Chan-Vese Algorithm Research Articles

Region Based Segmentation in Presence of Intensity Inhomogeneity Using Legendre Polynomials

We propose a novel region based segmentation method capable of segmenting objects in presence of significant intensity variation. Current solutions use some form of local processing to tackle intra-region inhomogeneity, which makes such methods susceptible to local minima. In this letter, we present a framework which generalizes the traditional Chan-Vese algorithm. In contrast to existing local techniques, we represent the illumination of the regions of interest in a lower dimensional subspace using a set of pre-specified basis functions. This representation enables us to accommodate heterogeneous objects, even in presence of noise. We compare our results with three state of the art techniques on a dataset focusing on biological/biomedical images with tubular or filamentous structures. Quantitatively, we achieve a 44% increase in performance, which demonstrates efficacy of the method.

Accuracy of manual and automated rectal contours using helical tomotherapy image guidance scans during prostate radiotherapy.

94 Background: Prostate radiotherapy can be delivered using daily image-guided helical tomotherapy. Previous work has shown that contouring the rectum on the kV planning CT scan has a Jaccard conformity index (JCI) of 0.78 for different oncologists (inter-observer variability) and 0.82 for a single oncologist (intra-observer variability) (Lutgendorf-Caucig C et al. Feasibility of CBCT-based target and normal structure delineation in prostate cancer radiotherapy: multi-observer and image multi-modality study. Radiother Oncol. 2011;98(2):154-61.). Using the daily image guidance MV CT scan we have developed automated methods to contour the rectum in order to investigate the dose delivered over a course of treatment. We sought to quantify the accuracy of MV manual and automated contours. Methods: A single oncologist (JES) contoured the rectum on 370 MV scans for 10 participants treated with helical tomotherapy to prostate and pelvic lymph nodes. Accuracy of MV manual contours was tested using a scalar algorithm to enlarge and reduce the contours and intra-observer re-contouring at a 3-month interval. Automated contouring, incorporating the Chan-Vese algorithm, was developed and outputs were compared with manual contours. Results: JES could identify differences in MV manual contour size at the level of ±2.2 mm, equivalent to 1.7 pixels. The median JCI for MV re-contouring was 0.87 with inter-quartile range (IQR) 0.78 to 0.90. When compared with manual contours, automated outputs had a median JCI of 0.79 (IQR 0.74 to 0.79). These results were obtained after 3 iterations, each taking less than 10 seconds. Conclusions: Manual contouring using MV scans was accurate, at a level of approximately 2 mm, and reproducible, with JCI of 0.87. The time taken to contour was approximately 20 minutes per scan. Automated contouring was also reproducible with JCI of 0.79 and, in contrast, took less than a minute per scan. Both manual and automated methods produced results comparable to those for contouring using kV scans. We plan to use auto-contouring to calculate accumulated dose to the rectum in an initial cohort of 100 participants. These doses will be correlated with toxicity as part of the VoxTox Study.

Chan-Vese segmentation model for faster and accurate evaluation of yarn packing density

Yarn packing density is an important parameter in correlating the internal structure of the yarn to its final properties and performance. The current methods of determining the yarn packing density have three main drawbacks: they are labor dependent, they are time consuming, and they approximate many parameters of the fibers and the yarn. The current work utilizes the Chan-Vese (CV) segmentation model to detect the actual contours of the yarn and the fibers inside its cross-section. This algorithm allows the automation of the process which reduces the time required for the analysis and makes it independent from the human element. The method also avoids the approximations involved in the traditional methods by calculating the actual areas of the fibers and the yarn which makes it more precise in determining the yarn packing density. A user-friendly graphical user interface (GUI) was developed to utilize the CV algorithm and calculate different yarn parameters such as the yarn's radial packing density, the yarn's effective diameter, and the yarn's effective packing density. The area ratio factor (ARF) was used as a quick indicator of the accuracy of fiber segmentation. The CV algorithm was described in detail and tested with ring-spun yarns from different materials and the results were found to be in agreement with the reported values of such yarns. The introduced method can be extended to the evaluation of fibrous materials other than yarns.

Automatic local parameterization of the Chan Vese active contour model’s force coefficients using edge information

Image segmentation is an important field of computer vision that includes algorithms that segment images into foreground and background. Active Contours (AC) is a family of segmentation techniques that has gained high popularity in recent years due to the advantages it provides, including contour deformation, detail preservation and multiple objects identification. One of the most popular AC algorithms is the Chan–Vese (CV) algorithm, which uses region information. This paper presents a novel algorithmic scheme named Automatic Local Ratio (ALR) that extends the original CV algorithm in order to automatically determine its force coefficient’s values, using edge information. Moreover, due to the use of edge information, this scheme provides an alternative to typical edge stopping functions. The proposed scheme is tested on real images, providing superior performance both in terms of qualitative and quantitative results compared to the CV using Gaussian regularization, the SBGFRLS and Fast Edge Integration algorithms.

