Level Set Based Segmentation Method Research Articles

A novel hybrid approach to detect brain tumor in MRI images

Image segmentation is the fundamental step in medical image analysis. Segmentation is a procedure to separate similar portions of images showing resemblance in different features such as color, intensity, or texture. Grayscale images are mostly used for the segmentation of medical images. Tumors are commonly stated as the abnormal growth of tissues and the brain tumor is a diseased part in the body tissues that is an abnormal mass in which the growth rate of cells is irrepressible. The mortality rate of people has raised over the past years due to brain tumors, hence this area has gained the attention of researchers. Automatic detection of brain tumors is a challenging task because it involves pathology, functional physics of MRI along with intensity and shapes analysis of MR image. After all, tumor shape, size, location, and intensity vary for each infected case. In this work, a novel hybrid approach is implemented by combing watershed segmentation, level set segmentation and K means clustering. First, the image is preprocessed by removing the skull. Watershed segmentation is applied to this preprocessed image. Level set segmentation is applied to the previous step. Finally, k means clustering is applied as the final step to detect tumor parts accurately. This Hybrid approach is compared with other four techniques such as Threshold segmentation, K means clustering, Watershed segmentation, and Level set-based segmentation methods. Statistical and Visual analysis is performed. It is found that the hybrid approach has better specificity, accuracy, and precision among all four techniques. Further, it is able to detect tumors more accurately. This research could help clinicians in surgical planning, treatment planning and accurately segmenting the tumor part with the most accurate method.

UVI Image Segmentation of Auroral Oval: Dual Level Set and Convolutional Neural Network Based Approach

The auroral ovals around the Earth’s magnetic poles are produced by the collisions between energetic particles precipitating from solar wind and atoms or molecules in the upper atmosphere. The morphology of auroral oval acts as an important mirror reflecting the solar wind-magnetosphere-ionosphere coupling process and its intrinsic mechanism. However, the classical level set based segmentation methods often fail to extract an accurate auroral oval from the ultraviolet imager (UVI) image with intensity inhomogeneity. The existing methods designed specifically for auroral oval extraction are extremely sensitive to the contour initializations. In this paper, a novel deep feature-based adaptive level set model (DFALS) is proposed to tackle these issues. First, we extract the deep feature from the UVI image with the newly designed convolutional neural network (CNN). Second, with the deep feature, the global energy term and the adaptive time-step are constructed and incorporated into the local information based dual level set auroral oval segmentation method (LIDLSM). Third, we extract the contour of the auroral oval through the minimization of the proposed energy functional. The experiments on the UVI image data set validate the strong robustness of DFALS to different contour initializations. In addition, with the help of deep feature-based global energy term, the proposed method also obtains higher segmentation accuracy in comparison with the state-of-the-art level set based methods.

Tumor Classification in Breast Magnetic Resonance Images (MRI) Using the Level Set–Based Segmentation Method and Gabor-Haralik Feature

Tumor Classification in Breast Magnetic Resonance Images (MRI) Using the Level Set–Based Segmentation Method and Gabor-Haralik Feature

Fully automated, level set-based segmentation for knee MRIs using an adaptive force function and template: data from the osteoarthritis initiative.

BackgroundThis study focuses on osteoarthritis (OA), which affects millions of adults and occurs in knee cartilage. Diagnosis of OA requires accurate segmentation of cartilage structures. Existing approaches to cartilage segmentation of knee imaging suffer from either lack of fully automatic algorithm, sub-par segmentation accuracy, or failure to consider all three cartilage tissues.MethodsWe propose a novel segmentation algorithm for knee cartilages with level set-based segmentation method and novel template data. We used 20 normal subjects from osteoarthritis initiative database to construct new template data. We adopt spatial fuzzy C-mean clustering for automatic initialization of contours. Force function of our algorithm is modified to improve segmentation performance.ResultsThe proposed algorithm resulted in dice similarity coefficients (DSCs) of 87.1, 84.8 and 81.7 % for the femoral, patellar, and tibial cartilage, respectively from 10 subjects. The DSC results showed improvements of 8.8, 4.3 and 3.5 % for the femoral, patellar, and tibial cartilage respectively compared to existing approaches. Our algorithm could be applied to all three cartilage structures unlike existing approaches that considered only two cartilage tissues.ConclusionsOur study proposes a novel fully automated segmentation algorithm adapted for three types of knee cartilage tissues. We leverage state-of-the-art level set approach with newly constructed knee template. The experimental results show that the proposed method improves the performance by an average of 5 % over existing methods.

