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Kidney segmentation from abdominal computed tomography (CT) images is essential for computer-aided kidney diagnosis, pathology detection, and surgical planning. This paper introduces a kidney segmentation method for clinical contrast-enhanced CT images. First, it begins with shape-based preprocessing to remove the spine and ribs. Second, a novel clustering algorithm and an initial kidney selection strategy are utilized to locate the initial slices and contours. Finally, an adaptive narrow-band approach based on active contours is developed, followed by a clustering postprocessing to address issues with concave parts. Experimental results demonstrate the high segmentation performance of the proposed method, achieving a Dice Similarity Coefficient of 97.4 ± 1.0% and an Average Symmetric Surface Distance of 0.5 ± 0.2 mm across twenty sequences. Notably, this method eliminates the need for manually setting initial contours and can handle intensity inhomogeneity and varying kidney shapes without extensive training or statistical modeling. [ABSTRACT FROM AUTHOR]

This paper presents a study of the segmentation of medical images. The paper provides a solid introduction to image enhancement along with image segmentation fundamentals. In the first step, the morphological operations are employed to ensure image detail protection and noise-immunity. The objective of using morphological operations is to remove the defects in the texture of the image. Secondly, the Fuzzy C-Means (FCM) clustering algorithm is used to modify membership function based only on the spatial neighbors instead of the distance between pixels within local spatial neighbors and cluster centers. The proposed technique is very simple to implement and significantly fast since it is not necessary to compute the distance between the neighboring pixels and the cluster centers. It is also efficient when dealing with noisy images because of its ability to efficiently improve the membership partition matrix. Simulation results are performed on different medical image modalities. Ultrasonic (Us), X-ray (Mammogram), Computed Tomography (CT), Positron Emission Tomography (PET), and Magnetic Resonance (MR) images are the main medical image modalities used in this work. The obtained results illustrate that the proposed technique can achieve good results with a short time and efficient image segmentation. Simulation results on different image modalities show that the proposed technique can achieve segmentation accuracies of 98.83%, 99.71%, 99.83%, 99.85%, and 99.74% for Us, Mammogram, CT, PET, and MRI images, respectively. [ABSTRACT FROM AUTHOR]

Automation of Antimicrobial Susceptibility Test (AST) for faster results and the cheaper cost is gaining importance for effective medical diagnosis. Among all the available methods for AST, disk diffusion is the cheaper one, requires less skill, and it is applicable for telemetry. The diameters of the Inhibition zones are calculated using image processing and computer vision techniques to provide easy and automated data for further processing, however, the result of a particular antibiotic is Susceptible (S), Intermediate (I), or resistance (R). In this paper, the agar plate images, which were prepared for the AST, have been taken with 23 antibiotics and 12 bacteria and the image bank of about 170 images with 513 inhibition zones has been developed. For the diameter calculation of the inhibition zones, four methods are developed. In the first method, the diameter is calculated using the watershed segmentation method which requires manual interference, in the second method, modified edge detection is used instead of edge detection in the watershed segmentation, in the third method a semi-automatic method is developed using segmentation with active contours, and in the fourth one, an automatic method is used based on the fuzzy clustering along with Level set methods (FCLSM). The evaluation is done using kappa and correlation indices. The results show that these methods are giving better results within the tolerable limits for automation. In this paper, the same agar plate image with 11 antibiotics is taken to compare developed methods with the manual method and satisfying results are obtained. [ABSTRACT FROM AUTHOR]

The active contour model is a widely used technique for automatic object contour extraction. Existing methods based on this model can perform with high accuracy, even in the case of complex contours, but challenging issues remain, like the need for precise contour initialization for high curvature boundary segments or the handling of cluttered backgrounds. To deal with such issues, this paper presents a salient object extraction method, the first step of which is the introduction of an improved edge map that incorporates edge direction as a feature. The direction information in the small neighborhoods of image feature points is extracted, and the images’ prominent orientations are defined for direction-selective edge extraction. Using such improved edge information, we provide a highly accurate shape contour representation, which we also combine with texture features. The principle of the paper is to interpret an object as the fusion of its components: its extracted contour and its inner texture. Our goal in fusing textural and structural information is twofold: it is applied for automatic contour initialization, and it is also used to establish an improved external force field. This fusion then produces highly accurate salient object extractions. We performed extensive evaluations, which confirm that the presented object extraction method outperforms parametric active contour models and achieves higher efficiency than the majority of the evaluated automatic saliency methods. [ABSTRACT FROM AUTHOR]

