An adaptive snake model for ultrasound image segmentation: modified trimmed mean filter, ramp integration and adaptive weighting parameters

The snake model is a widely-used approach to finding the boundary of the object of interest in an ultrasound image. However, due to the speckles, the weak edges and the tissue-related textures in an ultrasound image, conventional snake models usually cannot obtain the desired boundary satisfactorily. In this paper, we propose a new adaptive snake model for ultrasound image segmentation. The proposed snake model is composed of three major techniques, namely, the modified trimmed mean (MTM) filtering, ramp integration and adaptive weighting parameters. With the advantages of the mean and median filters, the MTM filter is employed to alleviate the speckle interference in the segmentation process. The weak edge enhancement by ramp integration attempts to capture the slowly varying edges, which are hard to capture by conventional snake models. The adaptive weighting parameter allows weighting of each energy term to change adaptively during the deformation process. The proposed snake model has been verified on the phantom and clinical ultrasound images. The experimental results showed that the proposed snake model achieves a reasonable performance with an initial contour placed 10 to 20 pixels away from the desired boundary. The mean minimal distances from the derived boundary to the desired boundary have been shown to be less than 3.5 (for CNR > or = 0.5) and 2.5 pixels, respectively, for the phantom and ultrasound images.     

Cell-based dual snake model: a new approach to extracting highly winding boundaries in the ultrasound images

Two common deficiencies of most conventional deformable models are the need to place the initial contour very close to the desired boundary and the incapability of capturing a highly winding boundary for sonographic boundary extraction. To remedy these two deficiencies, a new deformable model (namely, the cell-based dual snake model) is proposed in this paper. The basic idea is to apply the dual snake model in the cell-based deformation manner. While the dual snake model provides an effective mechanism allowing a distant initial contour, the cell-based deformation makes it possible to catch the winding characteristics of the desired boundary. The performance of the proposed cell-based dual snake model has been evaluated on synthetic images with simulated speckles and on the clinical ultrasound (US) images. The experimental results show that the mean distances from the derived to the desired boundary points are 0.9 +/- 0.42 pixels and 1.29 +/- 0.39 pixels for the synthetic and the clinical US images, respectively.     

3-D breast ultrasound segmentation using active contour model

In this study, we made use of the discrete active contour model to overcome the natural properties of ultrasound (US) images, speckle, noise and tissue-related textures, to segment the breast tumors precisely. Determination of the real tumor boundary with the snake-deformation process requires an initial contour estimate. However, the manual way to sketch an initial contour is very time-consuming. Thus, we propose an automatic initial contour-finding method that not only maintains the tumor shape, but also is close to the tumor boundary and inside the tumor. During the deformation process, to prevent the snake trapping into the false position caused by tissue-related texture or speckle, we added the edge information as an image feature to define the external force. In addition, because the 3-D volume of a tumor is essentially constructed by a sequence of 2-D images, our method for finding boundaries of a tumor can be extended to 3-D cases. By precisely counting the volume of the 3-D images, we can get the volume of tumor. Finally, we will show that the proposed techniques have rather good performance and lead to a satisfactory result in comparison with the estimated volume and physician's estimate.     

Fetal abdominal contour extraction and measurement in ultrasound images

A novel method is developed for the fetal abdominal contour extraction and measurement in ultrasound images. Fetal abdominal circumference (AC) is one of the standardized measurements in the antepartum ultrasound monitoring. Among several standardized measurements, AC is best correlated with fetal growth but is also the most difficult to be accurately measured. To overcome the difficulties in the abdominal contour extraction, the proposed method is a four-step procedure that integrates several image segmentation techniques. The proposed method is able to make the best use of the strength of different segmentation algorithms, while avoiding their deficiencies. An enhanced instantaneous coefficient of variation (ICOV) edge detector is first developed to detect edges of the abdominal contour and alleviate the effects of most speckle noise. Then, the Fuzzy C-Means clustering is employed to distinguish salient edges attributable to the abdominal contour from weak edges due to the other texture. Subsequently, the iterative Hough transform is applied to determine an elliptical contour and obtain an initial estimation of the AC. Finally, the gradient vector field (GVF) snake adapts the initial ellipse to the real edges of the abdominal contour. Quantitative validation of the proposed method on synthetic images under different imaging conditions achieves satisfactory segmentation accuracy (98.78%+/-0.16%). Experiments on 150 clinical images are carried out in three aspects: comparisons between inter-observer and inter-run variation, the fitness analysis between the automatically detected ellipse and the manual delineation, and the accuracy comparisons between automatic measurements and manual measurements in estimation of fetal weight (EFW). Experimental results show that the proposed method can provide consistent and accurate measurements. The reductions of the mean absolute difference and the standard deviation of EFW based on automatic measurements are about 1.2% and 2.1%, respectively, which indicate its potential in clinical antepartum monitoring application.     

