Optical Coherence Optical coherence tomography is a powerful high-resolution imaging method with a broad biomedical application. Nonetheless, OCT images suffer from a multiplicative artefacts so-called speckle, a result of coherent imaging of system. Digital filters become ubiquitous means for speckle reduction. Addressing the fact that there still a room for despeckling in OCT, we proposed an intelligent speckle reduction framework based on OCT tissue morphological, textural and optical features that through a trained network selects the winner filter in which adaptively suppress the speckle noise while preserve structural information of OCT signal. These parameters are calculated for different steps of the procedure to be used in designed Artificial Neural Network decider that select the best denoising technique for each segment of the image. Results of training shows the dominant filter is BM3D from the last category.

Adabi, S., Mohebbikarkhoran, H., Mehregan, D., Conforto, S., Nasiriavanaki, M. (2017). An intelligent despeckling method for swept source optical coherence tomography images of skin. In Progress in Biomedical Optics and Imaging - Proceedings of SPIE (pp.101372B). SPIE [10.1117/12.2255565].

An intelligent despeckling method for swept source optical coherence tomography images of skin

Adabi, Saba;Conforto, Silvia;
2017-01-01

Abstract

Optical Coherence Optical coherence tomography is a powerful high-resolution imaging method with a broad biomedical application. Nonetheless, OCT images suffer from a multiplicative artefacts so-called speckle, a result of coherent imaging of system. Digital filters become ubiquitous means for speckle reduction. Addressing the fact that there still a room for despeckling in OCT, we proposed an intelligent speckle reduction framework based on OCT tissue morphological, textural and optical features that through a trained network selects the winner filter in which adaptively suppress the speckle noise while preserve structural information of OCT signal. These parameters are calculated for different steps of the procedure to be used in designed Artificial Neural Network decider that select the best denoising technique for each segment of the image. Results of training shows the dominant filter is BM3D from the last category.
9781510607194
Adabi, S., Mohebbikarkhoran, H., Mehregan, D., Conforto, S., Nasiriavanaki, M. (2017). An intelligent despeckling method for swept source optical coherence tomography images of skin. In Progress in Biomedical Optics and Imaging - Proceedings of SPIE (pp.101372B). SPIE [10.1117/12.2255565].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/329210
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