Since the advent of U-Net, fully convolutional deep neural networks and its many variants have completely changed the modern landscape of deep-learning based medical image segmentation. However, the over-dependence of these methods on pixel-level classification and regression has been identified early on as a problem. Especially when trained on medical databases with sparse available annotation, these methods are prone to generate segmentation artifacts such as fragmented structures, topological inconsistencies and islands of pixel. These artifacts are especially problematic in medical imaging since segmentation is almost always a pre-processing step for some downstream evaluations like surgical planning, visualization, prognosis, or treatment planning. However, one common thread across all these downstream tasks is the demand of anatomical consistency. To ensure the segmentation result is anatomically consistent, approaches based on Markov/ Conditional Random Fields, Statistical Shape Models, Active Contours are becoming increasingly popular over the past 5 years. In this review paper, a broad overview of recent literature on bringing explicit anatomical constraints for medical image segmentation is given, the shortcomings and opportunities are discussed and the potential shift towards implicit shape modelling is elaborated. We review the most relevant papers published until the submission date and provide a tabulated view with method details for quick access. [ABSTRACT FROM AUTHOR]

Detection of relative changes in circulating blood volume is important to guide resuscitation and manage a variety of medical conditions, including sepsis, trauma, dialysis, and congestive heart failure. Recent studies have shown that estimates of circulating blood volume can be obtained from the cross-sectional area of the internal jugular vein (IJV) from ultrasound images. However, accurate segmentation and tracking of the IJV in ultrasound imaging is a challenging task and is significantly influenced by a number of parameters, such as the image quality, shape, and temporal variation. In this paper, we propose a novel adaptive polar active contour (Ad-PAC) algorithm for the segmentation and tracking of the IJV in ultrasound videos. In the proposed algorithm, the parameters of the Ad-PAC algorithm are adapted based on the results of segmentation in previous frames. The Ad-PAC algorithm is applied to 65 ultrasound videos captured from 13 healthy subjects, with each video containing 450 frames. The results show that spatial and temporal adaptation of the energy function significantly improves segmentation performance when compared with the current state-of-the-art active contour algorithms. [ABSTRACT FROM AUTHOR]

Deep learning-based approaches to delineating 3D structure depend on accurate annotations to train the networks. Yet in practice, people, no matter how conscientious, have trouble precisely delineating in 3D and on a large scale, in part because the data is often hard to interpret visually and in part because the 3D interfaces are awkward to use. In this paper, we introduce a method that explicitly accounts for annotation inaccuracies. To this end, we treat the annotations as active contour models that can deform themselves while preserving their topology. This enables us to jointly train the network and correct potential errors in the original annotations. The result is an approach that boosts performance of deep networks trained with potentially inaccurate annotations. [ABSTRACT FROM AUTHOR]

Quantitative assessment of retinal layer thickness in spectral domain-optical coherence tomography (SD-OCT) images is vital for clinicians to determine the degree of ophthalmic lesions. However, due to the complex retinal tissues, high-level speckle noises and low intensity constraint, how to accurately recognize the retinal layer structure still remains a challenge. To overcome this problem, this paper proposes an adaptive-guided-coupling-probability level set method for retinal layer segmentation in SD-OCT images. Specifically, based on Bayes's theorem, each voxel probability representation is composed of two probability terms in our method. The first term is constructed as neighborhood Gaussian fitting distribution to characterize intensity information for each intra-retinal layer. The second one is boundary probability map generated by combining anatomical priors and adaptive thickness information to ensure surfaces evolve within a proper range. Then, the voxel probability representation is introduced into the proposed segmentation framework based on coupling probability level set to detect layer boundaries. A total of 1792 retinal B-scan images from 4 SD-OCT cubes in healthy eyes, 5 cubes in abnormal eyes with central serous chorioretinaopathy and 5 SD-OCT cubes in abnormal eyes with age-related macular disease are used to evaluate the proposed method. The experiment demonstrates that the segmentation results obtained by the proposed method have a good consistency with ground truth, and the proposed method outperforms six methods in the layer segmentation of uneven retinal SD-OCT images. [ABSTRACT FROM AUTHOR]

