Conditional spatial biased intuitionistic clustering technique for brain MRI image segmentation

In clinical research, it is crucial to segment the magnetic resonance (MR) brain image for studying the internal tissues of the brain. To address this challenge in a sustainable manner, a novel approach has been proposed leveraging the power of unsupervised clustering while integrating conditional spatial properties of the image into intuitionistic clustering technique for segmenting MRI images of brain scans. In the proposed technique, an Intuitionistic-based clustering approach incorporates a nuanced understanding of uncertainty inherent in the image data. The measure of uncertainty is achieved through calculation of hesitation degree. The approach introduces a conditional spatial function alongside the intuitionistic membership matrix, enabling the consideration of spatial relationships within the image. Furthermore, by calculating weighted intuitionistic membership matrix, the algorithm gains the ability to adapt its smoothing behavior based on the local context. The main advantages are enhanced robustness with homogenous segments, lower sensitivity to noise, intensity inhomogeneity and accommodation of degree of hesitation or uncertainty that may exist in the real-world datasets. A comparative analysis of synthetic and real datasets of MR brain images proves the efficiency of the suggested approach over different algorithms. The paper investigates how the suggested research methodology performs in medical industry under different circumstances including both qualitative and quantitative parameters such as segmentation accuracy, similarity index, true positive ratio, false positive ratio. The experimental outcomes demonstrate that the suggested algorithm outperforms in retaining image details and achieving segmentation accuracy

Mammography Images Segmentation Based on Fuzzy Set and Thresholding

Breast cancer is the most widespread cancer that influences ladies about the world. Early recognition of breast tumor is a standout amongst the hugest variables influencing the probability of recuperation from the illness. Hence, mammography remains the most precise and best device for distinguishing breast malignancy. This paper presents a method for segment the boundary of breast masses regions in mammograms via a proposed algorithm based on fuzzy set techniques. Firstly, it was used data set (mini-MIAS) for evaluate algorithm. it was preprocessing the data set to remove noise and propose a fuzzy set by using fuzzy inference system by generated two input parameters (employs image gradient), then used thresholding filter. Then it was evaluated this proposed method, qualitative and quantitative results were obtained to demonstrate the efficiency of this method and confirm the possibility of its use in improving the diagnosis

Spatial fuzzy c-mean sobel algorithm with grey wolf optimizer for MRI brain image segmentation

Segmentation is the process of dividing the original image into multiple sub regions called segments in such a way that there is no intersection between any two regions. In medical images, the segmentation is hard to obtain due to the intensity similarity among various regions and the presence of noise in medical images. One of the most popular segmentation algorithms is Spatial Fuzzy C-means (SFCM). Although this algorithm has a good performance in medical images, it suffers from two issues. The first problem is lack of a proper strategy for point initialization step, which must be performed either randomly or manually by human. The second problem of SFCM is having inaccurate segmented edges. The goal of this research is to propose a robust medical image segmentation algorithm that overcomes these weaknesses of SFCM for segmenting magnetic resonance imaging (MRI) brain images with less human intervention. First, in order to find the optimum initial points, a histogram based algorithm in conjunction with Grey Wolf Optimizer (H-GWO) is proposed. The proposed H-GWO algorithm finds the approximate initial point values by the proposed histogram based method and then by taking advantage of GWO, which is a soft computing method, the optimum initial values are found. Second, in order to enhance SFCM segmentation process and achieve higher accurate segmented edges, an edge detection algorithm called Sobel was utilized. Therefore, the proposed hybrid SFCM-Sobel algorithm first finds the edges of the original image by Sobel edge detector algorithm and finally extends the edges of SFCM segmented images to the edges that are detected by Sobel. In order to have a robust segmentation algorithm with less human intervention, the H-GWO and SFCM-Sobel segmentation algorithms are integrated to have a semi-automatic robust segmentation algorithm. The results of the proposed H-GWO algorithms show that optimum initial points are achieved and the segmented images of the SFCM-Sobel algorithm have more accurate edges as compared to recent algorithms. Overall, quantitative analysis indicates that better segmentation accuracy is obtained. Therefore, this algorithm can be utilized to capture more accurate segmented in images in the era of medical imaging

