Rule-based detection of intrathoracic airway trees

New sensitive and reliable methods for assessing alterations in regional lung structure and function are critically important for the investigation and treatment of pulmonary diseases. Accurate identification of the airway tree will provide an assessment of airway structure and will provide a means by which multiple volumetric images of the lung at the same lung volume over time can be used to assess regional parenchymal changes. The authors describe a novel rule-based method for the segmentation of airway trees from three-dimensional (3-D) sets of computed tomography (CT) images, and its validation. The presented method takes advantage of a priori anatomical knowledge about pulmonary airway and vascular trees and their interrelationships. The method is based on a combination of 3-D seeded region growing that is used to identify large airways, rule-based two-dimensional (2-D) segmentation of individual CT slices to identify probable locations of smaller diameter airways, and merging of airway regions across the 3-D set of slices resulting in a tree-like airway structure. The method was validated in 40 3-mm-thick CT sections from five data sets of canine lungs scanned via electron beam CT in vivo with lung volume held at a constant pressure. The method's performance was compared with that of the conventional 3-D region growing method. The method substantially outperformed an existing conventional approach to airway tree detection.     

Segmentation of intrathoracic airway trees: a fuzzy logic approach

Three-dimensional (3-D) analysis of airway trees extracted from computed tomography (CT) image data can provide objective information about lung structure and function. However, manual analysis of 3-D lung CT images is tedious, time consuming and, thus, impractical for routine clinical care. The authors have previously reported an automated rule-based method for extraction of airway trees from 3-D CT images using a priori knowledge about airway-tree anatomy. Although the method's sensitivity was quite good, its specificity suffered from a large number of falsely detected airways. The authors present a new approach to airway-tree detection based on fuzzy logic that increases the method's specificity without compromising its sensitivity. The method was validated in 32 CT image slices randomly selected from five volumetric canine electron-beam CT data sets. The fuzzy-logic method significantly outperformed the previously reported rule-based method (p<0.002)     

Pulmonary airways: 3-D reconstruction from multislice CT and clinical investigation

In the framework of computer-aided diagnosis, this paper proposes a novel functionality for the computerized tomography (CT)-based investigation of the pulmonary airways. It relies on an energy-based three-dimensional (3-D) reconstruction of the bronchial tree from multislice CT acquisitions, up to the sixth- to seventh-order subdivisions. Global and local analysis of the reconstructed airways is possible by means of specific visualization modalities, respectively, the CT bronchography and the virtual bronchoscopy. The originality of the 3-D reconstruction approach consists in combining axial and radial propagation potentials to control the growth of a subset of low-order airways extracted from the CT volume by means of a robust mathematical morphology operator-the selective marking and depth constrained (SMDC) connection cost. The proposed approach proved to be robust with respect to a large spectrum of airway pathologies, including even severe stenosis (bronchial lumen obstruction/collapse). Validated by expert radiologists, examples of airway 3-D reconstructions are presented and discussed for both normal and pathological cases. They highlight the interest in considering CT bronchography and virtual bronchoscopy as complementary tools for clinical diagnosis and follow-up of airway diseases.     

Automatic lung segmentation for accurate quantitation of volumetricX-ray CT images

Segmentation of pulmonary X-ray computed tomography (CT) images is a precursor to most pulmonary image analysis applications. This paper presents a fully automatic method for identifying the lungs in three-dimensional (3-D) pulmonary X-ray CT images. The method has three main steps. First, the lung region is extracted from the CT images by gray-level thresholding. Then, the left and right lungs are separated by identifying the anterior and posterior junctions by dynamic programming. Finally, a sequence of morphological operations is used to smooth the irregular boundary along the mediastinum in order to obtain results consistent with those obtained by manual analysis, in which only the most central pulmonary arteries are excluded from the lung region. The method has been tested by processing 3-D CT data sets from eight normal subjects, each imaged three times at biweekly intervals with lungs at 90% vital capacity. We present results by comparing our automatic method to manually traced borders from two image analysts. Averaged over all volumes, the root mean square difference between the computer and human analysis is 0.8 pixels (0.54 mm). The mean intrasubject change in tissue content over the three scans was 2.75% +/- 2.29% (mean +/- standard deviation).     