Traditional iris segmentation methods give good results when the iris images are taken under ideal imaging conditions. However, the segmentation accuracy of an iris recognition system significantly influences its performance especially in nonideal iris images. This paper proposes a novel segmentation method for nonideal iris images. Two algorithms are proposed for pupil segmentation in iris images which are captured under visible and near infrared light. Then, a fusion of an expanding and a shrinking active contour is developed for iris segmentation by integrating a new pressure force to the active contour model. Thereafter, a noncircular iris normalization scheme is adopted to effectively unwrap the segmented iris. In addition, a novel method for closed eye detection is proposed. The proposed scheme is robust in finding the exact iris boundary and isolating the eyelids of the iris images. Experimental results on CASIA V4.0, MMU2, UBIRIS V1, and UBIRIS V2 iris databases indicate a high level of accuracy using the proposed technique. Moreover, the comparison results with the state-of-the-art iris segmentation algorithms revealed considerable improvement in segmentation accuracy and recognition performance while being computationally more efficient. [ABSTRACT FROM AUTHOR]

Diabetic foot (DF) complications are associated with temperature variations. The occurrence of DF ulceration could be reduced by using a contactless thermal camera. The aim of our study is to provide a decision support tool for the prevention of DF ulcers. Thus, the segmentation of the plantar foot in thermal images is a challenging step for a non-constraining acquisition protocol. This paper presents a new segmentation method for plantar foot thermal images. This method is designed to include five pieces of prior information regarding the aforementioned images. First, a new energy term is added to the snake of Kass et al. in order to force its curvature to match that of the prior shape, which has a known form. Second, we defined the initial contour as the downsized prior-shape contour, which is placed inside the plantar foot surface in a vertical orientation. This choice makes the snake avoid strong false boundaries present outside the plantar region when evolving. As a result, the snake produces a smooth contour that rapidly converges to the true boundaries of the foot. The proposed method is compared to two classical prior-shape snake methods, that of Ahmed et al. and that of Chen et al. A database of 50 plantar foot thermal images was processed. The results show that the proposed method outperforms the previous two methods with a root-mean-square error of 5.12 pixels and a dice similarity coefficient of 94%. The segmentation of the plantar foot regions in the thermal images helped us to assess the point-to-point temperature differences between the two feet in order to detect hyperthermia regions. The presence of such regions is the pre-sign of ulcers in the diabetic foot. Furthermore, our method was applied to hyperthermia detection to illustrate the promising potential of thermography in the case of the diabetic foot. Associated with a friendly acquisition protocol, the proposed segmentation method is the first step for a future mobile smartphone-based plantar foot thermal analysis for diabetic foot patients. [ABSTRACT FROM AUTHOR]

Binarization is an important step in reading text documents automatically through optical character recognition. Old document images often suffer from degradations that make their binarization a challenging task. In this paper, a new binarization technique for degraded document images is presented. The proposed technique is based on active contours evolving according to intrinsic geometric measures of the document image. The image contrast that is defined by the local image maximum and minimum is used to automatically generate the initialization map of our active contour model; an average thresholding is also used to produce the final delineation and binarization. The proposed implementation benefits from the level set framework, which allows the simultaneous application of a large variety of forces at the stroke-background interface. Our binarization method involves the combination of those forces in a specific way. The efficiency of the proposed method is shown on both recent and historical document images of the Document Image Binarization Contest (DIBCO) datasets that include different types of degradations. The results are compared to a number of known techniques from the literature. [ABSTRACT FROM AUTHOR]

This paper presents the boundary detection of atrium and ventricle in echocardiographic images. In case of mitral regurgitation, atrium and ventricle may get dilated. To examine this, doctors draw the boundary manually. Here the aim of this paper is to evolve the automatic boundary detection for carrying out segmentation of echocardiography images. Active contour method is selected for this purpose. There is an enhancement of Chan-Vese paper on active contours without edges. Our algorithm is based on Chan-Vese paper active contours without edges, but it is much faster than Chan-Vese model. Here we have developed a method by which it is possible to detect much faster the echocardiographic boundaries. The method is based on the region information of an image. The region-based force provides a global segmentation with variational flow robust to noise. Implementation is based on level set theory so it easy to deal with topological changes. In this paper, Newton-Raphson method is used which makes possible the fast boundary detection. [ABSTRACT FROM AUTHOR]

