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

2015 ◽  
Author(s):  
Pankhuri Agarwal ◽  
Sandeep Kumar ◽  
Rahul Singh ◽  
Prateek Agarwal ◽  
Mahua Bhattacharya
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Brain Mri ◽  
Bias Field ◽  
Fuzzy C Means ◽  
Level Set Approach ◽  
Mri Image

Optimized Fuzzy Clustering Algorithms for Brain MRI Image Segmentation Based on Local Gaussian Probability and Anisotropic Weight Models

2018 ◽  
Vol 32 (09) ◽  
pp. 1857005 ◽  
Author(s):  
Ting Zhang
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Clinical Medicine ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Segmentation Algorithm ◽  
Fuzzy C Means ◽  
Anisotropic Weight ◽  
Mri Image

Brain Magnetic Resonance Imaging (MRI) image segmentation is one of the critical technologies of clinical medicine, and is the basis of three-dimensional reconstruction and downstream analysis between normal tissues and diseased tissues. However, there are various limitations in brain MRI images, such as gray irregularities, noise, and low contrast, reducing the accuracy of the brain MRI images segmentation. In this paper, we propose two optimization solutions for the fuzzy clustering algorithm based on local Gaussian probability fuzzy C-means (LGP-FCM) model and anisotropic weight fuzzy C-means (AW-FCM) model and apply it in brain MRI image segmentation. An FCM clustering algorithm is proposed based on AW-FCM. By introducing the new neighborhood weight calculation method, each point has the weight of anisotropy, effectively overcomes the influence of noise on the image segmentation. In addition, the LGP model is introduced in the objective function of fuzzy clustering, and a fuzzy clustering segmentation algorithm based on LGP-FCM is proposed. A clustering segmentation algorithm of adaptive scale fuzzy LGP model is proposed. The neighborhood scale corresponding to each pixel in the image is automatically estimated, which improves the robustness of the model and achieves the purpose of precise segmentation. Extensive experimental results demonstrate that the proposed LGP-FCM algorithm outperforms comparison algorithms in terms of sensitivity, specificity and accuracy. LGP-FCM can effectively segment the target regions from brain MRI images.

scholarly journals A Variational Level Set Approach Based on Local Entropy for Image Segmentation and Bias Field Correction

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Jian Tang ◽  
Xiaoliang Jiang
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Energy Function ◽  
Bias Field ◽  
Superior Performance ◽  
Local Entropy ◽  
Distribution Fitting ◽  
Field Estimation ◽  
Variational Level Set ◽  
Level Set Approach

Image segmentation has always been a considerable challenge in image analysis and understanding due to the intensity inhomogeneity, which is also commonly known as bias field. In this paper, we present a novel region-based approach based on local entropy for segmenting images and estimating the bias field simultaneously. Firstly, a local Gaussian distribution fitting (LGDF) energy function is defined as a weighted energy integral, where the weight is local entropy derived from a grey level distribution of local image. The means of this objective function have a multiplicative factor that estimates the bias field in the transformed domain. Then, the bias field prior is fully used. Therefore, our model can estimate the bias field more accurately. Finally, minimization of this energy function with a level set regularization term, image segmentation, and bias field estimation can be achieved. Experiments on images of various modalities demonstrated the superior performance of the proposed method when compared with other state-of-the-art approaches.

scholarly journals Smallest Univalue Segment Assimilating Nucleus approach to Brain MRI Image Segmentation using Fuzzy C-Means and Fuzzy K-Means Algorithms

2017 ◽  
Vol 16 (7) ◽  
pp. 7065-7076 ◽  
Author(s):  
AJALA Funmilola Alaba ◽  
AKANDE Noah Oluwatobi ◽  
ADEYEMO Isiaka Akinkunmi ◽  
Ogundokun Roseline Oluwaseun
Keyword(s):  
Image Segmentation ◽  
Brain Mri ◽  
False Negative ◽  
Similarity Index ◽  
Brain Magnetic Resonance Imaging ◽  
Bias Field ◽  
True Negative ◽  
Fuzzy C Means ◽  
Segmentation Technique ◽  
Segmentation Task

Image segmentation still remains an important task in image processing and analysis. Sequel to any segmentation process, preprocessing activities carried out on the images have a great effect on the accuracy of the segmentation task. This paper therefore laid emphasis on the preprocessing stage of brain Magnetic Resonance Imaging (MRI) images Smallest Univalue Segment Assimilating Nucleus (SUSAN) and bias field correction algorithms. Subsequently, brain tissue extraction tool was employed in extracting non-brain tissues from the brain image. Afterwards, Fuzzy K-Means (FKM) and Fuzzy C-Means (FCM) segmentation algorithms were employed for segmenting brain MRI images acquired from four different MRI databases into their White Matter (WM), Gray Matter (GM) and Cerebrospinal Fluid (CSF) constituents. Evaluation metrics such as cluster validity functions using partition coefficients and partition entropy; area error metrics such as false positive, true positive, true negative and false negative (FN); similarity index, sensitivity and specificity were used to evaluate the performance of both techniques. A comparative analysis of the experimental results revealed that in most instances, FKM segmentation technique is preferable to FCM segmentation technique for brain MRI segmentation task.

Sign in / Sign up

Export Citation Format

Share Document