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Cyst identification in retinal optical coherence tomography images using hidden Markov model
Scientific Reports ( IF 3.8 ) Pub Date : 2023-01-02 , DOI: 10.1038/s41598-022-27243-2
Niloofarsadat Mousavi 1 , Maryam Monemian 2 , Parisa Ghaderi Daneshmand 2 , Mohammad Mirmohammadsadeghi 3 , Maryam Zekri 1 , Hossein Rabbani 2
Affiliation  

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.



中文翻译:

使用隐马尔可夫模型识别视网膜光学相干断层扫描图像中的囊肿

光学相干断层扫描 (OCT) 是一种有用的成像方式,有助于从视网膜层进行捕获过程。在视网膜的显着疾病中,囊肿形成于视网膜层中。因此,识别视网膜层中的囊肿非常重要。在本文中,提出了一种快速检测囊性 OCT B 扫描的新方法。在所提出的方法中,隐马尔可夫模型 (HMM) 用于对包囊的存在进行数学建模。事实上,图像中囊肿的存在可以认为是一种隐藏状态。由于 OCT B 扫描中囊肿的存在取决于先前 B 扫描中囊肿的存在,因此 HMM 是对该过程建模的合适工具。在第一阶段,提取了许多特征,包括 Harris、KAZE、HOG、SURF、FAST、Min-Eigen 和深度 AlexNet 提取的特征。结果表明,具有最佳判别力的特征是 AlexNet 提取的特征。第一阶段提取的特征被用作观察向量来估计 HMM 参数。评估结果表明HMM在准确性方面的性能有所提高。

更新日期:2023-01-02
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