In this paper, an unsupervised image segmentation technique is proposed. Firstly, for obtaining a multiresolution representation of the original image, the probability model of the nonsubsampled contourlet coefficients of the image is established. A region-based active contour model is then applied to the multiresolution representation for segmenting the image. The proposed technique has been conducted on challenging images to illustrate the robust and accurate segmentations. At last, an in-depth study of the behaviors of the above techniques in response to the proposed model is given, and the segmentation results are compared with several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This paper proposes a computer assisted diagnostic system for the detection of melanoma in dermoscopy images. Clinical findings have concluded that in case of melanoma, the lesion borders exhibit differential structures such as pigment networks and streaks as opposed to normal skin spots, which have smoother borders. We aim at validating these findings by performing segmentation of the skin lesions followed by an extraction of the peripheral region of the lesion that is subjected to feature extraction and classification for detecting melanoma. For segmentation, we propose a novel active contours based method that takes an initial lesion contour followed by the usage of Kullback–Leibler divergence between the lesion and skin to fit a curve to the lesion boundaries. After segmentation of the lesion, its periphery is extracted to detect melanoma using image features that are based on local binary patterns. For validation of our algorithms, we have used the publicly available PH $^{2}$ and ISIC dermoscopy datasets. An extensive experimental analysis reveals two important findings: 1) the proposed segmentation method mimics the ground truth data; and 2) the most significant melanoma characteristics in the lesion actually lie on the lesion periphery. [ABSTRACT FROM AUTHOR]

In our changing world, floods are a threat of increasing concern. Within this context, flood mapping is important for both damage assessment and forecast improvement. Due to the suitability of synthetic aperture radar (SAR) for flood mapping, a broad range of SAR-based flood mapping algorithms has been developed during the past years. However, most of these algorithms were presented based on a single test case only and comparisons between methods are rare. This paper presents an in-depth assessment and comparison of the established pixel-based flood mapping approaches, including global and enhanced thresholding, active contour modeling and change detection. The methods were tested on medium-resolution SAR images of different flood events and lakes across the U.K. and Ireland and were evaluated on both accuracy and robustness. Results indicate that the most suited method depends on the area of interest and its characteristics as well as the intended use of the observation product. Due to its high robustness and good performance, tiled thresholding is suited for automated, near-real time flood detection and monitoring. Active contour models can provide higher accuracies but require long computation times that strongly increase with increasing image sizes, making them more appropriate for accurate flood mapping in smaller areas of interest. [ABSTRACT FROM AUTHOR]

The segmentation of synthetic aperture radar (SAR) images is a long-standing yet challenging task, not only because of the presence of speckle but also due to the variations of surface backscattering properties in the images. Tremendous investigations have been made to suppress the speckle effects for the segmentation of SAR images, whereas few works are devoted to dealing with the variations of backscattering intensities in the images. To overcome the two difficulties, this paper presents a novel SAR image segmentation method by exploiting a multiscale active contour model based on the nonlocal processing principle. More precisely, we first formulize the SAR segmentation problem with an active contour model by integrating the nonlocal interactions between pairs of patches inside and outside the segmented regions. Second, a multiscale strategy is proposed to speed up the nonlocal active contour segmentation procedure and to avoid falling into a local minimum for achieving more accurate segmentation results. Experimental results on simulated and real SAR images demonstrate the efficiency and feasibility of the proposed method: It can not only achieve precise segmentations for images with heavy speckle and nonlocal intensity variations but also be used for SAR images from different types of sensors. [ABSTRACT FROM PUBLISHER]

Road extraction from very high-resolution (VHR) satellite images plays an important role in the remote-sensing applications. Although tracing road features in satellite images has received much attention over the years, fully automated methods appear to be in their infancy. To tackle these limitations to some extent, this paper presents a novel object-based automatic road extraction method. The proposed method consists of five main steps. First, satellite images are segmented to generate objects. Then, two object-based filters are applied to compute object features to select road candidates. After that, the road class is extracted using the support vector machine (SVM) based on the extracted feature set. Finally, tensor voting (TV), active contour, and the geometrical information are integrated to eliminate road gaps and improve road smoothness. Experiments are conducted on nine test sites. It is experimentally demonstrated that the proposed method produces an excellent accuracy for the automatic road extraction from VHR satellite images. [ABSTRACT FROM PUBLISHER]

