Contour Definition and Tracking in Cardiac Imaging Through the Integration of Knowledge and Image Evidence

The main contribution of this paper is the use of simple processing techniques, incorporated in a new multistage approach, to automatically delineate left ventricle contours. Another contribution is the proposal of the centerline distances for contour comparison, which promises a more accurate measurement than the common method, based on the distance to the closest point. Edges are detected by Gaussian filtering at coarse and fine scale. The region of interest is defined as a binary map where coarse edges are extracted throughout image sequence. A contour template is matched against the gradient of the first image. Candidate boundary points are instantiated by scanning the coarse edge map perpendicularly to the matched template. A candidate contour is estimated from these points by maximizing an edge likelihood function. A region growing algorithm gives another candidate contour. Both edge and region candidate contours are then integrated with the edge map computed at fine scale by maximizing another likelihood function. Evaluation was carried out on 12 echocardiographic and 4 angiocardiographic sequences (for a total of 289 frames). Distances between computer-generated contours and the contours traced by three experts were within interobserver variability, unlike the results obtained by Acoustic Quantification and by a general-purpose deformable model.     

基于自适应标记分水岭算法的肝脏CT图像自动分割

目的为减少人工交互提出了基于自适应标记分水岭的CT系列图像肝脏区域自动分割算法。方法首先对图像进行形态学重构运算以平滑图像,然后计算多尺度形态学梯度,同时提出利用梯度图像非零的局部极小值点的均值进行自适应标记提取,以避免分水岭的过分割和欠分割,再结合肝脏为最大的实质性脏器和相邻图像的相似性实现CT系列图像的肝区自动分割。结果该算法能自动、快速地提取CT系列图像中的肝脏区域。结论分水岭算法能准确定位区域的边缘,通过选择合适的阈值对梯度图像进行标记以抑制分水岭的过分割,实现医学图像中感兴趣区域的自动分割。     

Automated seed detection and three-dimensional reconstruction. I. Seed localization from fluoroscopic images or radiographs.

An automated procedure for the detection of the position and the orientation of radioactive seeds on fluoroscopic images or scanned radiographs is presented. The extracted positions of seed centers and the orientations are used for three-dimensional reconstruction of permanent prostate implants. The extraction procedure requires several steps: correction of image intensifier distortions, normalization, background removal, automatic threshold selection, thresholding, and finally, moment analysis and classification of the connected components. The algorithm was tested on 75 fluoroscopic images. The results show that, on average, 92% of the seeds are detected automatically. The orientation is found with an error smaller than 50 for 75% of the seeds. The orientation of overlapping seeds (10%) should be considered as an estimate at best. The image processing procedure can also be used for seed or catheter detection in CT images, with minor modifications.     

A three-dimensional deformable model for segmentation of human prostate from ultrasound images.

Segmentation of human prostate from ultrasound (US) images is a crucial step in radiation therapy, especially in real-time planning for US image-guided prostate seed implant. This step is critical to determine the radioactive seed placement and to ensure the adequate dose coverage of prostate. However, due to the low contrast of prostate and very low signal-to-noise ratio in US images, this task remains as an obstacle. The manual segmentation of this object is time consuming and highly subjective. In this work, we have proposed a three-dimensional (3D) deformable surface model for automatic segmentation of prostate. The model has a discrete structure made from a set of vertices in the 3D space that form triangle facets. The model converges from an initial shape to its equilibrium iteratively, by a weighted sum of the internal and external forces. Internal forces are based on the local curvature of the surface and external forces are extracted from the volumetric image data by applying an appropriate edge filter. We have also developed a method for initialization of the model from a few initial contours that are drawn on different slices. During the deformation, a resampling procedure is used to maintain the resolution of the model. The entire model is applied in a multiscale scheme, which increases the robustness and speed, and guarantees a better convergence. The model is tested on real clinical data and initial results are very promising.     

An Image-Based Comprehensive Approach for Automatic Segmentation of Left Ventricle from Cardiac Short Axis Cine MR Images

Segmentation of the left ventricle is important in the assessment of cardiac functional parameters. Manual segmentation of cardiac cine MR images for acquiring these parameters is time-consuming. Accuracy and automation are the two important criteria in improving cardiac image segmentation methods. In this paper, we present a comprehensive approach to segment the left ventricle from short axis cine cardiac MR images automatically. Our method incorporates a number of image processing and analysis techniques including thresholding, edge detection, mathematical morphology, and image filtering to build an efficient process flow. This process flow makes use of various features in cardiac MR images to achieve high accurate segmentation results. Our method was tested on 45 clinical short axis cine cardiac images and the results are compared with manual delineated ground truth (average perpendicular distance of contours near 2 mm and mean myocardium mass overlapping over 90%). This approach provides cardiac radiologists a practical method for an accurate segmentation of the left ventricle.     

