Weighted Level Set Evolution Based on Local Edge Features for Medical Image Segmentation

Researchers recently apply an integrative approach to automate medical image segmentation for benefiting available methods and eliminating their disadvantages. Intensity inhomogeneity is a challenging and open problem in this area, which has received less attention by this approach. It has considerable effects on segmentation accuracy. This paper proposes a new kernel-based fuzzy level set algorithm by an integrative approach to deal with this problem. It can directly evolve from the initial level set obtained by Gaussian Kernel-Based FuzzyC-Means (GKFCM). The controlling parameters of level set evolution are also estimated from the results of GKFCM. Moreover the proposed algorithm is enhanced with locally regularized evolution based on an image model that describes the composition of real-world images, in which intensity inhomogeneity is assumed as a component of an image. Such improvements make level set manipulation easier and lead to more robust segmentation in intensity inhomogeneity. The proposed algorithm has valuable benefits including automation, invariant of intensity inhomogeneity, and high accuracy. Performance evaluation of the proposed algorithm was carried on medical images from different modalities. The results confirm its effectiveness for medical image segmentation.

Download Full-text

Fast Distance Preserving Level Set Evolution for Medical Image Segmentation

2006 9th International Conference on Control, Automation, Robotics and Vision ◽

10.1109/icarcv.2006.345357 ◽

2006 ◽

Cited By ~ 17

Author(s):

Chunming Li ◽

Chenyang Xu ◽

Kishori M. Konwar ◽

Martin D. Fox

Keyword(s):

Image Segmentation ◽

Level Set ◽

Medical Image ◽

Medical Image Segmentation ◽

Level Set Evolution

Download Full-text

Medical Image Segmentation Based on Level Set Combining with Region Information

2008 Fourth International Conference on Natural Computation ◽

10.1109/icnc.2008.512 ◽

2008 ◽

Cited By ~ 4

Author(s):

Yong Yang ◽

Shuying Huang ◽

Pan Lin ◽

Nini Rao

Keyword(s):

Image Segmentation ◽

Level Set ◽

Medical Image ◽

Medical Image Segmentation ◽

Region Information

Download Full-text

Medical image segmentation based on FCM and Level Set algorithm

2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS) ◽

10.1109/icsess.2016.7883054 ◽

2016 ◽

Cited By ~ 3

Author(s):

Sen Qian ◽

Guirong Weng

Keyword(s):

Image Segmentation ◽

Level Set ◽

Medical Image ◽

Medical Image Segmentation

Download Full-text

Combining interpolation and 3D level set method (I+3DLSM) for medical image segmentation

Electronics Letters ◽

10.1049/el.2015.3959 ◽

2016 ◽

Vol 52 (8) ◽

pp. 592-594 ◽

Cited By ~ 1

Author(s):

T. Doshi ◽

G. Di Caterina ◽

J. Soraghan ◽

L. Petropoulakis ◽

D. Grose ◽

...

Keyword(s):

Image Segmentation ◽

Level Set Method ◽

Level Set ◽

Medical Image ◽

Medical Image Segmentation

Download Full-text

Medical Imaging Importance in the Real World

Advances in Computer and Electrical Engineering - Challenges and Applications for Implementing Machine Learning in Computer Vision ◽

10.4018/978-1-7998-0182-5.ch006 ◽

2020 ◽

pp. 136-162

Author(s):

Ramgopal Kashyap

Keyword(s):

Image Segmentation ◽

Edge Detection ◽

Level Set ◽

Real World ◽

Medical Image ◽

Automatic Segmentation ◽

Medical Image Segmentation ◽

Image Resolution ◽

Variational Model ◽

Medical Applications

In the medical image resolution, automatic segmentation is a challenging task, and it's still an unsolved problem for most medical applications due to the wide variety connected with image modalities, encoding parameters, and organic variability. In this chapter, a review and critique of medical image segmentation using clustering, compression, histogram, edge detection, parametric, variational model. and level set-based methods is presented. Modes of segmentation like manual, semi-automatic, interactive, and automatic are also discussed. To present current challenges, aim and motivation for doing fast, interactive and correct segmentation, the medical image modalities X-ray, CT, MRI, and PET are discussed in this chapter.

Download Full-text