Skip to main content
Top
Published in: International Journal of Computer Assisted Radiology and Surgery 6/2021

12-05-2021 | Original Article

Detection of microaneurysms and hemorrhages based on improved Hessian matrix

Authors: Linying Yang, Shiju Yan, Yuanzhi Xie

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 6/2021

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Purpose

Knowing the early lesion detection of fundus images is very important to prevent blindness, and accurate lesion segmentation can provide doctors with diagnostic evidence. This study proposes a method based on improved Hessian matrix eigenvalue analysis to detect microaneurysms and hemorrhages in the fundus images of diabetic patients.

Methods

A two-step method including identification of lesion candidate regions and classification of candidate regions is adopted. In the first step, the method of eigenvalue analysis based on the improved hessian matrix was applied to enhance the image preprocessed. A dual-threshold method was used for segmentation. Then, blood vessels were gradually removed to obtain the lesion candidate regions. In the second step, all candidates were classified into three categories: microaneurysms, hemorrhages and the others.

Results

The proposed method has achieved a better performance compared with the existing algorithms on accuracy rates. The classification accuracy rates of microaneurysms and hemorrhages obtained by using our method were 94.4% and 94.0%, respectively, while the classification accuracy rates obtained by using Frangi’s filter based on the Hessian matrix to enhance the image were 90.9% and 92.1%.

