A Combination of Bias-Field Corrected Fuzzy C-Means and Level Set Approach for Brain MRI Image Segmentation

Agarwal, Pankhuri; Kumar, Sandeep; Singh, Rahul; Agarwal, Prateek; Bhattacharya, Mahua

doi:10.1109/iscmi.2015.16

Cited by 14 publications

(5 citation statements)

References 12 publications

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“…Most medical image modalities are grayscale, and m = (x, y) is considered a point in the medical image. This point and the position of m(t) will evolve at the overtime, t is the time for the m(t) point on the surface; this point is expressed below [ 22 ].…”

Section: Methodsmentioning

confidence: 99%

Thresholding for medical image segmentation for cancer using fuzzy entropy with level set algorithm

Maolood

Al-Salhi

2018

Open Medicine

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AbstractIn this study, an effective means for detecting cancer region through different types of medical image segmentation are presented and explained. We proposed a new method for cancer segmentation on the basis of fuzzy entropy with a level set (FELs) thresholding. The proposed method was successfully utilized to segment cancer images and then efficiently performed the segmentation of test ultrasound image, brain MRI, and dermoscopy image compared with algorithms proposed in previous studies. Results showed an excellent performance of the proposed method in detecting cancer image segmentation in terms of accuracy, precision, specificity, and sensitivity measures.

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Section: Methodsmentioning

confidence: 99%

Thresholding for medical image segmentation for cancer using fuzzy entropy with level set algorithm

Maolood

Al-Salhi

2018

Open Medicine

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“…e outcomes intensify and reinforce the traditional approaches with the harshness of the application. e tissues such as gray-level and white-level intensified are segmented with bias-filed corrected fuzzy C-means in [28]. A combination of the levelset and fuzzy method clusters the gray and white matter from the magnetic resonance images.…”

Section: Fuzzy-based Approachesmentioning

confidence: 99%

Machine Learning Techniques for Quantification of Knee Segmentation from MRI

Singla

Abugabah

et al. 2020

Complexity

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Magnetic resonance imaging (MRI) is precise and efficient for interpreting the soft and hard tissues. Moreover, for the detailed diagnosis of varied diseases such as knee rheumatoid arthritis (RA), segmentation of the knee magnetic resonance image is a challenging and complex task that has been explored broadly. However, the accuracy and reproducibility of segmentation approaches may require prior extraction of tissues from MR images. The advances in computational methods for segmentation are reliant on several parameters such as the complexity of the tissue, quality, and acquisition process involved. This review paper focuses and briefly describes the challenges faced by segmentation techniques from magnetic resonance images followed by an overview of diverse categories of segmentation approaches. The review paper also focuses on automatic approaches and semiautomatic approaches which are extensively used with performance metrics and sufficient achievement for clinical trial assistance. Furthermore, the results of different approaches related to MR sequences used to image the knee tissues and future aspects of the segmentation are discussed.

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“…There has been a lot of research done on the FCM method [30], [31], [32], [8], [1], [33], [34], [35]. The brain images of MRI need a computer analysis for the precise delineation of the tumors and then the reproducible method of image segmentation process.…”

Section: F Fuzzy C-means (Fcm)mentioning

confidence: 99%

“…This paper thus concludes a higher accuracy rate in the method of My FCM than the other methods. Agarwal et.al (2015) [30] had given a system of bias field correction technique integrated with the fuzzy c-means segmentation to partition the brain MRI image into two clusters GM and WM respectively. The level set segmentation is used at last to segment these regions.…”

Section: F Fuzzy C-means (Fcm)mentioning

confidence: 99%

A Survey on Advanced Segmentation Techniques for Brain MRI Image Segmentation

Nilakant

Menon

Vikram

2017

International Journal on Advanced Science, Engineering and Information Technology

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This paper presents a survey of advanced methods for segmenting the MRI (Magnetic Resonance Imaging) image of the brain. Segmentation of the brain is a challenging task because it requires more emphasized methods to differentiate each of the regions present in the brain image. The intensity differences between the different regions in the brain MRI image are very less, making it difficult to automate the entire segmentation process. Hence, a thorough understanding of the existing segmentation algorithm is essential for accurate segmentation. The segmentation algorithms surveyed in this work are Neural Network Model, Self-Organizing Maps, Radial Basis Function, Back Propagation, Fuzzy C-Means, Deformable Models, Level Set Models, Genetic Algorithm, Differential Evolutionary Algorithm, Hybrid Clustering and Artificial Intelligence. Such a survey would be helpful for researchers working in the field of brain image segmentation. The paper discusses the complexities in the segmentation algorithm and also the challenges in segmenting the brain MRI images. The segmentation outputs and analysis of the existing literature has also been discussed. The major criteria and their advantages in the segmentation of each algorithm have been reported accordingly in the observations.

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A Combination of Bias-Field Corrected Fuzzy C-Means and Level Set Approach for Brain MRI Image Segmentation

Cited by 14 publications

References 12 publications

Thresholding for medical image segmentation for cancer using fuzzy entropy with level set algorithm

Thresholding for medical image segmentation for cancer using fuzzy entropy with level set algorithm

Machine Learning Techniques for Quantification of Knee Segmentation from MRI

A Survey on Advanced Segmentation Techniques for Brain MRI Image Segmentation

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