Supervised vessel delineation in retinal fundus images with the automatic selection of B-COSFIRE filters

Strisciuglio, Nicola; Azzopardi, George; Vento, Mario; Petkov, Nicolai

doi:10.1007/s00138-016-0781-7

Cited by 92 publications

(45 citation statements)

References 40 publications

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“…The construction of the COPE feature vector can also be optimized by including in the classification system only those filters that are relevant for the application at hand. A feature selection scheme based on the relevance of the feature values described can be employed [79]. The optimization of the number of configured feature extractors and the implementation of parallelization strategies can jointly contribute to the implementation of a real-time system for intelligent audio surveillance on edge embedded systems.…”

Section: Discussionmentioning

confidence: 99%

Learning representations of sound using trainable COPE feature extractors

Strisciuglio

Vento

Petkov

2019

Pattern Recognition

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Sound analysis research has mainly been focused on speech and music processing. The deployed methodologies are not suitable for analysis of sounds with varying background noise, in many cases with very low signal-to-noise ratio (SNR).In this paper, we present a method for the detection of patterns of interest in audio signals. We propose novel trainable feature extractors, which we call COPE (Combination of Peaks of Energy). The structure of a COPE feature extractor is determined using a single prototype sound pattern in an automatic configuration process, which is a type of representation learning. We construct

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Section: Discussionmentioning

confidence: 99%

Learning representations of sound using trainable COPE feature extractors

Strisciuglio

Vento

Petkov

2019

Pattern Recognition

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“…The sensitivity of this study is better than [45,[47][48][49][50][51][52] whereas its specificity is better than [53] and its accuracy and AUC is respectively more successful than [48] and [45,51] for DRIVE dataset as in Table 4. The sensitivity of this study is more successful than [45,49,51,52,54,55] whereas the performance of its specificity is better than [53,55] and its accuracy and AUC is respectively higher than [48,53,55] and [45,51] for STARE dataset as in Table 5. The performance of the sensitivity of this study is higher than [56] whereas its accuracy is more successful than [57] for CHASE_DB1 dataset as in Table 6.…”

Section: Discussionmentioning

confidence: 95%

Retinal Vessel Segmentation via Structure Tensor Coloring and Anisotropy Enhancement

Nergiz

Akın

2017

Symmetry

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Abstract:Retinal vessel segmentation is one of the preliminary tasks for developing diagnosis software systems related to various retinal diseases. In this study, a fully automated vessel segmentation system is proposed. Firstly, the vessels are enhanced using a Frangi Filter. Afterwards, Structure Tensor is applied to the response of the Frangi Filter and a 4-D tensor field is obtained. After decomposing the Eigenvalues of the tensor field, the anisotropy between the principal Eigenvalues are enhanced exponentially. Furthermore, this 4-D tensor field is converted to the 3-D space which is composed of energy, anisotropy and orientation and then a Contrast Limited Adaptive Histogram Equalization algorithm is applied to the energy space. Later, the obtained energy space is multiplied by the enhanced mean surface curvature of itself and the modified 3-D space is converted back to the 4-D tensor field. Lastly, the vessel segmentation is performed by using Otsu algorithm and tensor coloring method which is inspired by the ellipsoid tensor visualization technique. Finally, some post-processing techniques are applied to the segmentation result. In this study, the proposed method achieved mean sensitivity of 0.8123, 0.8126, 0.7246 and mean specificity of 0.9342, 0.9442, 0.9453 as well as mean accuracy of 0.9183, 0.9442, 0.9236 for DRIVE, STARE and CHASE_DB1 datasets, respectively. The mean execution time of this study is 6.104, 6.4525 and 18.8370 s for the aforementioned three datasets respectively.

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“…line-endings) of various thickness (i.e. scale), we proposed to use several approaches based on information theory and machine learning to select an optimal subset of B-COSFIRE filters for the vessel delineation task [7,9]. We indicate this procedure with the dashed box named 'feature learning' in Figure 1.…”

Section: Methodsmentioning

confidence: 99%

Learning audio and image representations with bio-inspired trainable feature extractors

Strisciuglio

2018

ELCVIA

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Recent advancements in pattern recognition and signal processing concern the automatic learning of data representations from labeled training samples. Typical approaches are based on deep learning and convolutional neural networks, which require large amount of labeled training samples. In this work, we propose novel feature extractors that can be used to learn the representation of single prototype samples in an automatic configuration process. We employ the proposed feature extractors in applications of audio and image processing, and show their effectiveness on benchmark data sets.

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Supervised vessel delineation in retinal fundus images with the automatic selection of B-COSFIRE filters

Cited by 92 publications

References 40 publications

Learning representations of sound using trainable COPE feature extractors

Learning representations of sound using trainable COPE feature extractors

Retinal Vessel Segmentation via Structure Tensor Coloring and Anisotropy Enhancement

Learning audio and image representations with bio-inspired trainable feature extractors

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