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Guiding ziplock snakes with a priori information
Abstract
In this paper, we present a method to combine a grammatical model that encodes a priori shape information with the ziplock snakes presented by Neuenschwander et al. A competing mechanism is adopted to take advantage of the shape models without inducing excessive computation. The resulting model-based ziplock snakes have many advantages over the original model: they can accurately locate contour features, produce more refined results, and deal with multiple contours, missing image cues, and noise.
... The proposed approach is based on the idea that given convergence of the active contours mostly data-driven, appearance and geometrical data can be recovered from the resulting energy component value distribution. Contrary to other works that tried to embed partial shape information to guide the evolution of the contour [21], we consider the analysis of energy based derived features a natural way to explore the range of possible nematode shape configurations in a set of population images without having to build an specific model or making explicit constrains about objects interaction [19]. We leave to the active contour optimization process the task of locating salient linear structures and focus on exploiting the distribution of energy values for recognition of those contours corresponding to nematodes. ...
... This procedure is intended to raise the probability of accurate segmentation by progressively locating control points on the object surface. They can encode shape information explicitly [21] and provide faster convergence than geodesic snakes. ...
In this paper we study how shape information encoded in contour energy components values can be used for detection of microscopic organisms in population images. We proposed features based on shape and geometrical statistical data obtained from samples of optimized contour lines integrated in the framework of Bayesian inference for recognition of individual specimens. Compared with common geometric features the results show that patterns present in the image allow better detection of a considerable amount of individuals even in cluttered regions when sufficient shape information is retained. Therefore providing an alternative to building a specific shape model or imposing specific constrains on the interaction of overlapping objects. Department of telecommunication and information processing, Ghent University, St-Pieters Nieuwstraat 41, B-9000, Ghent, Belgium Centro de Vision y Robotica, Facultad de Ingenieria en Electricidad y Computación, ESPOL University, Km 30.5 via perimetral, 09015863, Guayaquil, Ecuador
... More specifically, the method proposed in [56] allows the segmentation of the IMC, the extraction of the atherosclerotic plaque, the delineation of the CCA diameter, and the grading of its stenosis. Nowadays, there are a few known commercial software-imaging systems supporting IMC segmentation available from different research groups [10, 103], which are based on snakes [9, 10, 15, 16, 39, 103], zip-lock snakes [102], gradient vector flow [107], dynamic programming (DP) segmentation [103], [45] , neural net- works [62], as well as integrated approaches [66, 68]. Recently, two different commercial software systems for ultrasound image [59], and video [57] analysis of the CCA, have been developed, can be downloaded, and used in the clinical praxis. ...
The determination of the wall thickness [intima-media thickness (IMT)], the delineation of the atherosclerotic carotid plaque, the measurement of the diameter in the common carotid artery (CCA), as well as the grading of its stenosis are important for the evaluation of the atherosclerosis disease. All these measurements are also considered to be significant markers for the clinical evaluation of the risk of stroke. A number of CCA segmentation techniques have been proposed in the last few years either for the segmentation of the intima-media complex (IMC), the lumen of the CCA, or for the atherosclerotic carotid plaque from ultrasound images or videos of the CCA. The present review study proposes and discusses the methods and systems introduced so far in the literature for performing automated or semi-automated segmentation in ultrasound images or videos of the CCA. These are based on edge detection, active contours, level sets, dynamic programming, local statistics, Hough transform, statistical modeling, neural networks, and an integration of the above methods. Furthermore, the performance of these systems is evaluated and discussed based on various evaluation metrics. We finally propose the best performing method that can be used for the segmentation of the IMC and the atherosclerotic carotid plaque in ultrasound images and videos. We end the present review study with a discussion of the different image and video CCA segmentation techniques, future perspectives, and further extension of these techniques to ultrasound video segmentation and wall tracking of the CCA. Future work on the segmentation of the CCA will be focused on the development of integrated segmentation systems for the complete segmentation of the CCA as well as the segmentation and motion analysis of the plaque and or the IMC from ultrasound video sequences of the CCA. These systems will improve the evaluation, follow up, and treatment of patients affected by advanced atherosclerosis disease conditions.