Four-chamber heart modeling and automatic segmentation for 3-D cardiac CT volumes using marginal space learning and steerable features

We propose an automatic four-chamber heart segmentation system for the quantitative functional analysis of the heart from cardiac computed tomography (CT) volumes. Two topics are discussed: heart modeling and automatic model fitting to an unseen volume. Heart modeling is a nontrivial task since the heart is a complex nonrigid organ. The model must be anatomically accurate, allow manual editing, and provide sufficient information to guide automatic detection and segmentation. Unlike previous work, we explicitly represent important landmarks (such as the valves and the ventricular septum cusps) among the control points of the model. The control points can be detected reliably to guide the automatic model fitting process. Using this model, we develop an efficient and robust approach for automatic heart chamber segmentation in 3-D CT volumes. We formulate the segmentation as a two-step learning problem: anatomical structure localization and boundary delineation. In both steps, we exploit the recent advances in learning discriminative models. A novel algorithm, marginal space learning (MSL), is introduced to solve the 9-D similarity transformation search problem for localizing the heart chambers. After determining the pose of the heart chambers, we estimate the 3-D shape through learning-based boundary delineation. The proposed method has been extensively tested on the largest dataset (with 323 volumes from 137 patients) ever reported in the literature. To the best of our knowledge, our system is the fastest with a speed of 4.0 s per volume (on a dual-core 3.2-GHz processor) for the automatic segmentation of all four chambers.      

Intrathoracic airway trees: segmentation and airway morphology analysis from low-dose CT scans

The segmentation of the human airway tree from volumetric computed tomography (CT) images builds an important step for many clinical applications and for physiological studies. Previously proposed algorithms suffer from one or several problems: leaking into the surrounding lung parenchyma, the need for the user to manually adjust parameters, excessive runtime. Low-dose CT scans are increasingly utilized in lung screening studies, but segmenting them with traditional airway segmentation algorithms often yields less than satisfying results. In this paper, a new airway segmentation method based on fuzzy connectivity is presented. Small adaptive regions of interest are used that follow the airway branches as they are segmented. This has several advantages. It makes it possible to detect leaks early and avoid them, the segmentation algorithm can automatically adapt to changing image parameters, and the computing time is kept within moderate values. The new method is robust in the sense that it works on various types of scans (low-dose and regular dose, normal subjects and diseased subjects) without the need for the user to manually adjust any parameters. Comparison with a commonly used region-grow segmentation algorithm shows that the newly proposed method retrieves a significantly higher count of airway branches. A method that conducts accurate cross-sectional airway measurements on airways is presented as an additional processing step. Measurements are conducted in the original gray-level volume. Validation on a phantom shows that subvoxel accuracy is achieved for all airway sizes and airway orientations.     

Automatic liver and lesion segmentation: a primary step in diagnosis of liver diseases

Computed Tomography (CT) images are widely used for diagnosis of liver diseases and volume measurement for liver surgery and transplantation. Segmentation of liver and lesion is regarded as a major primary step in computer-aided diagnosis of liver diseases. Lesion alone cannot be segmented automatically from the abdominal CT image since there are tissues external to the liver with similar intensity to the lesions. Therefore, it is necessary to segment the liver first so that lesion can then be segmented accurately from it. In this paper, an approach for automatic and effective segmentation of liver and lesion from CT images needed for computer-aided diagnosis of liver is proposed. The method uses confidence connected region growing facilitated by preprocessing and postprocessing functions for automatic segmentation of liver and Alternative Fuzzy C-Means clustering for lesion segmentation. The algorithm is quantitatively evaluated by comparing automatic segmentation results to the manual segmentation results based on volume measurement error, figure of merit, spatial overlap, false positive error, false negative error, and visual overlap.     

