Fuzzy Level Set Research Articles

Comparison of Fuzzy Level Set Search and Rescue Optimization and Clustering-Based Segmentation Techniques for Brain Tumor Detection

Abstract: Brain tumor detection is a critical task in medical imaging that requires accurate and efficient image segmentation techniques. This paper presents a comparative study of two advanced segmentation methods: Fuzzy Level Set Search and Rescue Optimization (FLSSRO) and Clustering-Based Segmentation (CBS). By examining the theoretical foundations, algorithmic structures, and performance metrics of these techniques, we aim to highlight their strengths and limitations in the context of brain tumor detection. Through experimental evaluation on benchmark MRI datasets, we analyze the efficacy, accuracy, and computational efficiency of both methods. The study concludes with insights into the appropriate application domains for each technique and potential future research directions.

Efficient Brain Tumour Segmentation Using Fuzzy Level Set Method and Intensity Normalization

This paper is developed to implement a fuzzy set technique with intensity normalization intended for the identification of location and tumor shape from an MRI image. Normally, the tumor can be an uncontrolled growth of tissue in any portion of the body. Here, different kinds of cancers have various conditions with the treatments. Hence, brain tumor segmentation is an essential topic in medical applications. The fuzzy level set technique is utilized to segment the tumor from the brain MRI images. Additionally, intensity normalization is utilized to enhance image quality. The proposed technique is implemented in MATLAB and the exhibitions are evaluated by performance scores and implementation scales of quality ratings. To recognize the exhibition of the proposed technique, it is compared with the different and conventional strategies, for example, mobilenetv2, resnet18, resnet50, and xception separately.

Computer-aided Diagnosis applied to MRI images of Brain Tumor using Spatial Fuzzy Level Set and ANN Classifier

The most vital organs in the human body are the brain, heart, and lungs. Because the brain controls and coordinates the operations of all other organs, normal brain function is vital. Brain tumour is a mass of tissues which interrupts the normal functioning of the brain, if left untreated will lead to the death of the subject. The classification of multiclass brain tumours using spatial fuzzy based level sets and artificial neural network (ANN) techniques is proposed in this paper. In the proposed method, images are preprocessed using Median Filtering technique, the boundaries of the Brain Tumor are obtained using Spatial Fuzzy based Level Set method, features are extracted using Gabor Wavelet and Gray-Level Run Length Matrix (GLRLM) methods. Finally ANN technique is used for the classification of the image into Normal or Benign Tumor or Malignant Tumor. The proposed method was implemented in the MATLAB working platform and achieved classification accuracy of 94%, which is significant compared to state-of-the-art classification techniques. Thus, the proposed method assist in differentiating between benign and malignant brain tumours, enabling doctors to provide adequate treatment.

ANALYSIS OF SPECIFIC PARAMETERS FOR SKIN TUMOR CLASSIFICATION

During the last years, computer vision-based diagnosis systems have been widely used in several hospitals and dermatology clinics, aiming mostly at the early detection of malignant melanoma tumor, which is among the most frequent types of skin cancer, versus other types of nonmalignant cutaneous diseases. They grow in melanocytes, the cells responsible for pigmentation. This type of cancer is increasing rapidly and its related mortality rate is increasing more modestly, and inversely proportional to the tumor’s thickness. The mortality rate can be decreased by earlier detection of suspicious lesions and better prevention. In this work, we are interested in extracting all specific attributes which can be used for computer-aided diagnosis of melanoma. In the first step of the proposed work, we applied the Dull Razor [Lee T et al., Dullrazor: A software approach to hair removal from images, Cancer Control Research, British Columbia Cancer Agency, Vancouver, Canada, Vol. 21, No. 6, pp. 533–543, 1997] technique to images to reduce the influence of small structures, hairs, bubbles, light reflection. In the second step, a new fuzzy level set algorithm is proposed in order to facilitate the medical image segmentation task. It is able to directly evolve from the initial segmentation proposed that uses a spatial fuzzy clustering approach. The controlling parameters of the level set evolution are also estimated from the results of the fuzzy clustering step. This step is essential to characterize the shape of the lesion and also to locate the tumor to be analyzed. In this paper, we have also treated the necessity to extract all the specific attributes used to develop a characterization methodology that enables specialists to take the best possible diagnosis. For this purpose, our proposal relies largely on visual observation of the tumor while dealing with some characteristics as color, texture or form. The method used in this paper is called ABCD. It requires calculating four factors: Asymmetry ([Formula: see text], Border ([Formula: see text], Color ([Formula: see text], and Diversity ([Formula: see text]. Finally, these parameters are used to construct a classification module based on artificial neural network for the recognition of malignant melanoma.

