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In this paper, the honey bee mating optimization (HBMO) algorithm is used to improve the detection of the concave region connected with the control points of active contour. In the traditional active contour model (ACM) method, the updating of control point is based on its local energy within a small searching window. As a result, it always results in the failure of precisely searching the boundary concavities. In order to vanquish these drawbacks, the HBMO-based snake algorithm is applied in this paper to search for the optimal position in a lager searching window around each control point. In this proposed algorithm, to each active contour there is a chromosome that includes several genes as well as the control points of active contour. These control points are moved iteratively by minimizing the total energy of the active contour. Experimental results reveal that the proposed HBMO-based snake algorithm can locate the object boundary of concavity more precisely without requiring large number of computational time.

In this paper, the multi-population particle swarm optimization (PSO) is utilized to enhance the concavity searching capability for the control points of active contour model (ACM). In the traditional methods for ACM, each control point searches its new position in a small nearby window. Consequently, the boundary concavities cannot be searched accurately. Some improvements have been made in the past to enlarge the searching space, yet they are still time-consuming. To overcome these drawbacks, a multi-population particle swarm optimization technique is adopted in this paper to reduce the search time but in a larger searching window. In the proposed scheme, to each control point in the contour there is a corresponding swarm of particles with the best swarm particle as the new control point. The proposed optimizer not only inherits the spirit of the original PSO in each swarm but also shares information of the surrounding swarms. Experimental results demonstrate that the proposed method can improve the search of object concavities without extra computation time.

This research article proposes a novel edge scaled method with local and global region based statistical information for inhomogeneous image segmentation. We coordinate region force (local and global) term with geodesic edge term in level set formulation. The vital energy of the proposed model utilizes both global and local terms fused with compacted geodesic edge term in an additive fashion, which uses image gradient information to segment feeble boundaries inside images. The initialization of the level set is always vulnerable and its execution is liable to suitable initialization, which requires manual intercession. In this regard, the proposed method is extended and automated by integrating the proposed method with FCM (fuzzy c-means) clustering. Modified active contour method gets suitable initialization from FCM clustering, which eliminates the initial contour problem existed in the traditional region-based active contour methods. A development in this paper is to interface FCM with level set strategy by morphological tasks. Moreover, the uninterrupted segmentation scheme confirms the effectiveness of the proposed method in modern intelligent and automatic systems. The experimental analysis is performed on synthetic and real inhomogeneous images. The visual and quantitative results confirm the proficiency of the proposed technique and show that proposed method achieves better accuracy results compared to previous methods.

Supervised active contour models can use information extracted from supervised samples to guide contour evolution. However, their applicability is limited by the accuracy of the probabilistic models they use, especially when processing images with intensity inhomogeneity. In this paper, an unsupervised activity contour model with Self Organizing Maps (SOM) is proposed. The proposed model employs the self-organizing neural network to perform clustering calculation and the clustering center is called local self-organizing clustering center. An adaptive sign function is used to control the direction of curve evolution. To improve the stability of curve evolution, an improved double-well potential function is proposed. The experiment results show that our model can effectively segment images with intensity inhomogeneity.

The segmentation of images under biased conditions such as low contrast, high-intensity inhomogeneity, and noise is a challenge for any image segmentation model. The ideal image segmentation model must be capable of segmenting images with maximum accuracy and a minimum false-positive rate under biased conditions. In this paper, we propose a region-based active contour model (ACM), called global signed pressure and K-means clustering based on local correntropy with the exponential family (GSLCE), to address segmentation challenges under biased conditions. An adaptive weighted function is formulated based on the global and local image differences such that a single weighted function can drive both the global and local intensities. Further, the Riemannian steepest descent method is used for convergence of the proposed GSLCE energy function, and a Gaussian kernel is applied for spatial smoothing to obviate the computationally expensive level-set re-initialization. The experimental results show that, compared with state-of-the-art ACMs, the proposed GSLCE model obtained the best visual image segmentation results for synthetic and real images under biased conditions. Further, the qualitative and quantitative experimental results validate that the proposed model outperforms the state-of-the-art ACMs by yielding higher values of performance metrics. Moreover, the proposed GSLCE model requires substantially less processing time compared to the state-of-the-art ACMs.

