A self-adaptive matched filter for retinal blood vessel detection

Chakraborti, Tapabrata; Jha, D.; Chowdhury, Ananda S.; Jiang, Xiaoyi

doi:10.1007/s00138-014-0636-z

Cited by 80 publications

(43 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The flow chart for the proposed method is shown in Fig.2. Firstly it's necessary to gain the Hessian Matrix's eigenanalysis of each pixel of the image at different scales [10,11]. Fig.…”

Section: Multi-scale Hessian Matrix With Top-hat Operationmentioning

confidence: 99%

See 1 more Smart Citation

Image Enhancement based Improved Multi-scale Hessian Matrix for Coronary Angiography

Chen¹,

Yin²,

Xiao³

et al. 2015

IJCA

View full text Add to dashboard Cite

The coronary angiography image is easy to be affected by many factors, such as vascular thickness varied huge, complex background noise, uneven illumination intensity and so on. The coronary angiography image is more difficult to deal with compared with other similar medical images. By using Hessian matrix multi-scale vascular detection method, the vicinity of blood vessels will yield a lot of background noise, and the small tiny blood vessels become blurred or even lost, which seriously affect the experiment results. In this paper, an improved multi-scale Hessian matrix is presented, combined with morphological top-hat operation for the detection of coronary angiography. The experimental results demonstrate the effectiveness of the proposed method.

show abstract

“…The flow chart for the proposed method is shown in Fig.2. Firstly it's necessary to gain the Hessian Matrix's eigenanalysis of each pixel of the image at different scales [10,11]. Fig.…”

Section: Multi-scale Hessian Matrix With Top-hat Operationmentioning

confidence: 99%

“…To enhance vascular image in a linear structure, the most common method is vascular enhancement filter based on Hessian matrix introduced in paper [8,9,10,11,12,13]. Multiscale fusion in the multi-scale vessel enhancement filter is used to solve the vascular characteristics that the size of blood vessel images is different.…”

Section: Introductionmentioning

confidence: 99%

Image Enhancement based Improved Multi-scale Hessian Matrix for Coronary Angiography

Chen¹,

Yin²,

Xiao³

et al. 2015

IJCA

View full text Add to dashboard Cite

show abstract

“…The training data or hand labelled ground truths are not needed for the design of algorithm in these approaches. These approaches are matched filter-based (Chaudhuri et al;1989;Hoover et al;Chanwimaluang and Fan;Cinsdikici and Aydın;2009;Zhang et al;Chakraborti et al;, scale space-based (Martínez-Pérez et al;Vlachos and Dermatas;Wang et al;, tracking-based , model-based (Szpak and Tapamo;Xiao et al;, adaptive thresholding-based (Jiang and Mojon;Cornforth et al;2005;Akram and Khan;, mathematical morphology-based (Zana and Klein;Mendonca and Campilho;Jiménez et al;Miri and Mahloojifar;2012a) and clustering-based (Tolias and Panas;1998;Yang et al;Kande et al;2011a,b;Sun et al;Saffarzadeh et al;. Chaudhuri et al (1989) implemented matched filter response (MFR) by initially approximating the intensity of gray-level profiles of the cross-sections of retinal vessels using a Gaussian shaped curve.…”

Section: Introductionmentioning

confidence: 99%

“…The improved technique was, however, faced with the inability to segment the thinner vessels. Chakraborti et al (2014) implemented an unsupervised segmentation technique that combines vesselness filter and matched filter using orientation histogram for the segmentation of retinal vessels. Martínez-Pérez et al (1999) used a combination of scale space analysis and region growing to segment the vessel network.…”

Section: Introductionmentioning

confidence: 99%

Automatic Retinal Vessel Detection and Tortuosity Measurement

Mapayi¹,

Tapamo²,

Viriri³

et al. 2016

Image Anal Stereol

View full text Add to dashboard Cite

As retinopathies continue to be major causes of visual loss and blindness worldwide, early detection and management of these diseases will help achieve significant reduction of blindness cases. However, an efficient automatic retinal vessel segmentation approach remains a challenge. Since efficient vessel network detection is a very important step needed in ophthalmology for reliable retinal vessel characterization, this paper presents the study on the combination of difference image and k-means clustering for the segmentation of retinal vessels. Stationary points in the vessel center-lines are used to model the detection of twists in the vessel segments. The combination of arc-chord ratio with stationary points is used to compute tortuosity index. Experimental results show that the proposed k-means combined with difference image achieved a robust segmentation of retinal vessels. A maximum average accuracy of 0.9556 and a maximum average sensitivity of 0.7581 were achieved on DRIVE database while a maximum average accuracy of 0.9509 and a maximum average sensitivity of 0.7666 were achieved on STARE database. When compared with the previously proposed techniques on DRIVE and STARE databases, the proposed technique yields higher mean sensitivity and mean accuracy rates in the same range of very good specificity. In a related development, a non-normalized tortuosity index that combined distance metric and the vessel twist frequency proposed in this paper also achieved a strong correlation of 0.80 with the expert ground truth.

show abstract

“…This methodology could handle the vessel crossings and bifurcations as against the vesselness filter which looked only for elongated structures. Then, Chakraborti [19] introduced a selfadaptive MF which is a synergistic combination of vesselness and matched filter yielding an accuracy of 93.70%. In a broad spectrum, these filter based approaches are based on intensity of the image and are susceptible to intensity inhomogeneity.…”

mentioning

confidence: 99%