Hybrid fuzzy level set approach for multiple sclerosis lesions assessment in magnetic resonance brain images

Hybrid fuzzy level set approach for multiple sclerosis lesions assessment in magnetic resonance brain images

Hybrid fuzzy level set approach for multiple sclerosis lesions assessment in magnetic resonance brain images

Hybrid fuzzy level set approach for multiple sclerosis lesions assessment in magnetic resonance brain images

MATrix LABoratory Algorithm in Ultrasound Image-Guided General Drug Anesthesia along with Lumbar and Sacral Plexus Block in Hip Replacement under

Objective. The study aimed to explore the application of ultrasound image-guided general drug anesthesia combined with lumbar and sacral plexus block based on MATrix LABoratory (MATLAB) algorithm in hip arthroplasty and to study its clinical effect. Methods. The classic geodesic active contour (GAC) algorithm and the improved fuzzy clustering level set algorithm were used to segment ultrasound images of waist plexus, and then their segmentation effects were compared. Both algorithms are from the MATrix LABoratory (MATLAB) platform. A total of 60 patients undergoing hip arthroplasty were selected and randomly enrolled into control and experimental groups. The control group accepted general drug anesthesia, and the experimental group accepted ultrasound-guided lumbar and sacral plexus block combined with general anesthesia. The mean arterial pressure and heart rate at t0 (before anesthesia), t1 (before ventilation), t2 (when the skin was incised), t3 (when the prosthesis was implanted), t4 (when the incision was closed), and t5 (at the end of ventilation) were observed, and the intraoperative sufentanil dosage and 24 h analgesic dosage, the incidence of postoperative delirium, and the incidence of cognitive dysfunction were recorded. Results. The improved fuzzy clustering level set algorithm was better than the GAC model algorithm in image segmentation and running time. In contrast with the control group, the average arterial pressure and heart rate of the experimental group at the four time points of t1, t2, t3, and t5 were obviously reduced ( P < 0.05). In contrast with the control group, the amount of sufentanil and analgesics in the experimental group was obviously reduced ( P < 0.05), and the incidence of postoperative cognitive dysfunction and delirium was obviously reduced ( P < 0.05). Conclusion. The improved fuzzy clustering level set algorithm is superior to the GAC model in image segmentation and running time. Under its guidance, the lumbar and sacral plexus block combined with general anesthesia has a good clinical effect in hip arthroplasty, which is better than simple general anesthesia.

Numerical Treatment for Solving Fuzzy Volterra Integral Equation by Sixth Order Runge-Kutta Method

There has recently been considerable focus on finding reliable and more effective numerical methods for solving different mathematical problems with integral equations. The Runge–Kutta methods in numerical analysis are a family of iterative methods, both implicit and explicit, with different orders of accuracy, used in temporal and modification for the numerical solutions of integral equations. Fuzzy Integral equations (known as FIEs) make extensive use of many scientific analysis and engineering applications. They appear because of the incomplete information from their mathematical models and their parameters under fuzzy domain. In this paper, the sixth order Runge-Kutta is used to solve second-kind fuzzy Volterra integral equations numerically. The proposed method is reformulated and updated for solving fuzzy second-kind Volterra integral equations in general form by using properties and descriptions of fuzzy set theory. Furthermore a Volterra fuzzy integral equation, based on the parametric form of a fuzzy numbers, transforms into two integral equations of the second kind in the crisp case under fuzzy properties. We apply our modified method using the specific example with a linear fuzzy integral Volterra equation to illustrate the strengths and accurateness of this process. A comparison of evaluated numerical results with the exact solution for each fuzzy level set is displayed in the form of table and figures. Such results indicate that the proposed approach is remarkably feasible and easy to use.