Accurate measurement of intrathoracic airways

Airway geometry measurements can provide information regarding pulmonary physiology and pathophysiology. There has been considerable interest in measuring intrathoracic airways in two-dimensional (2-D) slices from volumetric X-ray computed tomography (CT). Such measurements can be used to evaluate and track the progression of diseases affecting the airways. A popular airway measurement method uses the “half-max” criteria, in which the gray level at the airway wall is estimated to be halfway between the minimum and maximum gray levels along a ray crossing the edge. However, because the scanning process introduces blurring, the half-max approach may not be applicable across all airway sizes. The authors propose a new measurement method based on a model of the scanning process. In their approach, they examine the gray-level profile of a ray crossing the airway wall and use a maximum-likelihood method to estimate the airway inner and outer radius. Using CT scans of a physical phantom, the authors present results showing that the new approach is more accurate than the half-max method at estimating wall location for thin-walled airways     

Image segmentation via adaptive K-mean clustering andknowledge-based morphological operations with biomedical applications

Image segmentation remains one of the major challenges in image analysis. In medical applications, skilled operators are usually employed to extract the desired regions that may be anatomically separate but statistically indistinguishable. Such manual processing is subject to operator errors and biases, is extremely time consuming, and has poor reproducibility. We propose a robust algorithm for the segmentation of three-dimensional (3-D) image data based on a novel combination of adaptive K-mean clustering and knowledge-based morphological operations. The proposed adaptive K-mean clustering algorithm is capable of segmenting the regions of smoothly varying intensity distributions. Spatial constraints are incorporated in the clustering algorithm through the modeling of the regions by Gibbs random fields. Knowledge-based morphological operations are then applied to the segmented regions to identify the desired regions according to the a priori anatomical knowledge of the region-of-interest. This proposed technique has been successfully applied to a sequence of cardiac CT volumetric images to generate the volumes of left ventricle chambers at 16 consecutive temporal frames. Our final segmentation results compare favorably with the results obtained using manual outlining. Extensions of this approach to other applications can be readily made when a priori knowledge of a given object is available.     

Computer-aided kidney segmentation on abdominal CT images.

In this paper, an effective model-based approach for computer-aided kidney segmentation of abdominal CT images with anatomic structure consideration is presented. This automatic segmentation system is expected to assist physicians in both clinical diagnosis and educational training. The proposed method is a coarse to fine segmentation approach divided into two stages. First, the candidate kidney region is extracted according to the statistical geometric location of kidney within the abdomen. This approach is applicable to images of different sizes by using the relative distance of the kidney region to the spine. The second stage identifies the kidney by a series of image processing operations. The main elements of the proposed system are: 1) the location of the spine is used as the landmark for coordinate references; 2) elliptic candidate kidney region extraction with progressive positioning on the consecutive CT images; 3) novel directional model for a more reliable kidney region seed point identification; and 4) adaptive region growing controlled by the properties of image homogeneity. In addition, in order to provide different views for the physicians, we have implemented a visualization tool that will automatically show the renal contour through the method of second-order neighborhood edge detection. We considered segmentation of kidney regions from CT scans that contain pathologies in clinical practice. The results of a series of tests on 358 images from 30 patients indicate an average correlation coefficient of up to 88% between automatic and manual segmentation.     

一种基于多层CT影像的肺部结节分割方法

本文针对多层CT影像中肺部结节分割问题，提出一种交互式手动分割结节的方法，用Canny算子代替传统Live-Wire算法中的拉普拉斯算子，解决了拉普拉斯算子边缘检测存在伪边缘和边缘间断的问题，实验证明利用该方法可以快速的为医生分割出感兴趣的结节区域，为定量分析和三维重建提供了前提，从分割和三维可视化的结果来看，该分割方法的分割结果是较为理想的．     

Shape-driven three-dimensional watersnake segmentation of biological membranes in electron tomography