The task of segmenting cell nuclei and cytoplasm in pap smear images is one of the most challenging tasks in automated cervix cytological analysis due to specifically the presence of overlapping cells. This paper introduces a multi-pass fast watershed-based method (MPFW) to segment both nucleus and cytoplasm from large cell masses of overlapping cervical cells in three watershed passes. The first pass locates the nuclei with barrier-based watershed on the gradient-based edge map of a pre-processed image. The next pass segments the isolated, touching, and partially overlapping cells with a watershed transform adapted to the cell shape and location. The final pass introduces mutual iterative watersheds separately applied to each nucleus in the largely overlapping clusters to estimate the cell shape. In MPFW, the line-shaped contours of the watershed cells are deformed with ellipse fitting and contour adjustment to give a better representation of cell shapes. The performance of the proposed method has been evaluated using synthetic, real extended depth-of-field, and multi-layers cervical cytology images provided by the first and second overlapping cervical cytology image segmentation challenges in ISBI 2014 and ISBI 2015. The experimental results demonstrate superior performance of the proposed MPFW in terms of segmentation accuracy, detection rate, and time complexity, compared with recent peer methods. [ABSTRACT FROM AUTHOR]

Minimal paths are regarded as a powerful and efficient tool for boundary detection and image segmentation due to its global optimality and the well-established numerical solutions such as fast marching method. In this paper, we introduce a flexible interactive image segmentation model based on the Eikonal partial differential equation (PDE) framework in conjunction with region-based homogeneity enhancement. A key ingredient in the introduced model is the construction of local geodesic metrics, which are capable of integrating anisotropic and asymmetric edge features, implicit region-based homogeneity features and/or curvature regularization. The incorporation of the region-based homogeneity features into the metrics considered relies on an implicit representation of these features, which is one of the contributions of this work. Moreover, we also introduce a way to build simple closed contours as the concatenation of two disjoint open curves. Experimental results prove that the proposed model indeed outperforms state-of-the-art minimal paths-based image segmentation approaches. [ABSTRACT FROM AUTHOR]

The Voronoi diagram-based dual-front scheme is known as a powerful and efficient technique for addressing the image segmentation and domain partitioning problems. In the basic formulation of existing dual-front approaches, the evolving contour can be considered as the interfaces of adjacent Voronoi regions. Among these dual-front models, a crucial ingredient is regarded as the geodesic metrics by which the geodesic distances and the corresponding Voronoi diagram can be estimated. In this paper, we introduce a new dual-front model based on asymmetric quadratic metrics. These metrics considered are built by the integration of the image features and a vector field derived from the evolving contour. The use of the asymmetry enhancement can reduce the risk for the segmentation contours being stuck at false positions, especially when the initial curves are far away from the target boundaries or the images have complicated intensity distributions. Moreover, the proposed dual-front model can be applied for image segmentation in conjunction with various region-based homogeneity terms. The numerical experiments on both synthetic and real images show that the proposed dual-front model indeed achieves encouraging results. [ABSTRACT FROM AUTHOR]

Detection and extraction of an object of interest and accurate boundaries segmentation in a given image has been of interest in the last decades due to its application in different fields. To successfully segment a single object, interactive/selective segmentation techniques has been developed as a supplement to the existing global segmentation techniques. Even though existing interactive/selective segmentation techniques perform well in segmenting the images with prominent edges, those methods are less efficient or even fail in segmenting images having multi-regions of different intensity scale. In this paper, we design a new variational selective segmentation model which incorporates the idea of area-based fitting term along with a signed pressure force function based on a generalized average into a variational energy function. The new model is capable to capture the object of interest which can be single or multi-region within the object of interest. To evaluate the performance of our new model, we compare our results with state of the art models by showing same efficiency and reliability on detecting single-region and an outperforming for multi-region selective segmentation. Comparison tests were carried out on synthetic and real data images. [ABSTRACT FROM AUTHOR]

This paper presents an approach for pulse rate extraction from videos. The core of the presented approach is a novel method to segment and track a suitable region of interest (ROI). The proposed method combines level sets with subject-individual Gaussian Mixture Models to yield a time varying ROI. The ROI builds up from multiple homogeneous skin areas under constraints regarding the area and contour length of the ROI. Together with state of the art signal processing methods our approach yields an Mean Average Error (MAE) of 2.3 bpm, 1.4 bpm and 2.7 bpm on own data, the PURE database and the UBFC-rPPG database, respectively. Therewith, our method performs equal or better compared to widely used approaches (e.g. the KLT tracker instead of the proposed image processing yields an MAE of 2.6 bpm, 2.6 bpm and 4.4 bpm). Such results and the 2nd place with a MAE of 7.92 bpm in the 1st Challenge on Remote Physiological Signal Sensing prove the applicability of the proposed method. The taken approach, however, bears further potential for optimization in the context of photoplethysmography imaging and should be transferable to other segmentation tasks as well. [ABSTRACT FROM AUTHOR]

