Fuzzy c-means algorithm for medical image segmentation

An unsupervised fuzzy clustering technique, fuzzy c-means (FCM) clustering algorithm has been widely used in image segmentation. However, the FCM algorithm always converges to strict local minima, starting from an initial guess of the membership degrees. To overcome this limitation of FCM algorithm, a fuzzy evolutional c-mean (FECM) algorithm is presented in this paper. We combine the classical FCM with an evolutional algorithm and we introduce the sharing operator for taking into account the spatial information.

Medical image segmentation is an indispensable process in viewing and measuring various structures in the brain. However, medical images are inherently low contrast, vague boundaries, and high correlative. The traditional fuzzy c-means (FCM) clustering algorithm considers only the pixel attributes. This leads to accuracy degradation with image segmentation. To solve this problem, this paper proposes a robust segmentation technique, called a Generalized Spatial Fuzzy C-Means (GSFCM) algorithm, that utilizes both given pixel attributes and the spatial local information which is weighted correspondingly to neighbor elements based on their distance attributes. This improves the segmentation performance dramatically. Experimental results with several magnetic resonance (MR) images show that the proposed GSFCM algorithm outperforms the traditional FCM algorithms in the various cluster validity functions.

Local segmentation of images using an improved fuzzy C-means clustering algorithm based on self-adaptive dictionary learning

Image segmentation plays an important role in medical image processing. Fuzzy c-means (FCM) is one of the popular clustering algorithms for medical image segmentation. But FCM is highly vulnerable to noise due to not considering the spatial information in image segmentation. This paper introduces medium mathematics system which is employed to process fuzzy information for image segmentation. It establishes the medium similarity measure based on the measure of medium truth degree (MMTD) and uses the correlation of the pixel and its neighbors to define the medium membership function. An improved FCM medical image segmentation algorithm based on MMTD which takes some spatial features into account is proposed in this paper. The experimental results show that the proposed algorithm is more antinoise than the standard FCM, with more certainty and less fuzziness. This will lead to its practicable and effective applications in medical image segmentation.

Deep learning techniques are recently exploited for segmentation of medical images. These deep learning techniques have accomplished state-of-art conduct for automatic medical image segmentation. Image segmentation helps in quantitative and qualitative analysis of the medical images, which leads to diagnosis of various diseases. Manual segmentation of medical images is a laborious task that prevents early diagnosis of diseases. For these particular reasons, automated techniques play a major role in medical image segmentation. The recent research is focused on deep learning algorithms for efficient automatic medical image segmentation. Deep learning algorithms are classified as supervised as well as unsupervised learning. Supervised learning consists of convolutional neural networks (CNNs) and unsupervised learning consists of stacked auto-encoder, restricted Boltzmann’s machine (RBM), and deep belief networks. CNNs comprise convolutional layer, pooling layer, dropout layer, and fully connected layer. The convolutional layer carries out the convolution operation with a set of kernels along with weights and added biases individually creating a new feature map on the input image at each layer. Stacked auto-encoder models are designed by insertion of different layers termed to be auto-encoder layers in the form of stack. These layers take image as an input and extracts different features in the form of feature maps in an unsupervised mode lacking labeled data. It is a model that takes input data, gathers feature representations from this, and then uses these feature representations to restructure output data. According to the literature survey, auto-encoder layers are trained independently after which the full network is used to make a prediction by fine-tuning it using supervised training. The neurons present in deep belief nets are densely connected which helps in rapid and accurate learning of a good set of parameters. RBMs are a type of Markov random fields, consisting of input layer or visible layer and a hidden layer that brings hidden feature representation. There are bidirectional connections between the nodes, so latent feature representation extracted from an input vector and vice versa. This chapter provides an outline on the state of deep learning algorithms for medical image segmentation, highlighting those facets that are frequently useful for brain tumor segmentation. In addition, comparative analysis of the deep learning algorithms is discussed. This concludes that with the different algorithms for segmentation tumor regions from brain MRI images, deep learning has proven to be the most effective in the recent trends.

