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A region-based level-set active contour is the preferred choice for image-segmentation tasks, when the region of interest is defined by weak edges. While, the minimization of an energy functional leads to the contour’s conformation to object boundary, the energy is composed of contour’s internal and image-dependent external energies. In such a model, the overall motion (expansion/shrinkage) of contour is controlled by its area-energy, whereas the length-energy controls the contour’s elasticity. Traditionally, these two internal energies are weighted by scalar constants, which remain fixed throughout the level-set evolution. Both the internal energies are responsible for the contour’s regularization only, whereas the contour converges due to the minimization of external energies. Further, inappropriate weighting results into an inaccurate segmentation either because of premature convergence, or leakage beyond the object boundary. To address these issues, an adaptive spatially weighted region-based active contour (ASWRAC) is proposed. The weights are assigned adaptively to the internal energy terms at successive steps of evolution. This basically removes the need of weight initialization for an improved segmentation process. Additionally, the proposed weights are modeled as vectors, depending on the local regional statistics of the image. This causes the originally internal energies to also reflect external characteristics. Moreover, the time-step used in the discrete implementation of level-set evolution is also managed adaptively. This restricts the contour’s leakage beyond the region of interest. The suggested technique is tested on brain MR slices and other biomedical images from public databases. Comparison of the obtained results with state-of-the-art techniques shows its superiority in segmentation accuracy measured using the metrics: dice Similarity Coefficient, sensitivity, and specificity.

Intensity inhomogeneity often causes considerable difficulties in image segmentation. In order to tackle this problem, we propose a novel region-based active contour model in a variational level set formulation. We first define a data fitting energy with a local Gaussian distribution fitting (LGDF) term, which induces a local force to attract the contour and stops it at object boundaries, and a local signed difference (LSD) term based on local entropy, which possesses both local separability and global consistency. This energy is then incorporated into a level set formulation with a level set regularization term that is necessary for accurate computation in the corresponding level set method. Experimental results show that the proposed model can not only segment images with intensity inhomogeneities and weak boundaries but also be robust to the noise, initial contours.

This paper presents a face recognition system which can identify the unknown identity effectively using the front-view facial features. In front-view facial feature extractions, we can capture the contours of eyes and mouth by the deformable template model because of their analytically describable shapes. However, the shapes of eyebrows, nostrils and face are difficult to model using a deformable template. We extract them by using the active contour model (snake). After the contours of all facial features have been captured, we calculate effective feature values from these extracted contours and construct databases for unknown identities classification. In the database generation phase, 12 models are photographed, and feature vectors are calculated for each portrait. In the identification phase if any one of these 12 persons has his picture taken again, the system can recognize his identity.

Two familiar approaches to image segmentation are the salient contour extraction approach and the closed-contour deformation approach. The former uses Gestalt laws to link individual edge elements and construct segmentation boundaries. However, it is often difficult to have both closure and precision of the boundary addressed at the same time. The latter starts with a closed contour and deforms the contour to localize the segmentation boundary more precisely whilst maintaining the closure. The approach does not have the closure problem, but how to assign a proper initial contour for it remains an open issue. In this work, we propose a scheme that puts together the two approaches to let them work complementarily. Specifically, we design a salient contour extraction process that extracts a proper initialization of the closed contours; the process looks into edge evidence and proximity to the desired segmentation boundaries. Then, a region-based active contour in a level set formulation is adopted to refine the contour position to locate the segmentation boundaries more precisely. The scheme requires neither manual input on contour initialization nor prior knowledge about the imaged scene. Experiments on extensive benchmarking image-sets are presented to illustrate the performance of the scheme.

Abdomen related diseases are responsible of many deaths every year. These deaths can be reduced by early diagnosis of abdomen diseases. Computer aided diagnosis (CAD) can play vital role in early detection of diseases. Hence, a novel CAD is proposed in this paper that can diagnose abdomen diseases like Hepatocellular carcinoma, cysts and Calculi using statistical curvelet texture descriptors. The proposed CAD is divided into four stages: (a) Image segmentation using active contours, (b) feature extraction, (c) feature selection and (d) abdomen disease classification. The regions of interest (ROIs) are segmented from 120CT images using active contour models. The statistical features are extracted from segmented ROIs. Further, the classifiers are used to evaluate the ability of feature set in diagnosis various diseases of abdomen. The performance metrics indicates that the proposed CAD achieves accuracy of 87.9% using curvelet coefficient features and neural network as classifier.

