STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Image Processing & Pattern Recognition Progress Review Article

Survey on Retinal OCT Image Preprocessing, Segmentation, and Deep Learning Based Classification

  1. Ranjitha Rajan Assistant Professor, Department of Electronics and Communication Engineering, Amal Jyothi College of Engineering, Koovappally
  2. S.N. Kumar Department of Electrical and Electronics Engineering, Amal Jyothi College of Engineering, Koovappally

Abstract

Optical coherence tomography (OCT) is a non-invasive technique that generates high-resolution, detailed cross-sectional images of biological tissues. By utilizing low-coherence interferometry, OCT enables visualization of tissue microstructure with micron-scale resolution, making it useful in various medical fields such as ophthalmology, cardiology, and dermatology. In ophthalmology, OCT is extensively used for diagnosing and monitoring retinal diseases like macular degeneration and diabetic retinopathy, allowing doctors to assess changes in tissue morphology over time. Moreover, OCT plays a crucial role in guiding surgical procedures, assessing treatment efficacy, and advancing our understanding of disease pathogenesis. Its ability to provide detailed, real-time images with minimal patient discomfort has made OCT a cornerstone technology in modern medical imaging, revolutionizing patient care and research practices. This survey comprehensively reviews the recent advances in preprocessing, segmentation, and deep learning-based classification of retinal OCT images, highlighting the latest techniques, challenges, and future directions for improving diagnostic accuracy and clinical applications.

Keywords

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