Medical images are information carriers that visually reflect and record the anatomical structure of the human body, and play an important role in clinical diagnosis, teaching and research, etc. Modern medicine has become increasingly inseparable from the intelligent processing of medical images. In recent years, there have been more and more attempts to apply deep learning theory to medical image segmentation tasks, and it is imperative to explore a simple and efficient deep learning algorithm for medical image segmentation. In this paper, we investigate the segmentation of lung nodule images. We address the above-mentioned problems of medical image segmentation algorithms and conduct research on medical image fusion algorithms based on a hybrid channel-space attention mechanism and medical image segmentation algorithms with a hybrid architecture of Convolutional Neural Networks (CNN) and Visual Transformer. To the problem that medical image segmentation algorithms are difficult to capture long-range feature dependencies, this paper proposes a medical image segmentation model SW-UNet based on a hybrid CNN and Vision Transformer (ViT) framework. Self-attention mechanism and sliding window design of Visual Transformer are used to capture global feature associations and break the perceptual field limitation of convolutional operations due to inductive bias. At the same time, a widened self-attentive vector is used to streamline the number of modules and compress the model size so as to fit the characteristics of a small amount of medical data, which makes the model easy to be overfitted. Experiments on the LUNA16 lung nodule image dataset validate the algorithm and show that the proposed network can achieve efficient medical image segmentation on a lightweight scale. In addition, to validate the migratability of the model, we performed additional validation on other tumor datasets with desirable results. Our research addresses the crucial need for improved medical image segmentation algorithms. By introducing the SW-UNet model, which combines CNN and ViT, we successfully capture long-range feature dependencies and break the perceptual field limitations of traditional convolutional operations. This approach not only enhances the efficiency of medical image segmentation but also maintains model scalability and adaptability to small medical datasets. The positive outcomes on various tumor datasets emphasize the potential migratability and broad applicability of our proposed model in the field of medical image analysis.

Medical image analysis often faces challenges due to noise, which can obscure crucial diagnostic information and hinder precise segmentation. Traditional denoising methods often fail to effectively suppress noise while preserving image details, resulting in blurred or overly smoothed outputs. To address this, we propose an improved fuzzy clustering algorithm that introduces an innovative integration of fast bilateral filtering and adaptive parameter tuning, offering superior noise reduction and enhanced medical image segmentation accuracy. Our method introduces a novel combination of fast bilateral filtering and an enhanced fuzzy C-means (FCM) algorithm, which effectively balances noise suppression and detail preservation, outperforming existing methods in both accuracy and efficiency. The fast bilateral filter efficiently preserves edge details while reducing spatial and local intensity variations, serving as a robust preprocessing step that mitigates noise-induced clustering errors. Additionally, we introduce an innovative strategy that calculates the absolute difference between the original and filtered images to enhance clustering accuracy in noisy environments. To improve convergence speed and computational efficiency, we refine the FCM objective function by incorporating a logarithmic summation of membership degrees from previous iterations, reducing iteration counts and accelerating convergence. Finally, we apply sharpening and median filtering techniques to refine segmentation outputs and enhance detail clarity. Experimental results on benchmark medical images demonstrate that our proposed method achieves superior noise suppression, improved segmentation accuracy, and faster convergence compared to conventional FCM and recent denoising techniques.

This paper presents a modified intuitionistic fuzzy clustering (IFCM) algorithm for medical image segmentation. IFCM is a variant of the conventional fuzzy C -means (FCM) based on intuitionistic fuzzy set (IFS) theory. Unlike FCM, IFCM considers both membership and nonmembership values. The existing IFCM method uses Sugeno’s and Yager’s IFS generators to compute nonmembership value. But for certain parameters, IFS constructed using above complement generators does not satisfy the elementary condition of intuitionism. To overcome this problem, this paper adopts a new IFS generator. Further, Hausdorff distance is used as distance metric to calculate the distance between cluster center and pixel. Extensive experimentations are carried out on standard datasets like brain, lungs, liver and breast images. This paper compares the proposed method with other IFS based methods. The proposed algorithm satisfies the elementary condition of intuitionism. Further, this algorithm outperforms other methods with the use of various cluster validity functions.