A fully automated level-set based segmentation method of thoracic and lumbar vertebral bodies in Computed Tomography images.

Spine is a structure commonly involved in several diseases. Identification and segmentation of the vertebral structures are of relevance to many medical applications related to the spine such as diagnosis, therapy or surgical intervention. However, the development of automatic and reliable methods are an unmet need. This work presents a fully automatic segmentation method of thoracic and lumbar vertebral bodies from Computed Tomography images. The procedure can be divided into four main stages: firstly, seed points were detected in the spinal canal in order to generate initial contours in the segmentation process, automating the whole process. Secondly, a processing step is performed to improve image quality. Third step was to carry out the segmentation using the Selective Binary Gaussian Filtering Regularized Level Set method and, finally, two morphological operations were applied in order to refine the segmentation result. The method was tested in clinical data coming from 10 trauma patients. To evaluate the result the average value of the DICE coefficient was calculated, obtaining a 90.86 ± 1.87% in the whole spine (thoracic and lumbar regions), a 86.08 ± 1.73% in the thoracic region and a 95,61 ±2,25% in the lumbar region. The results are highly competitive when compared to the results obtained in previous methods, especially for the lumbar region.

A unified variational segmentation framework with a level-set based sparse composite shape prior

Image segmentation plays an essential role in many medical applications. Low SNR conditions and various artifacts makes its automation challenging. To achieve robust and accurate segmentation results, a good approach is to introduce proper shape priors. In this study, we present a unified variational segmentation framework that regularizes the target shape with a level-set based sparse composite prior. When the variational problem is solved with a block minimization/decent scheme, the regularizing impact of the sparse composite prior can be observed to adjust to the most recent shape estimate, and may be interpreted as a ‘dynamic’ shape prior, yet without compromising convergence thanks to the unified energy framework. The proposed method was applied to segment corpus callosum from 2D MR images and liver from 3D CT volumes. Its performance was evaluated using Dice Similarity Coefficient and Hausdorff distance, and compared with two benchmark level-set based segmentation methods. The proposed method has achieved statistically significant higher accuracy in both experiments and avoided faulty inclusion/exclusion of surrounding structures with similar intensities, as opposed to the benchmark methods.

사전정보를 이용한 가우시안 커널 레벨 셋 알고리즘 기반 무릎 관절 연골 자기공명영상 분할기법

무릎 관절 연골은 두께가 얇아 대부분 무릎 질환의 원인이 되고 있다. 그러므로 무릎 자기공명영상에서 관절 연골 분할은 무릎 질환의 정확한 진단을 위한 필수조건이다. 특히 수동이 아닌 전자동 방식으로 무릎 관절 연골을 분할하여야만 효과적인 무릎 질환 진단을 할 수 있다. 본 논문에서는 뇌 자기공명영상에서 대표적으로 사용되는 레벨 셋 기반의 영상 분할 기법을 분석하여 무릎 자기공명영상에 적용 시 문제점을 파악하고 이를 해결함으로써, 무릎 자기공명영상에 레벨 셋 기반 영상분할 방식을 적용하였다. 이는 본 논문에서 제안하는 분할기법을 사용할 경우 무릎 관절 연골 분할에 대한 모든 과정이 전자동화 되어 기존 반자동화 방식보다 빠른 처리가 가능하며, 3차원 형상화를 통해 보다 정확한 진단에 도움을 줄 수 있다. 또한 우리는 제안하고 있는 분할기법이 기존 대표적인 무릎 관절 분할보다 더 높은 정확도를 갖는 것을 실험을 통해 확인할 수 있었다. The thickness of knee joint cartilage causes most diseases of knee. Therefore, an articular cartilage segmentation of knee magnetic resonance imaging (MRI) is required to diagnose a knee diagnosis correctly. In particular, fully automatic segmentation method of knee joint cartilage enables an effective diagnosis of knee disease. In this paper, we analyze a well-known level-set based segmentation method in brain MRI, and apply that method to knee MRI with solving some problems from different image characteristics. The proposed method, a fully automatic segmentation in whole process, enables to process faster than previous semi-automatic segmentation methods. Also it can make a three-dimension visualization which provides a specialist with an assistance for the diagnosis of knee disease. In addition, the proposed method provides more accurate results than the existing methods of articular cartilage segmentation in knee MRI through experiments.

