Comparison of dictionary learning methods for reverberation suppression in photoacoustic microscopy : Invited presentation
| Autor: | Song Hu, John A. Hossack, Bo Ning, Sushanth G. Sathyanarayana |
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| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Reverberation K-SVD Computer science Initialization Image (mathematics) Set (abstract data type) 03 medical and health sciences 030104 developmental biology 0302 clinical medicine Photoacoustic microscopy Unsupervised learning Algorithm Dictionary learning 030217 neurology & neurosurgery |
| Zdroj: | CISS |
| Popis: | Dictionary learning is an unsupervised learning method to abstract image data into a set of learned basis vectors. In prior work, the efficacy of the K-SVD dictionary learning algorithm in suppressing reverberation in volumetric photoacoustic microscopy (PAM) data was demonstrated. In this work, we compare the K-SVD algorithm against the method of optimal directions (MOD). The generalization error and reverberation suppression performance of the two algorithms were compared. The K-SVD was found to have a lower average generalization error (5.69x104 ±9.09x103 (a.u.)) when compared to the MOD (8.27x104 ±1.33x104 (a.u.)) for identical training data, initialization, sparsity (3 atoms per A-line) and number of iterations (5). Both algorithms were observed to suppress the reverberation to a similar extent (18.8 ± 1.12 dB for the K-SVD and 18.3 ± 1.2 dB for the MOD). Our data show that irrespective of the method used, sparse dictionary learning can significantly suppress reverberations in PAM. |
| Databáze: | OpenAIRE |
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