Due to the significant complexity of membrane morphology and the generally poor image quality in electron tomographic volumes, current automatic methods for segmentation of membranes perform poorly. Users must resort to manual tracing of recognized patterns on 2-D slices of the volume, a method that suffers from subjectivity and is very labor intensive, preventing quantitative analyses of tomographic data that require comparative analyses of many volumes. To overcome these limitations, we develop an automatic 3-D segmentation method that fully exploits the prior knowledge about the shape of the membranes as well as the 3-D information provided by the tomograms, and systematically combines this knowledge with the image data to improve segmentation results. The method is based on the watersnake framework. By mathematically reformulating the traditional watershed segmentation as an energy minimization problem, the watersnake inherits the many strengths of the watershed method while overcoming the limitations of the traditional energy-based segmentation methods. In our previous work (H. Nguyen et al., 2003), the original watersnake model was successfully modified by incorporating smoothness into watershed segmentation. In this work, we further extend that model to incorporate into the energy function various constraints representing our prior knowledge about the global shape of the cellular features to be segmented. Segmentation can, therefore, be accomplished via minimization of the energy function subject to the shape prior constraints. Finally, the mathematical framework is further extended from 2-D to 3-D so that segmentation can be carried out in 3-D to take advantage of the additional information provided by the tomograms. We apply this method for the automatic extraction of biological membranes of varying complexities including those of bacterial walls and mitochondrial boundaries.     

一种全自动的肺裂分割方法

CT（Computer Tomography）图像中自动分割肺裂是很困难的,肺裂往往存在不完整、形变、断裂和附裂等现象.本文提出一种融合肺部解剖结构特征来实现自动分割肺裂的方法.首先结合肺部气管和动脉血管信息定位肺裂感兴趣区域.然后利用肺裂方向信息增强肺裂,并利用多剖面滤波器滤除噪声从而对肺裂进行预分割.最后融合已定位的肺裂感兴趣区域和肺裂预分割结果来自动分割肺裂.与人工参考对比,提出的算法在人体左肺和右肺中分割的肺裂的F₁-score中值分别为0.881和0.878.     

Real-time volume rendering visualization of dual-modality PET/CT images with interactive fuzzy thresholding segmentation.

Three-dimensional (3-D) visualization has become an essential part for imaging applications, including image-guided surgery, radiotherapy planning, and computer-aided diagnosis. In the visualization of dual-modality positron emission tomography and computed tomography (PET/CT), 3-D volume rendering is often limited to rendering of a single image volume and by high computational demand. Furthermore, incorporation of segmentation in volume rendering is usually restricted to visualizing the presegmented volumes of interest. In this paper, we investigated the integration of interactive segmentation into real-time volume rendering of dual-modality PET/CT images. We present and validate a fuzzy thresholding segmentation technique based on fuzzy cluster analysis, which allows interactive and real-time optimization of the segmentation results. This technique is then incorporated into a real-time multi-volume rendering of PET/CT images. Our method allows a real-time fusion and interchangeability of segmentation volume with PET or CT volumes, as well as the usual fusion of PET/CT volumes. Volume manipulations such as window level adjustments and lookup table can be applied to individual volumes, which are then fused together in real time as adjustments are made. We demonstrate the benefit of our method in integrating segmentation with volume rendering in its application to PET/CT images. Responsive frame rates are achieved by utilizing a texture-based volume rendering algorithm and the rapid transfer capability of the high-memory bandwidth available in low-cost graphic hardware.     

An efficient tissue classifier for building patient-specific finiteelement models from X-ray CT images

The authors developed an efficient semiautomatic tissue classifier for X-ray computed tomography (CT) images which can be used to build patient- or animal-specific finite element (FE) models for bioelectric studies. The classifier uses a gray scale histogram for each tissue type and three-dimensional (3-D) neighborhood information. A total of 537 CT images from four animals (pigs) were classified with an average accuracy of 96.5% compared to manual classification by a radiologist. The use of 3-D, as opposed to 2-D, information reduced the error rate by 78%. Models generated using minimal or full manual editing yielded substantially identical voltage profiles. For the purpose of calculating voltage gradients or current densities in specific tissues, such as the myocardium, the appropriate slices need to be fully edited, however. The authors' classifier offers an approach to building FE models from image information with a level of manual effort that can be adjusted to the need of the application     