In this paper, we propose and present a novel algorithm for medical image segmentation (MIS). By analyzing the current state-of-the-art related algorithms, we introduce the multi-band active contour model based limit function to make the multilayer segmentation available. With the development of image segmentation technology, the development of medical image segmentation technology also got very big, because there is no find common, accepted effect ideal is suitable for medical image segmentation method, almost existing each kind of segmentation method has application in the field of medical image segmentation. Furtherly, with the optimized aims of being robust to the noise and avoiding the bad effluence on the result, we adopt the kernel method and new initialization curve. This model suffers from low noise robustness, and model algorithm is difficult to achieve. Integrated segmentation technology refers to two or more technology is used, combined with their own advantages, so they can on the accuracy or efficiency to achieve better performance than when using a single. A new penalty term is introduced to improve numerical stability and the step length is increased to improve efficiency. As far as the robustness and effectiveness are concerned, our method is better than the existing medical image segmentation algorithms. Experimental analysis verifies the success of our method.

Medical image segmentation is an essential step for most consequent image analysis tasks. Medical images can be segmented manually, but the accuracy of image segmentation using the automated segmentation algorithms is more when compared with the manual calculations. In this paper, an automated segmentation and classification of tissues are proposed for MR brain images. To classify MR brain image into three segments such as Grey Matter (GM), White Matter (WM) and Cerebro-Spinal Fluid (CSF). Classification of brain into tissues helps to diagnose several diseases such as tumors, Alzheimer's disease, stroke, multiple sclerosis. An unsupervised clustering technique such as Fuzzy C-Means (FCM) algorithm has been widely used in segmenting the images. The spatial information is not fully utilized by the conventional clustering algorithm and hence it is not applicable for clustering a noisy image. We incorporate a method for image clustering called out as Reformulated Fuzzy Local information C-Means Clustering algorithm [RFLICM] which is a variant of traditional Clustering algorithm by considering both spatial and gray level information. In RFLICM, spatial distance is replaced by local coefficient of variation in a fuzzy manner. Experiments are conducted on brain images to validate the performance of the proposed technique in segmenting the medical images and the efficiency achieved in the presence of salt and pepper noise is 99.86%. Standard FCM, Fuzzy Local information C-means clustering algorithm [FLICM], Reformulated Fuzzy Local information C-means clustering algorithm [RFLICM] are compared to explore the accuracy of our proposed approach. Clustering results show that RFLICM segmentation method is appropriate for classifying tissues in brain MR image.

Problem statement: Segmentation plays an important role in medical imaging. Segmentation of an image is the division or separation of the image into dissimilar regions of similar attribute. In this study we proposed a methodology that integrates clustering algorithm and marker controlled watershed segmentation algorithm for medical image segmentation. The use of the conservative watershed algorithm for medical image analysis is pervasive because of its advantages, such as always being able to construct an entire division of the image. On the other hand, its disadvantages include over segmentation and sensitivity to false edges. Approach: In this study we proposed a methodology that integrates K-Means clustering with marker controlled watershed segmentation algorithm and integrates Fuzzy C-Means clustering with marker controlled watershed segmentation algorithm separately for medical image segmentation. The Clustering algorithms are unsupervised learning algorithms, while the marker controlled watershed segmentation algorithm makes use of automated thresholding on the gradient magnitude map and post-segmentation merging on the initial partitions to reduce the number of false edges and over-segmentation. Results: In this study, we compared K-means clustering and marker controlled watershed algorithm with Fuzzy C-means clustering and marker controlled watershed algorithm. And also we showed that our proposed method produced segmentation maps which gave fewer partitions than the segmentation maps produced by the conservative watershed algorithm. Conclusion: Integration of K-means clustering with marker controlled watershed algorithm gave better segmentation than integration of Fuzzy C-means clustering with marker controlled watershed algorithm. By reducing the amount of over segmentation, we obtained a segmentation map which is more diplomats of the several anatomies in the medical images.

