A Modified Robust FCM Model with Spatial Constraints for Brain MR Image Segmentation

Medical image processing is typically performed to diagnose a patient's brain tumor prior to surgery. In this study, a technique in denoising and segmentation was developed to improve medical image processing. The proposed approach employs multiple modules. In the first module, the noisy brain tumor image is transformed into multiple low‐ and high‐pass tetrolet coefficients. In the second module, multiple low‐pass tetrolet coefficients are applied through a modified transform‐based gamma correction method. Generalized cross‐validation is used on multiple high‐pass tetrolet coefficients to obtain the best threshold value. In the third module, all enhanced coefficients are applied to the partial differential equation method. In the final module, the denoised image is applied to Atanassov's intuitionistic fuzzy set histon‐based fuzzy clustering method with centroid optimization using an elephant herding method. Accordingly, the tumor part is segmented from the nontumor part in the magnetic resonance imaging brain images. The method was assessed in terms of peak signal‐to‐noise ratio, mean square error, specificity, sensitivity, and accuracy. The experimental results showed that the suggested method is superior to traditional methods.

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“…The cluster centroid is presented as follows 17 :

{cl}_{n} = \sum_{italicij = 1}^{B \times C} ((), \frac{{((), μ_{HIFS} ((), I_{italicij}))}^{italicfv} μ (I_{ij})}{{(μ_{italicHIFS} (I_{ij}))}^{fv}})

…”

Section: Implementation Of the Proposed Image Segmentation Methodsmentioning

confidence: 99%

Modified transform‐based gamma correction for MRI tumor image denoising and segmentation by optimized histon‐based elephant herding algorithm

Kollem

Reddy²,

Rao

2020

Int J Imaging Syst Tech

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“…Song et al [23] developed a modified robust fuzzy c-means (MRFCM) algorithm for brain MR image segmentation at a local level. The results of segmentation are compared with state-of-the-art algorithms based on fuzzy clustering.…”

Section: Expert Systemsmentioning

confidence: 99%

eHealth and Artificial Intelligence

Impedovo

Pirlo

2019

Information

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Artificial intelligence is changing the healthcare industry from many perspectives: diagnosis, treatment, and follow-up. A wide range of techniques has been proposed in the literature. In this special issue, 13 selected and peer-reviewed original research articles contribute to the application of artificial intelligence (AI) approaches in various real-world problems. Papers refer to the following main areas of interest: feature selection, high dimensionality, and statistical approaches; heart and cardiovascular diseases; expert systems and e-health platforms.

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“…However, the probabilistic model is tedious and statistical in the global scope of the eye image, the obtained pupil threshold is a large range of values, and the segmented pupil often contains other non-pupil regions, for the pupil information characteristics are not clear badly segmented. In terms of the clustering segmentation algorithm mentioned in the literature [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ], feature extraction segmentation of images has worked well for segmentation of infrared electrical appliances images, brain MRI images, fundus vascular images, and breast density images. Reference [ 12 ] used the k-means clustering segmentation algorithm to classify eye images.…”

Section: Introductionmentioning

confidence: 99%

A Pupil Segmentation Algorithm Based on Fuzzy Clustering of Distributed Information

Bai

Wang

2021

Sensors

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Pupil segmentation is critical for line-of-sight estimation based on the pupil center method. Due to noise and individual differences in human eyes, the quality of eye images often varies, making pupil segmentation difficult. In this paper, we propose a pupil segmentation method based on fuzzy clustering of distributed information, which first preprocesses the original eye image to remove features such as eyebrows and shadows and highlight the pupil area; then the Gaussian model is introduced into global distribution information to enhance the classification fuzzy affiliation for the local neighborhood, and an adaptive local window filter that fuses local spatial and intensity information is proposed to suppress the noise in the image and preserve the edge information of the pupil details. Finally, the intensity histogram of the filtered image is used for fast clustering to obtain the clustering center of the pupil, and this binarization process is used to segment the pupil for the next pupil localization. Experimental results show that the method has high segmentation accuracy, sensitivity, and specificity. It can accurately segment the pupil when there are interference factors such as light spots, light reflection, and contrast difference at the edge of the pupil, which is an important contribution to improving the stability and accuracy of the line-of-sight tracking.

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A Modified Robust FCM Model with Spatial Constraints for Brain MR Image Segmentation

Cited by 17 publications

References 20 publications

Modified transform‐based gamma correction for MRI tumor image denoising and segmentation by optimized histon‐based elephant herding algorithm

Modified transform‐based gamma correction for MRI tumor image denoising and segmentation by optimized histon‐based elephant herding algorithm

eHealth and Artificial Intelligence

A Pupil Segmentation Algorithm Based on Fuzzy Clustering of Distributed Information

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