scholarly journals Active Contour Driven by Local Region Statistics and Maximum A Posteriori Probability for Medical Image Segmentation

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Xiaoliang Jiang ◽  
Bailin Li ◽  
Qiang Wang ◽  
Jiajia Liu
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Active Contour ◽  
Medical Images ◽  
Bias Field ◽  
Medical Image Segmentation ◽  
Local Region ◽  
Posteriori Probability ◽  
Field Estimation ◽  
Level Set Formulation

This paper presents a novel active contour model in a variational level set formulation for simultaneous segmentation and bias field estimation of medical images. An energy function is formulated based on improved Kullback-Leibler distance (KLD) with likelihood ratio. According to the additive model of images with intensity inhomogeneity, we characterize the statistics of image intensities belonging to each different object in local regions as Gaussian distributions with different means and variances. Then, we use the Gaussian distribution with bias field as a local region descriptor in level set formulation for segmentation and bias field correction of the images with inhomogeneous intensities. Therefore, image segmentation and bias field estimation are simultaneously achieved by minimizing the level set formulation. Experimental results demonstrate desirable performance of the proposed method for different medical images with weak boundaries and noise.

Composite fuzzy-wavelet-based active contour for medical image segmentation

2020 ◽  
Vol 37 (9) ◽  
pp. 3525-3541
Author(s):  
Hiren Mewada ◽  
Amit V. Patel ◽  
Jitendra Chaudhari ◽  
Keyur Mahant ◽  
Alpesh Vala
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Legendre Polynomial ◽  
Active Contour ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Segmentation ◽  
Low Resolution ◽  
Content Type ◽  
Proposed Model

Purpose In clinical analysis, medical image segmentation is an important step to study the anatomical structure. This helps to diagnose and classify abnormality in the image. The wide variations in the image modality and limitations in the acquisition process of instruments make this segmentation challenging. This paper aims to propose a semi-automatic model to tackle these challenges and to segment medical images. Design/methodology/approach The authors propose Legendre polynomial-based active contour to segment region of interest (ROI) from the noisy, low-resolution and inhomogeneous medical images using the soft computing and multi-resolution framework. In the first phase, initial segmentation (i.e. prior clustering) is obtained from low-resolution medical images using fuzzy C-mean (FCM) clustering and noise is suppressed using wavelet energy-based multi-resolution approach. In the second phase, resultant segmentation is obtained using the Legendre polynomial-based level set approach. Findings The proposed model is tested on different medical images such as x-ray images for brain tumor identification, magnetic resonance imaging (MRI), spine images, blood cells and blood vessels. The rigorous analysis of the model is carried out by calculating the improvement against noise, required processing time and accuracy of the segmentation. The comparative analysis concludes that the proposed model withstands the noise and succeeds to segment any type of medical modality achieving an average accuracy of 99.57%. Originality/value The proposed design is an improvement to the Legendre level set (L2S) model. The integration of FCM and wavelet transform in L2S makes model insensitive to noise and intensity inhomogeneity and hence it succeeds to segment ROI from a wide variety of medical images even for the images where L2S failed to segment them.

Two-Step Modified Nash Equilibrium Method for Medical Image Segmentation Based on an Improved C-V Model

2018 ◽  
Vol 8 (9) ◽  
pp. 1826-1834
Author(s):  
Tian Chi Zhang ◽  
Jian Pei Zhang ◽  
Jing Zhang ◽  
Melvyn L. Smith
Keyword(s):  
Image Segmentation ◽  
Nash Equilibrium ◽  
Level Set ◽  
Medical Images ◽  
Medical Image Segmentation ◽  
Clustering Problem ◽  
Object Contour ◽  
Segmentation Methods ◽  
Maximum Similarity ◽  
Segmentation Problem

One of the most established region-based segmentation methods is the region based C-V model. This method formulates the image segmentation problem as a level set or improved level set clustering problem. However, the existing level set C-V model fails to perform well in the presence of noisy and incomplete data or when there is similarity between the objects and background, especially for clustering or segmentation tasks in medical images where objects appear vague and poorly contrasted in greyscale. In this paper, we modify the level set C-V model using a two-step modified Nash equilibrium approach. Firstly, a standard deviation using an entropy payoff approach is employed and secondly a two-step similarity clustering based approach is applied to the modified Nash equilibrium. One represents a maximum similarity within the clustered regions and the other the minimum similarity between the clusters. Finally, an improved C-V model based on a two-step modified Nash equilibrium is proposed to smooth the object contour during the image segmentation. Experiments demonstrate that the proposed method has good performance for segmenting noisy and poorly contrasting regions within medical images.

scholarly journals Brain Image Segmentation Based on Fuzzy Clustering

2018 ◽  
Vol 28 (3) ◽  
pp. 220
Author(s):  
Shatha J. Mohammed
Keyword(s):  
Image Segmentation ◽  
Performance Evaluation ◽  
Brain Tumour ◽  
Level Set ◽  
Fuzzy Clustering ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Segmentation ◽  
Brain Image ◽  
The Brain

The segmentation performance is topic to suitable initialization and best configuration of supervisory parameters. In medical image segmentation, the segmentation is very important when the diagnosing becomes very hard in medical images which are not properly illuminated. This paper proposes segmentation of brain tumour image of MRI images based on spatial fuzzy clustering and level set algorithm. After performance evaluation of the proposed algorithm was carried on brain tumour images, the results showed confirm its effectiveness for medical image segmentation, where the brain tumour is detected properly.

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