A dual-snake model of high penetrability for ultrasound image boundary extraction

Most deformable models require the initial contour to be placed close to the boundary of the object of interest for boundary extraction of ultrasound (US) images, which is impractical in many clinical applications. To allow a distant initial contour, a new dual-snake model promising high penetrability through the interference of the noises is proposed in this paper. The proposed dual-snake model features a new far-reaching external force, called the discrete gradient flow, a connected component-weighted image force, and an effective stability evaluation of two underlying snakes. The experimental results show that, with a distant initial contour, the mean distance from the derived boundary to the desired boundary is less than 1.4 pixels, and most snake elements are within 2.7 pixels of the desired boundaries for the synthetic images with CNR ≥ 1. For the clinical US images, the mean distance is less than 1.9 pixels, and most snake elements are within 3 pixels of the desired boundaries. (E-mail: chung@lotus.mc.ntu.edu.tw)     

Discriminant snakes for 3D reconstruction of anatomical organs

In this work a new statistic deformable model for 3D segmentation of anatomical organs in medical images is proposed. A statistic discriminant snake performs a supervised learning of the object boundary in an image slice to segment the next slice of the image sequence. Each part of the object boundary is projected in a feature space generated by a bank of Gaussian filters. Then, clusters corresponding to different boundary pieces are constructed by means of linear discriminant analysis. Finally, a parametric classifier is generated from each contour in the image slice and embodied into the snake energy-minimization process to guide the snake deformation in the next image slice. The discriminant snake selects and classifies image features by the parametric classifier and deforms to minimize the dissimilarity between the learned and found image features. The new approach is of particular interest for segmenting 3D images with anisotropic spatial resolution, and for tracking temporal image sequences. In particular, several anatomical organs from different imaging modalities are segmented and the results compared to expert tracings.     

Diffusion-enhanced visualization and quantification of vascular anomalies in three-dimensional rotational angiography: results of an in-vitro evaluation

Three-dimensional rotational angiography (3DRA) is a new and promising technique for obtaining high-resolution isotropic 3D images of vascular structures. However, due to the relatively high noise level and the presence of other background structures in clinical 3DRA images, noise reduction is inevitable. In this paper, we evaluate a number of linear and nonlinear diffusion techniques for this purpose. Specifically, we analyze the effects of these techniques on the threshold-based visualization and quantification of vascular anomalies in 3DRA images. The results of in-vitro experiments indicate that edge-enhancing anisotropic diffusion filtering is most suitable: the increase in the user-dependency of visualizations and quantifications is considerably less with this technique compared to linear filtering techniques, and it is better at reducing noise near edges than isotropic nonlinear diffusion. However, in view of the memory and computation-time requirements of this technique, the latter scheme may be considered a useful alternative.     

Segmentation of breast tumor in three-dimensional ultrasound images using three-dimensional discrete active contour model

In this paper, we apply the three-dimensional (3-D) active contour model to a 3-D ultrasonic data file for segmenting of the breast tumor. The 3-D ultrasonic file is composed of a series of two-dimensional (2-D) images. Most of traditional techniques of 2-D image segmentation will not use the information between adjacent images. To suit the property of the 3-D data, we introduce the concept of the 3-D stick, the 3-D morphologic process and the 3-D active contour model. The 3-D stick can get over the problem that the ultrasonic image is full of speckle noise and highlight the edge information in images. The 3-D morphologic process helps to determine the contour of the tumor and the resulting contour can be regarded as the initial contour of the active contour model. Finally, the 3-D active contour model will make the initial contour approach to the real contour of the tumor. However, there is emphasis on these 3-D techniques that they do not consist of a series of 2-D techniques. When they work, they will consider the horizontal, vertical and depth directions at the same time. The use of these 3-D techniques not only segments the 3-D shape but also obtains the volume of the tumor. The volume of the tumor calculated by the proposed method will be compared with the volume calculated by the VOCAL software with the physician's manually drawn shape and it shows that the performance of our method is satisfactory.     

k-means based hybrid wavelet and curvelet transform approach for denoising of remotely sensed images

