Fuzzy Active Contour Model Research Articles

An efficient image classification and segmentation method for crime investigation applications

AbstractThe field of forensic science is experiencing significant growth, largely driven by the increasing integration of holographic and immersive technologies, along with their associated head-mounted displays. These immersive systems have become increasingly vital in resolving critical crimes as they facilitate communication, interaction, and collaboration. Given the sensitive nature of their work, crime investigators require substantial technical support. There is a pressing need for accurate documentation and archiving of crime scenes, which can be addressed by leveraging 3D scanned scenes to accurately represent evidence and expected scenarios. This study aims to develop an enhanced AR. system that can be deployed on hologram facilities such as the Microsoft HoloLens. The proposed system encompasses two main approaches, namely image classification and image segmentation. Image classification utilizes various deep learning models, including lightweight convolutional neural networks (CNNs) and convolutional Long-Short Term Memory (ConvLSTM). Additionally, the image segmentation approach is based on the fuzzy active contour model (FACM). The effectiveness of the proposed system was evaluated for both classification and segmentation tasks, utilizing metrics such as accuracy, sensitivity, precision, and F1 score. The simulation results indicate that the proposed system achieved a 99% accuracy rate in classification and segmentation tasks, positioning it as an effective solution for detecting bloodstain patterns in AR applications.

Fully convolutional neural network with attention gate and fuzzy active contour model for skin lesion segmentation

Fully convolutional neural network with attention gate and fuzzy active contour model for skin lesion segmentation

Fuzzy Active Contour Model With Markov Random Field for Change Detection

The traditional active contour models are sensitive to the speckle noise in the synthetic aperture radar (SAR) images. In this paper, the Markov random field (MRF) theory is incorporated into the fuzzy active contour model to detect the changes of multitemporal SAR images. In the proposed method, neighboring information is considered to modify the pointwise prior probability for exploiting the mutual and spatial information. In addition, we incorporate MRF into the fuzzy active contour model and get the resulting MRF-based energy function. Finally, we drive the associated first variation of the energy function to compute the fuzzy membership. Due to the introduction of MRF, the proposed MRF-based fuzzy active contour model is robust to the speckle noise in the SAR images and can achieve accurate change detection results. Experiments on four SAR image datasets demonstrate that the proposed MRF-based fuzzy active contour model is able to accurately segment the difference image and has better performance in comparison with other change detection techniques.

Multichannel convolutional neural network-based fuzzy active contour model for medical image segmentation

Multichannel convolutional neural network-based fuzzy active contour model for medical image segmentation

Superpixel-based brain tumor segmentation in MR images using an extended local fuzzy active contour model

In this paper, to deal with poor boundaries in the presence of noise and heterogeneity of magnetic resonance (MR) images, a new region-based fuzzy active contour model based on techniques of curve evolution is introduced for the brain tumor segmentation. On the other hand, since brain MR images intrinsically contain significant amounts of dark areas such as cerebrospinal fluid, therefore for properly declining the heterogeneity of classes and better segmentation results, the proposed fuzzy energy-based function has been extended to consider three distinct regions; target, dark tissues with a dark background and the rest of the foreground. Moreover, due to the inevitable dependency of pixel-based models on the initial contour, artifact, and inhomogeneity of MR images, we have used superpixels as basic atomic units not only to reduce the sensitivity to the mentioned factors but also to reduce the computational cost of the algorithm. Results show that the proposed method outperforms the accuracy of the state-of-the-art models in both real and synthetic brain MR images.

State-of-the-art fuzzy active contour models for image segmentation

Image segmentation is the initial step for every image analysis task. A large variety of segmentation algorithm has been proposed in the literature during several decades with some mixed success. Among them, the fuzzy energy-based active contour models get attention to the researchers during last decade which results in development of various methods. A good segmentation algorithm should perform well in a large number of images containing noise, blur, low contrast, region in-homogeneity, etc. However, the performances of the most of the existing fuzzy energy-based active contour models have been evaluated typically on the limited number of images. In this article, our aim is to review the existing fuzzy active contour models from the theoretical point of view and also evaluate them experimentally on a large set of images under the various conditions. The analysis under a large variety of images provides objective insight into the strengths and weaknesses of various fuzzy active contour models. Finally, we discuss several issues and future research direction on this particular topic.