FCM Clustering Algorithms for Segmentation of Brain MR Images

The study of brain disorders requires accurate tissue segmentation of magnetic resonance (MR) brain images which is very important for detecting tumors, edema, and necrotic tissues. Segmentation of brain images, especially into three main tissue types: Cerebrospinal Fluid (CSF), Gray Matter (GM), and White Matter (WM), has important role in computer aided neurosurgery and diagnosis. Brain images mostly contain noise, intensity inhomogeneity, and weak boundaries. Therefore, accurate segmentation of brain images is still a challenging area of research. This paper presents a review of fuzzy c-means (FCM) clustering algorithms for the segmentation of brain MR images. The review covers the detailed analysis of FCM based algorithms with intensity inhomogeneity correction and noise robustness. Different methods for the modification of standard fuzzy objective function with updating of membership and cluster centroid are also discussed

Approche robuste pour la segmentation et la classification d’images m´edicales

Image segmentation is a vital process in various fields, including robotics, object recognition, and medical imaging. In medical imaging, accurate segmentation of brain tissues from MRI images is crucial for diagnosing and treating brain disorders such as Alzheimer’s disease, epilepsy, schizophrenia, multiple sclerosis, and cancer. This thesis proposes an automatic fuzzy method for brain MRI segmentation. Firstly, the proposed method aims to improve the efficiency of the Fuzzy C-Means (FCM) algorithm by reducing the need for manual intervention in cluster initialization and determining the number of clusters. For this purpose, we introduce an adaptive splitmerge technique that effectively divides the image into several homogeneous regions using a multi-threshold method based on entropy information. During the merge process, a new distance metric is introduced to combine the regions that are both highly similar within the merged region and effectively separated from others. The cluster centers and numbers obtained from the adaptive split-merge step serve as the initial parameters for the FCM algorithm. The obtained fuzzy partitions are evaluated using a novel proposed validity index. Secondly, we present a novel method to address the challenge of noisy pixels in the FCM algorithm by incorporating spatial information. Specifically, we assign a crucial role to the central pixel in the clustering process, provided it is not corrupted with noise. However, if it is corrupted with noise, its influence is reduced. Furthermore, we propose a novel quantitative metric for replacing the central pixel with one of its neighbors if it can improve the segmentation result in terms of compactness and separation. To evaluate the effectiveness of the proposed method, a thorough comparison with existing clustering techniques is conducted, considering cluster validity functions, segmentation accuracy, and tissue segmentation accuracy. The evaluation comprises comprehensive qualitative and quantitative assessments, providing strong evidence of the superior performance of the proposed approach

A Novel Distance between Vague Sets and Its Applications in Decision Making

A novel distance between vague sets (VSs) is presented after the inadequacies of existing distance measures between vague sets are analyzed by artificial vague sets. The proposed method investigates the assignment of degree of hesitation to the membership and nonmembership degree, and the properties are also discussed. The performances of the new method are illustrated by pattern classification problem. Finally, the proposed method is applied into multicriteria fuzzy decision making, where the linear programming method is taken to generate optimal weights for every criterion and the best alternative is obtained by the weighted sum of distance measures between each alternative and the idea alternative with respect to a set of criteria. The experimental results show the effectiveness of the proposed method

Educational data mining using fuzzy sets to facilitate usability and user experience - an approach to integrate artificial intelligence and human-computer interaction

Artificial Intelligence (AI) and Human-Computer Interaction (HCI) have the common goal of enhancing effectiveness of a system and making it easier for people to use. AI accomplishes that by demonstrating intelligent behavior on a machine, whereas HCI involves the design approach required to obtain usability and user experience. This study integrates AI and HCI techniques in a real-world application complementing the aims of each field. A web based system was developed for a school board in Eastern Canada by following the user-centered approach of HCI. In the course of designing a good interface, it was found that fuzzy inference of AI was going on in users’ minds when they formed conceptual models to understand the application. The interface was evaluated by applying heuristic evaluation, cognitive walkthroughs and user feedback. It was shown that usability and user experience can be improved by employing fuzzy set techniques. Therefore, fuzzy set modeling can serve as a user centered method for HCI design. Furthermore, data gathering techniques of HCI helped to define the cognitive processes that could be replicated with the aid of fuzzy sets.Master of Science (MSc) in Computational Science