Proceedings ArticleDOI
Intuitionistic Fuzzy Clustering Method with Spatial Information for MRI Image Segmentation
Dhirendra Kumar,Ramesh Kumar Agrawal,Jyoti Singh Kirar +2 more
- pp 1-7
TLDR
The segmentation performance of the proposed I FCM with spatial neighborhood information (IFCMSNI) method is compared with FCM, IFCM, FCMS, FLICM and IIFCM methods in terms of dice score and average segmentation accuracy and the experimental finding endorses the proposed method for image segmentation.Abstract:
Fuzzy c-means is one of the popular clustering technique which has been utilized for medical image analysis. Intuitionistic fuzzy set theory based clustering is an extension of fuzzy c-means which is used for medical image segmentation due to its promising nature for handling the vagueness and uncertainty. The performance of image segmentation is not good in the presence of noise. Many fuzzy and intuitionistic fuzzy set theory based clustering methods have been reported in the literature to handle noise in the segmentation process. In the process of handling noise, most of these methods use smoothing which ignores the important structural information (such as edges and other fine details). In this research work to address this issue, the optimization problem of the proposed IFCM with spatial neighborhood information (IFCMSNI) method is formulated with a novel spatial regularization term which is based on the neighborhood membership value with the advantage of both the intuitionistic fuzzy set theory and a spatial regularization term to handle noise associated with medical images. In the proposed method, the image is represented in the form of Intuitionistic Fuzzy Sets (IFSs) using Sugeno’s negation function. In order to validate the effectiveness of the proposed method, experiments have been carried out on a synthetic image dataset and two publicly available human brain MRI dataset. The segmentation performance of the proposed method is compared with FCM, IFCM, FCMS, FLICM and IIFCM methods in terms of dice score and average segmentation accuracy. The experimental finding endorses the proposed method for image segmentation.read more
Citations
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Journal ArticleDOI
Bias-Corrected Intuitionistic Fuzzy C-Means With Spatial Neighborhood Information Approach for Human Brain MRI Image Segmentation
TL;DR: In this paper , an intuitionistic fuzzy set (IFS) theory based bias-corrected intuitionistic c-means with spatial neighborhood information (SNI) method for magnetic resonance imaging (MRI) image segmentation is proposed.
Journal ArticleDOI
Kernel picture fuzzy clustering with spatial neighborhood information for MRI image segmentation
TL;DR: The comparison with the state-of-the-art methods shows that the proposed picture fuzzy clustering method provides better segmentation performance in terms of average segmentation accuracy and Dice score.
Journal ArticleDOI
An Instance Segmentation and Clustering Model for Energy Audit Assessments in Built Environments: A Multi-Stage Approach.
Youness Arjoune,Sai Peri,Niroop Sugunaraj,Avhishek Biswas,Debanjan Sadhukhan,Prakash Ranganathan +5 more
TL;DR: In this paper, the authors demonstrate a novel process using deep-learning methods to segment more than 100,000 thermal images collected from an UAS, to quantify the heat loss for a building envelope, multiple stages of computations need to be performed: object detection (using Mask-RCNN/Faster R-CNN), estimating the surface temperature (using two clustering methods), and finally calculating the overall heat transfer coefficient (e.g., the U-value).
Journal ArticleDOI
A noise robust kernel fuzzy clustering based on picture fuzzy sets and KL divergence measure for MRI image segmentation
Journal ArticleDOI
Fuzzy k-plane clustering method with local spatial information for segmentation of human brain MRI image
TL;DR: In this article, the authors incorporated local spatial information in the Fuzzy k-plane clustering method to handle the noise present in the image and showed that the proposed FkPC_S method is superior in comparison with 10 related methods in the presence of noise.
References
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Journal ArticleDOI
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