Automatic segmentation algorithm for the extraction of lumen region and boundary from endoscopic images

A new segmentation algorithm for lumen region detection and boundary extraction from gastro-intestinal (GI) images is presented. The proposed algorithm consists of two steps. First, a preliminary region of interest (ROI) representing the GI lumen is segmented by an adaptive progressive thresholding (APT) technique. Then, an adaptive filter, the Iris filter, is applied to the ROI to determine the actual region. It has been observed that the combined APT-Iris filter technique can enhance and detect the unclear boundaries in the lumen region of GI images and thus produces a more accurate lumen region, compared with the existing techniques. Experiments are carried out to determine the maximum error on the extracted boundary with respect to an expert-annotated boundary technique. Investigations show that, based on the experimental results obtained from 50 endoscopic images, the maximum error is reduced by up to 72 pixels for a 256 × 256 image representation compared with other existing techniques. In addition, a new boundary extraction algorithm, based on a heuristic search on the neighbourhood pixels, is employed to obtain a connected single pixel width outer boundary using two preferential sequence windows. Experimental results are also presented to justify the effectiveness of the proposed algorithm.     

医用肠道内窥镜图像纹理分析的寻径方法研究

描述采用纹理分析法进行寻径引导内窥镜头部的方法，该方法首先对内窥镜图像进行边缘提取．然后对边缘线进行骨架提取，最后对边缘线进行分组连接和决策，计算出内窥镜头部偏摆所需的数据。通过实验验证了该方法的可行性及可靠性，该方法的应用可弥补传统方法中因暗区不明显而无法引导内窥镜的不足．提高了内窥镜视觉导航的精度和可靠性。     

基于CT医学图像的边缘提取研究

为了实现人体器官的三维重建,如何准确、有效地提取二维医学图像的边缘成了首要解决的问题.我们提出一种新的图像边缘提取方法,该方法先将原始CT图像二值化,然后利用数学形态运算对二值化图像进行预处理,最后利用Canny算子提取图像边缘.通过肾脏CT图像边缘提取结果表明,该方法简单、高效、性能优越.     

Endocardial Border Detection in Cardiac Magnetic Resonance Images Using Level Set Method

Segmentation of the left ventricle in MRI images is a task with important diagnostic power. Currently, the evaluation of cardiac function involves the global measurement of volumes and ejection fraction. This evaluation requires the segmentation of the left ventricle contour. In this paper, we propose a new method for automatic detection of the endocardial border in cardiac magnetic resonance images, by using a level set segmentation-based approach. To initialize this level set segmentation algorithm, we propose to threshold the original image and to use the binary image obtained as initial mask for the level set segmentation method. For the localization of the left ventricular cavity, used to pose the initial binary mask, we propose an automatic approach to detect this spatial position by the evaluation of a metric indicating object’s roundness. The segmentation process starts by the initialization of the level set algorithm and ended up through a level set segmentation. The validation process is achieved by comparing the segmentation results, obtained by the automated proposed segmentation process, to manual contours traced by tow experts. The database used was containing one automated and two manual segmentations for each sequence of images. This comparison showed good results with an overall average similarity area of 97.89%.     

Gray-scale edge detection for gastric tumor pathologic cell images by morphological analysis

For the purpose of analyzing gastric tumor pathologic cell images, a novel method is developed with gray-scale edge detection of mathematical morphology in this study. In combination with texture features of the image under investigation, this paper works on edge detection with various structuring elements (SEs) and gray-scale values. The results of the experiment are presented, and we found several advantages by using the morphological edge detection scheme for the analysis of gastric tumor pathologic cell images. Meanwhile, the results of the binary morphological edge detection are given for comparison.     

