Medical image segmentation based on level set with new local fitting energy

Shen, Changchun; Yu, Hengguo; Liu, Xiaoyun; Chen, Wufan

doi:10.1109/miaca.2010.5528316

Cited by 2 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nevertheless, as far as the level-set methods are concerned, speed is required in the acquisition as well as improved resolution of the imaging modalities [3,26,32,47]. With respect to the level-set function ( ) , a common representation involves defining the segmentation contour as well as the region on an image I(x) for the purpose of minimizing the following energy function [118]:…”

Section: Level-set Methodsmentioning

confidence: 99%

Biomedical Image Segmentation: A Survey

Alzahrani

Boufama

2021

SN COMPUT. SCI.

View full text Add to dashboard Cite

Medical Image Segmentation is the process of segmenting and detecting boundaries of anatomical structures in various types of 2D and 3D-medical images. The latter come from different modalities, such as Magnetic Resonance Imaging (MRI), X-Rays, Positron Emission Tomography (PET)/Single-Photon Emission Computed Tomography, Computed Tomography (CT), and Ultrasound (US). It is a key supporting technology for medical applications including diagnostics, planning, monitoring, and guidance. Hence, a large number of segmentation methods have been published in past decades. This paper presents a comprehensive review of the current medical segmentation techniques. In particular, we reviewed the most important medical segmentation methods that have been utilized for almost all types of medical images. We grouped these methods into categories and then compared, contrasted, and highlighted their main advantages and limitations.

show abstract