Image segmentation is one of the most common steps in digital image processing. It classifies a digital image into different segments. There are many algorithms for image segmentation such as thresholding, edge detection, and region growing, which finding a suitable algorithm for medical image segmentation is a challenging task. This is due to noise, low contrast, and steep light variations of medical images. The main goal of this paper is improving the performance of fuzzy c-means clustering. Improving is achieved using parallel implementation of this algorithm. Fuzzy c-means clustering is an important iterative clustering algorithm, but it is computationally intensive and it uses the same data between the iterations. The center of the clusters changes in each iteration, which requires considerable amount of time for large data sets. The parallel fuzzy c-means clustering is implemented by using task pipeline concept in CUDA technology. The experimental results show that the performance is improved up to 23.35×. After that watershed algorithm is applied for the final segmentation. The implementation results show that the accuracy of diagnosis in magnetic resonance imaging 97/33% is improved. This improvement is achieved using enhancing edges and reducing noises in images.

Medical image segmentation is a strategy for extricating the ideal parts and highlights from the info medical image information. The presentation of classification stage depends on initial stages like preprocessing and segmentation. The traditional Fuzzy c-means (FCM) clustering algorithms have been generally utilized for grayscale and color image segmentation. In this work, we propose a super-pixel based FCM clustering algorithm that is altogether more hearty than best in clustering algorithms for image segmentation. We initially acquire a preprocessing stage by super-pixel image with exact contour for background separation. As opposed to customary neighboring window of fixed size and shape, the super-pixel image gives better adaptive and irregular local spatial neigh-borhoods that are helpful for improving Interstitial lung disease (ILD) image segmentation. Also after that the results are compared with preprocessing performed by adaptive median filtering to stay away from the noise effect on ILD images followed by Contrast-limited adaptive histogram equalization (CLAHE) enhancement to improve the image quality and then segmented by FCM. The outcomes are obtained for various number of clusters segmented with FCM with super-pixel approach and result are improve as contrast to conventional FCM and Otsu method on ILD images.

Image segmentation is an important process to extract information from complex medical images. Segmentation has wide application in medical field. The main objective of image segmentation is to partition an image into mutually exclusive and exhausted regions such that each region of interest is spatially contiguous and the pixels within the region are homogeneous with respect to a predefined criterion. Widely used homogeneity criteria include values of intensity, texture, color, range, surface normal and surface curvatures. During the past, many researchers in the field of medical imaging and soft computing have made significant survey in the field of image segmentation. This paper aims to develop an improved method of segmentation using Fuzzy- Neuro logic to detect various tissues like white matter, gray matter; cerebral spinal fluid and tumor for a given magnetic resonance image data set. Generally magnetic resonance images always contain a significant amount of noise caused by operator performance, equipment, and the environment, which can lead to serious inaccuracies. So segmentation of such medical images is a challenging problem in the field of image analysis. Several diagnostics are based on proper segmentation of the digitized image. Segmentation of medical images is needed for applications involving estimation of the boundary of an object, classification of tissue abnormalities, shape analysis, contour detection. In particular Fuzzy-Neuro logic segmentation algorithm is used to provide satisfactory results compared to K-means, Fuzzy C-Means, Neural Network and Fuzzy logic.

Image segmentation plays an important role in medical image processing. Fuzzy c-means (FCM) clustering is one of the popular clustering algorithms for medical image segmentation. However, FCM has the problems of depending on initial clustering centers, falling into local optimal solution easily, and sensitivity to noise disturbance. To solve these problems, this paper proposes a hybrid artificial fish swarm algorithm (HAFSA). The proposed algorithm combines artificial fish swarm algorithm (AFSA) with FCM whose advantages of global optimization searching and parallel computing ability of AFSA are utilized to find a superior result. Meanwhile, Metropolis criterion and noise reduction mechanism are introduced to AFSA for enhancing the convergence rate and antinoise ability. The artificial grid graph and Magnetic Resonance Imaging (MRI) are used in the experiments, and the experimental results show that the proposed algorithm has stronger antinoise ability and higher precision. A number of evaluation indicators also demonstrate that the effect of HAFSA is more excellent than FCM and suppressed FCM (SFCM).