Fuzzy Active Contour Model Using Fractional-Order Diffusion Based Edge Indicator and Fuzzy Local Fitted Image

Local region-based active contour models (ACMs) can effectively segment images corrupted by intensity inhomogeneity, however, they always converge to local minimum and are sensitive to the initial position of contour. In this paper, a novel fuzzy ACM is proposed to tackle these problems. In order to deal with intensity inhomogeneity, the fuzzy local fitted image is first defined and utilized for constructing a local-region based fuzzy energy term, which is minimized in a variational level set framework to accurately segment inhomogeneous images. Second, the fractional-order diffusion based edge indicator is used to scale the local fuzzy energy term to reduce the effect of intensity inhomogeneity. Third, the fuzzy signed pressure force (FSPF) function defined by local image information is used for constructing the weighted area term to further improve the accuracy of the developed model. Finally, the global FSPF is formulated and used as an adaptive force, which can drive the level set function (LSF) to adaptively move up or down according to image intensity information. Therefore, the initial contour can be initialized as a constant function, which eliminates the problem caused by contour initialization. Moreover, the global FSPF makes the proposed model not easy to fall into local minimum. The results of experiments on synthetic and real images validate the accuracy of the proposed model for inhomogeneous image segmentation.

Unsupervised Multiregion Partitioning of Fully Polarimetric SAR Images With Advanced Fuzzy Active Contours

This article proposes an unsupervised multiregion segmentation method for fully polarimetric synthetic aperture radar (polSAR) images based on the improved fuzzy active contour model. Different from most of the active contour models that are based on the utilization of only statistical information, the proposed method makes better use of information from polarimetric data. In addition to the statistical information, an edge detector modified from the ratio of exponentially weighted averages (ROEWA) operator, a sliding window algorithm for the total received power, and a ratio operator with respect to scattering mechanisms are integrated to the proposed active contour model. We then present a layer-based fuzzy active contour framework to solve our model. The general fuzzy active contour framework is computationally much more efficient compared with the level set-based framework; however, it cannot be applied to the multiregion segmentation of SAR images due to its low robustness to strong noise. The proposed approach includes the advantages of the general fuzzy active contour framework and has good robustness. Using two fully polSAR images demonstrates that the proposed method can achieve higher efficiency and a better segmentation performance in comparison with the commonly used active contour methods.

Fuzzy energy based active contour model for multi-region image segmentation

In this article, we present a new multi-phase pseudo 0.5 level set framework on fuzzy energy based active contour model to segment images into more than two regions. The proposed method is a generalization of fuzzy active contour based on 2-phase segmentation (object and background), developed by Krinidis and Chatzis. The proposed method needs only log2n pseudo 0.5 level set functions for n phase piece-wise constant case. In piece-wise smooth case, only two pseudo 0.5 level set functions are sufficient to represent any partition based on ‘the four colo theorem. The proposed fuzzy active contour model can segment images into multiple regions instead of two regions (object and background) based on curve evolution. In this article, instead of solving the Euler-Lagrange equation, a multi-phase pseudo 0.5 level set based optimization is proposed to speed up the convergence. Finally, the proposed method is compared with state-of-the-art techniques on several images. Analysis (both qualitative and quantitative) of the results concludes that the proposed method segments images into multiple regions in a better way as compared to the existing ones.

A fuzzy energy-based active contour model with adaptive contrast constraint for local segmentation

Image segmentation is to divide an image into different parts or extract some interested objects. Active contour model and fuzzy clustering are two widely used segmentation methods, which have been integrated into an effective model in recent years. Local segmentation is often needful in medical image processing. In view of local segmentation on inhomogeneous images, a new average fuzzy energy-based active contour model is proposed in this paper, in which the total fuzzy energy integrates the approximate weighted average and arithmetic average variances of the image. And an adaptive contrast constraint condition is introduced to prevent the curve from falling into local minimum, which further improves the robustness of the segmentation model to initial contour. Experimental results on synthetic and medical images demonstrate that the proposed model has considerable improvements in terms of segmentation accuracy and robustness compared to several existing local segmentation models.

