scholarly journals Automatic Spine Tissue Segmentation from MRI Data Based on Cascade of Boosted Classifiers and Active Appearance Model

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Dominik Gaweł ◽  
Paweł Główka ◽  
Tomasz Kotwicki ◽  
Michał Nowak
Keyword(s):  
Cross Validation ◽  
Intraclass Correlation ◽  
Machine Learning Techniques ◽  
Appearance Model ◽  
Lumbosacral Spine ◽  
Active Appearance Model ◽  
Tissue Segmentation ◽  
Algorithm Convergence ◽  
Active Appearance ◽  
Fold Cross Validation

The study introduces a novel method for automatic segmentation of vertebral column tissue from MRI images. The paper describes a method that combines multiple stages of Machine Learning techniques to recognize and separate different tissues of the human spine. For the needs of this paper, 50 MRI examinations presenting lumbosacral spine of patients with low back pain were selected. After the initial filtration, automatic vertebrae recognition using Cascade Classifier takes place. Afterwards the main segmentation process using the patch based Active Appearance Model is performed. Obtained results are interpolated using centripetal Catmull–Rom splines. The method was tested on previously unseen vertebrae images segmented manually by 5 physicians. A test validating algorithm convergence per iteration was performed and the Intraclass Correlation Coefficient was calculated. Additionally, the 10-fold cross-validation analysis has been done. Presented method proved to be comparable to the physicians (FF=90.19±1.01%). Moreover results confirmed a proper algorithm convergence. Automatically segmented area correlated well with manual segmentation for single measurements (r¯=0.8336) and for average measurements (r¯=0.9068) with p=0.05. The 10-fold cross-validation analysis (FF=91.37±1.13%) confirmed a good model generalization resulting in practical performance.

MAPPING 2D TO 3D FORENSIC FACIAL RECOGNITION VIA BIO- INSPIRED ACTIVE APPEARANCE MODEL

2015 ◽  
Vol 78 (2-2) ◽  
Author(s):  
Siti Zaharah Abd. Rahman ◽  
Siti Norul Huda Sheikh Abdullah ◽  
Lim Eng Hao ◽  
Mohammed Hasan Abdulameer ◽  
Nazri Ahmad Zamani ◽  
...  
Keyword(s):  
Forensic Analysis ◽  
Support Vector ◽  
Appearance Model ◽  
Active Appearance Model ◽  
Video Footage ◽  
Digital Forensic ◽  
Facial Information ◽  
Active Appearance ◽  
2D Model ◽  
2D To 3D

This research done is to solve the problems faced by digital forensic analysts in identifying a suspect captured on their CCTV. Identifying the suspect through the CCTV video footage is a very challenging task for them as it involves tedious rounds of processes to match the facial information in the video footage to a set of suspect’s images. The biggest problem faced by digital forensic analysis is modeling 2D model extracted from CCTV video as the model does not provide enough information to carry out the identification process. Problems occur when a suspect in the video is not facing the camera, the image extracted is the side image of the suspect and it is difficult to make a matching with portrait image in the database. There are also many factors that contribute to the process of extracting facial information from a video to be difficult, such as low-quality video. Through 2D to 3D image model mapping, any partial face information that is incomplete can be matched more efficiently with 3D data by rotating it to matched position. The first methodology in this research is data collection; any data obtained through video recorder. Then, the video will be converted into an image. Images are used to develop the Active Appearance Model (the 2D face model is AAM) 2D and AAM 3D. AAM is used as an input for learning and testing process involving three classifiers, which are Random Forest, Support Vector Machine (SVM), and Neural Networks classifier. The experimental results show that the 3D model is more suitable for use in face recognition as the percentage of the recognition is higher compared with the 2D model.

scholarly journals Comparison of structural MRI brain measures between 1.5T and 3T: data from the Lothian Birth Cohort 1936

2021 ◽  
Author(s):  
Colin R Buchanan ◽  
Susana Munoz Maniega ◽  
Maria del Carmen Valdes Hernandez ◽  
Lucia Ballerini ◽  
Gayle Barclay ◽  
...  
Keyword(s):  
Cross Validation ◽  
Intraclass Correlation ◽  
Structural Mri ◽  
Community Dwelling ◽  
Lothian Birth Cohort ◽  
Cortical Regions ◽  
Absolute Measurements ◽  
Structural Networks ◽  
And Diffusion ◽  
Fold Cross Validation

Multi-scanner MRI studies are reliant on understanding the apparent differences in imaging measures between different scanners. We provide a comprehensive analysis of T1-weighted and diffusion MRI (dMRI) structural brain measures between a 1.5T GE Signa Horizon HDx and a 3T Siemens Magnetom Prisma using 91 community-dwelling older participants (aged 82 years). Although we found considerable differences in absolute measurements (global tissue volumes were measured as ~6-11% higher and fractional anisotropy was 33% higher at 3T than at 1.5T), between-scanner consistency was good to excellent for global volumetric and dMRI measures (intraclass correlation coefficient [ICC] range: 0.612-0.993) and fair to good for 68 cortical regions (FreeSurfer) and cortical surface measures (mean ICC: 0.504-0.763). Between-scanner consistency was fair for dMRI measures of 12 major white matter tracts (mean ICC: 0.475-0.564), and the general factors of these tracts provided excellent consistency (ICC > 0.769). Whole-brain structural networks provided good to excellent consistency for global metrics (ICC > 0.612). Although consistency was poor for individual network connections (mean ICCs: 0.275-0.280), this was driven by a large difference in network sparsity (0.599 versus 0.334), and consistency was improved when comparing only the connections present in every participant (mean ICCs: 0.533-0.647). Regression-based k-fold cross-validation showed that, particularly for global volumes, between-scanner differences could be largely eliminated (R2 range 0.615-0.991). We conclude that low granularity measures of brain structure can be reliably matched between the scanners tested, but caution is warranted when combining high granularity information from different scanners.

Robust Active Appearance Model Matching

2005 ◽  
pp. 114-125 ◽  
Author(s):  
Reinhard Beichel ◽  
Horst Bischof ◽  
Franz Leberl ◽  
Milan Sonka
Keyword(s):  
Appearance Model ◽  
Active Appearance Model ◽  
Model Matching ◽  
Active Appearance
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