Ship Target Image Segmentation Algorithm Based on Fuzzy Level Set

The effective recognition of ship target is very important for the protection of ocean rights, and the image segmentation is the precondition of the ship target recognition. To solve the two problems existing in the traditional level set graph, namely the increase of image segmentation and the increase of computation, the Fuzzy C-Means (FCM) and level set are introduced. A new algorithm for image segmentation based on fuzzy level set is proposed in this paper. The algorithm can use spatial fuzzy clustering to realize the parameter estimation and evolutionary adjustment. The algorithm is applied to optical remote sensing ship target image segmentation in complex sea environment, and the effectiveness of the proposed algorithm is verified by experiment results.

Intuitionistic Level Set Segmentation for Medical Image Segmentation

Introduction: Image segmentation is one of the basic practices that involve dividing an image into mutually exclusive partitions. Learning how to partition an image into different segments is considered as one of the most critical and crucial step in the area of medical image analysis. Objective: The primary objective of the work is to design an integrated approach for automating the process of level set segmentation for medical image segmentation. This method will help to overcome the problem of manual initialization of parameters. Methods: In the proposed method, input image is simplified by the process of intuitionistic fuzzification of an image. Further segmentation is done by intuitionistic based clustering technique incorporated with local spatial information (S-IFCM). The controlling parameters of level set method are automated by S-IFCM, for defining anatomical boundaries. Results: Experimental results were carried out on MRI and CT-scan images of brain and liver. The results are compared with existing Fuzzy Level set segmentation; Spatial Fuzzy Level set segmentation using MSE, PSNR and Segmentation Accuracy. Qualitatively results achieved after proposed segmentation technique shows more clear definition of boundaries. The attain PSNR and MSE value of propose algorithm proves the robustness of algorithm. Segmentation accuracy is calculated for the segmentation results of the T-1 weighted axial slice of MRI image with 0.909 value. Conclusion: The proposed method shows good accuracy for the segmentation of medical images. This method is a good substitute for the segmentation of different clinical images with different modalities and proves to give better result than fuzzy technique.

A medical analytical system using intelligent fuzzy level set brain image segmentation based on improved quantum particle swarm optimization

Medical image segmentation demonstrates a significant part in curative image exploration and dispensation, is a multifaceted and perplexing assignment for reckoning efficiency and dissection precision. Segmenting an image is essential to dissection different components of the image, which is prominent fact to identify region of defect accurately. An Intelligent Fuzzy Level Set Method (IFLSM) along with an over-all search proficiency of Improved Quantum Particle Swarm Optimization (IQPSO) for image segmentation is proposed to improve the steadiness and meticulousness thus aiming at reduction of opening sensitivity. The proposed algorithm aims at optimizing the opening contours by utilizing the IQPSO method in addition with intelligent fuzzy clustering method, and segments the image using enhanced Level Set Method (LSM). A stable cluster head is identified using the comprehensive quest aptitude of IQPSO. The iteration period will also provide a pre-segmentation contour which is nearer to Region of Interest (ROI). The implementation of the proposed work for segmenting brain tissues through Magnetic Image Resonance (MRI) images provides an optimized result which is 15% more than the original FLSM algorithm. The obtained contours from the proposed work shows more stability than the original FLSM. The proposed work shows a promising significant improvement in the image segmentation process.

α-level Fuzzy Soft Sets

In this paper, based on soft lattices, with the help of fuzzy level (cut) set, -level fuzzy soft sets and -level fuzzy soft lattices are defined, and the structure and characteristics of our definitions are explained with examples, at the same time, their di erences and relations are compared with classic soft set.