This article presents a new technique for denoising of remotely sensed images based on multi-resolution analysis (MRA). Multi-resolution techniques provide a coarse-to-fine and scale-invariant decomposition of images for image processing and analysis. The multi-resolution image analysis methods have the ability to analyse the image in an adaptive manner, capturing local as well as global information. Further, noise, as one of the biggest obstacles for image analysis and for further processing, is effectively handled by multi-resolution methods. The article aims at the analysis of noise filtering of image using wavelets and curvelets methods on multispectral images acquired by the QuickBird and medium-resolution Landsat Thematic Mapper satellite systems. To improve the performance of noise filtering, an iterative thresholding scheme and a hybrid approach based on wavelet and curvelet transforms are proposed for restoring the image from its noisy version. Two comparative measures are used for evaluation of the performance of the methods for denoising. One of them is the peak signal-to-noise ratio and the second is the ability of the noise filtering scheme to preserve the sharpness of the edges. By both of these comparative measures, the hybrid approach of curvelet and wavelet for heterogeneous and homogeneous areas with iterative threshold has proved to be better than the others. Results are illustrated using QuickBird and Landsat images for proposed methods and compared with wavelets and curvelet-based denoising.     

Compensation of log-compressed images for 3-D ultrasound

In this study, a Bayesian approach was used for 3-D reconstruction in the presence of multiplicative noise and nonlinear compression of the ultrasound (US) data. Ultrasound images are often considered as being corrupted by multiplicative noise (speckle). Several statistical models have been developed to represent the US data. However, commercial US equipment performs a nonlinear image compression that reduces the dynamic range of the US signal for visualization purposes. This operation changes the distribution of the image pixels, preventing a straightforward application of the models. In this paper, the nonlinear compression is explicitly modeled and considered in the reconstruction process, where the speckle noise present in the radio frequency (RF) US data is modeled with a Rayleigh distribution. The results obtained by considering the compression of the US data are then compared with those obtained assuming no compression. It is shown that the estimation performed using the nonlinear log-compression model leads to better results than those obtained with the Rayleigh reconstruction method. The proposed algorithm is tested with synthetic and real data and the results are discussed. The results have shown an improvement in the reconstruction results when the compression operation is included in the image formation model, leading to sharper images with enhanced anatomical details.     

Direct segmentation of the major white matter tracts in diffusion tensor images

Diffusion-weighted images of the human brain are acquired more and more routinely in clinical research settings, yet segmenting and labeling white matter tracts in these images is still challenging. We present in this paper a fully automated method to extract many anatomical tracts at once on diffusion tensor images, based on a Markov random field model and anatomical priors. The approach provides a direct voxel labeling, models explicitly fiber crossings and can handle white matter lesions. Experiments on simulations and repeatability studies show robustness to noise and reproducibility of the algorithm, which has been made publicly available.     

Speckles Suppression Techniques for Ultrasound Images

Most medical images have a poorer signal to noise ratio than scenes taken with a digital camera, which often leads to incorrect diagnosis. Speckles suppression from ultrasound images is one of the most important concerns in computer-aided diagnosis. This article proposes two novel, robust and efficient ultrasound images denoising techniques. The first technique is the enhanced ultrasound images denoising (EUID) technique, which estimates automatically the speckle noise amount in the ultrasound images by estimating important input parameters of the filter and then denoising the image using the sigma filter. The second technique is the ultrasound image denoising using neural network (UIDNN) that is based on the second-order difference of pixels with adaptive threshold value in order to identify random valued speckles from images to achieve high efficient image restoration. The performances of the proposed techniques are analyzed and compared with those of other image denoising techniques. The experimental results show that the proposed techniques are valuable tools for speckles suppression, being accurate, less tedious, and preventing typical human errors associated with manual tasks in addition to preserving the edges from the image. The EUID algorithm has nearly the same peak signal to noise ratio (PSNR) as Frost and speckle-reducing anisotropic diffusion 1, whereas it achieves higher gains, on average—0.4 dB higher PSNR—than the Lee, Kuan, and anisotropic diffusion filters. The UIDNN technique outperforms all the other techniques since it can determine the noisy pixels and perform filtering for these pixels only. Generally, when relatively high levels of noise are added, the proposed algorithms show better performances than the other conventional filters.     

