Gradient Vector Flow Snake Algorithm Research Articles

Edge Segmentation Algorithm‐Based Prevention of Recurrent Laryngeal Nerve Injury in Treatment of Thyroid Nodules Using Ultrasound Images

The study was aimed to explore the segmentation effects of different algorithms on thyroid nodule ultrasound images, so as to better protect the recurrent laryngeal nerve during thyroid surgery. Specifically, 186 patients with thyroid nodules were selected as the research objects. The segmentation performances of the gradient vector flow (GVF) Snake, Watershed, and Snake algorithms were compared from 6 aspects of image segmentation effects, pixel accuracy (PA), Intersection over Union (IOU) value, algorithm running time, postoperative recurrent laryngeal nerve injury, intraoperative bleeding volume, and postoperative drainage volume. It was found that the average PA value (0.954) and the IOU value (0.866) of the GVF Snake algorithm were obviously higher than those of the other two algorithms. The total incidence of recurrent laryngeal nerve injury based on the GVF Snake algorithm (4.69%) was obviously lower than that of the Snake algorithm (19.35%) and the watershed algorithm (16.13%). The bleeding volume and postoperative drainage volume based on the GVF Snake algorithm were less versus the other two algorithms ( P < 0.05 ). In conclusion, the GVF Snake algorithm demonstrates ideal segmentation effects, which is suggested in the treatment of thyroid nodules to better protect the recurrent laryngeal nerve.

An improved cluster-based snake model for automatic agricultural field boundary extraction from high spatial resolution imagery

ABSTRACT Agricultural field boundary information is important and often required for the geosciences and the agricultural sector. In this paper, a novel method is developed to extract sub-boundaries within the permanent boundaries of agricultural land parcels from high-resolution optical satellite imagery using an improved cluster-based snake model. The method takes the advantage of the results of an automatic fuzzy c-means (FCM) clustering and edge detection to compute external forces for an improved gradient vector flow (GVF) snake model. The GVF snake algorithm is improved by using an automatic seeding model based on clustering results and image moment functions. To seed the improved GVF algorithm, an ellipse is automatically delineated for each cluster within agricultural parcel by utilizing image moment functions (in particular silhouette moments). The GVF snake model is then implemented for each seed, one seed at a time. Active contours tend to have curve shapes rather than straight lines due to their structure that consists of several connected nodes within each contour. Therefore, the final accurate results are obtained after performing a three-stage line simplification operation. The experiments of the method were conducted on 20 test fields in a study area located near to the town of Karacabey, Turkey, using the 4-m resolution IKONOS multispectral (xs) image, the 2.44-m resolution QuickBird xs image, and the 0.61-m resolution QuickBird pan-sharpened (PS) image. Experimental results demonstrate that using both the clustering and edge detection results as external forces for the improved GVF snake model increases the accuracy of the results. In addition, the developed method showed a fairly good performance in extracting sub-boundaries for the fields comprising crops with an inherent high inner heterogeneity, such as rice and corn. The method can potentially be applied in the extraction of within-field sub-boundaries from high-resolution satellite imagery in agricultural areas.

Medical Data Point Clouds Reconstruction Algorithm Based on Tensor Product B-Spline Approximation in Virtual Surgery

A complete 3D visualization method for virtual liver lesion model was proposed by taking patients’ abdomen CT slices as data source. Firstly, Gradient Vector Flow-Snake algorithm that combines with region force was adopted to fast and accurately extract the contour points in patients’ CT slices. Then 3D cloud data of these contour points was simplified uniformly, and a distance-field-based method of distribution field fitting for B-spline surface was presented to fast establish the lesion model roughly. Then, an interactive node fine tuning method Interactive Marching Nodes was proposed, so the model can be optimized according to requirements of users. Finally, realistic lesion model was generated through texture mapping. Experiments suggest that this approach is suitable for various kinds of lesions with the whole process taking only a few minutes, and the generated model has high precision, which is of some significance in the study of virtual surgeries.

