Model-based classification of nonstationary vocal fold vibrations

2006 ◽  
Vol 120 (2) ◽  
pp. 1012-1027 ◽  
Author(s):  
Tobias Wurzbacher ◽  
Raphael Schwarz ◽  
Michael Döllinger ◽  
Ulrich Hoppe ◽  
Ulrich Eysholdt ◽  
...  
Keyword(s):  
Vocal Fold ◽  
Model Based

scholarly journals Manual versus Automatic Classification of Laryngeal Lesions based on Vascular Patterns in CE+NBI Images

2020 ◽  
Vol 6 (3) ◽  
pp. 70-73
Author(s):  
Nazila Esmaeili ◽  
Alfredo Illanes ◽  
Axel Boese ◽  
Nikolaos Davaris ◽  
Christoph Arens ◽  
...  
Keyword(s):  
Vocal Fold ◽  
Narrow Band ◽  
Narrow Band Imaging ◽  
Automatic Classification ◽  
Vascular Pattern ◽  
Objective Interpretation ◽  
Laryngeal Lesions ◽  
Malignant Lesions ◽  
Manual Classification

AbstractLongitudinal and perpendicular changes in the blood vessels of the vocal fold have been related to the advancement from benign to malignant laryngeal cancer stages. The combination of Contact Endoscopy (CE) and Narrow Band Imaging (NBI) provides intraoperative realtime visualization of vascular pattern in Larynx. The evaluation of these vascular patterns in CE+NBI images is a subjective process leading to differentiation difficulty and subjectivity between benign and malignant lesions. The main objective of this work is to compare multi-observer classification versus automatic classification of laryngeal lesions. Six clinicians visually classified CE+NBI images into benign and malignant lesions. For the automatic classification of CE+NBI images, we used an algorithm based on characterizing the level of the vessel’s disorder. The results of the manual classification showed that there is no objective interpretation, leading to difficulties to visually distinguish between benign and malignant lesions. The results of the automatic classification of CE+NBI images on the other hand showed the capability of the algorithm to solve these issues. Based on the observed results we believe that, the automatic approach could be a valuable tool to assist clinicians to classifying laryngeal lesions.

An unsupervised Hidden Markov Model-based system for the detection and classification of blue whale vocalizations off Chile

Bioacoustics ◽  
2019 ◽  
Vol 29 (2) ◽  
pp. 140-167 ◽  
Author(s):  
Susannah J. Buchan ◽  
Rodrigo Mahú ◽  
Jorge Wuth ◽  
Naysa Balcazar-Cabrera ◽  
Laura Gutierrez ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Blue Whale ◽  
Model Based

scholarly journals Model-Based Segmentation and Classification of Trajectories

Algorithmica ◽  
2017 ◽  
Vol 80 (8) ◽  
pp. 2422-2452
Author(s):  
Sander P. A. Alewijnse ◽  
Kevin Buchin ◽  
Maike Buchin ◽  
Stef Sijben ◽  
Michel A. Westenberg
Keyword(s):  
Model Based

scholarly journals Radiomics-Based Detection of Radionecrosis Using Harmonized Multiparametric MRI

Cancers ◽  
2022 ◽  
Vol 14 (2) ◽  
pp. 286
Author(s):  
Clément Acquitter ◽  
Lucie Piram ◽  
Umberto Sabatini ◽  
Julia Gilhodes ◽  
Elizabeth Moyal Cohen-Jonathan ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
Predictive Power ◽  
Clinical Observation ◽  
Predictive Performance ◽  
Multiparametric Mri ◽  
Random Forest Classifier ◽  
Additional Benefit ◽  
Image Normalization ◽  
Model Based

In this study, a radiomics analysis was conducted to provide insights into the differentiation of radionecrosis and tumor progression in multiparametric MRI in the context of a multicentric clinical trial. First, the sensitivity of radiomic features to the unwanted variability caused by different protocol settings was assessed for each modality. Then, the ability of image normalization and ComBat-based harmonization to reduce the scanner-related variability was evaluated. Finally, the performances of several radiomic models dedicated to the classification of MRI examinations were measured. Our results showed that using radiomic models trained on harmonized data achieved better predictive performance for the investigated clinical outcome (balanced accuracy of 0.61 with the model based on raw data and 0.72 with ComBat harmonization). A comparison of several models based on information extracted from different MR modalities showed that the best classification accuracy was achieved with a model based on MR perfusion features in conjunction with clinical observation (balanced accuracy of 0.76 using LASSO feature selection and a Random Forest classifier). Although multimodality did not provide additional benefit in predictive power, the model based on T1-weighted MRI before injection provided an accuracy close to the performance achieved with perfusion.

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