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Optimized signal detection and analysis methods for in vivo photoacoustic flow cytometry
- Source :
- SPIE Proceedings.
- Publication Year :
- 2017
- Publisher :
- SPIE, 2017.
-
Abstract
- Melanoma is known as a malignant tumor of melanocytes, which usually appear in the blood circulation at the metastasis stage of cancer. Thus the detection of circulating melanoma cells is useful for early diagnosis and therapy of cancer. Here we have developed an in vivo photoacoustic flow cytometry (PAFC) based on the photoacoustic effect to detect melanoma cells. However, the raw signals we obtain from the target cells contain noises such as environmental sonic noises and electronic noises. Therefore we apply correlation comparison and feature separation methods to the detection and verification of the in vivo signals. Due to similar shape and structure of cells, the photoacoustic signals usually have similar vibration mode. By analyzing the correlations and the signal features in time domain and frequency domain, we are able to provide a method for separating photoacoustic signals generated by target cells from background noises. The method introduced here has proved to optimize the signal acquisition and signal processing, which can improve the detection accuracy in PAFC.
- Subjects :
- 0301 basic medicine
Photoacoustic effect
Signal processing
Materials science
Signal
03 medical and health sciences
030104 developmental biology
0302 clinical medicine
Feature (computer vision)
In vivo
030220 oncology & carcinogenesis
Frequency domain
Detection theory
Time domain
Biomedical engineering
Subjects
Details
- ISSN :
- 0277786X
- Database :
- OpenAIRE
- Journal :
- SPIE Proceedings
- Accession number :
- edsair.doi...........56aef39ddeba27cdff98bc79f67c8bd3
- Full Text :
- https://doi.org/10.1117/12.2253495