Classification‐based framework for binarization on mice eye image in vivo with optical coherence tomography. Issue 7 (12th April 2022)

• Record Type: Journal Article
• Authors: Ma, Fei; Dai, Cuixia; Meng, Jing; Li, Ying; Zhao, Jingxiu; Zhang, Yuanke; Wang, Shengbo; Zhang, Xueting; Cheng, Ronghua
• Is Part Of: Journal of biophotonics. Volume 15:Issue 7(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-04-12
• Subjects: binarization threshold -- contrast‐limited image -- OCT mice eye database -- optical coherence tomography -- specific‐patch SVM framework; Photonics -- Periodicals; Optical materials -- Periodicals; Optics -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3605
• DOI: 10.1002/jbio.202100336
• Languages: English
• ISSNs: 1864-063X

Abstract

Abstract: Optical coherence tomography (OCT) angiography has drawn much attention in the medical imaging field. Binarization plays an important role in quantitative analysis of eye with optical coherence tomography. To address the problem of few training samples and contrast‐limited scene, we proposed a new binarization framework with specific‐patch SVM (SPSVM) for low‐intensity OCT image, which is open and classification‐based framework. This new framework contains two phases: training model and binarization threshold. In the training phase, firstly, the patches of target and background from few training samples are extracted as the ROI and the background, respectively. Then, PCA is conducted on all patches to reduce the dimension and learn the eigenvector subspace. Finally, the classification model is trained from the features of patches to get the target value of different patches. In the testing phase, the learned eigenvector subspace is conducted on the pixels of each patch. The binarization threshold of patch is obtained with the learned SVM model. We acquire a new OCT mice eye (OCT‐ME) database, which is publicly available at https://mip2019.github.io/spsvm . Extensive experiments were performed to demonstrate the effectiveness of the proposed SPSVM framework. Abstract : The distribution of specific ROI and Gabor features. (a) Selecting the ROI from sample; (b) The pixel distribution of ROI; (c) The Gabor filter bank at 6 orientations and 4 scales; (d) The patch feature of specific ROI with the Gabor filter bank.