Microscopy images segmentation algorithm based on shearlet neural network
| Autor: | Nemir Ahmed Al-Azzawi |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Control and Optimization
Computer Networks and Communications Computer science Electron microscopy image 030218 nuclear medicine & medical imaging Image (mathematics) 03 medical and health sciences 0302 clinical medicine Microscopy Machine learning Computer Science (miscellaneous) Segmentation Electrical and Electronic Engineering Instrumentation 030304 developmental biology 0303 health sciences Sequence Image segmentation Artificial neural network business.industry Deep learning Shearlet transform Pattern recognition Neural network Hardware and Architecture Control and Systems Engineering Shearlet Artificial intelligence business Information Systems |
| Popis: | Microscopic images are becoming important and need to be studied to know the details and how-to quantitatively evaluate decellularization. Most of the existing research focuses on deep learning-based techniques that lack simplification for decellularization. A new computational method for the segmentation microscopy images based on the shearlet neural network (SNN) has been introduced. The proposal is to link the concept of shearlets transform and neural networks into a single unit. The method contains a feed-forward neural network and uses a single hidden layer. The activation functions are depending on the standard shearlet transform. The proposed SNN is a powerful technology for segmenting an electron microscopic image that is trained without relying on the pre-information of the data. The shearlet neural networks capture the features of full accuracy and contextual information, respectively. The expected value for specific inputs is estimated by learning the functional configuration of a network for the sequence of observed value. Experimental results on the segmentation of two-dimensional microscopy images are promising and confirm the benefits of the proposed approach. Lastly, we investigate on a challenging datasets ISBI 2012 that our method (SNN) achieves superior outcomes when compared to classical and deep learning-based methods. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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