... The parameters α and β determine to which degree the contour is allowed to stretch or develop sharp bends. Because our goal is shape sampling rather than segmentation (Jiankang & Xiaobo, 2003), contour parameters should allow significant changes in control point spatial distribution. At the same time, the contour should be kept in the vicinity of the C. elegans medial axis, i.e. the location of minimum external energy. ...
In this work, we discuss the problems of automatic segmentation of Arabidopsis thaliana epidermal cell patterning in images captured using a Differential Interference Contrast (DIC) microscope. These images are difficult to analyze due to the non-linear nature of DIC optics. The border of objects appear as a combination of bright and dark shadows with variable thickens and contrast levels. Our segmentation approach exploits prior knowledge on the optical properties of A. thaliana cell walls. A set of matching filters and a scale space line detector are used to generate an enhanced image that shows a single response at the location of cellular walls. To reduce the uncertainty in low contrast cellular walls several images are captured at different orientations. After image fusion, we obtain a single image that can be segmented using well established algorithms. Experiments on a manually annotated DIC image data set demonstrated the effectiveness of the proposed scheme.
... Extensions to allow 3D volume segmentation were also developed as was the ability to change topology to handle objects with bifurcations or internal holes [McInerney and Terzopoulos, 1996; McInerney and Terzopoulos, 1999]. New snake models continue to be developed [Xu and Prince, 1998, Meegama and Rajapakse, 2003; Wang and Li, 2003; Wei et al., 2004; Li and Acton, 2007; Sum and Cheung, 2007]. Level-set methods were introduced to deformable models by casting the curve evolution problem in terms of front propagation rather than energy minimization [Malladi et al., 1995; Caselles et al., 1997a,b; Sethian, 1999]. ...
Automatic medical image segmentation is an unsolved problem that has captured the attention of many researchers. The purpose of this survey is to identify a representative set of methods that have been used for automatic medical image segmentation over the past 35 years and to provide an opportunity to view the transitions that have occurred as this research area has developed. To facilitate this, the existing research is divided into three generations, each generation adding an additional level of algorithmic complexity. These generations indicate progress towards accurate, fully-automatic, medical image segmentation and their identification provides a framework for classifying the wide variety of methods that have been devised. The first generation is composed of the simplest forms of image analysis such as the application of intensity thresholds and region growing. The second generation is characterized by the application of uncertainty models and optimization methods, and the third generation incorporates knowledge into the segmentation process. The progress toward accurate, fully-automatic segmentation is discussed and sources of segmentation software from industry and academia are identified, along with databases for segmentation validation.
... To overcome these limitations, the ziplock snake model was proposed ( Neuenschwander et al., 1997). Ziplock snakes need far less initialisation effort and are less affected by the shrinking effect from the internal energy term ( Wang and Li, 2003). Furthermore, the computational process is more robust because the active part whose energy is minimised is always quite close to the contour being extracted. ...
Road junctions are important components of a road network. However, they are usually not explicitly modelled in existing road extraction approaches. In this research, road junctions are modelled in detail as area objects and an approach is proposed for their automatic extraction through the use of an existing geospatial database. Prior knowledge derived from a topographic geospatial database is used to facilitate the extraction. A new snake-based approach is proposed that makes use of the "ziplock snake" concept and whose external force is a combination of the gradient vector flow (GVF) force and the balloon force in order to delineate the junction border. Road arm extraction results provide fixed boundary conditions for the proposed snake. The approach was tested using aerial black-and-white Digital Mapping Camera (DMC) ortho-images of 0.1 m ground resolution taken from suburban and rural areas. The results obtained demonstrate the validity of this approach. © 2008 The Authors. Journal Compilation © 2008 Remote Sensing and Photogrammetry Society and Blackwell Publishing Ltd.
... Optimization is carried out from the end points towards the center of the contour so that the initial control points will be located progressively on the object surface and increases the probalilities of a correct segmentation. Being parametric it can encode shape information [21] explicitly and provide faster convergence than geodesic snakes. ...
In this paper we present an approach to perform automated analysis of nematodes in population images. Occlusion, shape variability and structural noise make reliable recognition of individuals a task difficult. Our approach relies on shape and geometrical statistical data obtained from samples of segmented lines. We study how shape similarity in the objects of interest, is encoded in active contour energy component values and exploit them to define shape features. Without having to build a specific model or making explicit assumptions on the interaction of overlapping objects, our results show that a considerable number of individual can be extracted even in highly cluttered regions when shape information is consistent with the patterns found in a given sample set. Espol
... The variations involve different data models, different forms of curve parameterizations, and different forms of smoothing penalties on the curves [13, 12, 11, 9, 10] . Others deformable approaches allows a user to specify only the distant end points of the curve, without having to supply a complete polygonal approximation [59, 78]. ...