Automatic segmentation of left ventricle cavity from short-axis cardiac magnetic resonance images

In this paper, a computational framework is proposed to perform a fully automatic segmentation of the left ventricle (LV) cavity from short-axis cardiac magnetic resonance (CMR) images. In the initial phase, the region of interest (ROI) is automatically identified on the first image frame of the CMR slices. This is done by partitioning the image into different regions using a standard fuzzy c-means (FCM) clustering algorithm where the LV region is identified according to its intensity, size and circularity in the image. Next, LV segmentation is performed within the identified ROI by using a novel clustering method that utilizes an objective functional with a dissimilarity measure that incorporates a circular shape function. This circular shape-constrained FCM algorithm is able to differentiate pixels with similar intensity but are located in different regions (e.g. LV cavity and non-LV cavity), thus improving the accuracy of the segmentation even in the presence of papillary muscles. In the final step, the segmented LV cavity is propagated to the adjacent image frame to act as the ROI. The segmentation and ROI propagation are then iteratively executed until the segmentation has been performed for the whole cardiac sequence. Experiment results using the LV Segmentation Challenge validation datasets show that our proposed framework can achieve an average perpendicular distance (APD) shift of 2.23 ± 0.50 mm and the Dice metric (DM) index of 0.89 ± 0.03, which is comparable to the existing cutting edge methods. The added advantage over state of the art is that our approach is fully automatic, does not need manual initialization and does not require a prior trained model.     

A novel stepwise thresholding for fuzzy image segmentation

INTRODUCTION　　 In some imaging system,such as laser scanning confocal microscopy( LSCM)imaging,computerised tomography ( CT ) imaging,magnetic resonance ( RM )imaging,etc.,the image acquisition produces sometimes fuzzy images.The fuzzyimage can be described as the follows.( 1 ) Objects may appearto fuse together.( 2 )Objects in the same class can have different intensities and individual objects canthemselves have a wide range of intensity values.( 3) Intensity will be lower deepwith…     

Identifying same-cell contours in image stacks: a key step in making 3D reconstructions

Identification of contours belonging to the same cell is a crucial step in the analysis of confocal stacks and other image sets in which cell outlines are visible, and it is central to the making of 3D cell reconstructions. When the cells are close packed, the contour grouping problem is more complex than that found in medical imaging, for example, because there are multiple regions of interest, the regions are not separable from each other by an identifiable background and regions cannot be distinguished by intensity differences. Here, we present an algorithm that uses three primary metrics—overlap of contour areas in adjacent images, co-linearity of the centroids of these areas across three images in a stack, and cell taper—to assign cells to groups. Decreasing thresholds are used to successively assign contours whose membership is less obvious. In a final step, remaining contours are assigned to existing groups by setting all thresholds to zero and groups having strong hour-glass shapes are partitioned. When applied to synthetic data from isotropic model aggregates, a curved model epithelium in which the long axes of the cells lie at all possible angles to the transection plane, and a confocal image stack, algorithm assignments were between 97 and 100% accurate in sets having at least four contours per cell. The algorithm is not particularly sensitive to the thresholds used, and a single set of parameters was used for all of the tests. The algorithm, which could be extended to time-lapse data, solves a key problem in the translation of image data into cell information.     

Left-ventricle boundary detection from nuclear medicine images

We present here a new algorithm for segmentation of nuclear medicine images to detect the left-ventricle (LV) boundary. In this article, other image segmentation techniques, such as edge detection and region growing, are also compared and evaluated. In the edge detection approach, we explored the relationship between the LV boundary characteristics in nuclear medicine images and their radial orientations: we observed that no single brightness function (eg, maximum of first or second derivative) is sufficient to identify the boundary in every direction. In the region growing approach, several criteria, including intensity change, gradient magnitude change, gradient direction change, and running mean differences, were tested. We found that none of these criteria alone was sufficient to successfully detect the LV boundary. Then we proposed a simple but successful region growing method—Contour-Modified Region Growing (CMRG). CMRG is an easy-to-use, robust, and rapid image segmentation procedure. Based on our experiments, this method seems to perform quite well in comparison to other automated methods that we have tested because of its ability to handle the problems of both low signal-to-noise ratios (SNR) as well as low image contrast without any assumptions about the shape of the left ventricle.     

Automated cerebrum segmentation from three-dimensional sagittal brain MR images

We present a fully automated cerebrum segmentation algorithm for full three-dimensional sagittal brain MR images. First, cerebrum segmentation from a midsagittal brain MR image is performed utilizing landmarks, anatomical information, and a connectivity-based threshold segmentation algorithm as previously reported. Recognizing that cerebrum in laterally adjacent slices tends to have similar size and shape, we use the cerebrum segmentation result from the midsagittal brain MR image as a mask to guide cerebrum segmentation in adjacent lateral slices in an iterative fashion. This masking operation yields a masked image (preliminary cerebrum segmentation) for the next lateral slice, which may truncate brain region(s). Truncated regions are restored by first finding end points of their boundaries, by comparing the mask image and masked image boundaries, and then applying a connectivity-based algorithm. The resulting final extracted cerebrum image for this slice is then used as a mask for the next lateral slice. The algorithm yielded satisfactory fully automated cerebrum segmentations in three-dimensional sagittal brain MR images, and had performance superior to conventional edge detection algorithms for segmentation of cerebrum from 3D sagittal brain MR images.     

