and software allows us to produce and report here the first clinical 3-D microwave tomographic images of the breast. Two clinical examples are selected out of 400+ exams conducted at the Dartmouth Hitchcock Medical Center (Lebanon, NH). The first example demonstrates the potential usefulness of our system for breast cancer screening while the second example focuses on therapy monitoring.     

Feature extraction of chromosomes from 3-D confocal microscope images

An investigation of local energy surface detection integrated with neural network techniques for image segmentation is presented, as applied in the feature extraction of chromosomes from image datasets obtained using an experimental confocal microscope. Use of the confocal microscope enables biologists to observe dividing cells (living or preserved) within a three-dimensional (3-D) volume, that can be visualised from multiple aspects, allowing for increased structural insight. The Nomarski differential interference contrast mode used for imaging translucent specimens, such as chromosomes, produces images not suitable for volume rendering. Segmentation of the chromosomes from this data is, thus, necessary. A neural network based on competitive learning, known as Kohonen's self-organizing feature map (SOFM) was used to perform segmentation, using a collection of statistics or features defining the image. Our past investigation showed that standard features such as the localized mean and variance of pixel intensities provided reasonable extraction of objects such as mitotic chromosomes, but surface detail was only moderately resolved. In this current work, a biologically inspired feature known as local energy is investigated as an alternative image statistic based on phase congruency in the image. This, along with different combinations of other image statistics, is applied in a SOFM, producing 3-D images exhibiting vast improvement in the level of detail and clearly isolating the chromosomes from the background. Index Terms-DIC, differential interference contrast, feature extraction, feature space, image segmentation, local energy, Morlet wavelet, phase congruency, self organizing feature map, SOFM.     

边缘检测算法在医学超声液性病变图像中的应用

医学超声液性病变图像多见数个无回声区,呈"蜂窝状",边界不清晰,为了清晰地提取医学超声液性病变图像的边缘,进一步为临床诊断提供可靠依据,在此将几种不同的边缘检测算法应用于医学超声液态病变图像中,经实验结果得出,经典的边缘检测算法不能很好地提取图像的边缘,而基于Snake模型的边缘检测算法,人为设定边缘控制点,智能动态调整曲线,获得了很好的边缘提取效果,具有很高的临床应用价值。