Compressed sensing of human breast optical coherence 3-D image volume data using predictive coding

Diego Song; Christine P. Hendon

doi:10.1117/12.2605934

7 March 2022 Compressed sensing of human breast optical coherence 3-D image volume data using predictive coding

Diego Song, Christine P. Hendon

Proceedings Volume PC11948, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVI; PC119480Z (2022) https://doi.org/10.1117/12.2605934
Event: SPIE BiOS, 2022, San Francisco, California, United States

Abstract

This study aims to explore Denoising Predictive Coding (DN-PC), a new compressed sensing (CS) algorithm for reconstruction of optical coherence tomography (OCT) image volumes. In preliminary work, the algorithm has yielded reconstructions of OCT images of various biological samples, with accuracy and computation time superior to other CS methods. Here we have assembled an image bank of representative OCT images of normal breast such as adipose and stroma, and pathology such as breast cancer. We apply DN-PC for compression at decreasing a-line sampling rates to evaluate a relation between reconstruction behavior and breast tissue structure.

Conference Presentation

Citation Download Citation

Diego Song and Christine P. Hendon "Compressed sensing of human breast optical coherence 3-D image volume data using predictive coding", Proc. SPIE PC11948, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVI, PC119480Z (7 March 2022); https://doi.org/10.1117/12.2605934

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