TU‐F‐BRF‐05: Level Set Based Segmentation with a Dynamic Shape Prior

Purpose:To perform accurate segmentation on low‐SNR MR images subject to motion artifacts and signal voids.Methods:We have developed a novel level set based segmentation method that incorporates a dynamic shape prior. In contrast to conventional shape prior models that are either based on a single template or statistical models, we modeled the shape prior as sparse linear combinations of templates in a shape library. The proposed method was applied to segment real‐time kidney geometry from an abdominal MR series acquired under respiration and compared with the Chan‐Vese approach. Synthetic occlusions were further introduced to assess performance robustness. We compared the segmentation results with the ground truth contours and evaluated the segmentation accuracy based on the Dice similarity coefficient.Results:The proposed method successfully segmented all kidneys in the testing series. The corresponding shape priors were reasonably reconstructed from a small number of shape templates in the library, as expected. On testing dataset without occlusion, the proposed method achieved mean DSC of 0.97, which was more accurate than the Chan‐Vese approach with a DSC of 0.93. When tested on dataset with synthetic occlusions, the proposed method made robust inference on the occlusion sites and achieved mean DSC of 0.95, compared to 0.76 from the Chan‐Vese method, demonstrating the advantage of incorporating the proposed shape prior.Conclusion:We have developed a novel level set based segmentation method with a novel regularization to incorporate a dynamic shape prior. The shape prior is dynamically updated during the segmentation process as a sparse linear combination of templates from a shape library. Preliminary results have demonstrated the ability of the proposed method in improving segmentation accuracy, especially when noises and/or signal voids are present. Future work will consider dynamically update and evolve the shape library to encode shape priors as segmentations are being performed.

Techniques to derive geometries for image-based Eulerian computations.

The performance of three frequently used level set-based segmentation methods is examined for the purpose of defining features and boundary conditions for image-based Eulerian fluid and solid mechanics models. The focus of the evaluation is to identify an approach that produces the best geometric representation from a computational fluid/solid modeling point of view. In particular, extraction of geometries from a wide variety of imaging modalities and noise intensities, to supply to an immersed boundary approach, is targeted. Two- and three-dimensional images, acquired from optical, X-ray CT, and ultrasound imaging modalities, are segmented with active contours, k-means, and adaptive clustering methods. Segmentation contours are converted to level sets and smoothed as necessary for use in fluid/solid simulations. Results produced by the three approaches are compared visually and with contrast ratio, signal-to-noise ratio, and contrast-to-noise ratio measures. While the active contours method possesses built-in smoothing and regularization and produces continuous contours, the clustering methods (k-means and adaptive clustering) produce discrete (pixelated) contours that require smoothing using speckle-reducing anisotropic diffusion (SRAD). Thus, for images with high contrast and low to moderate noise, active contours are generally preferable. However, adaptive clustering is found to be far superior to the other two methods for images possessing high levels of noise and global intensity variations, due to its more sophisticated use of local pixel/voxel intensity statistics. It is often difficult to know a priori which segmentation will perform best for a given image type, particularly when geometric modeling is the ultimate goal. This work offers insight to the algorithm selection process, as well as outlining a practical framework for generating useful geometric surfaces in an Eulerian setting.

