SEGMENTED SNAKE FOR CONTOUR DETECTION
Abstract
The active contour model, called snake, has been proved to be an effective method in contour detection. This method has been successfully employed in the areas of object recognition, computer vision, computer graphics and biomedical images. However, this model suffers from a great limitation, that is, it is difficult to locate concave parts of an object. In view of such a limitation, a segmented snake is designed and proposed in this paper. The basic idea of the proposed method is to convert the global optimization of a closed snake curve into local optimization on a number of open snake curves. The segmented snake algorithm consists of two steps. In the first step, the original snake model is adopted to locate the initial contour near the object boundary. In the second step, a recursive split-and-merge procedure is developed to determine the final object contour. The proposed method is able to locate all convex, concave and high curvature parts of an object accurately. A number of images are selected to evaluate the capability of the proposed algorithm and the results are encouraging.
References (0)
Cited by (54)
Particle method for segmentation of breast tumors in ultrasound images
2020, Mathematics and Computers in SimulationWe propose a new segmentation method based on multiple walking particles (WP) bouncing from the image edges. The particles are able to segment objects characterized by deep concavities as narrow as one pixel and handle single or multiple objects characterized by a noisy background and broken boundaries (“weak edge”, “boundary leakage”). The particles are designed to segment the image by permanently staying inside the object and repairing the boundaries where necessary. The proposed WP combine the advantages of the continuous diffusion models with the principles of multi-agent systems.
WP have been tested against recent active contours and the distance regularized level set method on a set of complex-shaped synthetic images and ultrasound (US) images of breast cancer (http://onlinemedicalimages.com). The method has also been compared with localizing region-based active contours, the fuzzy C-mean level set method, and morphological active contours. The WP are faster and more accurate for images characterized by low contrast, noise, broken boundaries, or boundary leakage. However, for good quality, simple shaped objects the WP work similarly to the conventional methods. There is still an important difference even in this case: the WP do not require initialization.
A video demo of the algorithm is at https://drive.google.com/drive/folders/1SlTphINKtdUwvdjjxiakFrwI2dDlIUAU
Level sets-based image segmentation approach using statistical shape priors
2019, Applied Mathematics and ComputationA robust 3-D segmentation technique incorporated with the level sets concept and based on both shape and intensity constraints is introduced. A partial differential equation (PDE) is derived to describe the evolution of the level set contours. This PDE does not contain weighting parameters that need to be tuned, which overcomes the drawbacks of other PDE approaches. The shape information is collected from a set of co-aligned manually segmented contours of the training data. A promising statistical approach is used to get the distribution of the intensity gray values. The introduced statistical approach is built by modeling the empirical PDF (normalized histogram of occurrences) for the intensity level distribution with a linear combination of Gaussians (LCG) incorporating both negative and positive components. An Expectation-Maximization (EM) algorithm is modified to deal with the LCGs, and we also proposed an EM-based sequential technique to acquire a close initial LCG approximation for the modified EM algorithm to start with. The PDF of the intensity levels is incorporated in the speed function of the moving level set to specify the evolution direction. Experimental results show how accurately the approach is in segmenting various types of 2-D and 3-D datasets comprising medical images.
The line- and block-like structures extraction via ingenious snake
2018, Pattern Recognition LettersActive contour model (ACM) plays an important role in computer vision and medical image analysis. The traditional ACMs were employed to extract closed contours of objects. While simultaneous extraction of line- and block-like objects, such as boundary contours, centerlines, as well as their topological relationship, remains open so far. Therefore, a novel ACM named "Ingenious Snake" is proposed to adaptively extract the feature curves. The proposed ingenious snake includes the following steps: 1) In the preprocessing, the line- and block-like structures are classified with k-means clustering, following up with morphological operation and enhancement. The gradient vector flow (GVF) field is then acquired from the resultant ridge feature map. 2) For the automatic initialization, the ridge-points are extracted by using the local phase measurement of GVF field, then the two-category of object ridgelines are obtained fast. 3) Finally, the contour deformation and curves evolvement are implemented with a management strategy. The resultant contours and centerlines well characterize the objects of interest. In the experiments, we compare the existing initialization methods and the adaptive extraction with a series of phantoms and testing images. The visual and quantitative assessments of structure extraction are satisfying in terms of effectiveness and accuracy.