A Two Phase Method for Solving the Distribution Problem in a Fuzzy Setting

In this paper, a new method for the solution of distribution problem in a fuzzy setting is presented. It consists of two phases. In the first of them, the problem is formulated as the classical, fully fuzzy transportation problem. A new, straightforward numerical method for solving this problem is proposed. This method is implemented using the -cut approximation of fuzzy values and the probability approach to interval comparison. The method allows us to provide the straightforward fuzzy extension of a simplex method. It is important that the results are fuzzy values. To validate our approach, these results were compared with those obtained using the competing method and those we got using the Monte–Carlo method. In the second phase, the results obtained in the first one (the fuzzy profit) are used as the natural constraints on the parameters of multiobjective task. In our approach to the solution of distribution problem, the fuzzy local criteria based on the overall profit and contracts breaching risks are used. The particular local criteria are aggregated with the use of most popular aggregation modes. To obtain a compromise solution, the compromise general criterion is introduced, which is the aggregation of aggregating modes with the use of level-2 fuzzy sets. As the result, a new two phase method for solving the fuzzy, nonlinear, multiobjective distribution problem aggregating the fuzzy local criteria based on the overall profit and contracts breaching risks has been developed. Based on the comparison of the results obtained using our method with those obtained by competing one, and on the results of the sensitivity analysis, we can conclude that the method may be successfully used in applications. Numerical examples illustrate the proposed method.

Brightness preserving optimized weighted bi‐histogram equalization algorithm and its application to MR brain image segmentation

AbstractMedical image segmentation is crucial for neuroscience research and computer‐aided diagnosis. However, intensity inhomogeneity and existence of noise in magnetic resonance images lead to incorrect segmentation. In this article, an effective method called enhanced fuzzy level set algorithm is presented to segment the white matter, gray matter, and cerebrospinal fluid automatically in contrast‐enhanced brain images. In this method, first, exposure threshold is computed to divide the input histogram into two sub‐histograms of different gray levels. The input histogram is clipped using a mean gray level to control the excessive enhancement rate. Then, these two sub‐histograms are modified and equalized independently to get a better contrast enhanced image. Finally, an enhanced fuzzy level set algorithm is employed to facilitate image segmentation. The extensive experimental results proved the outstanding performance of the proposed algorithm compared with other existing methods. The results conform its effectiveness for MR brain image segmentation.

An Adaptive Fuzzy Level Set Model With Local Spatial Information for Medical Image Segmentation and Bias Correction

Medical image segmentation is still a challenging task due to noise and intensity inhomogeneity. An adaptive fuzzy level set model (AFLSM) with local spatial information is presented in this paper for accurately segmenting medical images and correcting bias field. A weighting scheme that can ensure each pixel in the neighborhood to have anisotropic weight is first introduced to remove noisy pixels and hence improve the robustness to noise. Then, a linear combination of orthogonal basis functions is used to represent bias field to ensure its smoothly and slowly varying property. Besides, to improve the robustness to initialization, this adaptive fuzzy level set model fuses a level set model with the membership function of fuzzy clustering, which can adaptively adjust the evolution of level set function. Finally, the distance regularization term in energy formulation is redefined with a novel double-well potential function to inherently maintain the accuracy and stability of the AFLSM. The AFLSM is first represented in the two-phase case and subsequently extended to the multi-phase formulation. The numerous visual segmentation results and quantitative evaluation can demonstrate the performance of the AFLSM on synthetic and real medical images. Comparison with the state-of-the-art models shows that the AFLSM can achieve better segmentation results with an improvement of 0.2286 ± 0.1477 in Dice coefficient and 0.1350 ± 0.0661 in Jaccard similarity coefficient in terms of robustness and the capability to correct bias field, respectively.

Fluorescence microscopy image segmentation based on graph and fuzzy methods: A comparison with ensemble method

Accurate segmentation of fluorescence images has become increasingly important for recognizing cell nucleus that have the phenotype of interest in biomedical applications. In this study an ensemble based method is proposed for the segmentation of cell cancer microscopy images. The ensemble is constructed and compared using Bayes graph-cut algorithm, binary graph-cut algorithm, spatial fuzzy C-means, and fuzzy level set algorithm, which were chosen for their accuracy and efficiency in the segmentation area. We investigate the performance of each method separately and finally compare the results with the ensemble method. Experiments are conducted over two datasets with different cell types. At 95% confidence level, the ensemble based method represents the best among all the implemented algorithms. Also ensemble method depicts better results in comparison with other state-of-the-art segmentation methods.

