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Cyst Identification in Retinal Optical Coherence Tomography Images Using Hidden Markov Model Publisher Pubmed



Mousavi N1 ; Monemian M2 ; Ghaderi Daneshmand P2 ; Mirmohammadsadeghi M3 ; Zekri M1 ; Rabbani H2
Authors
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Authors Affiliations
  1. 1. Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
  2. 2. Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
  3. 3. Negah Hospital, Tehran, Iran

Source: Scientific Reports Published:2023


Abstract

Optical Coherence Tomography (OCT) is a useful imaging modality facilitating the capturing process from retinal layers. In the salient diseases of retina, cysts are formed in retinal layers. Therefore, the identification of cysts in the retinal layers is of great importance. In this paper, a new method is proposed for the rapid detection of cystic OCT B-scans. In the proposed method, a Hidden Markov Model (HMM) is used for mathematically modelling the existence of cyst. In fact, the existence of cyst in the image can be considered as a hidden state. Since the existence of cyst in an OCT B-scan depends on the existence of cyst in the previous B-scans, HMM is an appropriate tool for modelling this process. In the first phase, a number of features are extracted which are Harris, KAZE, HOG, SURF, FAST, Min-Eigen and feature extracted by deep AlexNet. It is shown that the feature with the best discriminating power is the feature extracted by AlexNet. The features extracted in the first phase are used as observation vectors to estimate the HMM parameters. The evaluation results show the improved performance of HMM in terms of accuracy. © 2023, The Author(s).
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