Efficient level set formulation for segmentation and correction with application to medical images

2019 ◽  
Author(s):  
Yunyun Yang ◽  
Wenjing Jia ◽  
Xiu Shu
Keyword(s):  
Level Set ◽  
Medical Images ◽  
Efficient Level ◽  
Level Set Formulation

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.

Biomedical Image Segmentation Using Spatial Kernel Fuzzy C-Means Based Level Set Formulation

2012 ◽  
Vol 2 (2) ◽  
pp. 200-205
Author(s):  
Raghotham Reddy Ganta ◽  
Syed Zaheeruddin ◽  
Narsimha Baddiri ◽  
R. Rameshwar Rao
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Biomedical Image ◽  
Level Set Formulation

scholarly journals Biometric Foetal Contour Extraction using Hybrid Level Set

2020 ◽  
Author(s):  
Rachana Jaiswal ◽  
Srikant Satarkar
Keyword(s):  
Feature Extraction ◽  
Level Set ◽  
Medical Images ◽  
Traditional Approach ◽  
Ultrasound Images ◽  
Contour Extraction ◽  
Processing Technologies ◽  
Biometric Measurements ◽  
Ultrasonic Images ◽  
Automatic Technique

In medical imaging, accurate anatomical structure extraction is important for diagnosis and therapeutic interventional planning. So, for easier, quicker and accurate diagnosis of medical images, image processing technologies may be employed in analysis and feature extraction of medical images. In this paper, some modifications to level set algorithm are made and modified algorithm is used for extracting contour of foetal objects in an image. The proposed approach is applied on foetal ultrasound images. In traditional approach, foetal parameters are extracted manually from ultrasound images. Due to lack of consistency and accuracy of manual measurements, an automatic technique is highly desirable to obtain foetal biometric measurements. This proposed approach is based on global & local region information for foetal contour extraction from ultrasonic images. The primary goal of this research is to provide a new methodology to aid the analysis and feature extraction from foetal images.

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