Conclusion

This study demonstrated a methodology for enhancing images by using eigenvalue analysis based on the improved Hessian matrix and segmentation by using double thresholds. The proposed method is beneficial to improve the detection accuracy of microaneurysms and hemorrhages in fundus images.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Literature
1.
go back to reference Sidibé D, Sadek I, Mériaudeau F (2015) Discrimination of retinal images containing bright lesions using sparse coded features and SVM. Comput Biol Med 62:175–184CrossRef Sidibé D, Sadek I, Mériaudeau F (2015) Discrimination of retinal images containing bright lesions using sparse coded features and SVM. Comput Biol Med 62:175–184CrossRef
2.
go back to reference Amin J, Sharif M, Yasmin M, Ali H, Fernandes SL (2017) A method for the detection and classification of diabetic retinopathy using structural predictors of bright lesions. J Comput Sci 19:153–164CrossRef Amin J, Sharif M, Yasmin M, Ali H, Fernandes SL (2017) A method for the detection and classification of diabetic retinopathy using structural predictors of bright lesions. J Comput Sci 19:153–164CrossRef
3.
go back to reference Veiga D, Martins N, Ferreira M, Monteiro J (2018) Automatic microaneurysm detection using laws texture masks and support vector machines. Comput Methods Biomech Biomed Eng Imaging Vis 6(4):405–416CrossRef Veiga D, Martins N, Ferreira M, Monteiro J (2018) Automatic microaneurysm detection using laws texture masks and support vector machines. Comput Methods Biomech Biomed Eng Imaging Vis 6(4):405–416CrossRef
4.
go back to reference Derwin DJ, Selvi ST, Singh OJ (2020) Discrimination of microaneurysm in color retinal images using texture descriptors. SIViP 14(2):369–376CrossRef Derwin DJ, Selvi ST, Singh OJ (2020) Discrimination of microaneurysm in color retinal images using texture descriptors. SIViP 14(2):369–376CrossRef
5.
go back to reference Akram MU, Khalid S, Khan SA (2013) Identification and classification of microaneurysms for early detection of diabetic retinopathy. Pattern Recogn 46(1):107–116CrossRef Akram MU, Khalid S, Khan SA (2013) Identification and classification of microaneurysms for early detection of diabetic retinopathy. Pattern Recogn 46(1):107–116CrossRef
6.
go back to reference Lachure J, Deorankar AV, Lachure S, Gupta S, Jadhav R (2015) Diabetic retinopathy using morphological operations and machine learning. In: 2015 IEEE international advance computing conference (IACC). IEEE, pp 617–622 Lachure J, Deorankar AV, Lachure S, Gupta S, Jadhav R (2015) Diabetic retinopathy using morphological operations and machine learning. In: 2015 IEEE international advance computing conference (IACC). IEEE, pp 617–622
7.
go back to reference Sisodia DS, Nair S, Khobragade P (2017) Diabetic retinal fundus images: preprocessing and feature extraction for early detection of diabetic retinopathy. Biomed Pharmacol J 10(2):615–626CrossRef Sisodia DS, Nair S, Khobragade P (2017) Diabetic retinal fundus images: preprocessing and feature extraction for early detection of diabetic retinopathy. Biomed Pharmacol J 10(2):615–626CrossRef
8.
go back to reference Fleming AD, Philip S, Goatman KA, Olson JA, Sharp PF (2006) Automated microaneurysm detection using local contrast normalization and local vessel detection. IEEE Trans Med Imaging 25(9):1223–1232CrossRef Fleming AD, Philip S, Goatman KA, Olson JA, Sharp PF (2006) Automated microaneurysm detection using local contrast normalization and local vessel detection. IEEE Trans Med Imaging 25(9):1223–1232CrossRef
9.
go back to reference Walter T, Massin P, Erginay A, Ordonez R, Jeulin C, Klein JC (2007) Automatic detection of microaneurysms in color fundus images. Med Image Anal 11(6):555–566CrossRef Walter T, Massin P, Erginay A, Ordonez R, Jeulin C, Klein JC (2007) Automatic detection of microaneurysms in color fundus images. Med Image Anal 11(6):555–566CrossRef
10.
go back to reference Inoue T, Hatanaka Y, Okumura S, Muramatsu C, Fujita H (2013) Automated microaneurysm detection method based on eigenvalue analysis using hessian matrix in retinal fundus images. In: 2013 35th annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 5873–5876 Inoue T, Hatanaka Y, Okumura S, Muramatsu C, Fujita H (2013) Automated microaneurysm detection method based on eigenvalue analysis using hessian matrix in retinal fundus images. In: 2013 35th annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 5873–5876
11.
go back to reference Mazlan N, Yazid H, Arof H, Isa HM (2020) Automated microaneurysms detection and classification using multilevel thresholding and multilayer perceptron. J Med Biol Eng 40:1–15CrossRef Mazlan N, Yazid H, Arof H, Isa HM (2020) Automated microaneurysms detection and classification using multilevel thresholding and multilayer perceptron. J Med Biol Eng 40:1–15CrossRef
12.
go back to reference Frangi AF, Niessen WJ, Vincken KL, Viergever MA (1998). ultiscale vessel enhancement filtering. In: International conference on medical image computing and computer-assisted intervention. Springer, Berlin, Heidelberg, pp 130–137 Frangi AF, Niessen WJ, Vincken KL, Viergever MA (1998). ultiscale vessel enhancement filtering. In: International conference on medical image computing and computer-assisted intervention. Springer, Berlin, Heidelberg, pp 130–137