Automated segmentation to find the endocardial boundary of the left heart ventricle from magnetic resonance (MR) images has shown to be a difficult task. One of the major problems related to the detection of the boundary are the shortcomings typical of discrete data, such as sampling artifacts and noise, which may cause the shape boundaries to be indistinct and disconnected. Furthermore, the structures inside the ventricular cavities, such as papillary muscles, are often indistinguishable from structures of interest for diagnostic analysis, such as the moving inner heart boundary. Thus, segmentation is error-prone and often incomplete. The aim of this work is to develop a model towards an automatic segmentation of the endocardial border. The proposed method is composed of two phases: The segmentation phase uses a bottom-up multi-scale analysis, based mainly on morphological scale-space processing by decomposing the image into a number of scales of different structure size. As a result of the decomposition, the structures adjacent to the endocardial border are located, and finally an estimated boundary is obtained regardless of those structures. The refinement phase subsequently asserts prior information about local structure around defined points along the shape boundary in order to obtain the best accuracy of the endocardial segmentation.
... For the calculation of the snake parameters, a(s), and b(s), we took into consideration the irregular spacing between the contour points of the snake and were calculated as proposed in [7]. We have chosen in our study the initial values of a i (s) = 0.6, b i (s) = 0.4 and c i (s) = 2, to start the snake deformation, which is consistent with other studies [6, 33, 39]. The extracted final snake contours (seeFig. ...
Ultrasound measurements of the human carotid artery walls are conventionally obtained by manually tracing interfaces between tissue layers. In this study we present a snakes segmentation technique for detecting the intima-media layer of the far wall of the common carotid artery (CCA) in longitudinal ultrasound images, by applying snakes, after normalization, speckle reduction, and normalization and speckle reduction. The proposed technique utilizes an improved snake initialization method, and an improved validation of the segmentation method. We have tested and clinically validated the segmentation technique on 100 longitudinal ultrasound images of the carotid artery based on manual measurements by two vascular experts, and a set of different evaluation criteria based on statistical measures and univariate statistical analysis. The results showed that there was no significant difference between all the snakes segmentation measurements and the manual measurements. For the normalized despeckled images, better snakes segmentation results with an intra-observer error of 0.08, a coefficient of variation of 12.5%, best Bland-Altman plot with smaller differences between experts (0.01, 0.09 for Expert1 and Expert 2, respectively), and a Hausdorff distance of 5.2, were obtained. Therefore, the pre-processing of ultrasound images of the carotid artery with normalization and speckle reduction, followed by the snakes segmentation algorithm can be used successfully in the measurement of IMT complementing the manual measurements. The present results are an expansion of data published earlier as an extended abstract in IFMBE Proceedings (Loizou et al. IEEE Int X Mediterr Conf Medicon Med Biol Eng POS-03 499:1-4, 2004).
The connection between humans and digital technologies has been documented extensively in the past decades but needs to be evaluated through the current global pandemic. Artificial Intelligence(AI), with its two strands, Machine Learning (ML) and Semantic Reasoning, has proven to be a great solution to provide efficient ways to prevent, diagnose and limit the spread of COVID-19. IoT solutions have been widely proposed for COVID-19 disease monitoring, infection geolocation, and social applications. In this paper, we investigate the usage of the three technologies for handling the COVID-19 pandemic. For this purpose, we surveyed the existing ML applications and algorithms proposed during the pandemic to detect COVID-19 disease using symptom factors and image processing. The survey includes existing approaches including semantic technologies and IoT systems for COVID-19. Based on the survey result, we classified the main challenges and the solutions that could solve them. The study proposes a conceptual framework for pandemic management and discusses challenges and trends for future research.
