Multi-path convolutional neural network in fundus segmentation of blood vessels

• Autorzy: Tian Chun , Fang Tao , Fan Yingle , Wu Wei

Identyfikatory

DOI

10.1016/j.bbe.2020.01.011

• Języki publikacji: EN

Abstrakty

EN

There is a close correlation between retinal vascular status and physical diseases such as eye lesions. Retinal fundus images are an important basis for diagnosing diseases such as diabetes, glaucoma, hypertension, coronary heart disease, etc. Because the thickness of the retinal blood vessels is different, the minimum diameter is only one or two pixels wide, so obtaining accurate measurement results becomes critical and challenging. In this paper, we propose a new method of retinal blood vessel segmentation that is based on a multi-path convolutional neural network, which can be used for computer-based clinical medical image analysis. First, a low-frequency image characterizing the overall characteristics of the retinal blood vessel image and a high-frequency image characterizing the local detailed features are respectively obtained by using a Gaussian low-pass filter and a Gaussian high-pass filter. Then a feature extraction path is constructed for the characteristics of the low- and high-frequency images, respectively. Finally, according to the response results of the low-frequency feature extraction path and the high-frequency feature extraction path, the whole blood vessel perception and local feature information fusion coding are realized, and the final blood vessel segmentation map is obtained. The performance of this method is evaluated and tested by DRIVE and CHASE_DB1. In the experimental results of the DRIVE database, the evaluation indexes accuracy (Acc), sensitivity (SE), and specificity (SP) are 0.9580, 0.8639, and 0.9665, respectively, and the evaluation indexes Acc, SE, and SP of the CHASE_DB1 database are 0.9601, 0.8778, and 0.9680, respectively. In addition, the method proposed in this paper could effectively suppress noise, ensure continuity after blood vessel segmentation, and provide a feasible new idea for intelligent visual perception of medical images.

Słowa kluczowe

EN

image segmentation; convolutional neural network; dilated convolution; visual perception; feature fusion

PL

segmentacja obrazu; konwolucyjna sieć neuronowa; postrzeganie wzrokowe

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2020
• Tom: Vol. 40, no. 2
• Strony: 583--595
• Opis fizyczny: Bibliogr. 34 poz., rys., tab., wykr.

Twórcy

autor

Tian Chun

• Laboratory of Pattern Recognition and Image Processing, Hangzhou Dianzi University, Hangzhou, China

autor

Fang Tao

• Laboratory of Pattern Recognition and Image Processing, Hangzhou Dianzi University, Hangzhou, China

autor

Fan Yingle

• Laboratory of Pattern Recognition and Image Processing, Hangzhou Dianzi University, Hangzhou 310018, China

autor

Wu Wei

• Laboratory of Pattern Recognition and Image Processing, Hangzhou Dianzi University, Hangzhou, China

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).