Convolutional autoencoder-based reconstruction of vascular structures in photoacoustic images

Israr Ul Haq, Yoshinobu Kawahara

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

1 被引用数 (Scopus)

抄録

In medical imaging, a number of imaging modalities have been used to visualize the structural information of different internal organs in the human body and some modalities can even visualize structures at a cellular level for diagnostic and treatment purposes. Optical resolution photoacoustic microscopy (OR-PAM) is one of the emerging imaging modalities to analyze the anatomy and functionality of tissues non-invasive. It is a hybrid imaging technology that combines photoacoustic (PA) contrast and acoustic resolution to reconstruct images of tissues in humans and animals. However, in OR-PAM the received ultrasonic signal by the thermal expansion of tissues has a low signal-to-noise ratio because most of the signal power is lost in the conversion process from light to acoustic waves which makes it difficult to visualize the structural information in the PA images. Traditional denoising methods such as wiener filter, bandpass filter, wavelet-based denoising, noise reduction by SVD and dictionary-based denoising methods can denoise PA images to some extent but it is still a difficult task to preserve structural information in the images by such methods. In this research, a convolutional autoencoder (CAE) based model is proposed to denoise and learn the structural patterns of blood vessels in PA images. To achieve this task, a CAE model is first trained between noisy and Gabor filtered sub-images, those contain the patterns of different vascular structures. Then, the trained model is used to approximate the denoise version of the input noisy sub-images of blood vessels. The proposed model is trained and tested on PA images of blood vessels of a mouse ear, acquired by the OR-PAM imaging system and the results show that our proposed method can effectively approximate and reconstruct the noisy vascular structures than traditional images denoising filtering methods.

本文言語英語
ホスト出版物のタイトルBiomedical Spectroscopy, Microscopy, and Imaging
編集者Jurgen Popp, Csilla Gergely
出版社SPIE
ISBN(電子版)9781510634909
DOI
出版ステータス出版済み - 2020
イベントBiomedical Spectroscopy, Microscopy, and Imaging 2020 - Virtual, Online, フランス
継続期間: 4 6 20204 10 2020

出版物シリーズ

名前Proceedings of SPIE - The International Society for Optical Engineering
11359
ISSN(印刷版)0277-786X
ISSN(電子版)1996-756X

会議

会議Biomedical Spectroscopy, Microscopy, and Imaging 2020
Countryフランス
CityVirtual, Online
Period4/6/204/10/20

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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