Knee cartilage segmentation using active shape models and local binary patterns

2014 ◽  
Author(s):  
Germán González ◽  
Boris Escalante-Ramírez
Keyword(s):  
Local Binary Patterns ◽  
Knee Cartilage ◽  
Active Shape Models ◽  
Shape Models ◽  
Active Shape

scholarly journals Creating a 2D Active Shape Model Using itk::ImagePCAShapeModelEstimator

2011 ◽  
Author(s):  
John Durkin ◽  
David Miller ◽  
Kenneth Urish
Keyword(s):  
Active Contour ◽  
Active Shape Model ◽  
Knee Cartilage ◽  
Shape Model ◽  
Active Shape Models ◽  
Contour Segmentation ◽  
Shape Models ◽  
Segmentation Algorithms ◽  
Active Shape ◽  
Weak Boundaries

Although many variations of active contour segmentation algorithms exist, most are based on solely edge criteria and breakdown or leak at weak boundaries. One solution to this problem is constraining the segmented area to only statistically possible shapes with the guidance of a shape model. The purpose of this document is to fill the void in the ITK user guide on building active shape models. We describe how to create a 2d active shape model of articular femoral knee cartilage using ITK’s ImagePCAShapeModelEstimator. Sample code and example images are provided for displaying the initial principle components of variation. Shape models built with our code can be used for segmentation with itk::GeodesicActiveContourShapePriorLevelSetImageFilter.

scholarly journals Deformable Models for Segmentation Based on Local Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Jimena Olveres ◽  
Erik Carbajal-Degante ◽  
Boris Escalante-Ramírez ◽  
Enrique Vallejo ◽  
Carla María García-Moreno
Keyword(s):  
Local Binary Patterns ◽  
Distance Measures ◽  
Local Analysis ◽  
Hermite Transform ◽  
Active Shape Models ◽  
Shape Models ◽  
Segmentation Methods ◽  
Segmentation Algorithms ◽  
Image Modality ◽  
New Algorithms

Segmentation tasks in medical imaging represent an exhaustive challenge for scientists since the image acquisition nature yields issues that hamper the correct reconstruction and visualization processes. Depending on the specific image modality, we have to consider limitations such as the presence of noise, vanished edges, or high intensity differences, known, in most cases, as inhomogeneities. New algorithms in segmentation are required to provide a better performance. This paper presents a new unified approach to improve traditional segmentation methods as Active Shape Models and Chan-Vese model based on level set. The approach introduces a combination of local analysis implementations with classic segmentation algorithms that incorporates local texture information given by the Hermite transform and Local Binary Patterns. The mixture of both region-based methods and local descriptors highlights relevant regions by considering extra information which is helpful to delimit structures. We performed segmentation experiments on 2D images including midbrain in Magnetic Resonance Imaging and heart’s left ventricle endocardium in Computed Tomography. Quantitative evaluation was obtained with Dice coefficient and Hausdorff distance measures. Results display a substantial advantage over the original methods when we include our characterization schemes. We propose further research validation on different organ structures with promising results.

Active shape models with optimised texture features for radiotherapy

2014 ◽  
Author(s):  
K. Cheng ◽  
D. Montgomery ◽  
F. Yang ◽  
D. B. McLaren ◽  
S. McLaughlin ◽  
...  
Keyword(s):  
Texture Features ◽  
Active Shape Models ◽  
Shape Models ◽  
Active Shape
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