Localized Patch-Based Fuzzy Active Contours for Image Segmentation.

This paper presents a novel fuzzy region-based active contour model for image segmentation. By incorporating local patch-energy functional along each pixel of the evolving curve into the fuzziness of the energy, we construct a patch-based energy function without the regurgitation term. Its purpose is not only to make the active contour evolve very stably without the periodical initialization during the evolution but also to reduce the effect of noise. In particular, in order to reject local minimal of the energy functional, we utilize a direct method to calculate the energy alterations instead of solving the Euler-Lagrange equation of the underlying problem. Compared with other fuzzy active contour models, experimental results on synthetic and real images show the advantages of the proposed method in terms of computational efficiency and accuracy.

Novel fuzzy active contour model with kernel metric for image segmentation

In this paper, a novel region-based fuzzy active contour model with kernel metric is proposed for a robust and stable image segmentation. This model can detect the boundaries precisely and work well with images in the presence of noise, outliers and low contrast. It segments an image into two regions – the object and the background by the minimization of a predefined energy function. Due to the kernel metric incorporated in the energy and the fuzziness of the energy, the active contour evolves very stably without the reinitialization for the level set function during the evolution. Here the fuzziness provides the model with a strong ability to reject local minima and the kernel metric is employed to construct a nonlinear version of energy function based on a level set framework. This new fuzzy and nonlinear version of energy function makes the updating of region centers more robust against the noise and outliers in an image. Theoretical analysis and experimental results show that the proposed model achieves a much better balance between accuracy and efficiency compared with other active contour models.

A Local Statistical Fuzzy Active Contour Model for Change Detection

In this letter, a statistical active contour model exploiting the local information is proposed for classifying changed and unchanged regions in the difference image. It incorporates the local information and fuzzy logic for the purpose of enhancing the changed information and of reducing the effect of speckle noise. The fuzzy length term and the fuzzy penalty term are added into the fuzzy energy function. In particular, in order to avoid deriving the complex fuzzy membership updating function, we solve the Euler-Lagrange equation to minimize the fuzzy energy function instead of calculating the fuzzy energy alterations directly. The experimental results on three synthetic aperture radar images confirm the performance of the proposed model over some existing methods.

Application of fuzzy inference and active contour model for detection of fovea and its center in a fundus image

Detecting the center of the fovea and its boundary in a human retinal image is a challenging task due to the irregularity of the avascular foveal region. Several attempts have been made in order to detect the boundary using fluorescein angiographic images. However, no attempt was made until now in detecting the boundary of fovea directly from fundus images. In this paper, a novel method for detecting the center of the fovea and its boundary in a fundus image is proposed. A fuzzy rule-based technique has been implemented along with a gradient vector flow (GVF)-based active contour technique in order to detect the boundary of the foveal avascular zone as well as its center. The proposed method exhibits to be promising in detecting fovea regions in wide range of images.

Automatic Building Extraction Using a Fuzzy Active Contour Model

Automatic Building Extraction Using a Fuzzy Active Contour Model

Change Detection in Synthetic Aperture Radar Images Based on Fuzzy Active Contour Models and Genetic Algorithms

This paper presents an unsupervised change detection approach for synthetic aperture radar images based on a fuzzy active contour model and a genetic algorithm. The aim is to partition the difference image which is generated from multitemporal satellite images into changed and unchanged regions. Fuzzy technique is an appropriate approach to analyze the difference image where regions are not always statistically homogeneous. Since interval type-2 fuzzy sets are well-suited for modeling various uncertainties in comparison to traditional fuzzy sets, they are combined with active contour methodology for properly modeling uncertainties in the difference image. The interval type-2 fuzzy active contour model is designed to provide preliminary analysis of the difference image by generating intermediate change detection masks. Each intermediate change detection mask has a cost value. A genetic algorithm is employed to find the final change detection mask with the minimum cost value by evolving the realization of intermediate change detection masks. Experimental results on real synthetic aperture radar images demonstrate that change detection results obtained by the improved fuzzy active contour model exhibits less error than previous approaches.