An irregular growth in brain cells causes brain tumors. In recent years, a considerable rate of increment in medical cases regarding brain tumors has been observed, affecting adults and children. However, it is highly curable in recent times only if detected in the early time of tumor growth. Moreover, there are many sophisticated approaches devised by researchers for predicting the tumor regions and their stages. In addition, Magnetic Resonance Imaging (MRI) is utilized commonly by radiologists to evaluate tumors. In this paper, the input image is from a database, and brain tumor segmentation is performed using various segmentation techniques. Here, the comparative analysis is performed by comparing the performance of segmentation approaches, like Hybrid Active Contour (HAC) model, Bayesian Fuzzy Clustering (BFC), Active Contour (AC), Fuzzy C-Means (FCM) clustering technique, Sparse (Sparse FCM), and Black Hole Entropy Fuzzy Clustering (BHEFC) model. Moreover, segmentation technique performance is evaluated with the Dice coefficient, Jaccard coefficient, and segmentation accuracy. The proposed method shows high Dice and Jaccard coefficients of 0.7809 and 0.6456 by varying iteration with the REMBRANDT dataset and a better segmentation accuracy of 0.9789 by changing image size in the Brats-2015 database.

Intra-cardiac blood flow imaging and visualization is challenging due to the processes involved in generating velocity fields of flow within specific chambers of interest. Visual analysis of cardiac flow or wall deformation is crucial for an accurate examination of the heart.

Cardiac chamber boundary encapsulation is one of the key implementations for region definition. To provide intelligible results describing flow within the human heart, cardiac chamber segmentation is a pre-requisite so that fluid motion information can be presented within a region of interest defined by the chamber boundary. A technique that is used to establish contouring along the cardiac wall is described mathematically. This article also sets the practical foundation for flow vector synthesis and visualization in the cardiac discipline. We have outlined conceptual development and the construction of flow field based on a three-dimensional Cartesian grid that can give a greater insight into the blood dynamics within the heart.

We developed a framework that is able to present both anatomical as well as flow information by overlaying velocity fields over medical images and displaying them in cine-mode. By addressing most of the methods involved from the programming perspective, procedural execution and memory efficiency have been considered. Our implemented system can be used to examine abnormal blood motion behaviour or discover flow phenomena in normal or defective hearts.

A novel energy functional based on the Mumford–Shah model is established for performing automatic image segmentation. And in order to optimize the global model using graph-based methods, we develop a localized formula. Then, we propose a merging predicate for determining whether an edge connecting two neighboring pixels or regions merge. The dynamic graph merging (DGM) method is applied based on this merging predicate. That is, those edges with large energy merge and the edges with low energy are remained, such that the energy functional is minimized. Compared with other graph-based segmentation methods, our algorithm based on DGM has an important characteristic which is its ability to produce good segmentation on some complex texture images. Another characteristic is that this segmentation algorithm can avoid the “shrinking bias” problem. We also apply DGM to interactive image segmentation and find the results to be encouraging too.

This research paper proposed a newer strategic method for the extraction of tumor from magnetic resonance imaging scans by employing a region-based active contour model (ACM). The earlier methods have applied the process of contour initialization randomly and updating the energy of the contour at every iteration. The proposed method used wavelet-based feature set to initiate the contour and restricts the energy update procedure. The efficiency of the presented technique in terms of tumor extraction is measured through qualitative and quantitative measures further compared with its counterparts Vese–Chan multiphase model, ACM and Selective binary. Gaussian filtering regularized level set and non active contour based models.

Time-to-contact (TTC) provides vital information for obstacle avoidance and for the visual navigation of a robot. In this paper, we present a novel method to estimate the TTC information of a moving object for monocular mobile robots. In specific, the contour of the moving object is extracted first using an active contour model; then the height of the motion contour and its temporal derivative are evaluated to generate the desired TTC estimates. Compared with conventional techniques employing the first-order derivatives of optical flow, the proposed estimator is less prone to errors of optical flow. Experiments using real-world images are conducted and the results demonstrate that the developed method can successfully achieve TTC with an average relative error (ARVE) of 0.039 with a single calibrated camera.