Initial results for automated computational modeling ofpatient-specific electromagnetic hyperthermia

Developments in finite-difference time-domain (FD-TD) computational modeling of Maxwell's equations, super-computer technology, and computed tomography (CT) imagery open the possibility of accurate numerical simulation of electromagnetic (EM) wave interactions with specific, complex, biological tissue structures. One application of this technology is in the area of treatment planning for EM hyperthermia. In this paper, we report the first highly automated CT image segmentation and interpolation scheme applied to model patient-specific EM hyperthermia. This novel system is based on sophisticated tools from the artificial intelligence, computer vision, and computer graphics disciplines. It permits CT-based patient-specific hyperthermia models to be constructed without tedious manual contouring on digitizing pads or CRT screens. The system permits in principle near real-time assistance in hyperthermia treatment planning. We apply this system to interpret actual patient CT data, reconstructing a 3-D model of the human thigh from a collection of 29 serial CT images at 10 mm intervals. Then, using FD-TD, we obtain 2-D and 3-D models of EM hyperthermia of this thigh due to a waveguide applicator. We find that different results are obtained from the 2-D and 3-D models, and conclude that full 3-D tissue models are required for future clinical usage.     

Anatomy-Guided Lung Lobe Segmentation in X-Ray CT Images

The human lungs are divided into five distinct anatomic compartments called the lobes, which are separated by the pulmonary fissures. The accurate identification of the fissures is of increasing importance in the early detection of pathologies, and in the regional functional analysis of the lungs. We have developed an automatic method for the segmentation and analysis of the fissures, based on the information provided by the segmentation and analysis of the airway and vascular trees. This information is used to provide a close initial approximation to the fissures, using a watershed transform on a distance map of the vasculature. In a further refinement step, this estimate is used to construct a region of interest (ROI) encompassing the fissures. The ROI is enhanced using a ridgeness measure, which is followed by a 3-D graph search to find the optimal surface within the ROI. We have also developed an automatic method to detect incomplete fissures, using a fast-marching based segmentation of a projection of the optimal surface. The detected incomplete fissure is used to extrapolate and smoothly complete the fissure. We evaluate the method by testing on data sets from normal subjects and subjects with mild to moderate emphysema.      

Automatic registration of CT and MR brain images using correlation of geometrical features

Describes an automated approach to register CT and MR brain images. Differential operators in scale space are applied to each type of image data, so as to produce feature images depicting "ridgeness". The resulting CT and MR feature images show similarities which can be used for matching. No segmentation is needed and the method is devoid of human interaction. The matching is accomplished by hierarchical correlation techniques. Results of 2-D and 3-D matching experiments are presented. The correlation function ensures an accurate match even if the scanned volumes to be matched do not completely overlap, or if some of the features in the images are not similar.     

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).     

Three-dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical CT images

Small pulmonary nodules are a common radiographic finding that presents an important diagnostic challenge in contemporary medicine. While pulmonary nodules are the major radiographic indicator of lung cancer, they may also be signs of a variety of benign conditions. Measurement of nodule growth rate over time has been shown to be the most promising tool in distinguishing malignant from nonmalignant pulmonary nodules. In this paper, we describe three-dimensional (3-D) methods for the segmentation, analysis, and characterization of small pulmonary nodules imaged using computed tomography (CT). Methods for the isotropic resampling of anisotropic CT data are discussed. 3-D intensity and morphology-based segmentation algorithms are discussed for several classes of nodules. New models and methods for volumetric growth characterization based on longitudinal CT studies are developed. The results of segmentation and growth characterization methods based on in vivo studies are described. The methods presented are promising in their ability to distinguish malignant from nonmalignant pulmonary nodules and represent the first such system in clinical use.