Intra-retinal Layer Segmentation of Macular OCT Images using Edge Superpixels and Manifold Ranking Method

Gao, Zhijun; Bu, Wei; Wu, Xiangqian; Zhen, Yalin

doi:10.14257/ijfgcn.2017.10.6.08

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…At present, the main diagnostic methods of lung cancer include X-ray, low-dose spiral CT, nuclear magnetic resonance, biopsy, and so on. According to the National Lung Cancer Screening Experiment of the United States in 2012, low-dose spiral CT can effectively screen lung cancer and reduce mortality [ 3 , 4 ]. Although low-dose spiral CT can improve the survival rate, a large number of CT data and subtle difficult to distinguish nodules cause visual fatigue of doctors, leading to misdiagnosis and missed diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Texture recognition of pulmonary nodules based on volume local direction ternary pattern

et al. 2020

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In recent years, the incidence of lung cancer has been increasing. Lung cancer detection is based on computed tomography (CT) imaging of the lung area to determine whether there are pulmonary nodules. And then judge what's good and what's bad. However, due to the traditional way of manual reading and lack of experience and other problems. This leads to visual fatigue and misdiagnosis and missed diagnosis. In order to detect pulmonary nodules early and accurately, a new assistant diagnosis method for pulmonary nodules is proposed. Firstly, the image is preprocessed and denoised by median filter, the lung parenchyma is segmented by random walk algorithm and the region of interest is extracted, and then, according to the continuity of the CT slices, the texture feature extraction method of pulmonary nodules based on volume local direction ternary pattern is used to extract the features. Finally, the pulmonary nodules are identified and classified by the assistant diagnosis model of pulmonary nodules based on Stacking algorithm. In order to illustrate the validity of the diagnosis model, the experiments are carried out by cross-validation of ten folds. Experiments using data from LIDC database show that the accuracy, sensitivity and specificity of the proposed method are 82.2%, 85.7%, and 78.8%, respectively. Texture Recognition method based on volume vocal direction ternary pattern is feasible for the identification of pulmonary nodules and provides a reference value for doctor-assisted diagnosis.

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Section: Introductionmentioning

confidence: 99%

Texture recognition of pulmonary nodules based on volume local direction ternary pattern

et al. 2020

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Automatic Annotation Algorithm of Medical Radiological Images using Convolutional Neural Network

Wang

Cai³

2021

Pattern Recognition Letters

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Intra-retinal Layer Segmentation of Macular OCT Images using Edge Superpixels and Manifold Ranking Method

Abstract: In order to effectively segment the eleven surfaces of ten retinal layers in optical coherence tomography (OCT)

Cited by 2 publications

References 13 publications

Texture recognition of pulmonary nodules based on volume local direction ternary pattern

Texture recognition of pulmonary nodules based on volume local direction ternary pattern

Automatic Annotation Algorithm of Medical Radiological Images using Convolutional Neural Network

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