Automated classification of maxillofacial cysts in cone beam CT images using contourlet transformation and Spherical Harmonics

2017 ◽  
Vol 139 ◽  
pp. 197-207 ◽  
Author(s):  
Fatemeh Abdolali ◽  
Reza Aghaeizadeh Zoroofi ◽  
Yoshito Otake ◽  
Yoshinobu Sato
Keyword(s):  
Spherical Harmonics ◽  
Cone Beam Ct ◽  
Ct Images ◽  
Cone Beam ◽  
Automated Classification

Cone Beam CT Images of Normal and Paralyzed Facial Nerves

2010 ◽  
Vol 143 (2_suppl) ◽  
pp. P228-P229
Author(s):  
Masahiro Komori ◽  
Naoaki Yanagihara ◽  
Yasuyuki Hinohira ◽  
Hiroshi Aritomo
Keyword(s):  
Cone Beam Ct ◽  
Ct Images ◽  
Cone Beam ◽  
Facial Nerves

scholarly journals The Ability of Different Observers in Measuring Dimensions of Objects and Cavities in Cone Beam-CT Images

2019 ◽  
Vol 11 (4) ◽  
pp. 139-143
Author(s):  
Fatemeh Eskandarloo ◽  
Amir Eskandarloo ◽  
Payam Amini
Keyword(s):  
Cone Beam Ct ◽  
Ct Images ◽  
Cone Beam

scholarly journals Hybrid Scheme for Automated Classification of Pulmonary Nodules Using PET/CT Images and Patient Information

2020 ◽  
Vol 10 (12) ◽  
pp. 4225
Author(s):  
Ayumi Yamada ◽  
Atsushi Teramoto ◽  
Masato Hoshi ◽  
Hiroshi Toyama ◽  
Kazuyoshi Imaizumi ◽  
...  
Keyword(s):  
Patient Information ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
Machine Learning Algorithms ◽  
Automated Classification ◽  
Hybrid Scheme ◽  
Laboratory Test Results ◽  
Pet Ct ◽  
Functional Features

The classification of pulmonary nodules using computed tomography (CT) and positron emission tomography (PET)/CT is often a hard task for physicians. To this end, in our previous study, we developed an automated classification method using PET/CT images. In actual clinical practice, in addition to images, patient information (e.g., laboratory test results) is available and may be useful for automated classification. Here, we developed a hybrid scheme for automated classification of pulmonary nodules using these images and patient information. We collected 36 conventional CT images and PET/CT images of patients who underwent lung biopsy following bronchoscopy. Patient information was also collected. For classification, 25 shape and functional features were first extracted from the images. Benign and malignant nodules were identified using machine learning algorithms along with the images’ features and 17 patient-information-related features. In the leave-one-out cross-validation of our hybrid scheme, 94.4% of malignant nodules were identified correctly, and 77.7% of benign nodules were diagnosed correctly. The hybrid scheme performed better than that of our previous method that used only image features. These results indicate that the proposed hybrid scheme may improve the accuracy of malignancy analysis.

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