Comparison of GWO-SVM and Random Forest Classifiers in a LevelSet based approach for Bladder wall segmentation and characterisation using MR images-Reference-Cited by-同舟云学术

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Comparison of GWO-SVM and Random Forest Classifiers in a LevelSet based approach for Bladder wall segmentation and characterisation using MR images

Published:2022-04-19 Issue: Volume: Page:
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Container-title:2022 Eleventh International Conference on Image Processing Theory, Tools and Applications (IPTA)
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Author:

Trigui Rania¹,Adel Mouloud¹,Di Bisceglie Mathieu²,Wojak Julien¹,Pinol Jessica³,Faure Alice³,Chaumoitre Katia²

Affiliation:

1. Aix Marseille Univ, Institut Fresnel,CNRS, Centrale Marseille,Marseille,France

2. Marseille University Hospital Center,Paediatric Surgery Department,Marseille,France

3. North Hospital, Aix-Marseille University,Medical Imaging Service,Marseille,France

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9784103/9784113/09784127.pdf?arnumber=9784127

Reference22 articles.

1. Deep‐learning convolutional neural network: Inner and outer bladder wall segmentation in CT urography

2. Three-dimensional texture features from intensity and high-order derivative maps for the discrimination between bladder tumors and wall tissues via MRI

3. Characterization of Texture Features of Bladder Carcinoma and the Bladder Wall on MRI

4. Contrast-enhanced CT Texture Parameters as Predictive Markers of High-risk Urodynamic Features in Adult Patients with Spina Bifida

5. Pelvic anatomy and MRI

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