This paper proposes a coastline detection method for polarimetric synthetic aperture radar (SAR) images based on region-based and edge-based active contour models. It can be used to detect coastline accurately and fast. In this method, the region-based and edge-based active contour models are effectively combined by an important property of the likelihood ratio edge detector in polarimetric SAR images, which is proved by theory. Using low-resolution image obtained by multilook processing, we detect accurate and continued coarse coastlines by a region-based level set method. The property of the likelihood ratio edge detector of polarimetric SAR images along the coastline region is then analyzed. The coarse detection result is finally refined using a fast snake active contour model based on the edge property. Polarimetric SAR data acquired by RADARSAT-2 over a Singapore region and TerraSAR-X over a Berkeley region are both used to test the proposed algorithm. The experimental results show that the coastline is fast and accurately detected in different initial scales. The proposed method greatly reduces the data processing time compared with the coastline detection method based on a single scale. [ABSTRACT FROM PUBLISHER]

In target detection of optical remote sensing images, two main obstacles for aircraft target detection are how to extract the candidates in complex gray-scale-multi background and how to confirm the targets in case the target shapes are deformed, irregular or asymmetric, such as that caused by natural conditions (low signal-to-noise ratio, illumination condition or swaying photographing) and occlusion by surrounding objects (boarding bridge, equipment). To solve these issues, an improved active contours algorithm, namely region-scalable fitting energy based threshold (TRSF), and a corner-convex hull based segmentation algorithm (CCHS) are proposed in this paper. Firstly, the maximal variance between-cluster algorithm (Otsu's algorithm) and region-scalable fitting energy (RSF) algorithm are combined to solve the difficulty of targets extraction in complex and gray-scale-multi backgrounds. Secondly, based on inherent shapes and prominent corners, aircrafts are divided into five fragments by utilizing convex hulls and Harris corner points. Furthermore, a series of new structure features, which describe the proportion of targets part in the fragment to the whole fragment and the proportion of fragment to the whole hull, are identified to judge whether the targets are true or not. Experimental results show that TRSF algorithm could improve extraction accuracy in complex background, and that it is faster than some traditional active contours algorithms. The CCHS is effective to suppress the detection difficulties caused by the irregular shape. [ABSTRACT FROM AUTHOR]

Sclera blood veins have been investigated recently as a biometric trait which can be used in a recognition system. The sclera is the white and opaque outer protective part of the eye. This part of the eye has visible blood veins which are randomly distributed. This feature makes these blood veins a promising factor for eye recognition. The sclera has an advantage in that it can be captured using a visible-wavelength camera. Therefore, applications which may involve the sclera are wide ranging. The contribution of this paper is the design of a robust sclera recognition system with high accuracy. The system comprises of new sclera segmentation and occluded eye detection methods. We also propose an efficient method for vessel enhancement, extraction, and binarization. In the feature extraction and matching process stages, we additionally develop an efficient method, that is, orientation, scale, illumination, and deformation invariant. The obtained results using UBIRIS.v1 and UTIRIS databases show an advantage in terms of segmentation accuracy and computational complexity compared with state-of-the-art methods due to Thomas, Oh, Zhou, and Das. [ABSTRACT FROM PUBLISHER]

This paper presents a novel technique for the extraction of the left ventricle borders from echocardiograms with prior information. Although the literature includes many successful prior based methods, priors that include both image and non-image related features are rare for the contour extraction. We classify these features as local and global priors where the local priors refer to the locally definable features of the target borders and global priors refer to the geometric shape properties. The local priors, which include image, motion, and local shape information, are learned with AdaBoost. The scores produced by AdaBoost for the target images are combined with the global shape prior under a level set framework. The main contributions of this paper are to learn different types of local features efficiently with machine learning and to combine these features with the geometric shape information for the contour extraction task. The system is validated on the real echocardiograms and synthetic images. The results indicate that using local and global priors together produces better extraction results and the contours extracted by the proposed system are in accord with the expert delineated borders. [ABSTRACT FROM AUTHOR]

