Cost-effective broad learning-based ultrasound biomicroscopy with 3D reconstruction for ocular anterior segmentation

Saba Ghazanfar Ali, Yan Chen, Bin Sheng, Huating Li, Qiang Wu, Po Yang, Khan Muhammad, Geng Yang

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

Anterior Chamber Angle (ACA) assessment plays an important role for the diagnosis of glaucoma. Most of the existing techniques relied on Anterior Segment Optical Coherence Tomography (AS-OCT) or Swept Source Optical Coherence Tomography (SS-OCT). We proposed a system for 360° overview of iridocorneal angle of anterior chamber (ICAAC) via Ultrasound Biomicroscopy (UBM). UBM approach acquires the visualization of anterior segment components as well as diseased structures (glaucoma). Our system consists of a new pairing scheme of feature descriptors, i.e. (FREAK, BRISK), (SURF, BRISK) and Broad Learning System (BLS) for 3D reconstruction and segmentation of ICAAC. The 360° overview of 2D ICAAC gives global conception for ACA assessment. 3D images provide a detailed assessment with the amount of opposition’s and synechiae in angle-closure suspects, angle-closure and angle-closure glaucoma in bright light conditions. Extensive evaluations are performed on dataset consists of 650 ICAAC images in five directions of 65 subjects with 10 samples per subject (5 left eye and 5 right eye) from Shanghai Sixth People’s Hospital. Experiments showed that our approach achieves an overall accuracy of 98.72% with training and testing time 29.26(s), 1.232(s) respectively.

Original languageEnglish
Pages (from-to)35105-35122
Number of pages18
JournalMultimedia Tools and Applications
Volume80
Issue number28-29
DOIs
StatePublished - Nov 2021
Externally publishedYes

Keywords

  • 3D reconstruction
  • Broad learning system
  • Iridocorneal angle of anterior chamber
  • Machine learning
  • Medical data analytics
  • Ultrasound biomicroscopy

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