基于知识的三维核医学图像左心室心肌区的提取

从心脏PET或SPECT图像中提取完整的心肌区域是定量分析心功能的前提。心脏的PET和SPECT图像边界模糊，在病理状态下可能有局部显像缺失，致使图像分割困难。本研究提出一种基于医学知识的快速推进法，利用拟合的椭球模型将边界演化推进到局部低显像区，从而分割出一个完整的左心室心肌区域。实验图像测试和实际图像分割表明这种算法对于有显像缺失的三维核医学心脏图像的分割是有效的。     

Estimation of the breast skin-line in mammograms using multidirectional Gabor filters

Segmentation of the breast region is a fundamental step in any system for computerized analysis of mammograms. In this work, we propose a novel procedure for the estimation of the breast skin-line based upon multidirectional Gabor filtering. The method includes an adaptive values-of-interest (VOI) transformation, extraction of the skin–air ribbon by Otsu's thresholding method and the Euclidean distance transform, Gabor filtering with 18 real kernels, and a step for suppression of false edge points using the magnitude and phase responses of the filters. On a test set of 361 images from different acquisition modalities (screen-film and full-field digital mammograms), the average Hausdorff and polyline distances obtained were 2.85 mm and 0.84 mm, respectively, with reference to the ground-truth boundaries provided by an expert radiologist. When compared with the results obtained by other state-of-the-art methods on the same set of images and with respect to the same ground-truth boundaries, our method mostly outperformed the other approaches. The results demonstrate the effectiveness and robustness of the proposed algorithm.     

The enhancement of radiotherapy verification images by an automated edge detection technique.

Adaptive histogram equalization techniques are known to be effective for the enhancement of contrast in portal images acquired during radiotherapy treatments. A significant drawback is the loss of definition on the edges of the treatment field. Analysis of this problem shows that it can be remedied by separating the treatment field from the background prior to the enhancement, and using only the pixels within the field boundary in the enhancement procedure. An edge extraction algorithm has been developed for delineating the treatment field in portal images, and consists of four modules that are applied to the original portal image in sequence. In the first step, edges are enhanced with a derivative of Gaussian operator that assures high response to the field edges relative to anatomical or other edges in the image. Pixels for which the response of the edge operator was the strongest are subsequently connected by an edge following algorithm to produce a raw contour of the field. In the last two steps the contour is refined by converting it into straight line segments and appending to the contour any parts of the field edge that might have been missed out during the initial edge following. The final contour encloses exclusively those pixels that belong to the treatment field, and the adaptive histogram equalization is applied selectively to this region. The combination of edge detection and selective enhancement was shown to produce images of superior contrast on the patient's anatomical features as well as accurate definition of treatment field edges.     

Anatomical landmark localization in breast dynamic contrast-enhanced MR imaging

In this article, we present a novel approach to localize anatomical features—breast costal cartilage—in dynamic contrast-enhanced MRI using level sets. Current breast MRI diagnosis involves magnetic-resonance compatible needles for localization [12]. However, if the breast costal cartilage structure can be used as an alternative to the MR needle, this will not only assist in avoiding invasive procedures, but will also facilitate monitoring of the movement of breasts caused by cardiac and respiratory motion. This article represents a novel algorithm for achieving reliable detection and extraction of costal cartilage structures, which can be used for the analysis of motion artifacts, with possible shape variations of the structure caused by uptake of contrast agent, as well as a potential for the registration of breast. The algorithm represented in this article is to extract volume features from post-contrast MR images at three different time slices for the analysis of motion artifacts, and we validate the current algorithm according to the anatomic structure. This utilizes the level-set method [18] for the size selection of the region of interest. The variable shape of contours acquired from a level-set-based segment image actually determines the feature region of interest, which is used as a guide to achieve initial masks for feature extraction. Following this, the algorithm uses a K-means method for classification of the feature regions from other types of tissue and morphological operations with a choice of an appropriate structuring element to achieve reliable masks and extraction of features. The segments of features can be therefore obtained with the application of extracted masks for subsequent motion analysis of breast and for potential registration purposes.