A modified fuzzy C-means image segmentation algorithm for use with uneven illumination patterns

Ma, Lei; Staunton, Richard C.

doi:10.1016/j.patcog.2007.02.005

Cited by 80 publications

(43 citation statements)

References 10 publications

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“…The application of fuzzy logic for biomedical image segmentation proved superior results in terms of segmentation errors, in comparison with other methods developed in [14], [15], [17], taking as reference the manually contour-based segmented version. A main advantage of our method is that knowledge is directly represented in the image space by means of fuzzy sets.…”

Section: Discussionmentioning

confidence: 95%

“…These values act as a measure of the segmentation errors, and in general are low enough for further steps of image analysis (classification, 3D reconstruction, etc.). In order to compare our results with previous ones, obtained for the same image modality (CT), we used the Fuzzy c-Means (FCM) clustering algorithm [1], [15], that yet does not address the intensity inhomogeneity artifact. Also, we have applied other traditional approach -Otsu's multithreshold segmentation method, as shown in [14] and [17].…”

Section: Deffuzification Processmentioning

confidence: 99%

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A Fuzzy Rules-Based Segmentation Method for Medical Images Analysis

Costin

2013

INT J COMPUT COMMUN

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Abstract:Medical imaging mainly manages and processes missing, ambiguous, complementary, redundant and distorted data and information has a strong structural character. This paper reports a new (semi)automated and supervised method for the segmentation of brain structures using a rule-based fuzzy system. In the field of biomedical image analysis fuzzy logic acts as a unified framework for representing and processing both numerical and symbolic information, as well as structural information constituted mainly by spatial relationships. The developed application is for the segmentation of brain structures in CT (computer tomography) images. Promising results show the superiority of this knowledge-based approach over best traditional techniques in terms of segmentation errors. The quantitative assessment of this method is made by comparing manually and automatic segmented brain structures by using some indexes evaluating the accuracy of contour detection and spatial location. Though the proposed methodology has been implemented and successfully used for modeldriven in medical imaging, it is general enough and may be applied to any imagistic object that can be expressed by expert knowledge and morphological images.

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Section: Discussionmentioning

confidence: 95%

Section: Deffuzification Processmentioning

confidence: 99%

A Fuzzy Rules-Based Segmentation Method for Medical Images Analysis

Costin

2013

INT J COMPUT COMMUN

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“…It has the added advantage of being able to produce images which slice through the brain in both horizontal and vertical planes [5][6][7][8]. It also helps in the early detection of abnormal changes in tissues and organs [2].…”

Section: Mrimentioning

confidence: 99%

“…MRI brain image segmentation gets considerably benefit from fuzzy clustering methods from above methods. The data of MR image widely presented so far appears to be quite uncertain [4][5][6][7]. Clustering is the principally accepted method for medical image segmentation.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Various Segmentation Techniques on MRI of Brain Tissue

Suryawanshi¹,

Chowhan²,

Kulkarni³

2017

IJCA

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Accuracy of segmentation methods is of great importance in brain image analysis. Tissue classification in Magnetic Resonance brain images (MRI) is an important issue in the analysis of several brain dementias. This paper reviews the performance of segmentation techniques that are used on Brain MRI. A large variety of algorithms for segmentation of Brain MRI have been developed. This paper aims at to study the performance segmentation process on MR images of the human brain, using Fuzzy c-means (FCM), Kernel based Fuzzy c-means clustering (KFCM), Spatial Fuzzy c-means (SFCM) and Improved Fuzzy c-means (IFCM). The review covers imaging modalities, MRI and methods for noise reduction and segmentation approaches. After applying all methods on MRI brain images, which are degraded by saltpepper noise, it is demonstrated that the IFCM algorithm performs more robust to noise than the standard FCM algorithm. We conclude with the trend of future research in brain segmentation by changing norms in FCM, for better results.

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“…d nl is the distance measurement influenced by non-local spatial information. This added non local term is obtained from the non local means (NL-means) algorithm [17] for image denoising. The nonlocal constraint determined by all points whose neighborhood configurations look like the neighborhood of the pixel of interest.…”

Section: Region-based Enhanced Possibilistic Fuzzy C-means (Epfcm)mentioning

confidence: 99%

Brain Tumor Segmentation on MRI Brain Images with Fuzzy Clustering and GVF Snake Model

Rajendran¹,

Dhanasekaran²

2014

INT J COMPUT COMMUN

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Deformable or snake models are extensively used for medical image segmentation, particularly to locate tumor boundaries in brain tumor MRI images. Problems associated with initialization and poor convergence to boundary concavities, however, has limited their usefulness. As result of that they tend to be attracted towards wrong image features. In this paper, we propose a method that combine region based fuzzy clustering called Enhanced Possibilistic Fuzzy C-Means (EPFCM) and Gradient vector flow (GVF) snake model for segmenting tumor region on MRI images. Region based fuzzy clustering is used for initial segmentation of tumor then result of this is used to provide initial contour for GVF snake model, which then determines the final contour for exact tumor boundary for final segmentation. The evaluation result with tumor MRI images shows that our method is more accurate and robust for brain tumor segmentation.

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A modified fuzzy C-means image segmentation algorithm for use with uneven illumination patterns

Cited by 80 publications

References 10 publications

A Fuzzy Rules-Based Segmentation Method for Medical Images Analysis

A Fuzzy Rules-Based Segmentation Method for Medical Images Analysis

Performance Evaluation of Various Segmentation Techniques on MRI of Brain Tissue

Brain Tumor Segmentation on MRI Brain Images with Fuzzy Clustering and GVF Snake Model

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