13.
go back to reference Srivastava R, Wong DW, Duan L, Liu J, Wong TY (2015) Red lesion detection in retinal fundus images using Frangi-based filters. In: 2015 37th Annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 5663–5666 Srivastava R, Wong DW, Duan L, Liu J, Wong TY (2015) Red lesion detection in retinal fundus images using Frangi-based filters. In: 2015 37th Annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 5663–5666
14.
go back to reference Zhou L, Li P, Yu Q, Qiao Y, Yang J (2016) Automatic hemorrhage detection in color fundus images based on gradual removal of vascular branches. In: 2016 IEEE international conference on image processing (ICIP). IEEE, pp 399–403 Zhou L, Li P, Yu Q, Qiao Y, Yang J (2016) Automatic hemorrhage detection in color fundus images based on gradual removal of vascular branches. In: 2016 IEEE international conference on image processing (ICIP). IEEE, pp 399–403
15.
go back to reference Seoud L, Hurtut T, Chelbi J, Cheriet F, Langlois JP (2015) Red lesion detection using dynamic shape features for diabetic retinopathy screening. IEEE Trans Med Imaging 35(4):1116–1126CrossRef Seoud L, Hurtut T, Chelbi J, Cheriet F, Langlois JP (2015) Red lesion detection using dynamic shape features for diabetic retinopathy screening. IEEE Trans Med Imaging 35(4):1116–1126CrossRef
16.
go back to reference Hatanaka Y (2020) Retinopathy analysis based on deep convolution neural network. Adv Exp Med Biol 1213:107–120CrossRef Hatanaka Y (2020) Retinopathy analysis based on deep convolution neural network. Adv Exp Med Biol 1213:107–120CrossRef
17.
go back to reference Eftekhari N, Pourreza HR, Masoudi M, Ghiasi-Shirazi K, Saeedi E (2019) Microaneurysm detection in fundus images using a two-step convolutional neural network. Biomed Eng Online 18(1):1–16CrossRef Eftekhari N, Pourreza HR, Masoudi M, Ghiasi-Shirazi K, Saeedi E (2019) Microaneurysm detection in fundus images using a two-step convolutional neural network. Biomed Eng Online 18(1):1–16CrossRef
18.
go back to reference Chudzik P, Majumdar S, Calivá F, Al-Diri B, Hunter A (2018) Microaneurysm detection using fully convolutional neural networks. Comput Methods Prog Biomed 158:185–192CrossRef Chudzik P, Majumdar S, Calivá F, Al-Diri B, Hunter A (2018) Microaneurysm detection using fully convolutional neural networks. Comput Methods Prog Biomed 158:185–192CrossRef
19.
go back to reference Porwal P, Pachade S, Kamble R, Kokare M, Deshmukh G, Sahasrabuddhe V, Meriaudeau F (2018) Indian diabetic retinopathy image dataset (IDRiD): a database for diabetic retinopathy screening research. Data 3(3):25CrossRef Porwal P, Pachade S, Kamble R, Kokare M, Deshmukh G, Sahasrabuddhe V, Meriaudeau F (2018) Indian diabetic retinopathy image dataset (IDRiD): a database for diabetic retinopathy screening research. Data 3(3):25CrossRef
20.
go back to reference Van Grinsven MJ, van Ginneken B, Hoyng CB, Theelen T, Sánchez CI (2016) Fast convolutional neural network training using selective data sampling: application to hemorrhage detection in color fundus images. IEEE Trans Med Imaging 35(5):1273–1284CrossRef Van Grinsven MJ, van Ginneken B, Hoyng CB, Theelen T, Sánchez CI (2016) Fast convolutional neural network training using selective data sampling: application to hemorrhage detection in color fundus images. IEEE Trans Med Imaging 35(5):1273–1284CrossRef
21.
go back to reference Jerman T, Pernuš F, Likar B, Špiclin Ž (2016) Enhancement of vascular structures in 3D and 2D angiographic images. IEEE Trans Med Imaging 35(9):2107–2118CrossRef Jerman T, Pernuš F, Likar B, Špiclin Ž (2016) Enhancement of vascular structures in 3D and 2D angiographic images. IEEE Trans Med Imaging 35(9):2107–2118CrossRef
22.
go back to reference Jerman T, Pernuš F, Likar B, Špiclin Ž (2015) Beyond Frangi: an improved multiscale vesselness filter. In: Medical imaging 2015: image processing, vol 9413. International Society for Optics and Photonics, p 94132A Jerman T, Pernuš F, Likar B, Špiclin Ž (2015) Beyond Frangi: an improved multiscale vesselness filter. In: Medical imaging 2015: image processing, vol 9413. International Society for Optics and Photonics, p 94132A
23.
go back to reference Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66CrossRef Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66CrossRef
24.
go back to reference Wu B, Zhu W, Shi F, Zhu S, Chen X (2017) Automatic detection of microaneurysms in retinal fundus images. Comput Med Imaging Graph 55:106–112CrossRef Wu B, Zhu W, Shi F, Zhu S, Chen X (2017) Automatic detection of microaneurysms in retinal fundus images. Comput Med Imaging Graph 55:106–112CrossRef
Metadata
Title
Detection of microaneurysms and hemorrhages based on improved Hessian matrix
Authors
Linying Yang
Shiju Yan
Yuanzhi Xie
Publication date
12-05-2021
Publisher
Springer International Publishing
Published in
International Journal of Computer Assisted Radiology and Surgery / Issue 6/2021
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI
https://doi.org/10.1007/s11548-021-02358-5

Other articles of this Issue 6/2021

International Journal of Computer Assisted Radiology and Surgery 6/2021 Go to the issue

Premium Partner