Inhomogeneity correction and hybrid-based segmentation in cardiac MRI

Image segmentation is an important step in medical image analysis and segmentation of ventricles in Cardiac Magnetic Resonance (CMR) images is challenging due to an in-built artefact called intensity-inhomogeneity. The short-axis cine Magnetic Resonance Images (MRI) recorded under a steady-state free precision protocol were corrected for intensity-inhomogeneity using Bias Corrected Fuzzy C-Means (BCFCM) method, Level Set (LS) and Multiplicative Intrinsic Component Optimisation (MICO) methods. The statistical measures show that bias correction by BCFCM has better performance than MICO and LS. In addition, the original and bias corrected images are validated by Multifractal Analysis (MFA). The results show that in bias corrected images, the low frequency components are removed thereby enhancing the sharpness of the ventricular boundaries. Further, ventricular segmentation is performed using the proposed automatic hybrid Sobel edge detector with optimised level set method. The validation parameters of segmented results show that the ventricular detection in bias corrected images matches better with ground truth.

Global minimisation of fuzzy level set for image segmentation

Level set is an important method in image segmentation, and some models based on level set method have obtained great success, such as Chan and Vese (C-V) and its convex formulation, local binary fitting (LBF) model. However, these models have two drawbacks to be simultaneously solved. One is the non-convexity of energy functional; the other difficulty is segmenting objects in the background of inhomogeneous intensity. In order to simultaneously cope with these shortcomings, a fuzzy level set energy functional model is proposed. Firstly, a fuzzy factor is introduced in the original LBF model to describe the intensity inhomogeneity. Besides, the edge information is also integrated into the proposed model to improve the robustness of extracting objects. Finally, a regularisation optimisation method is introduced to obtain the global minimisation. Experimental results confirm the proposed method is robust to initialisation and could segment objects with inhomogeneous intensity.

Global minimisation of fuzzy level set for image segmentation

Level set is an important method in image segmentation, and some models based on level set method have obtained great success, such as Chan and Vese (C-V) and its convex formulation, local binary fitting (LBF) model. However, these models have two drawbacks to be simultaneously solved. One is the non-convexity of energy functional; the other difficulty is segmenting objects in the background of inhomogeneous intensity. In order to simultaneously cope with these shortcomings, a fuzzy level set energy functional model is proposed. Firstly, a fuzzy factor is introduced in the original LBF model to describe the intensity inhomogeneity. Besides, the edge information is also integrated into the proposed model to improve the robustness of extracting objects. Finally, a regularisation optimisation method is introduced to obtain the global minimisation. Experimental results confirm the proposed method is robust to initialisation and could segment objects with inhomogeneous intensity.

Automatic Subretinal Fluid Segmentation of Retinal SD-OCT Images With Neurosensory Retinal Detachment Guided by Enface Fundus Imaging.

Accurate segmentation of neurosensory retinal detachment (NRD) associated subretinal fluid in spectral domain optical coherence tomography (SD-OCT) is vital for the assessment of central serous chorioretinopathy (CSC). A novel two-stage segmentation algorithm was proposed, guided by Enface fundus imaging. In the first stage, Enface fundus image was segmented using thickness map prior to detecting the fluid-associated abnormalities with diffuse boundaries. In the second stage, the locations of the abnormalities were used to restrict the spatial extent of the fluid region, and a fuzzy level set method with a spatial smoothness constraint was applied to subretinal fluid segmentation in the SD-OCT scans. Experimental results from 31 retinal SD-OCT volumes with CSC demonstrate that our method can achieve a true positive volume fraction (TPVF), false positive volume fraction (FPVF), and positive predicative value (PPV) of 94.3%, 0.97%, and 93.6%, respectively, for NRD regions. Our approach can also discriminate NRD-associated subretinal fluid from subretinal pigment epithelium fluid associated with pigment epithelial detachment with a TPVF, FPVF, and PPV of 93.8%, 0.40%, and 90.5%, respectively. We report a fully automatic method for the segmentation of subretinal fluid. Our method shows the potential to improve clinical therapy for CSC.