scholarly journals LOGISMOS—Layered Optimal Graph Image Segmentation of Multiple Objects and Surfaces: Cartilage Segmentation in the Knee Joint

2010 ◽  
Vol 29 (12) ◽  
pp. 2023-2037 ◽  
Author(s):  
Yin Yin ◽  
Xiangmin Zhang ◽  
Rachel Williams ◽  
Xiaodong Wu ◽  
Donald D Anderson ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Knee Joint ◽  
Multiple Objects ◽  
Optimal Graph

scholarly journals An improved model for joint segmentation and registration based on linear curvature smoother

2016 ◽  
Vol 10 (4) ◽  
pp. 314-324 ◽  
Author(s):  
Mazlinda Ibrahim ◽  
Ke Chen ◽  
Lavdie Rada
Keyword(s):  
Image Segmentation ◽  
Medical Imaging ◽  
Large Deformation ◽  
Active Contour ◽  
Numerical Results ◽  
Variational Model ◽  
Multiple Objects ◽  
Proposed Model ◽  
Challenging Tasks ◽  
Improved Model

Image segmentation and registration are two of the most challenging tasks in medical imaging. They are closely related because both tasks are often required simultaneously. In this article, we present an improved variational model for a joint segmentation and registration based on active contour without edges and the linear curvature model. The proposed model allows large deformation to occur by solving in this way the difficulties other jointly performed segmentation and registration models have in case of encountering multiple objects into an image or their highly dependence on the initialisation or the need for a pre-registration step, which has an impact on the segmentation results. Through different numerical results, we show that the proposed model gives correct registration results when there are different features inside the object to be segmented or features that have clear boundaries but without fine details in which the old model would not be able to cope.

An evaluation metric for image segmentation of multiple objects

2009 ◽  
Vol 27 (8) ◽  
pp. 1223-1227 ◽  
Author(s):  
Mark Polak ◽  
Hong Zhang ◽  
Minghong Pi
Keyword(s):  
Image Segmentation ◽  
Multiple Objects ◽  
Evaluation Metric

scholarly journals Computed Tomographic Image Analysis Based on FEM Performance Comparison of Segmentation on Knee Joint Reconstruction

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Seong-Wook Jang ◽  
Young-Jin Seo ◽  
Yon-Sik Yoo ◽  
Yoon Sang Kim
Keyword(s):  
Image Segmentation ◽  
Knee Joint ◽  
Movement Analysis ◽  
3D Models ◽  
Performance Comparison ◽  
Fe Analysis ◽  
Joint Movement ◽  
Computed Tomographic ◽  
Ct Data ◽  
Scan Data

The demand for an accurate and accessible image segmentation to generate 3D models from CT scan data has been increasing as such models are required in many areas of orthopedics. In this paper, to find the optimal image segmentation to create a 3D model of the knee CT data, we compared and validated segmentation algorithms based on both objective comparisons and finite element (FE) analysis. For comparison purposes, we used 1 model reconstructed in accordance with the instructions of a clinical professional and 3 models reconstructed using image processing algorithms (Sobel operator, Laplacian of Gaussian operator, and Canny edge detection). Comparison was performed by inspecting intermodel morphological deviations with the iterative closest point (ICP) algorithm, and FE analysis was performed to examine the effects of the segmentation algorithm on the results of the knee joint movement analysis.

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