Selective Search and Intensity Context Based Retina Vessel Image Segmentation

Overview

Journal J Med Syst

Specialty Medical Informatics

Date 2017 Feb 15

PMID 28194685

Citations 3

Authors

Zhaohui Tang

Jin Zhang

Weihua Gui

Affiliations

Soon will be listed here.

Abstract

In the framework of computer-aided diagnosis of eye disease, a new contextual image feature named influence degree of average intensity is proposed for retinal vessel image segmentation. This new feature evaluates the influence degree of current detected pixel decreasing the average intensity of the local row where that pixel located. Firstly, Hessian matrix is introduced to detect candidate regions, for the reason of accelerating segmentation. Then, the influence degree of average intensity of each pixel is extracted. Next, contextual feature vector for each pixel is constructed by concatenating the 8 feature neighbors. Finally, a classifier is built to classify each pixel into vessel or non-vessel based on its contextual feature. The effectiveness of the proposed method is demonstrated through receiver operating characteristic analysis on the benchmarked databases of DRIVE and STARE. Experiment results show that our method is comparable with the state-of-the-art methods. For example, the average accuracy, sensitivity, specificity achieved on the database DRIVE and STARE are 0.9611, 0.8174, 0.9747 and 0.9547, 0.7768, 0.9751, respectively.

Citing Articles

Segmentation and Classification Approaches of Clinically Relevant Curvilinear Structures: A Review.

Kv R, Prasad K, Peralam Yegneswaran P J Med Syst. 2023; 47(1):40.

PMID: 36971852 PMC: 10042761. DOI: 10.1007/s10916-023-01927-2.

pyHIVE, a health-related image visualization and engineering system using Python.

Zhang R, Zhao R, Zhao X, Wu D, Zheng W, Feng X BMC Bioinformatics. 2018; 19(1):452.

PMID: 30477418 PMC: 6258460. DOI: 10.1186/s12859-018-2477-7.

A Computer-Aided Decision Support System for Detection and Localization of Cutaneous Vasculature in Dermoscopy Images Via Deep Feature Learning.

Kharazmi P, Zheng J, Lui H, Jane Wang Z, Lee T J Med Syst. 2018; 42(2):33.

PMID: 29318397 DOI: 10.1007/s10916-017-0885-2.

References

Soares J, Leandro J, Cesar Junior R, Jelinek H, Cree M . Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE Trans Med Imaging. 2006; 25(9):1214-22. DOI: 10.1109/tmi.2006.879967. View

Waheed A, Usman Akram M, Khalid S, Waheed Z, A Khan M, Shaukat A . Hybrid Features and Mediods Classification based Robust Segmentation of Blood Vessels. J Med Syst. 2015; 39(10):128. DOI: 10.1007/s10916-015-0316-1. View

Hoover A, Kouznetsova V, Goldbaum M . Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans Med Imaging. 2000; 19(3):203-10. DOI: 10.1109/42.845178. View

Marin D, Aquino A, Gegundez-Arias M, Bravo J . A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features. IEEE Trans Med Imaging. 2010; 30(1):146-58. DOI: 10.1109/TMI.2010.2064333. View

Staal J, Abramoff M, Niemeijer M, Viergever M, van Ginneken B . Ridge-based vessel segmentation in color images of the retina. IEEE Trans Med Imaging. 2004; 23(4):501-9. DOI: 10.1109/TMI.2004.825627. View

Akram U, Khan S . Automated detection of dark and bright lesions in retinal images for early detection of diabetic retinopathy. J Med Syst. 2011; 36(5):3151-62. DOI: 10.1007/s10916-011-9802-2. View

Ricci E, Perfetti R . Retinal blood vessel segmentation using line operators and support vector classification. IEEE Trans Med Imaging. 2007; 26(10):1357-65. DOI: 10.1109/TMI.2007.898551. View

Welikala R, Dehmeshki J, Hoppe A, Tah V, Mann S, Williamson T . Automated detection of proliferative diabetic retinopathy using a modified line operator and dual classification. Comput Methods Programs Biomed. 2014; 114(3):247-61. DOI: 10.1016/j.cmpb.2014.02.010. View

Wang X, Yang M, Zhu S, Lin Y . Regionlets for Generic Object Detection. IEEE Trans Pattern Anal Mach Intell. 2015; 37(10):2071-84. DOI: 10.1109/TPAMI.2015.2389830. View

10.

Lupascu C, Tegolo D, Trucco E . FABC: retinal vessel segmentation using AdaBoost. IEEE Trans Inf Technol Biomed. 2010; 14(5):1267-74. DOI: 10.1109/TITB.2010.2052282. View

11.

Imani E, Javidi M, Pourreza H . Improvement of retinal blood vessel detection using morphological component analysis. Comput Methods Programs Biomed. 2015; 118(3):263-79. DOI: 10.1016/j.cmpb.2015.01.004. View

12.

Fraz M, Remagnino P, Hoppe A, Uyyanonvara B, Rudnicka A, Owen C . Blood vessel segmentation methodologies in retinal images--a survey. Comput Methods Programs Biomed. 2012; 108(1):407-33. DOI: 10.1016/j.cmpb.2012.03.009. View

13.

Zana F, Klein J . Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. IEEE Trans Image Process. 2008; 10(7):1010-9. DOI: 10.1109/83.931095. View

14.

Bankhead P, Scholfield C, McGeown J, Curtis T . Fast retinal vessel detection and measurement using wavelets and edge location refinement. PLoS One. 2012; 7(3):e32435. PMC: 3299657. DOI: 10.1371/journal.pone.0032435. View

15.

Wang L, Kallem V, Bansal M, Eledath J, Sawhney H, Karp K . Interactive retinal vessel extraction by integrating vessel tracing and graph search. Med Image Comput Comput Assist Interv. 2014; 16(Pt 2):567-74. DOI: 10.1007/978-3-642-40763-5_70. View

16.

Rahebi J, Hardalac F . Retinal blood vessel segmentation with neural network by using gray-level co-occurrence matrix-based features. J Med Syst. 2014; 38(8):85. DOI: 10.1007/s10916-014-0085-2. View

17.

Bhuiyan A, Nath B, Ramamohanarao K, Kawasaki R, Wong T . Automated analysis of retinal vascular tortuosity on color retinal images. J Med Syst. 2010; 36(2):689-97. DOI: 10.1007/s10916-010-9536-6. View

18.

Fraz M, Remagnino P, Hoppe A, Uyyanonvara B, Rudnicka A, Owen C . An ensemble classification-based approach applied to retinal blood vessel segmentation. IEEE Trans Biomed Eng. 2012; 59(9):2538-48. DOI: 10.1109/TBME.2012.2205687. View

19.

Ganjee R, Azmi R, Gholizadeh B . An improved retinal vessel segmentation method based on high level features for pathological images. J Med Syst. 2014; 38(9):108. DOI: 10.1007/s10916-014-0108-z. View

20.

Zhao Y, Rada L, Chen K, Harding S, Zheng Y . Automated Vessel Segmentation Using Infinite Perimeter Active Contour Model with Hybrid Region Information with Application to Retinal Images. IEEE Trans Med Imaging. 2015; 34(9):1797-807. DOI: 10.1109/TMI.2015.2409024. View