基于Hesse矩陣和多尺度分析的視網(wǎng)膜動靜脈血管管徑測量方法

肖志濤; 崔寧; 吳駿; 耿磊; 張芳; 溫佳; 童軍; 劉曉婷; 楊嵩

doi:10.11999/JEIT160165

基于Hesse矩陣和多尺度分析的視網(wǎng)膜動靜脈血管管徑測量方法

doi: 10.11999/JEIT160165

肖志濤¹,
崔寧¹,
吳駿¹,
耿磊¹,
張芳¹,
溫佳¹,
童軍¹,
劉曉婷¹,
楊嵩¹

基金項目:

國家自然科學(xué)基金(61401439)，天津市科技支撐計劃重點項目(14ZCZDGX00033)，天津市應(yīng)用基礎(chǔ)與前沿技術(shù)研究計劃項目(15JCYBJC16600)，高等學(xué)校博士學(xué)科點專項科研基金(20131201110001)

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出版歷程
- 收稿日期: 2016-02-24
- 修回日期: 2016-06-22
- 刊出日期: 2016-11-19

Measurement of Retinal Diameters of Artery and Vein Based on Hesse Matrix and Multi-scale Analysis

Funds:

The National Natural Science Foundation of China (61401439), Tianjin Science and Technology Supporting Projection (14ZCZDGX00033), Tianjin Research Program of Application Foundation and Advanced Technology (15JCYBJC 16600), Specialized Research Fund for the Doctoral Program of Higher Education of China (20131201110001)

摘要

摘要: 許多全身性疾病會引起視網(wǎng)膜血管管徑及動靜脈血管比例(Arteriolar-to-Venular diameter Ratios, AVR)的變化，因此對視網(wǎng)膜血管管徑進行準確的量化分析對病情診斷具有重要的意義。該文提出一種視網(wǎng)膜動靜脈血管管徑及AVR的自動測量方法。首先，在分割血管網(wǎng)絡(luò)的基礎(chǔ)上，依據(jù)Hesse矩陣檢測線狀結(jié)構(gòu)的優(yōu)勢，結(jié)合多尺度分析準確定位血管方向并計算血管管徑；然后利用廣義回歸神經(jīng)網(wǎng)絡(luò)(General Regression Neural Network, GRNN)分類器對動靜脈血管骨架線上的點進行準確分類；最后計算感興趣區(qū)域(Region Of Interest, ROI)內(nèi)的AVR。對REVIEW和DRIVE數(shù)據(jù)庫進行實驗，驗證了該文方法的有效性。
- 視網(wǎng)膜血管 /
- Hesse矩陣 /
- 管徑測量 /
- 廣義回歸神經(jīng)網(wǎng)絡(luò) /
- 視網(wǎng)膜血管管徑與動靜脈管徑比
Abstract: Many systemic diseases can cause changes of the diameters of retinal vessels and Arteriolar-to-Venular diameter Ratios (AVR), so it is of great importance to make quantitative analysis of the diameter of retinal vessels accurately in the diagnosis of the disease. An automatic method measuring the diameters of the artery and the vein and the AVR is proposed. Firstly, based on the segmentation of vascular network, the diameters of retinal vessels are measured according to the advantage of Hesse matrix for detecting line-like structure, and accurate localization of vascular direction with multi-scale analysis. Secondly, a General Regression Neural Network (GRNN) classifier is used to classify the artery and the vein points. Finally, the AVR in the Region Of Interest (ROI) is calculated. The validity of the proposed method is demonstrated by testing on the DRIVE and the REVIEW database.
- Retinal vessels /
- Hesse matrix /
- Diameter measurement /
- General Regression Neural Network (GRNN) /
- Arteriolar-to-Venular diameter Ratios (AVR)

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參考文獻(17)

李春艷, 徐力. 糖尿病視網(wǎng)膜血管管徑的研究進展[J]. 醫(yī)學(xué)綜述, 2015, 25(17): 3173-3176. doi: 10.3969/j.issn.1006-2084. 2015.17.038.