100 lines of code for shape-based object localization
2016, Pattern RecognitionWe introduce a simple and effective concept for localizing objects in densely cluttered edge images based on shape information. The shape information is characterized by a binary template of the object's contour, provided to search for object instances in the image. We adopt a segment-based search strategy, in which the template is divided into a set of segments. In this work, we propose our own segment representation that we call one-pixel segment (OPS), in which each pixel in the template is treated as a separate segment. This is done to achieve high flexibility that is required to account for intra-class variations. OPS representation can also handle scale changes effectively. A dynamic programming algorithm uses the OPS representation to realize the search process, enabling a detailed localization of the object boundaries in the image. The concept's simplicity is reflected in the ease of implementation, as the paper's title suggests. The algorithm works directly with very noisy edge images extracted using the Canny edge detector, without the need for any preprocessing or learning steps. We present our experiments and show that our results outperform those of very powerful, state-of-the-art algorithms.
Automatic optimal filament segmentation with sub-pixel accuracy using generalized linear models and B-spline level-sets
2016, Medical Image AnalysisBiological filaments, such as actin filaments, microtubules, and cilia, are often imaged using different light-microscopy techniques. Reconstructing the filament curve from the acquired images constitutes the filament segmentation problem. Since filaments have lower dimensionality than the image itself, there is an inherent trade-off between tracing the filament with sub-pixel accuracy and avoiding noise artifacts. Here, we present a globally optimal filament segmentation method based on B-spline vector level-sets and a generalized linear model for the pixel intensity statistics. We show that the resulting optimization problem is convex and can hence be solved with global optimality. We introduce a simple and efficient algorithm to compute such optimal filament segmentations, and provide an open-source implementation as an ImageJ/Fiji plugin. We further derive an information-theoretic lower bound on the filament segmentation error, quantifying how well an algorithm could possibly do given the information in the image. We show that our algorithm asymptotically reaches this bound in the spline coefficients. We validate our method in comprehensive benchmarks, compare with other methods, and show applications from fluorescence, phase-contrast, and dark-field microscopy.
Defeating line-noise CAPTCHAs with multiple quadratic snakes
2013, Computers and SecurityOptical character recognition (OCR) is one of the fundamental problems in artificial intelligence and image processing, but recent progress in OCR represents a security challenge for Web sites that throttle requests with image based CAPTCHAs (Completely Automated Public Turing Tests to Tell Computers and Humans Apart). A CAPTCHA is challenge-response test placed within web forms to determine whether the user is human. Unfortunately, algorithms capable of solving image based CAPTCHAs can be used to create spam accounts and design malicious denial of service (DoS) attacks, causing financial and social damage. The problem of defeating digital image CAPTCHAs is thus twofold. On the one hand, it is an important problem in artificial intelligence and image processing. On the other hand, publicly available CAPTCHAs that are not tested against state of the art machine recognition algorithms may make the systems vulnerable to attack by software bots.
This paper considers a very important subclass of text CAPTCHAs, those characterized by salt and pepper noise combined with line (curve) noise. Thus far, attacks on CAPTCHAs with this type of noise have used relatively simple image processing methods with some success, but state-of-the-art segmentation methods have not been fully exploited. In this paper, we propose and benchmark two strong segmentation methods. The first method is a modification of a multiple quadratic snake proposed for road extraction from satellite images. The second competing method is a boundary tracing routine available in the OpenCV open source library.
A first numerical experiment indicates excellent accuracy for both methods. A second experiment on human recognition shows that the CAPTCHAs used in the study are already near the threshold of being too hard for humans. Finally, a third numerical experiment presents a more difficult set of CAPTCHAs with the addition of anti-binarization methods. The snake-based method is shown to be more resilient to anti-binarization schemes than boundary tracing and state-of-the art projection-based attacks on CAPTCHAs.
Since CAPTCHAs corrupted by small line noise are shown to be difficult for humans and relatively easy for our algorithm, CAPTCHA designers should introduce more challenging distortions into their CAPTCHAs, lest the security of systems based on them be compromised.