Myocardial infarction is one of the major life-threatening diseases. The cause is atherosclerosis i.e. the occlusion of the coronary artery by deposition of plaque on its walls. The severity of plaque deposition in the artery depends on the characteristics of the plaque. Hence, the classification of the type of plaque is crucial for assessing the risk of atherosclerosis and predicting the chances of myocardial infarction. This paper proposes prediction of atherosclerotic risk by non-invasive ultrasound image segmentation and textural feature extraction. The intima-media complex is segmented using a snakes-based segmentation algorithm on the arterial wall in the ultrasound images. Then, the plaque is extracted from the segmented intima-media complex. The features of the plaque are obtained by computing Hu’s moment invariants. Visual pattern recognition independent of position, size, orientation and parallel projection could be done using these moment invariants. For the classification of the features of the plaque, an SVM classifier is used. The performance shows improvement in accuracy using lesser number of features than previous works. The reduction in feature size is achieved by incorporating segmentation in the pre-processing stage. Tenfold cross-validation protocol is used for training and testing the classifier. An accuracy of 97.9% is obtained with only two features. This proposed technique could work as an adjunct tool in quick decision-making for cardiologists and radiologists. The segmentation step introduced in the preprocessing stage improved the feature extraction technique. An improvement in performance is achieved with much less number of features.
Segmentation of carotid intima-media (IM) borders from ultrasound images is of great importance for predicting cardiovascular risks. In this paper, we have developed a fully automatic approach to sequentially segment the carotid IM borders in each image throughout ultrasound sequences. Firstly, the first frame of an ultrasound sequence is automatically segmented using edge detectors and dynamic programming, and then the rest frames are segmented successively under the state-space framework. Under this framework, we developed a variant of the snake method for a precise measurement. The evaluation of our segmentation result is done by comparison with average manual delineations of 3 physicians on a total of 65 sequences. The accuracy of our method is high (segmentation error is 32.1±37.5μm for LI and 35.0±41.5μm for MA). The BA plot and the linear regression also demonstrate that our method is in agreement with the ground truth. This study strengthens the potential of the state-space and snake-based approach in segmenting IM borders for clinical diagnosis by demonstrating a fully automatic scheme.
The paper presents a fully automatic method of video segmentation that exploits both colour and motion information. A variation
of the active contour technique is applied. The method is developed for real-time applications and therefore its low complexity
is of high importance. The major part of contour migration is driven by very efficient algorithm known as Fast Marching. The
result is then locally enhanced using more computationally exhaustive still image segmentation.
Experiments on model organisms are used to extend the understanding of complex biological processes. In Caenorhabditis elegans studies, populations of specimens are sampled to measure certain morphological properties and a population is characterized based on statistics extracted from such samples. Automatic detection of C. elegans in such culture images is a difficult problem. The images are affected by clutter, overlap and image degradations. In this paper, we exploit shape and appearance differences between C. elegans and non-C. elegans segmentations. Shape information is captured by optimizing a parametric open contour model on training data. Features derived from the contour energies are proposed as shape descriptors and integrated in a probabilistic framework. These descriptors are evaluated for C. elegans detection in culture images. Our experiments show that measurements extracted from these samples correlate well with ground truth data. These positive results indicate that the proposed approach can be used for quantitative analysis of complex nematode images.
The Saliency Network proposed by Shashua and Ullman (1988) is a well-known approach to the problem of extracting salient curves from images while performing gap completion. This paper analyzes the Saliency Network. The Saliency Network is attractive for several reasons. First, the network generally prefers long and smooth curves over short or wiggly ones. While computing saliencies, the network also fills in gaps with smooth completions and tolerates noise. Finally, the network is locally connected, and its size is proportional to the size of the image.
Nevertheless, our analysis reveals certain weaknesses with the method. In particular, we show cases in which the most salient element does not lie on the perceptually most salient curve. Furthermore, in some cases the saliency measure changes its preferences when curves are scaled uniformly. Also, we show that for certain fragmented curves the measure prefers large gaps over a few small gaps of the same total size. In addition, we analyze the time complexity required by the method. We show that the number of steps required for convergence in serial implementations is quadratic in the size of the network, and in parallel implementations is linear in the size of the network. We discuss problems due to coarse sampling of the range of possible orientations. Finally, we consider the possibility of using the Saliency Network for grouping. We show that the Saliency Network recovers the most salient curve efficiently, but it has problems with identifying any salient curve other than the most salient one.