In this paper, a novel texture segmentation algorithm is proposed to partition the complex aerial insulator images into sub-regions with closed smooth contours. Firstly, Gray Level Co-occurrence Matrix (GLCM) is employed to extract the texture features of insulators and is calculated by the rapid Gray Level Co-occurrence Integrated Algorithm (GLCIA). We divide the extracted texture features into two categories: one with the stronger discriminative ability and the other with weaker ability. The second category is optimized by Principal Component Analysis (PCA) to better distinguish the different texture objects with low contrast. Then, a new convex energy functional is defined by taking the non-convex model of the Texture Descriptor Active Contour (TDAC) into a global minimization framework (GMAC) during segmentation. The proposed energy functional can avoid the existence of local minima in the minimization of the TDAC. A fast dual formulation is introduced for the efficient evolution of the contour. The experimental results on synthetic and real aerial insulator remote sensing images have shown that the proposed algorithm obtains more satisfactory segmentation compared to the classical models in terms of accuracy, efficiency and independence of initial contour. The influence of the algorithm parameters is also analyzed. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a robust variational segmentation model capable of overcoming the problem of the negative effects of outliers. The proposed method is based on the combination of the characteristics of the generalized mean with the concept of the active contours. The optimization problem raising in this combination employs a power method technique. We demonstrate the performance of the proposed model on a series of sample images from diverse modalities and show an outperforming proposed model in comparison with the state-of-the-art methods. The proposed method shows better or equivalent performance in terms of accuracy and robustness than the conventional state-of-the-art models. The validation of the efficiency of the proposed two-phase algorithm is further extended to vector-valued images and multi-phase formulation. [ABSTRACT FROM AUTHOR]

The images used in various practices are often disturbed by noise, such as Gaussian noise, speckled noise, and salt and pepper noise. Images with noise are one of the challenges for segmentation, since the noise may cause inaccurate segmented results. To cope with the effect of noise on images during segmentation, a novel active contour model is proposed in this paper. The newly proposed model consists of fitting term, regularization term and penalty term. The fitting term is designed using a Gaussian kernel function and fractional order differentiation with an adaptively defined fractional order, which applies different orders to different pixels. The regularization term is applied to maintain the smoothness of curves. In order to ensure stable evolution of curves, a penalty term is added into the proposed model. Comparison experiments are conducted to show the effectiveness and efficiency of the proposed model. [ABSTRACT FROM AUTHOR]

Hybrid active contour models with the combination of region and edge information have attracted great interests in image segmentation. To the best of our knowledge, however, the theoretical foundation of these hybrid models with level set evolution is insufficient and limited. More specifically, the weighting factors of their energy terms are difficult to select and are often empirically determined without definite theoretical basis. This problem is particularly prominent in the case of multi-object segmentation when more level set functions must be computed simultaneously. To cope with these challenges, this paper proposes a new level set approach for constructing hybrid active contour models with reliable energy weights, where the weights of region and edge terms can be constrained by the optimization condition deduced from the proposed method. It can be regarded as a general approach since many existing region-based models can be easily used to construct new hybrid models using their equivalent two-phase formulations. Some representative as well as state-of-the-art models are taken as examples to demonstrate the generality of our method. The respective comparative studies validate that under the guidance of the optimization condition, segmentation accuracy, robustness, and computational efficiency can be improved compared with the original models which are used to construct the new hybrid ones. [ABSTRACT FROM AUTHOR]

The active contour models with level set evolution have been visited with a vast number of methods for image segmentation. They can be mainly classified into region-based and edge-based models, and it has been validated that the hybrid variants combining both region and edge information can improve the segmentation performance. However, to the best of our knowledge, the theoretical foundation of collaboration mechanism between the region and the edge information is limited. Specifically, most existing hybrid models are just combining all the energy terms together, resulting in great challenges of choosing an appropriate weight coefficient for each term and accommodating different modalities of imaging. To overcome these difficulties, this paper proposes a region and edge synergetic level set framework named RESLS. It provides an approach to construct new hybrid level set models using a normalized intensity indicator function that allows the region information easily embedding into the edge-based model. In this case, the energy weights of region and edge terms can be constrained by the global optimization condition deduced from the framework. Some representative as well as state-of-the-art models are taken as examples to demonstrate the generality of our method. The experiments validate that under the guidance of the optimization condition, the weighting parameter of each term can be reliably chosen. Meanwhile, the segmentation accuracy, robustness, and computational efficiency of RESLS can be improved compared with its component models. [ABSTRACT FROM AUTHOR]