Broken Character Image Restoration Using Genetic Snake Algorithm: Deep Concavity Problem

Active contours also known as snakes became a familiar and widely used in the field of image segmentation and restoration of historical documents in last few decades. Gradient Vector Flow (GVF) snake successes in overcome of converge to boundary concavities which represents the drawback of traditional snakes. Deep concavity problem it has become Obstacle faced GVF snake when restoring broken characters of historical documents. In this study we proposed algorithm to use genetic algorithm with GVF snake algorithm in order to optimize snake points to get right positions in deep concavity boundaries, also adding a Divergence factor as the third force to enhance the restoring and recognizing results. The experimental results show that our proposed algorithm has more capture than GVF alone.

Sistema de detección asistido por ordenador para ictus

This paper presents a system for detection of ischemic and haemorrhagic stroke in computed tomography images. Firstly, the original image is converted into grey scale and the noise is removed using the bilateral filter. Then skull regions are removed by a morphological function. The image is classified into infarct or haemorrhagic stroke based on the mean and standard deviation. A method for manual segmentation was also implemented. This method is based on the use of deformable models, specifically applying the gradient vector flow Snake algorithm. The results are segmented images for abnormal region. Experiments carried out on computed tomography images show the accuracy of the proposed system.

Image Processing for Identification of Sea-Ice Floes and the Floe Size Distributions

An unmanned aerial vehicle was used as a mobile sensor platform to collect sea-ice features at Ny-Alesund in early May 2011, and several image processing algorithms have been applied to samples of sea-ice images to extract useful information about sea ice. The sea-ice statistics given by the floe size distribution, being an important parameter for climate and wave- and structure-ice analysis, is challenging to calculate due to difficulties in ice floe identification, particularly the separation of seemingly connected ice floes. In this paper, the gradient vector flow (GVF) snake algorithm is applied to solve this problem. To evolve the GVF snake algorithm automatically, an initialization based on the distance transform is proposed to detect individual ice floes, and the morphological cleaning is afterward applied to smoothen the shape of each identified ice floe. Based on the identification result, the image is separated into four different layers: ice floes, brash pieces, slush, and water. This makes it further possible to present a color map of the ice floes and brash pieces based on sizes, and the corresponding ice floe size distribution histogram. The proposed algorithm yields an acceptable identification result, and its effectiveness is demonstrated in a case study. A discussion on the methods and results concludes the paper.

TU-D-330A-07: A Fast Pseudo-1D Active Contour for Medical Image Segmentation

Purpose: A snake (active contour) is a deformable curve used in medical image processing to localize region boundaries. The time required for snake convergence to a solution is lengthened by increased data set size and complex external energy field computations. A new gradient vector flow (GVF) snake algorithm was developed to increase the convergence speed of the conventional GVF snake algorithm. Method and Materials: The new algorithm reformulates a 2D closed curve to a pseudo‐1D open curve. This transformation reduces the solution search space and the area for external energy field computation. Further, curve deformations are restricted to one dimension, thus reducing the complexity for both deformation and external field computation. A binary 512×512 test image was created and the algorithms were run using each of three initializations. Additionally, both algorithms were applied to a 512×512 thoracic CT section. Results: The new algorithm, on average, converged to a solution for the binary image in 11.1s compared with 14.6s for the conventional algorithm (24% improvement). The greatest gains were seen in the GVF calculation where the mean reduction in time for GVF calculation alone was 2.9s (32% improvement). The time for contour fitting was also consistently decreased, with a mean reduction for fitting of 0.6s (11% improvement). The new algorithm on average converged to a solution for the CT section in 4.01s compared with 7.15s for the conventional algorithm (44% improvement). Area‐of‐overlap measures for the contours generated by both algorithms exceeded 0.97 for both binary and CT test images.Conclusion: A new active contour algorithm was developed, and a consistent speed improvement over the conventional algorithm was measured. This new algorithm is compatible with other optimization techniques and shows potential for processing large medical imagedata sets.Conflict of Interest: S.G.A shareholder R2 Technology, Inc.