Muscles of mastication model-based MR image segmentation

Objective The muscles of mastication play a major role in the orodigestive system as the principal motive force for the mandible. An algorithm for segmenting these muscles from magnetic resonance (MR) images was developed and tested. Materials and methods Anatomical information about the muscles of mastication in MR images is used to obtain the spatial relationships relating the muscle region of interest (ROI) and head ROI. A model-based technique that involves the spatial relationships between head and muscle ROIs as well as muscle templates is developed. In the segmentation stage, the muscle ROI is derived from the model. Within the muscle ROI, anisotropic diffusion is applied to smooth the texture, followed by thresholding to exclude bone and fat. The muscle template and morphological operators are employed to obtain an initial estimate of the muscle boundary, which then serves as the input contour to the gradient vector flow snake that iterates to the final segmentation. Results The method was applied to segmentation of the masseter, lateral pterygoid and medial pterygoid in 75 images. The overlap indices (κ) achieved are 91.4, 92.1 and 91.2%, respectively. Conclusion A model-based method for segmenting the muscles of mastication from MR images was developed and tested. The results show good agreement between manual and automatic segmentations.     

A high-throughput active contour scheme for segmentation of histopathological imagery

In this paper a minimally interactive high-throughput system which employs a color gradient based active contour model for rapid and accurate segmentation of multiple target objects on very large images is presented. While geodesic active contours (GAC) have become very popular tools for image segmentation, they tend to be sensitive to model initialization. A second limitation of GAC models is that the edge detector function typically involves use of gray scale gradients; color images usually being converted to gray scale, prior to gradient computation. For color images, however, the gray scale gradient image results in broken edges and weak boundaries, since the other channels are not exploited in the gradient computation. To cope with these limitations, we present a new GAC model that is driven by an accurate and rapid object initialization scheme; hierarchical normalized cuts (HNCut). HNCut draws its strength from the integration of two powerful segmentation strategies—mean shift clustering and normalized cuts. HNCut involves first defining a color swatch (typically a few pixels) from the object of interest. A multi-scale, mean shift coupled normalized cuts algorithm then rapidly yields an initial accurate detection of all objects in the scene corresponding to the colors in the swatch. This detection result provides the initial contour for a GAC model. The edge-detector function of the GAC model employs a local structure tensor based color gradient, obtained by calculating the local min/max variations contributed from each color channel. We show that the color gradient based edge-detector function results in more prominent boundaries compared to the classical gray scale gradient based function. By integrating the HNCut initialization scheme with color gradient based GAC (CGAC), HNCut-CGAC embodies five unique and novel attributes: (1) efficiency in segmenting multiple target structures; (2) the ability to segment multiple objects from very large images; (3) minimal human interaction; (4) accuracy; and (5) reproducibility. A quantitative and qualitative comparison of the HNCut-CGAC model against other state of the art active contour schemes (including a Hybrid Active Contour model (Paragios–Deriche) and a region-based AC model (Rousson–Deriche)), across 196 digitized prostate histopathology images, suggests that HNCut-CGAC is able to outperform state of the art hybrid and region based AC techniques. Our results show that HNCut-CGAC is computationally efficient and may be easily applied to a variety of different problems and applications.     

Breast ultrasound image enhancement using fuzzy logic

Breast cancer is still a serious disease in the world. Early detection is very essential for breast cancer prevention and diagnosis. Breast ultrasound (US) imaging has been proven to be a valuable adjunct to mammography in the detection and classification of breast lesions. Because of the fuzzy and noisy nature of the US images and the low contrast between the breast cancer and tissue, it is difficult to provide an accurate and effective diagnosis. This paper presents a novel algorithm based on fuzzy logic that uses both the global and local information and has the ability to enhance the fine details of the US images while avoiding noise amplification and overenhancement. We normalize the images and then fuzzify the normalized images based on the maximum entropy principle. Edge and textural information are extracted to describe the lesion features and the scattering phenomenon of US images and the contrast ratio measuring the degree of enhancement is computed and modified. The defuzzification process is used to obtain the enhanced US images. To demonstrate the performance of the proposed approach, the algorithm was tested on 86 breast US images. Experimental results confirm that the proposed method can effectively enhance the details of the breast lesions without overenhancement or underenhancement.     

A comparison of two algorithms for automated stone detection in clinical B-mode ultrasound images of the abdomen

Ultrasound (US) imaging is an indispensible technique for detection of abdominal stones which are a serious health hazard. Segmentation of stones from abdominal ultrasound images presents a unique challenge because these images contain strong speckle noise and attenuated artifacts. In clinical situations where a large number of stones must be identified, traditional methods such as manual identification become tedious and lack reproducibility too. The necessity of obtaining high reproducibility and the need to increase efficiency motivates the development of automated and fast procedures that segment out stones of all sizes and shapes in medical images by applying image segmentation techniques. In this paper we present and compare two fully automatic and unsupervised methods for robust stone detection in B-mode ultrasound images of the abdomen. Our approaches are based on the marker controlled watershed segmentation, along with some pre-processing and post-processing procedures that eliminate the inherent problems associated with medical ultrasound images. The first algorithm (Algorithm I) utilizes the advantage of the Speckle reducing anisotropic diffusion (SRAD) technique, along with unsharp filtering and histo- gram equalization for removal of speckle noise, and the second algorithm (Algorithm II) is based on the log decompression model which too serves as a tool for minimization of speckle. Experimental results obtained from processing a set of 50 ultrasound images ensure the robustness of both the proposed algorithms. Comparative results of both the algorithms based on efficiency and relative error in stone area have been provided.     