An Analysis of Segmentation Techniques to Identify Herbal Leaves from Complex Background

Herbal leaves are used widely in local medication. But now a day, an ordinary person has little knowledge about those herbs and he may not identify such herbals easily. As a first step of computer based recognition of herbs, an analysis has been made to identify the best method for segmenting leaves object from its background. This type of segmentation is a preprocessing step required in identification of species of leaves or plants. Several methods are available for detecting the objects based on global and local features of an image. In this paper we are examining various object detection techniques for segmenting leaves based on color, shape and texture. Features like local adaptive mean color, evidence based color model, color histogram techniques are used. Boundary structure model is used to detect the leaves based on boundary descriptors of an image and Chan-Vese algorithm is used to segment the leaves from complex background. To extract leaves from texture background, edge focusing algorithm is used. From our experimentation analysis, shape is the powerful characteristics of segmenting leaf images and Chan-Vese algorithm provides better results compared to other techniques without affecting the leaf colors, texture etc.

Detection of Glomerulosclerosis in Diabetic Nephropathy Using Contour-based Segmentation

We have proposed a method for detection of glomerulosclerosis in diabetic nephropathy using Contour-Based Segmentation. Pathological images of the glomerulosclerosis were acquired from various patients. It is a challenging task as 100% detection of Nephropathy disease with regular pathological procedure is not possible. We propose a solution to the problem of segmentation of the glomerulosclerosis images for the analysis of abnormalities. The proposed method is the modification of the original Chan-Vese algorithm, with the varied values of parameters. The proposed method achieved better segmentation and the results are encouraging.

Robust Contour Tracking Model Using a Variational Level-Set Algorithm

In this work, we provide a novel variational level-set based object contour tracking approach. Thus, a mathematically rigorous variant of the Chan-Vese algorithm for image segmentation via geometric active contour model is proposed here. With respect to the original contour detection algorithm, the level set function ϕ(t) defining the evolving contour S t = {x; ϕ(t, x) = 0} is iteratively computed from a nonlinear parabolic boundary value problem that is well posed in the space of functions with bounded variations. We provide a robust mathematical justification of the proposed level-set model.

Visualization of subcutaneous insulin injections by x-ray computed tomography

We report how the three-dimensional structure of subcutaneous injections of soluble insulin can be visualized by x-ray computed tomography using an iodine based contrast agent. The injections investigated are performed ex vivo in porcine adipose tissue. Full tomography scans carried out at a laboratory x-ray source with a total acquisition time of about 1 min yield CT-images with an effective pixel size of 109 × 109 μm2. The depots are segmented using a modified Chan–Vese algorithm and we are able to observe differences in the shape of the injection depot and the position of the depot in the skin among equally performed injections. To overcome the beam hardening artefacts, which affect the quantitative prediction of the volume injected, we additionally present results concerning the visualization of two injections using synchrotron radiation. The spatial concentration distribution of iodine is calculated to show the dilution of the insulin drug inside the depot. Characterisation of the shape of the depot and the spatial concentration profile of the injected fluid is important knowledge when improving the clinical formulation of an insulin drug, the performance of injection devices and when predicting the effect of the drug through biomedical simulations.

Information Hiding Based on Chan-Vese Algorithm

The process of data transfer via the Internet becomes an easy process as a result of the great advances in networking technologies, and now many people can communicate with each other easily and quickly through them. Because the online environment is general and open, the unauthorized one can control information were transmitted between any two parts and interception of getting access for it, because of that there is an emergency need for write covered, which is the science of hiding secret information in a digital cover such as an images, so it is impossible for the normal person and others unauthorized to detected or perceives. In this paper, the technology in the field of information hiding in the images is developed, where first, the cover (PNG, BMP) image is segmented using Chan-Vese algorithm, then the text will hide in the segmented image depending on the areas of clipping. The standards (PSNR, BER) are used to measure technical efficiency. In addition the algorithm of this technique is implemented in Matlab.

Pulmonary dynamics of anatomical structures of interest in 4DCT imaging

The present work shows an application of the Chan-Vese algorithm for the semi-automatic segmentation of anatomical structures of interest (lungs and lung tumor) in 4DCT images of the thorax, as well as their three-dimensional reconstruction. The segmentation and reconstruction were performed on 10 CT images, which make up an inspiration-expiration cycle. The maximum displacement was calculated for the case of the lung tumor using the reconstructions of the onset of inspiration, the onset of expiration, and the voxel information. The proposed method achieves appropriate segmentation of the studied structures regardless of their size and shape. The three-dimensional reconstruction allows us to visualize the dynamics of the structures of interest throughout the respiratory cycle. In the future, it is expected to have more evidence of the good performance of the proposed method and to have the feedback of the clinical expert, since the knowledge of the characteristics of anatomical structures, such as their dimension and spatial position, helps in the planning of Radiotherapy (RT) treatments, optimizing the radiation dose to cancer cells and minimizing it in healthy organs. Therefore, the information found in this work may be of interest for the planning of RT treatments.

Active Contours without Edges for Vector-Valued Images

In this paper, we propose an active contour algorithm for object detection in vector-valued images (such as RGB or multispectral). The model is an extension of the scalar Chan–Vese algorithm to the vector-valued case [1]. The model minimizes a Mumford–Shah functional over the length of the contour, plus the sum of the fitting error over each component of the vector-valued image. Like the Chan–Vese model, our vector-valued model can detect edges both with or without gradient. We show examples where our model detects vector-valued objects which are undetectable in any scalar representation. For instance, objects with different missing parts in different channels are completely detected (such as occlusion). Also, in color images, objects which are invisible in each channel or in intensity can be detected by our algorithm. Finally, the model is robust with respect to noise, requiring no a priori denoising step.