MRI Fuzzy Segmentation of Brain Tissue Using Neighborhood Attraction With Neural-Network Optimization

Shen, Suhung; Sandham, W.A.; Granat, Malcolm; Sterr, Annette

doi:10.1109/titb.2005.847500

Cited by 332 publications

(185 citation statements)

References 26 publications

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“…To overcome these drawbacks, Shen et al (2005) presented an improved algorithm. They found that the similarity function , is the key to segmentation success.…”

Section: Ifcm Algorithmmentioning

confidence: 99%

“…Therefore, they lose the continuity from FCM, which inevitably introduce computation issues. Recently, Shen et al (2005) introduced a new extension of FCM algorithm, called improved FCM (IFCM). They introduced two influential factors in segmentation that address the neighbourhood attraction.…”

Section: Introductionmentioning

confidence: 99%

“…It is therefore important to select suitable parameters such that the IFCM algorithm achieves superior partition performance compared to the FCM. In (Shen et al, 2005), an artificial neural network (ANN) was employed for computation of these two parameters. However, designing the neural network architecture and setting its parameters are always complicated which slow down the algorithm and may also lead to inappropriate attraction parameters and consequently degrade the partitioning performance (Haykin, 1998).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Application of Particle Swarm Optimization in Accurate Segmentation of Brain MR Images

Forghani¹,

Forouzanfar²,

Eftekhari³

et al. 2009

Particle Swarm Optimization

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“…To overcome these drawbacks, Shen et al (2005) presented an improved algorithm. They found that the similarity function , is the key to segmentation success.…”

Section: Ifcm Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Application of Particle Swarm Optimization in Accurate Segmentation of Brain MR Images

Forghani¹,

Forouzanfar²,

Eftekhari³

et al. 2009

Particle Swarm Optimization

View full text Add to dashboard Cite

“…Because of the complexity of the segmentation process automatic brain image segmentation requires several different approaches, where each approach utilizesdiverse induction ways such as region-based methods (Adams and Bischof, 1994;Alia et al, 2011;Chang and Li, 1994;Pohle and Toennies, 2001;Sijbers et al, 1997), classification-based methods (Bezdek et al, 1993;Dou et al, 2007;Kapur et al, 1996;Mokbel et al, 2000;Szilagyi et al, 2003;Van et al, 1999a;1999b;Wells et al, 1996;Xiaohe et al, 2008;Zhou and Rajapakse, 2008) boundary-based methods (Ashtari et al, 1990;Atkins and Mackiewich, 1998;Ji and Yan, 2002;McInerney and Terzopoulos, 1996) and others in (Beevi and Sathik, 2012;Clark et al, 1997;1998;Shen et al, 2005;Sonka et al, 1996;Cherfa et al, 2007;Zanaty and Aljahdali, 2010;Zhou and Bai, 2007). This intricacyhappens from the intrinsic nature, complicated structures of the MRI brain image (Alia et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…However, the main drawback of these applied algorithms is calculating the neighborhood term for each phase of iteration, that takes a long time (very timeconsuming) (Shen et al, 2005). New methods relies on the image histogram representation were suggested in the literature such as (Cai et al, 2007;Chen and Zhang, 2004;Chuang et al, 2006;Liao et al, 2008;Sijbers et al, 1997;Szilagyi et al, 2003;Liew and Hong, 2003) in order to solve time-consuming and decrease the computational demands of these algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Firefly Algorithm With Fuzzy-C Mean Algorithm for Mri Brain Segmentation

Alsmadi¹

2014

American Journal of Applied Sciences

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Image processing is one of the essential tasks to extract suspicious region and robust features from the Magnetic Resonance Imaging (MRI). A numbers of the segmentation algorithms were developed in order to satisfy and increasing the accuracy of brain tumor detection. In the medical image processing brain image segmentation is considered as a complex and challenging part. Fuzzy c-means is unsupervised method that has been implemented for clustering of the MRI and different purposes such as recognition of the pattern of interest and image segmentation. However; fuzzy c-means algorithm still suffers many drawbacks, such as low convergence rate, getting stuck in the local minima and vulnerable to initialization sensitivity. Firefly algorithm is a new population-based optimization method that has been used successfully for solving many complex problems. This paper proposed a new dynamic and intelligent clustering method for brain tumor segmentation using the hybridization of Firefly Algorithm (FA) with Fuzzy C-Means algorithm (FCM). In order to automatically segment MRI brain images and improve the capability of the FCM to automatically elicit the proper number and location of cluster centres and the number of pixels in each cluster in the abnormal (multiple sclerosis lesions) MRI images. The experimental results proved the effectiveness of the proposed FAFCM in enhancing the performance of the traditional FCM clustering. Moreover; the superiority of the FAFCM with other state-of-the-art segmentation methods is shown qualitatively and quantitatively. Conclusion: A novel efficient and reliable clustering algorithm presented in this work, which is called FAFCM based on the hybridization of the firefly algorithm with fuzzy c-mean clustering algorithm. Automatically; the hybridized algorithm has the capability to cluster and segment MRI brain images.

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A 3D visualization‐based augmented reality application for brain tumor segmentation

Guerroudji,

Amara,

Lichouri

et al. 2023

Computer Animation & Virtual

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SummaryEvery year on June 8th, the globe observes World Brain Tumor Day to raise awareness and educate people about brain cancer, encompassing both noncancerous (benign) and cancerous (malignant) growths. Research in the field of brain cancer plays a vital role in supporting medical professionals. In this context, augmented reality (AR) technology has emerged as a valuable tool, enabling surgeons to visualize underlying structures and offering a cost‐effective and time‐efficient alternative. Our study focuses on the efficient segmentation of brain tumor classes using Magnetic Resonance Imaging (MRI) and incorporates a three‐stage approach: preprocessing, segmentation, and 3D reconstruction & AR display. In the preprocessing stage, a Gaussian filter is applied to mitigate intensity heterogeneity. Segmentation and detection are achieved using active geometric contour models, complemented by morphological operations. To establish 3D brain tumor reconstruction, a genuine scene is virtually integrated using 3D Slicer software. The proposed methodology was validated using a genuine patient dataset comprising 496 MRI scans obtained from the local Bab El Oued university hospital center. The results demonstrate the effectiveness of our approach in achieving accurate 3D brain tumor reconstruction, efficient tumor extraction, and augmented reality visualization. The obtained segmentation results showcased an impressive accuracy of 98.61%, outperforming existing state‐of‐the‐art methods and affirming the efficacy of our proposed strategy.

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MRI Fuzzy Segmentation of Brain Tissue Using Neighborhood Attraction With Neural-Network Optimization

Cited by 332 publications

References 26 publications

Application of Particle Swarm Optimization in Accurate Segmentation of Brain MR Images

Application of Particle Swarm Optimization in Accurate Segmentation of Brain MR Images

A Hybrid Firefly Algorithm With Fuzzy-C Mean Algorithm for Mri Brain Segmentation

A 3D visualization‐based augmented reality application for brain tumor segmentation

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