Natural Image-Orientated Hybrid Filter Using Pulse Coupled Neural Network

2016 ◽  
pp. 271-277
Author(s):  
Yun-Dong Li ◽  
Jia-Hao Pan
Keyword(s):  
Neural Network ◽  
Natural Image ◽  
Hybrid Filter ◽  
Pulse Coupled Neural Network

Underwater Image Enhancement Method via the Fusion of Retinex and Pulse Coupled Neural Network

2020 ◽  
Author(s):  
Zhengzhong Gao ◽  
Junjun Chen ◽  
Lixing Yang ◽  
Ruixing Lv ◽  
Haipeng Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Enhancement ◽  
Pulse Coupled Neural Network ◽  
Underwater Image ◽  
Enhancement Method

Spatial Point-Data Reduction Using Pulse Coupled Neural Network

2010 ◽  
Vol 32 (1) ◽  
pp. 11-29 ◽  
Author(s):  
Yongsheng Sang ◽  
Zhang Yi ◽  
Jiliu Zhou
Keyword(s):  
Neural Network ◽  
Data Reduction ◽  
Pulse Coupled Neural Network ◽  
Spatial Point ◽  
Point Data

scholarly journals A New Retrieval Algorithm Based on Pulse Coupled Neural Network for Biomedical Images

2019 ◽  
Vol 9 (1S) ◽  
pp. 9-13
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Rapid Expansion ◽  
Image Feature ◽  
Experimental Result ◽  
Feature Descriptor ◽  
Resonance Imaging ◽  
Biomedical Images ◽  
Pulse Coupled Neural Network

The rapid expansion and improvement in medical science and technology lead to the generation of more image data in its regular activity such as computed tomography (CT), X-ray, magnetic resonance imaging (MRI) etc. To manage the medical images properly for clinical decision making, content-based medical image retrieval (CBMIR) system emerged. In this paper, Pulse Coupled Neural Network (PCNN) based feature descriptor is proposed for retrieval of biomedical images. Time series is used as an image feature which contains the entire information of the feature, based on which the similar biomedical images are retrieved in our work. Here, the physician can point out the disorder present in the patient report by retrieving the most similar report from related reference reports. Open Access Series of Imaging Studies (OASIS) magnetic resonance imaging dataset is used for the evaluation of the proposed approach. The experimental result of the proposed system shows that the retrieval efficiency is better than the other existing systems.

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