Homeomorphic brain image segmentation with topological and statistical atlases

Atlas-based segmentation techniques are often employed to encode anatomical information for the delineation of multiple structures in magnetic resonance images of the brain. One of the primary challenges of these approaches is to efficiently model qualitative and quantitative anatomical knowledge without introducing a strong bias toward certain anatomical preferences when segmenting new images. This paper explores the use of topological information as a prior and proposes a segmentation framework based on both topological and statistical atlases of brain anatomy. Topology can be used to describe continuity of structures, as well as the relationships between structures, and is often a critical component in cortical surface reconstruction and deformation-based morphometry. Our method guarantees strict topological equivalence between the segmented image and the atlas, and relies only weakly on a statistical atlas of shape. Tissue classification and fast marching methods are used to provide a powerful and flexible framework to handle multiple image contrasts, high levels of noise, gain field inhomogeneities, and variable anatomies. The segmentation algorithm has been validated on simulated and real brain image data and made freely available to researchers. Our experiments demonstrate the accuracy and robustness of the method and the limited influence of the statistical atlas.     

Image quality in non-gated versus gated reconstruction of tongue motion using magnetic resonance imaging: a comparison using automated image processing

Purpose  The use of gated or ECG triggered MR is a well-established technique and developments in coil technology have enabled this approach to be applied to areas other than the heart. However, the image quality of gated (ECG or cine) versus non-gated or real-time has not been extensively evaluated in the mouth. We evaluate two image sequences by developing an automatic image processing technique which compares how well the image represents known anatomy. Methods  Four subjects practised experimental poly-syllabic sentences prior to MR scanning. Using a 1.5 T MR unit, we acquired comparable gated (using an artificial trigger) and non-gated sagittal images during speech. We then used an image processing algorithm to model the image grey along lines that cross the airway. Each line involved an eight parameter non-linear equation to model of proton densities, edges, and dimensions. Results  Gated and non-gated images show similar spatial resolution, with non-gated images being slightly sharper (10% better resolution, less than 1 pixel). However, the gated sequences generated images of substantially lower inherent noise, and substantially better discrimination between air and tissue. Additionally, the gated sequences demonstrate a very much greater temporal resolution. Conclusion  Overall, image quality is better with gated imaging techniques, especially given their superior temporal resolution. Gated techniques are limited by the repeatability of the motions involved, and we have shown that speech to a metronome can be sufficiently repeatable to allow high-quality gated magnetic resonance imaging images. We suggest that gated sequences may be useful for evaluating other types of repetitive movement involving the joints and limb motions.     

Wavelet-based edge detection in ultrasound images

We introduce a new wavelet-based method for edge detection in ultrasound (US) images. Each beam that is analyzed is first transformed into the wavelet domain using the continuous wavelet transform (CWT). Because the CWT preserves both scale and time information, it is possible to separate the signal into a number of scales. The edge is localized by first determining the scale at which the power spectrum, based on the wavelet transform, has its maximum value. Next, at this scale we find the position of the peak for the squared CWT. This method does not depend on any threshold, after the range of scales have been determined. We suggest a range of scales for US images in general. Sample edge detections are demonstrated in US images of straight and jagged edges of simple structures submerged in water bath, and of an abdominal aorta aneurysm phantom.     

InSAR phase filtering using spatially adapted total generalized variation

Phase filtering is an important processing step in interferometric synthetic aperture radar (InSAR) measurement. It is a difficult task to achieve noise reduction and fringe preservation simultaneously. To solve the dilemma, a new noise reduction scheme for InSAR phase images is introduced based on total generalized variation (TGV). The TGV regularization has been mathematically proven to be able to preserve edges while still imposing high-order smoothness in homogeneous regions. Combined with the characteristics of phase image, a spatially adapted TGV model is used as a regularizer in phase filtering. The regularization parameters are adaptively selected according to the coherence and local phase image features. The presented model is realized in the complex domain in order to avoid the effect of phase wrapping. Experiment results from simulated and real data sets demonstrate the effectiveness of this algorithm.