Active Contours with Free Endpoints

Image segmentation methods with length regularized edge sets are known to have segments whose endpoints either terminate perpendicularly to the boundary of the domain, terminate at a triple junction where three segments connect, or terminate at a free endpoint where the segment does not connect to any other edges. However, level set based segmentation methods are only able to capture edge structures which contain the first two types of segments. In this work, we propose an extension to the level set based image segmentation method in order to detect free endpoint structures. By generalizing the curve representation used in Chan and Vese (Trans. Image Proces. 10(2):266---277, 2001; Int. J. Comput. Vis. 50(3):271---293, 2002) to also include free endpoint structures, we are able to segment a larger class of edge types. Since our model is formulated using the level set framework, the curve evolution inherits useful properties such as the ability to change its topology by splitting and merging. The numerical method is provided as well as experimental results on both synthetic and real images.

Mammography segmentation with maximum likelihood active contours

We present a computer-aided approach to segmenting suspicious lesions in digital mammograms, based on a novel maximum likelihood active contour model using level sets (MLACMLS). The algorithm estimates the segmentation contour that best separates the lesion from the background using the Gamma distribution to model the intensity of both regions (foreground and background). The Gamma distribution parameters are estimated by the algorithm. We evaluate the performance of MLACMLS on real mammographic images. Our results are compared to those of two leading related methods: The adaptive level set-based segmentation method (ALSSM) and the spiculation segmentation using level sets (SSLS) approach, and show higher segmentation accuracy (MLACMLS: 86.85% vs. ALSSM: 74.32% and SSLS: 57.11%). Moreover, our results are qualitatively compared with those of the Active Contour Without Edge (ACWOE) and show a better performance. Further, the suitability of using ML as the objective function as opposed to the KL divergence and to the energy functional of the ACWOE is also demonstrated. Our algorithm is also shown to be robust to the selection of a required single seed point.

Nonlinear Dynamical Shape Priors for Level Set Segmentation

The introduction of statistical shape knowledge into level set based segmentation methods was shown to improve the segmentation of familiar structures in the presence of noise, clutter or partial occlusions. While most work has been focused on shape priors which are constant in time, it is clear that when tracking deformable shapes certain silhouettes may become more or less likely over time. In fact, the deformations of familiar objects such as the silhouettes of a walking person are often characterized by pronounced temporal correlations.In this paper, we propose a nonlinear dynamical shape prior for level set based image segmentation. Specifically, we propose to approximate the temporal evolution of the eigenmodes of the level set function by means of a mixture of autoregressive models. We detail how such shape priors “with memory” can be integrated into a variational framework for level set segmentation. As an application, we experimentally validate that the nonlinear dynamical prior drastically improves the tracking of a person walking in different directions, despite large amounts of clutter and noise.

Dynamical statistical shape priors for level set-based tracking

In recent years, researchers have proposed introducing statistical shape knowledge into level set-based segmentation methods in order to cope with insufficient low-level information. While these priors were shown to drastically improve the segmentation of familiar objects, so far the focus has been on statistical shape priors which are static in time. Yet, in the context of tracking deformable objects, it is clear that certain silhouettes (such as those of a walking person) may become more or less likely over time. In this paper, we tackle the challenge of learning dynamical statistical models for implicitly represented shapes. We show how these can be integrated as dynamical shape priors in a Bayesian framework for level set-based image sequence segmentation. We assess the effect of such shape priors "with memory" on the tracking of familiar deformable objects in the presence of noise and occlusion. We show comparisons between dynamical and static shape priors, between models of pure deformation and joint models of deformation and transformation, and we quantitatively evaluate the segmentation accuracy as a function of the noise level and of the camera frame rate. Our experiments demonstrate that level set-based segmentation and tracking can be strongly improved by exploiting the temporal correlations among consecutive silhouettes which characterize deforming shapes.

Quantitative Evaluation of Contour Segmentation of Three Dimensional Image Using Level Set Method

Quantitative evaluation of level set contour segmentation method applied to three dimensional images detecting image edge based on threshold is discussed. The integration of two popular level set methods, i.e., reinitialization method and geodesic active contour method, leads to success in development of robust, with high accuracy, level set based segmentation method. The applications of this method to both synthetic and clinical medical images are shown. Accuracy of the propose method, in the view of both averaged contour location and standard deviation of curvature in the vicinity of contour, calculated using obtained level set function is discussed.