scholarly journals Shape features learned for object classification can predict behavioral discrimination of written symbols

2019 ◽  
Vol 19 (10) ◽  
pp. 32d
Author(s):  
Daniel Janini ◽  
Talia Konkle
Keyword(s):  
Object Classification ◽  
Shape Features ◽  
Behavioral Discrimination

A Study on Nuclei Shape Features at the Classification of Glioma Disease Stage Using CNN

2020 ◽  
Vol 140 (12) ◽  
pp. 1367-1368
Author(s):  
Daisuke Saito ◽  
Hiroharu Kawanaka ◽  
V. B. Surya Prasath ◽  
Bruce J. Aronow
Keyword(s):  
Disease Stage ◽  
Shape Features

Using Dempster-Shafer Methods for Object Classification in the Theater Ballistic Missile Environment

1999 ◽  
Author(s):  
Kimberly Coombs ◽  
Debra Freel ◽  
Douglas Lampert ◽  
Steven Brahm
Keyword(s):  
Object Classification ◽  
Ballistic Missile

scholarly journals Distinguishing Adenocarcinomas from Granulomas in the CT scan of the chest: performance degradation evaluation in the automatic segmentation framework

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Mahsa Bank Tavakoli ◽  
Mahdi Orooji ◽  
Mehdi Teimouri ◽  
Ramita Shahabifar
Keyword(s):  
State Of The Art ◽  
Automatic Segmentation ◽  
Texture Features ◽  
Standards Of Care ◽  
Shape Features ◽  
Selection Methods ◽  
Segmentation Framework ◽  
Vessel Tortuosity ◽  
Benign Nodules ◽  
Degradation Evaluation

Abstract Objective The most common histopathologic malignant and benign nodules are Adenocarcinoma and Granuloma, respectively, which have different standards of care. In this paper, we propose an automatic framework for the diagnosis of the Adenocarcinomas and the Granulomas in the CT scans of the chest from a private dataset. We use the radiomic features of the nodules and the attached vessel tortuosity for the diagnosis. The private dataset includes 22 CTs for each nodule type, i.e., adenocarcinoma and granuloma. The dataset contains the CTs of the non-smoker patients who are between 30 and 60 years old. To automatically segment the delineated nodule area and the attached vessels area, we apply a morphological-based approach. For distinguishing the malignancy of the segmented nodule, two texture features of the nodule, the curvature Mean and the number of the attached vessels are extracted. Results We compare our framework with the state-of-the-art feature selection methods for differentiating Adenocarcinomas from Granulomas. These methods employ only the shape features of the nodule, the texture features of the nodule, or the torsion features of the attached vessels along with the radiomic features of the nodule. The accuracy of our framework is improved by considering the four selected features.

Coupling Deep Textural and Shape Features for Sketch Recognition

2020 ◽  
Author(s):  
Qi Jia ◽  
Xin Fan ◽  
Meiyu Yu ◽  
Yuqing Liu ◽  
Dingrong Wang ◽  
...  
Keyword(s):  
Shape Features ◽  
Sketch Recognition
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