H.5.7. Segmentation
Ali Fahmi Jafargholkhanloo; Mousa Shamsi; Mahdi Bashiri Bawil
Abstract
Magnetic Resonance Imaging (MRI) often suffers from noise and Intensity Non-Uniformity (INU), making segmentation a challenging task. The Fuzzy C-Means (FCM) algorithm, a widely used clustering method for image segmentation, is highly sensitive to noise and its convergence rate depends on data distribution. ...
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Magnetic Resonance Imaging (MRI) often suffers from noise and Intensity Non-Uniformity (INU), making segmentation a challenging task. The Fuzzy C-Means (FCM) algorithm, a widely used clustering method for image segmentation, is highly sensitive to noise and its convergence rate depends on data distribution. FCM employs the Euclidean distance metric, which fails to adapt to variations in data point distributions within compact and similarly shaped clusters. Additionally, this metric is not locally adaptive to different cluster shapes. This paper introduces a Conditional Spatial Gustafson-Kessel Clustering Algorithm based on Information Theory (CSGKIT) to address these challenges. First, information theory is incorporated to enhance the algorithm's robustness against noise and improve segmentation accuracy. Second, the Mahalanobis distance replaces the Euclidean distance to better accommodate cluster shapes during the clustering process. Finally, a conditional spatial approach uses a fuzzy-weighted membership matrix to incorporate local spatial interactions between neighboring pixels. The proposed CSGKIT algorithm is evaluated on two datasets: the BrainWeb simulated dataset and the Open Access Series of Imaging Studies (OASIS) dataset. Experimental results indicate that CSGKIT outperforms other FCM-based algorithms in segmentation accuracy across various tissue types.
H.5.7. Segmentation
Mohsen Erfani Haji Pour
Abstract
The segmentation of noisy images remains one of the primary challenges in image processing. Traditional fuzzy clustering algorithms often exhibit poor performance in the presence of high-density noise due to insufficient consideration of spatial features. In this paper, a novel approach is proposed that ...
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The segmentation of noisy images remains one of the primary challenges in image processing. Traditional fuzzy clustering algorithms often exhibit poor performance in the presence of high-density noise due to insufficient consideration of spatial features. In this paper, a novel approach is proposed that leverages both local and non-local spatial information, utilizing a Gaussian kernel to counteract high-density noise. This method enhances the algorithm's sensitivity to spatial relationships between pixels, thereby reducing the impact of noise. Additionally, a C+ means initialization approach is introduced to improve performance and reduce sensitivity to initial conditions, along with an automatic smoothing parameter tuning method. The evaluation results, based on the criteria of fuzzy assignment coefficient, fuzzy segmentation entropy, and segmentation accuracy, demonstrate a significant improvement in the performance of the proposed method.
H.5.7. Segmentation
Ehsan Ehsaeyan
Abstract
This paper presents a novel approach to image segmentation through multilevel thresholding, leveraging the speed and precision of the technique. The proposed algorithm, based on the Grey Wolf Optimizer (GWO), integrates Darwinian principles to address the common stagnation issue in metaheuristic algorithms, ...
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This paper presents a novel approach to image segmentation through multilevel thresholding, leveraging the speed and precision of the technique. The proposed algorithm, based on the Grey Wolf Optimizer (GWO), integrates Darwinian principles to address the common stagnation issue in metaheuristic algorithms, which often results in local optima and premature convergence. The search agents are efficiently steered across the search space by a dual mechanism of encouragement and punishment employed by our strategy, thereby curtailing computational time. This is implemented by segmenting the population into distinct groups, each tasked with discovering superior solutions. To validate the algorithm’s efficacy, 9 test images from the Pascal VOC dataset were selected, and the renowned energy curve method was employed for verification. Additionally, Kapur entropy was utilized to gauge the algorithm’s performance. The method was benchmarked against four disparate search algorithms, and its dominance was underscored by achieving the best outcomes in 20 out of 27 cases for image segmentation. The experimental findings collectively affirm that the Darwinian Grey Wolf Optimizer (DGWO) stands as a formidable instrument for multilevel thresholding.
H.5.7. Segmentation
V. Naghashi; Sh. Lotfi
Abstract
Image segmentation is a fundamental step in many of image processing applications. In most cases the image’s pixels are clustered only based on the pixels’ intensity or color information and neither spatial nor neighborhood information of pixels is used in the clustering process. Considering ...
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Image segmentation is a fundamental step in many of image processing applications. In most cases the image’s pixels are clustered only based on the pixels’ intensity or color information and neither spatial nor neighborhood information of pixels is used in the clustering process. Considering the importance of including spatial information of pixels which improves the quality of image segmentation, and using the information of the neighboring pixels, causes enhancing of the accuracy of segmentation. In this paper the idea of combining the K-means algorithm and the Improved Imperialist Competitive algorithm is proposed. Also before applying the hybrid algorithm, a new image is created and then the hybrid algorithm is employed. Finally, a simple post-processing is applied on the clustered image. Comparing the results of the proposed method on different images, with other methods, shows that in most cases, the accuracy of the NLICA algorithm is better than the other methods.