Segmentation of nerve fibers using multi-level gradient watershed and fuzzy systems

This paper presents an algorithm based on multi-level watershed segmentation combined with three fuzzy systems to segment a large number of myelinated nerve fibers in microscope images. The method can estimate various geometrical parameters of myelinated nerve fibers in peripheral nerves. It is expected to be a promising tool for the quantitative assessment of myelinated nerve fibers in related research. A novel multi-level watershed scheme iteratively detects pre-candidate nerve fibers. At each immersion level, watershed segmentation extracts the initial axon locations and obtains meaningful myelinated nerve fiber features. Thereafter, according to a priori characteristics of the myelinated nerve fibers, fuzzy rules reject unlikely pre-candidates and collect a set of candidates. Initial candidate boundaries are then refined by a fuzzy active contour model, which flexibly deforms contours according to the observed features of each nerve fiber. A final scan with a different set of fuzzy rules based on the a priori properties of the myelinated nerve fibers removes false detections. A particle swarm optimization method is employed to efficiently train the large number of parameters in the proposed fuzzy systems. The proposed method can automatically segment the transverse cross-sections of nerve fibers obtained from optical microscope images. Although the microscope image is usually noisy with weak or variable levels of contrast, the proposed system can handle images with a large number of myelinated nerve fibers and achieve a high fiber detection ratio. As compared to manual segmentation by experts, the proposed system achieved an average accuracy of 91% across different data sets. We developed an image segmentation system that automatically handles myelinated nerve fibers in microscope images. Experimental results showed the efficacy of this system and its superiority to other nerve fiber segmentation approaches. Moreover, the proposed method can be extended to other applications of automatic segmentation of microscopic images.

Interactive segmentation of the cerebral lobes with fuzzy inference in 3T MR images

Measurement of volume and surface area of the frontal, parietal, temporal and occipital lobes from magnetic resonance (MR) images shows promise as a method for use in diagnosis of dementia. This article presents a novel computer-aided system for automatically segmenting the cerebral lobes from 3T human brain MR images. Until now, the anatomical definition of cerebral lobes on the cerebral cortex is somewhat vague for use in automatic delineation of boundary lines, and there is no definition of cerebral lobes in the interior of the cerebrum. Therefore, we have developed a new method for defining cerebral lobes on the cerebral cortex and in the interior of the cerebrum. The proposed method determines the boundaries between the lobes by deforming initial surfaces. The initial surfaces are automatically determined based on user-given landmarks. They are smoothed and deformed so that the deforming boundaries run along the hourglass portion of the three-dimensional shape of the cerebrum with fuzzy rule-based active contour and surface models. The cerebrum is divided into the cerebral lobes according to the boundaries determined using this method. The reproducibility of our system with a given subject was assessed by examining the variability of volume and surface area in three healthy subjects, with measurements performed by three beginners and one expert user. The experimental results show that our system segments the cerebral lobes with high reproducibility.

Interactive 3-D Segmentation of the Frontal Lobe in 3.0T IR-FSPGR MR Images Using Fuzzy Rule-Based ACM

Volumetry and surface rendering of the cerebral lobes are effective for evaluating lobar atrophy. This paper proposes a novel computer-aided system for segmenting the frontal lobe from 3-D human brain IR-FSPGR MR images with the fuzzy rule-based active contour model (ACM). The proposed system uses the criterial curves consisting of the central sulcus and the Sylvian fissure, which are given by users with the proposed graphical user interface. The user-given criterial curves are optimized by the fuzzy rule-based ACM. The fuzzy rule-based ACM can represent physicians’ knowledge with fuzzy if-then rules. With these optimized curves and the anterior and posterior commissures, the frontal lobe is segmented automatically. The experimental results on three healthy volunteers operated by an expert user and three beginner users showed that our system could segment the frontal lobe with high repeatability by any users and to any subjects.