Fast an accurate iris segmentation system is presented.The Optical Correlation based Active Contour (OCAC) technique is described.A general study of the OCAC for iris segmentation and tracking is presented.Small computation time achieved by the algorithm is the advantage of the used method.The presented intelligent system perform a high accuracy in iris segmentation. Display Omitted Iris segmentation using active contours approaches is receiving increasing attention. In this paper, a self-ruling active contour approach based on the optical correlation algorithm is proposed. The novelty of this research effort is to apply the Optical Correlation based Active Contours (OCAC) on iris segmentation and tracking and highlight the advantages of its small computation time and better accuracy performance. Optical correlation computed with a numerical simulation of the Vander Lugt correlator is used to detect iris and pupil areas which used as an initial contours. As a result, these initial contours assists the method to calculate terms in an energy expression. In the proposed method, several references images called filters of iris and pupil have been introduced. Images from four iris datasets as CASIA v4, WVU non-ideal, MMU2, UBIRIS v2, and a motion video were used in the experiments phase. To present an aggregate overview of the proposed method advantages, we computed several parameters as iris and pupil centers localization errors, iris and pupil rays errors, three performance metrics (as Jaccard coefficient, Dice coefficient, Hausdroff distance), average segmentation error, and average execution time. We compare these segmentation performance parameters with several leading techniques demonstrating significantly improved results with the proposed OCAC technique.

An alternative Active Contour Model solution for medical images is introduced.A multi-population Cuckoo Search Strategy (MCSS) is implemented to boost ACM.Proposed method was applied on Magnetic Resonance Imaging (MRI) data.MCSS outperforms traditional ACM and ACM driven by multi-population PSO. In this paper, an alternative Active Contour Model (ACM) driven by Multi-population Cuckoo Search (CS) algorithm is introduced. This strategy assists the converging of control points towards the global minimum of the energy function, unlike the traditional ACM version which is often trapped in a local minimum. In the proposed methodology, each control point is constrained in a local search window, and its energy minimisation is performed through a Cuckoo Search via Levy flights paradigm. With respect to local search window, two shape approaches have been considered: rectangular shape and polar coordinates. Results showed that the CS method using polar coordinates is generally preferable to CS performed in rectangular shapes. Real medical and synthetic images were used to validate the proposed strategy, through three performance metrics as the Jaccard index, the Dice index and the Hausdorff distance. Applied specifically to Magnetic Resonance Imaging (MRI) images, the proposed method enables to reach better accuracy performance than the traditional ACM formulation, also known as Snakes and the use of Multi-population Particle Swarm Optimisation (PSO) algorithm.

A new online region-based active contour model (ORACM) is proposed in this paper. The classical geodesic active contour (GAC) model has only local segmentation property, although the Chan-Vese (C-V) model possesses global. An up-to-date active contour model (ACM with SBGFRLS) proposed in Zhang, Zhang, Song, and Zhou (2010) both has the properties of global/local segmentation and incorporates the GAC and the C-V models to raise active contours' performance on image segmentation. However it has two major disadvantages. First, it deforms the active contour model just using the gradient of current level set iteratively and so works too slowly. Second, it needs a parameter @a which plays major impact on the results and to be tuned according to input images. The proposed model ORACM eliminates these two disadvantages by using a new binary level set formula and a new regularization operation such as morphological opening and closing. Without changing segmentation accuracy, ORACM requires no parameter and less time over the traditional ACMs. Experiments on synthetic and real images demonstrate that the computational cost of ORACM with the morphological operations is 3.75 times less than the traditional ACMs on average.

Texture image segmentation is an important issue in computer vision applications. Active contour models are one of the powerful tools that are able to detect and segment textured objects against textured backgrounds. However, problems concerning the speed of the contour convergence in the texture image have limited their utility. This paper presents a fast and efficient texture energy function in the parametric active contour models. In the proposed method, we apply a novel version of the Walsh-Hadamard transform, called the Directional Walsh-Hadamard Transform or DWHT for calculating texture features of the energy function. This DWHT-based energy function is fast and easy to implement, and hence suitable for real time applications. We will show that the proposed method can reduce the execution time, while maintaining close accuracy and consequently it is more efficient than the previous active contour based methods for texture image segmentation.

Segmenting center of interests (COIs) from pictures is an important but highly challenging problem for researchers in computer vision and image processing. The capability of understanding the meanings of pictures by computers can lead to breakthroughs in a wide range of applications including Web image search and online picture-sharing communities. In this paper, a two-level strategy is presented, which consists of a rough segmentation stage and a fine segmentation stage. In the first level, a picture is partitioned into four regions by using a block clustering method based on color and texture features, and the COI within the picture is distinguished from the background according to the principles of photographic composition. This stage aims to determine the approximate region of the target. In the second level, a novel active contour model is established based on shape information and vector method, where the image energy is defined by a hue gradient and the external energy is generated from either a triangular inner force or a supplementary force. This stage tries to extract the boundary of the target accurately. Experimental results on photos downloaded from the Internet show the feasibility and effectiveness of the proposed method.

Segmentation of the left ventricle in echocardiographic images is a task with important diagnostic power. This paper describes a method for the calculation of the ventricle's contours in long- and short-axis views. The segmentation process starts with a complex border detection process, followed by the application of a generalized Hough transform to detect curves and ended up through an active-contour algorithm. Afterwards, parameters left ventricular area in a short-axis view, left ventricular length in a long-axis view, left ventricular volume, left ventricular mass, and heart-wall thickness are specifically calculated.