基于隨機森林的頻譜域光學(xué)相干層析技術(shù)的圖像視網(wǎng)膜神經(jīng)纖維層分割
doi: 10.11999/JEIT160663 cstr: 32379.14.JEIT160663
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2.
(南京理工大學(xué)計算機科學(xué)與工程學(xué)院 南京 210094) ②(福建省信息處理與智能控制重點實驗室(閩江學(xué)院) 福州 350121) ③(濟南大學(xué)信息科學(xué)與工程學(xué)院 濟南 250022)
基金項目:
國家自然科學(xué)基金(61671242)，中央高校基本科研業(yè)務(wù)費專項資金(30920140111004)，六大人才高峰(2014-SWYY-024)，福建省信息處理與智能控制重點實驗室(閩江學(xué)院)開放課題基(MJUKF201706)
Retinal Nerve Fiber Layer Segmentation of Spectral Domain Optical Coherence Tomography Images Based on Random Forest
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2.
(School of Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China)
Funds:
The National Natural Science Foundation of China (61671242), The Special Funds of Fundamental Research for the Central Universities (30920140111004), Six Big Talent Peals (2014-SWYY-024), The Open Fund Project of Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University)(MJUKF201706)
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摘要: 頻譜域光學(xué)相干層析技術(shù)是一種廣泛應(yīng)用于眼科疾病診斷的成像技術(shù)，而視網(wǎng)膜層分割對青光眼的診斷有很好的參考價值。該文利用隨機森林分類器尋找視網(wǎng)膜層間單像素寬的邊界，隨機森林分類器由12個特征訓(xùn)練產(chǎn)生，其中相對灰度特征和鄰域特征較好地解決灰度不均勻的分割誤差大問題。對10組帶有青光眼病變的視網(wǎng)膜圖像進行分割，并與傳統(tǒng)算法和Iowa軟件進行比較，平均邊界絕對誤差為9.202.57 m, 11.332.99 m和10.273.01 m。實驗結(jié)果表明，改進算法可以較好地分割視網(wǎng)膜神經(jīng)纖維層。
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關(guān)鍵詞:
- 頻域光學(xué)相干層技術(shù) /
- 青光眼 /
- 視網(wǎng)膜圖像分割 /
- 視網(wǎng)膜神經(jīng)纖維層 /
- 隨機森林
Abstract: Spectral Domain Optical Coherence Tomography (SD-OCT) imaging technique is widely used in the diagnosis of ophthalmology diseases. The segmentation of retinal layers plays a very important role in the diagnosis of glaucoma. In this paper, a random forest classifier is used which is trained by twelve different features to find the boundaries between layers. Whats more, the relative gray feature and the neighbor features are used to solve the problem of large errors under the condition of uneven illumination. In the last, the segmentation results of the proposed algorithm, a traditional algorithm and Iowa segmentation software on ten sets of retinal images are compared with manual segmentation, and the average absolute boundary errors are 9.202.57m, 11.332.99m, 10.273.01m, respectively. The experiments show that the proposed algorithm can segment the Retinal Never Fiber Layer (RNFL) better. -
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