LI Chunyan and XU Li. Research progress of diabetic retinal vascular caliber[J], Medical Recapitulate, 2015, 25(17): 3173-3176. doi: 10.3969/j.issn.1006-2084.2015.17.038.

NIEMEIJER M, XU X, DUMITRESCU A V, et al. Automated measurement of the arteriolar-to-venular width ratio in digital color fundus photographs[J]. IEEE Transactions on Medical Imaging, 2011, 30(11): 1941-1950. doi: 10.1109/TMI.2011.2159619.

GRISAN E and RUGGERI A. A divide et impera strategy for automatic classification of retinal vessels into arteries and veins[C]. The 25th Annual International Conference of the IEEE in Medicine and Biology Society, Cancun, Mexico, 2003, 1: 890-893. doi: 10.1109/IEMBS.2003.1279908.

姚暢, 陳后金. 一種基于先驗知識的視網(wǎng)膜血管寬度測量方法[J]. 中國科技論文在線, 2009, 4(1): 64-68. doi: 10.3969/ j.issn.2095-2783.2009.01.011.

YAO Chang and CHEN Houjin. Measurement of retinal vessel widths based on prior knowledge[J]. Sciencepaper Online, 2009, 4(1): 64-68. doi: 10.3969/j.issn.2095-2783.2009. 01.011.

LOWELL J, HUNTER A, STEEL D, et al. Quantitative measurement of retinal vessel widths from fundus images based on 2D modelling[J]. IEEE Transactions on Medical Imaging, 2004, 23(10): 1196-1204. doi: 10.1109/TMI.2004. 830524.

LUPA S CU C A, TEGOLO D, and TRUCCO E. Accurate estimation of retinal vessel width using bagged decision trees and an extended multiresolution Hermite model[J]. Medical Image Analysis, 2013, 17(8): 1164-1180. doi: 10.1016/j.media. 2013.07.006.

FRANGI A F, NIESSEN W J, VINCKEN K L, et al. Multiscale Vessel Enhancement Filtering[M]. Berlin, Germany, Medical Image Computing and Computer-Assisted Interventation, Springer, 1998: 130-137.

張新紅, 張帆, 崔延斌. 基于多尺度自適應(yīng)濾波的DSA血管增強[J]. 計算機工程與應(yīng)用, 2015, 51(14): 179-185. doi: 10. 3778/j.issn.1002-8331.1406-0457.

ZHANG Xinhong, ZHANG Fan, and CUI Yanbin. Blood vessel enhancement algorithm for DSA images based on adaptive multi-scale filtering[J]. Computer Engineering and Applications, 2015, 51(14): 179-185. doi: 10.3778/j.issn. 1002- 8331.1406-0457.

CHEN Y, BARCELOS C A Z, and MAIR B A. Smoothing and edge detection by time-varying coupled nonlinear diffusion equations[J]. Computer Vision and Image Understanding, 2001, 82(2): 85-100. doi: 10.1006/cviu. 2001.0903.

嚴頌華, 吳世才, 吳雄斌. 基于神經(jīng)網(wǎng)絡(luò)的高頻地波雷達目標到達角估計[J]. 電子與信息學(xué)報, 2008, 30(2): 339-342.

YAN Songhua, WU Shicai, and WU Xiongbin. DOA estimation based on neural network for HFGWR[J]. Journal of Electronics Information Technology, 2008, 30(2): 339-342.

LI X, XIAO Z, and ZHANG F, et al. Fundus optic disc localization method based on phase congruency[J]. International Journal of Digital Content Technology and Its Applications, 2013, 7(4): 726-733.

ZHANG B, ZHANG L, ZHANG L, et al. Retinal vessel extraction by matched filter with first-order derivative of Gaussian[J]. Computers in Biology and Medicine, 2010, 40(4): 438-445. doi: 10.1016/j.compbiomed.2010.02.008.

ZHAO Y Q, WANG X H, WANG X F, et al. Retinal vessels segmentation based on level set and region growing[J]. Pattern Recognition, 2014, 47(7): 2437-2446. doi: 10.1016/j. patcog.2014.01.006.

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