Hippocampus segmentation on magnetic resonance imaging is more significant for diagnosis, treatment and analyzing of neuropsychiatric disorders. Automatic segmentation is an active research field. Previous state-of-the-art hippocampus segmentation methods train their methods on healthy or Alzheimer’s disease patients from public datasets. It arises the question whether these methods are capable for recognizing the hippocampus in a different domain. Therefore, this study proposes a precise computational method for hippocampus segmentation from MRI of brain to assist physicians in the diagnosis of Alzheimer’s disease (HCS-MRI-DAD-LBP). Initially, the input images are pre-processed by Trimmed mean filter for image quality enhancement. Then the pre-processed images are given to ROI detection, ROI detection utilizes Weber’s law which determines the luminance factor of the image. In the region extraction process, Chan–Vese active contour model (ACM) and level sets are used (UACM). Finally, local binary pattern (LBP) is utilized to remove the erroneous pixel that maximizes the segmentation accuracy. The proposed model is implemented in MATLAB, and its performance is analyzed with performance metrics, like precision, recall, mean, variance, standard deviation and disc similarity coefficient. The proposed HCS-MRI-DAD-LBP method attains in OASIS dataset provides high disc similarity coefficient of 12.64%, 10.11% and 1.03% compared with the existing methods, like HCS-DAS-MLT, HCS-DAS-RNN and HCS-DAS-GMM and in ADNI dataset provides high precision of 20%, 9.09% and 1.05% compared with existing methods like HCS-MRI-DAD-CNN-ADNI, HCS-MRI-DAD-MCNN-ADNI and HCS-MRI-DAD-CNN-RNN-ADNI, respectively.

The paper presents an improved tensor-based active contour model in a variational level set formulation for medical image segmentation. In it, a new energy function is defined with a local intensity fitting term in intensity inhomogeneity of the image, and with a global intensity fitting term in intensity homogeneity domain. Weighting factor is chosen to balance these two intensity fitting terms, which can be calculated automatically by local entropy. The level set regularization term is to replace contour curve to find the minimum of the energy function. Particularly, structure tensor is applied to describe the image, which overcomes the disadvantage of image feature without structure information. The experimental results show that our proposed method can segment image efficiently whether it presents intensity inhomogeneity or not and wherever the initial contour is. Moreover, compared with the Chan–Vese model and local binary fitting model, our proposed model not only handles better intensity inhomogeneity, but also is less sensitive to the location of initial contour.

The major goal of this paper is to isolate tumor region from nontumor regions and the estimation of tumor volume. Accurate segmentation is not an easy task due to the varying size, shape and location of the tumor. After segmentation, volume estimation is necessary in order to accurately estimate the tumor volume. By exactly estimating the volume of abnormal tissue, physicians can do excellent prognosis, clinical planning and dosage estimation. This paper describes a new Euclidean Similarity factor (ESF) based active contour model with deep learning for segmenting the tumor region into complete, core and enhanced tumor portions. Initially, the ESF considers the spatial distances and intensity differences of the region automatically to detect the tumor region. It preserves the image details but removes the noisy details. Then, the 3D Convolutional Neural Network (3D CNN) segments the tumor by automatically extracting spatiotemporal features. Finally, the extended shoelace method estimates the volume of the tumor accurately for $n$-sided polygons. The simulation result achieves a high accuracy of 92% and Jaccard index of 0.912 and computes the tumor volume with effective performance than existing approaches.

Lung cancer detection has been a trending research area, as automating the medical diagnosis has significant benefits. Automatic identification of lung cancer from the CT images is considered as a significant technique in recent years. Even though various techniques are developed in the literature for lung cancer detection, designing an effective technique that can automatically detect lung cancer is challenging. Hence, this research aims to develop an automated lung cancer detection scheme through deep learning and hybrid optimization algorithm. Here, the CT images from the lung cancer database are pre-processed and provided to the lung segmentation, which is carried out by active contour. Then, the nodules in the segmented image are identified using the grid-based scheme. Several features, like intensity, wavelet, and scattering transform, are mined from the segmented image and given to the proposed salp-elephant herding optimization algorithm-based deep belief network (SEOA-DBN), for the classification. Here, SEOA is newly developed by considering the qualities of salp swarm algorithm (SSA) and elephant herding optimization (EHO). For the experimentation, lung CT images are considered from the standard database and compared with the various states of art techniques. From the results, it is evident that the proposed SEOA-based DBN achieved significant performance with 96% accuracy.

In this paper, we proposed a new active model to segment a given image, based on techniques of curve evolution and an energy minimization function. We use the semi-implicit scheme to implement the active model, instead of the classical explicit scheme, which is unconditionally stable and does not suffer from any time step size restriction. Finally, we present the algorithm and experimental results based on the semi-implicit scheme. Experimental results show that one can obtains a high quality edge contour.