Using a distance map and an active contour model to segment the carotid artery boundary from the lumen contour in proton density weighted magnetic resonance images.

Segmentation methods have assumed an important role in image-based diagnosis of several cardiovascular diseases. Particularly, the segmentation of the boundary of the carotid artery is demanded in the detection and characterization of atherosclerosis and assessment of the disease progression. In this article, a fully automatic approach for the segmentation of the carotid artery boundary in Proton Density Weighted Magnetic Resonance Images is presented. The approach relies on the expansion of the lumen contour based on a distance map built using the gray-weighted distance relative to the center of the identified lumen region in the image under analysis. Then, a Snake model with a modified weighted external energy based on the combination of a balloon force along with a Gradient Vector Flow-based external energy is applied to the expanded contour towards the correct boundary of the carotid artery. The average values of the Dice coefficient, Polyline distance, mean contour distance and centroid distance found in the segmentation of 139 carotid arteries were 0.83 ± 0.11, 2.70 ± 1.69 pixels, 2.79 ± 1.89 pixels and 3.44 ± 2.82 pixels, respectively. The segmentation results of the proposed approach were also compared against the ones obtained by related approaches found in the literature, which confirmed the outstanding performance of the new approach. Additionally, the proposed weighted external energy for the Snake model was shown to be also robust to carotid arteries with large thickness and weak boundary image edges.

Insight to Nanoparticle Size Analysis-Novel and Convenient Image Analysis Method Versus Conventional Techniques.

The aim of this paper is to introduce a new image analysis program "Nanoannotator" particularly developed for analyzing individual nanoparticles in transmission electron microscopy images. This paper describes the usefulness and efficiency of the program when analyzing nanoparticles, and at the same time, we compare it to more conventional nanoparticle analysis techniques. The techniques which we are concentrating here are transmission electron microscopy (TEM) linked with different image analysis methods and X-ray diffraction techniques. The developed program appeared as a good supplement to the field of particle analysis techniques, since the traditional image analysis programs suffer from the inability to separate the individual particles from agglomerates in the TEM images. The program is more efficient, and it offers more detailed morphological information of the particles than the manual technique. However, particle shapes that are very different from spherical proved to be problematic also for the novel program. When compared to X-ray techniques, the main advantage of the small-angle X-ray scattering (SAXS) method is the average data it provides from a very large amount of particles. However, the SAXS method does not provide any data about the shape or appearance of the sample.

An automatic segmentation system of acetabulum in sequential CT images for the personalized artificial femoral head design.

This paper describes an automatic and accurate segmentation method to extract the acetabulum tissue from sequential CT images. The hip joint consists of acetabulum and femoral head. In the personalized femoral head prosthesis designing by reverse engineering technology, obtaining the accurate acetabulum shape is the most important task. However, due to the necrotic femoral head's complex shape and the extremely narrow inter-bone region, obtaining the accurate acetabulum shape remains a challenging work. In this paper, we overcame these difficulties and developed an automatic segmentation method. First, we obtain the rough contour of the femoral head by utilizing the constraints of the great trochanter and the shape of femoral head in the initial slice. Second, we refine the rough contour by an orthogonal line edge detection approach and obtain a refined contour which will be used as the initial contour of the snake algorithm. Then, the snake algorithm is performed slice by slice upwards and downwards to generate the adjacent contours. During this process, the contour of the femoral head in a segmented slice is used as the initial contour of the next unsegmented slice. Finally, we can obtain the accurate sequential contours of the acetabulum by removing the femoral head and the femoral regions. And the 3D models of the acetabulum can be obtained correspondingly. The experimental result shows that the 3D models obtained by the proposed method are accurate and satisfactory. On this condition, we can reconstruct the personalized femoral head 3D models and design the personalized femoral head prosthesis.

Snake-based brain white matter fiber reconstruction.

Diffusion tensor imaging (DTI) is a tractography algorithm that provides the only means of mapping white matter fibers. Furthermore, because of its wealth of applications, diffusion MRI tractography is gaining importance in clinical and neuroscience research. This paper presents a novel brain white matter fiber reconstruction method based on the snake model by minimizing the energy function, which is composed of both external energy and internal energy. Internal energy represents the assembly of the interaction potential between connected segments, whereas external energy represents the differences between predicted DTI signals and measured DTI signals. Through comparing the proposed method with other tractography algorithms in the Fiber Cup test, the present method was shown to perform superiorly to the majority of the other methods. In fact, the proposed test performed the third best out of the ten available methods, which demonstrates that present method can accurately formulate the brain white matter fiber reconstruction.

Snake model-based lymphoma segmentation for sequential CT images.

The measurement of the size of lesions in follow-up CT examinations of cancer patients is important to evaluate the success of treatment. This paper presents an automatic algorithm for identifying and segmenting lymph nodes in CT images across longitudinal time points. Firstly, a two-step image registration method is proposed to locate the lymph nodes including coarse registration based on body region detection and fine registration based on a double-template matching algorithm. Then, to make the initial segmentation approximate the boundaries of lymph nodes, the initial image registration result is refined with intensity and edge information. Finally, a snake model is used to evolve the refined initial curve and obtain segmentation results. Our algorithm was tested on 26 lymph nodes at multiple time points from 14 patients. The image at the earlier time point was used as the baseline image to be used in evaluating the follow-up image, resulting in 76 total test cases. Of the 76 test cases, we made a 76 (100%) successful detection and 38/40 (95%) correct clinical assessment according to Response Evaluation Criteria in Solid Tumors (RECIST). The quantitative evaluation based on several metrics, such as average Hausdorff distance, indicates that our algorithm is produces good results. In addition, the proposed algorithm is fast with an average computing time 2.58s. The proposed segmentation algorithm for lymph nodes is fast and can achieve high segmentation accuracy, which may be useful to automate the tracking and evaluation of cancer therapy.

Application of Image Segment Method Based on GVF Snake Model for RA / 中国医学物理学杂志

Objective: To investigate the potential of gradient vector flow(GVF) Snake model as a method of image segment in radiographic absorptiometry method (RA) which is used to qualify bone mineral density. Methods: The Gradient Vector Flow model and the Region Growing method were applied in the segmentation of the middle phalanges image and aluminum wedge image separately in this paper. Then, the results can be compared. Results: The experiments shows that GVF Snake model is not only robust and practicable, but also segmentation results are in line with the actual border. Conclusions: GVF Snake model is very useful, and can be widely used in qualifying bone mineral density.

Survey of Medical Image Segmentation Based on Deformable Models / 医疗卫生装备

The classical deformable models and some new approaches for the past few years are surveyed,especially de-scribe two important methods of them: snake model and level-set model.Image segmentation is the bases of 3D recon-struction technology and medical visualization image,which are meaningful to disease diagnosis and therapy in clinical.It is not only a key step and critical technology in medical image processing and image analysis but also a classic puzzle.With the need of application,it is very important to continually research the image segmentation,to increasingly improve the old approaches and introduce the new and more effective ones.