We propose a snake-based approach that allows a user to specify only the distant end points of the curve he wishes to delineate without having to supply an almost complete polygonal approximation. This greatly simplifies the initialization process and yields excellent convergence properties. This is achieved by using the image information around the end points to provide boundary conditions and by introducing an optimization schedule that allows a snake to take image information into account first only near its extremities and then, progressively, toward its center. In effect, the snakes are clamped onto the image contour in a manner reminiscent of a ziplock being closed.
These snakes can be used to alleviate the often repetitive task practitioners face when segmenting images by eliminating the need to sketch a feature of interest in its entirety, that is, to perform a painstaking, almost complete, manual segmentation.
Dynamic programming is discussed as an approach to solving variational problems in vision. Dynamic programming ensures global optimality of the solution, is numerically stable, and allows for hard constraints to be enforced on the behavior of the solution within a natural and straightforward structure. As a specific example of the approach's efficacy, applying dynamic programming to the energy-minimizing active contours is described. The optimization problem is set up as a discrete multistage decision process and is solved by a time-delayed discrete dynamic programming algorithm. A parallel procedure for decreasing computational costs is discussed
this paper we present a sallehey measure based on curvature and curvature variation. The structures this measure emphasizes are also salient in hmnan perception, and they often correspond to objects of interest in the image. We present a method for computing the saliency by a simple iterative scheme, using a uniform network of locally connected processing elements. The network uses an optimization approach to produce a "sallehey map," which is a representation of the image emphasizing salient locations
Active contour models, or snakes, are effective and robust in contour extraction. In most papers on snakes, an initialization close to the desired contour is assumed to be provided, which is inappropriate in many cases. The ziplock snake model presented by Neuenschwander et al. (1997), however, needs only two user-supplied endpoints. The optimization process for a ziplock snake starts from the two endpoints and progresses towards the center of the snake. In this paper, we present a method to combine a grammatical model that encodes a priori shape information with the ziplock snakes. A competing mechanism is adopted to take advantage of the shape models without inducing excessive computation. The resulting model-based ziplock snakes have many advantages over the original model: They can accurately locate contour features, produce more refined results, and deal with multiple contours, missing image cues and noise
The Saliency Network proposed by Shashua and Ullman (1988) is a well-known approach to the problem of extracting salient curves from images while performing gap completion. This paper analyzes the Saliency Network. Although the network is attractive for a number reasons, our analysis reveals certain weaknesses with the method. In particular, we show cases in which the most salient element does not lie on the perceptually most salient curve. Furthermore, the saliency measure may change its preferences when curves are scaled uniformly. Also, for certain fragmented curves the measure prefers large gaps over a few small gaps of the same total size. We analyze the time complexity required by the method and discuss problems due to coarse sampling of the range of possible orientations. We show that with proper sampling the complexity of the network becomes cubic in the size of the network. Finally, we consider the possibility of using the Saliency Network for grouping. We show that the Saliency Network recovers the most salient curve efficiently, but it has problems with identifying any salient curve other than the most salient one
The theory of active contours models the problem of contour
recovery as an energy minimization process. The computational solutions
based on dynamic programming require that the energy associated with a
contour candidate can be decomposed into an integral of local energy
contributions. In this paper we propose a grammatical framework that can
model different local energy models and a set of allowable transitions
between these models. The grammatical encodings are utilized to
represent a priori knowledge about the shape of the object and the
associated signatures in the underlying images. The variability
encountered in numerical experiments is addressed with the energy
minimization procedure which is embedded in the grammatical framework.
We propose an algorithmic solution that combines a nondeterministic
version of the Knuth-Morris-Pratt algorithm for string matching with a
time-delayed discrete dynamic programming algorithm for energy
minimization. The numerical experiments address practical problems
encountered in contour recovery such as noise robustness and occlusion
The use of energy minimizing deformable models in various applications has become very popular. The issue of initializing such models, however, has not received much attention although the model's performance depends critically on its initial state. We aim at obtaining good convergence and segmentation properties from a minimum of a priori information.
photograph and biography not available at time of publication. Xiaobo Li (SM'91), photograph and biography not available at time of publi-cation
- Jiankang Wang
Jiankang Wang (M'00), photograph and biography not available at time of publication. Xiaobo Li (SM'91), photograph and biography not available at time of publi-cation.