Active contours as variational techniques have been widely applied in image processing and computer vision. In this paper, a shape based active contour is proposed for segmenting circular object from the cluttered background. Being different from the general shape based active contours that estimate the shape parameters by gradient descent method, the proposed shape based active contour directly estimates the parameters of the circular shape without any iteration, which overcomes the difficulty in the parameter initialisation. Therefore, the estimation of the circular shape parameter is robust. The experimental results show the good performance of the proposed shape based active contour on circle detection. [ABSTRACT FROM AUTHOR]

In this paper, we introduce new types of variational segmentation cost functions and associated active contour methods that are based on pairwise similarities or dissimilarities of the pixels. As a solution to a minimization problem, we introduce a new curve evolution framework, the graph partitioning active contours (GPAC). Using global features, our curve evolution is able to produce results close to the ideal minimization of such cost functions. New and efficient implementation techniques are also introduced in this paper. Our experiments show that GPAC solution is effective on natural images and computationally efficient. Experiments on gray-scale, color, and texture images show promising segmentation results. [ABSTRACT FROM AUTHOR]

Nowadays coastline extraction and tracking of its changes become of high importance because of the climate change, global warming and rapid growth of human population. Coastal areas play a significant role for the economy of the entire region. In this paper we propose a new methodology for automatic extraction of the coastline using aerial images. A combination of a four step algorithm is used to extract the coastline in a robust and generalizable way. First, noise distortion is reduced in order to ameliorate the input data for the next processing steps. Then, the image is segmented into two regions, land and sea, through the application of a local threshold to create the binary image. The result is further processed by morphological operators with the aim that small objects are being eliminated and only the objects of interest are preserved. Finally, we perform edge detection and active contours fitting in order to extract and model the coastline. These algorithmic steps are illustrated through examples, which demonstrate the efficacy of the proposed methodology. [ABSTRACT FROM AUTHOR]

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This paper presents a novel method exploiting fusion at the information level for urban road extraction from very high resolution (VHR) optical satellite images. Given a satellite image, we explore spectral and shape features computed at the pixel level, and use them to select road segments using two different methods (i.e., expectation maximization clustering and linearness filtering). A road centerline extraction method, which is relying on the outlier robust regression, is subsequently applied to extract accurate centerlines from road segments. After that, three different sets of information fusion rules are applied to jointly exploit results from these methods, which offer ways to address their own limitations. Two VHR optical satellite images are used to validate the proposed method. Quantitative results prove that information fusion following centerline extraction by multiple techniques is able to produce the best accuracy values for automatic urban road extraction from VHR optical satellite images. [ABSTRACT FROM PUBLISHER]

This paper proposes a method for segmenting the prostate on magnetic resonance (MR) images. A superpixel-based 3D graph cut algorithm is proposed to obtain the prostate surface. Instead of pixels, superpixels are considered as the basic processing units to construct a 3D superpixel-based graph. The superpixels are labeled as the prostate or background by minimizing an energy function using graph cut based on the 3D superpixel-based graph. To construct the energy function, we proposed a superpixel-based shape data term, an appearance data term, and two superpixel-based smoothness terms. The proposed superpixel-based terms provide the effectiveness and robustness for the segmentation of the prostate. The segmentation result of graph cuts is used as an initialization of a 3D active contour model to overcome the drawback of the graph cut. The result of 3D active contour model is then used to update the shape model and appearance model of the graph cut. Iterations of the 3D graph cut and 3D active contour model have the ability to jump out of local minima and obtain a smooth prostate surface. On our 43 MR volumes, the proposed method yields a mean Dice ratio of 89.3 \pm 1.9\\%. On PROMISE12 test data set, our method was ranked at the second place; the mean Dice ratio and standard deviation is 87.0\pm 3.2\\%. The experimental results show that the proposed method outperforms several state-of-the-art prostate MRI segmentation methods. [ABSTRACT FROM PUBLISHER]

This paper introduces a new approach to shoreline mapping, which provides critical input to nautical charting, coastal zone management, and legal boundary determination. We extract shorelines by fusing AVIRIS hyperspectral imagery (HSI) with a LiDAR-generated DEM using a multiphase active contour segmentation technique. Our approach employs a study of object spectra and a knowledge-based segmentation scheme for generating an initial solution followed by a contour evolution technique to achieve a subpixel level of accuracy, while maintaining low computational complexity. Introducing the DEM into shoreline delineation from HSI proves to be a useful tool in eliminating misclassifications and in increasing localization accuracy. Experimental results show that our integrated approach to mapping shorelines has promising outcomes that provide a way to exploit the rich information found in HSI. [ABSTRACT FROM PUBLISHER]