2019
1.
Tabone, Wilbert; Wilkinson, Michael HF; Gaalen, Anne EJV; Georgiadis, Janniko; Azzopardi, George
Alpha-tree segmentation of human anatomical photographic imagery Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: medical image analysis, segmentation
@inproceedings{tabone2019alpha,
title = {Alpha-tree segmentation of human anatomical photographic imagery},
author = {Wilbert Tabone and Michael HF Wilkinson and Anne EJV Gaalen and Janniko Georgiadis and George Azzopardi},
doi = {10.1145/3309772.3309776},
year = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
booktitle = {Proceedings of the 2nd International Conference on Applications of Intelligent Systems},
pages = {1--6},
abstract = {Segmentation of anatomical imagery is important in several areas, such as forensics, medical analysis and educational material. The manual segmentation of such images and the subsequent labelling of regions is a very laborious task. We propose an interactive segmentation scheme which we evaluate on a new data set of anatomical imagery. We use a morphological tree-based segmentation method, known as the alpha-tree, together with a Hu-moment thresholding mechanism in order to extract segments from a number of structures. Both qualitative and quantitative results in anatomical imagery of embalmed head, arm and leg specimens indicate that the proposed method can produce meaningful segmentation outputs, which could facilitate further refined labelling.},
keywords = {medical image analysis, segmentation},
pubstate = {published},
tppubtype = {inproceedings}
}
Segmentation of anatomical imagery is important in several areas, such as forensics, medical analysis and educational material. The manual segmentation of such images and the subsequent labelling of regions is a very laborious task. We propose an interactive segmentation scheme which we evaluate on a new data set of anatomical imagery. We use a morphological tree-based segmentation method, known as the alpha-tree, together with a Hu-moment thresholding mechanism in order to extract segments from a number of structures. Both qualitative and quantitative results in anatomical imagery of embalmed head, arm and leg specimens indicate that the proposed method can produce meaningful segmentation outputs, which could facilitate further refined labelling.
2.
Kind, Adrian; Azzopardi, George
An Explainable AI-Based Computer Aided Detection System for Diabetic Retinopathy Using Retinal Fundus Images Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: convnets, deep learning, medical image analysis, pattern recognition
@inproceedings{kind2019explainable,
title = {An Explainable AI-Based Computer Aided Detection System for Diabetic Retinopathy Using Retinal Fundus Images},
author = {Adrian Kind and George Azzopardi},
doi = {https://doi.org/10.1007/978-3-030-29888-3_37},
year = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
booktitle = {International Conference on Computer Analysis of Images and Patterns},
pages = {457--468},
organization = {Springer},
abstract = {Diabetic patients have a high risk of developing diabetic retinopathy (DR), which is one of the major causes of blindness. With early detection and the right treatment patients may be spared from losing their vision. We propose a computer-aided detection system, which uses retinal fundus images as input and it detects all types of lesions that define diabetic retinopathy. The aim of our system is to assist eye specialists by automatically detecting the healthy retinas and referring the images of the unhealthy ones. For the latter cases, the system offers an interactive tool where the doctor can examine the local lesions that our system marks as suspicious. The final decision remains in the hands of the ophthalmologists. Our approach consists of a multi-class detector, that is able to locate and recognize all candidate DR-defining lesions. If the system detects at least one lesion, then the image is marked as unhealthy. The lesion detector is built on the faster R-CNN ResNet 101 architecture, which we train by transfer learning. We evaluate our approach on three benchmark data sets, namely Messidor-2, IDRiD, and E-Ophtha by measuring the sensitivity (SE) and specificity (SP) based on the binary classification of healthy and unhealthy images. The results that we obtain for Messidor-2 and IDRiD are (SE: 0.965, SP: 0.843), and (SE: 0.83, SP: 0.94), respectively. For the E-Ophtha data set we follow the literature and perform two experiments, one where we detect only lesions of the type micro aneurysms (SE: 0.939, SP: 0.82) and the other when we detect only exudates (SE: 0.851, SP: 0.971). Besides the high effectiveness that we achieve, the other important contribution of our work is the interactive tool, which we offer to the medical experts, highlighting all suspicious lesions detected by the proposed system.},
keywords = {convnets, deep learning, medical image analysis, pattern recognition},
pubstate = {published},
tppubtype = {inproceedings}
}
Diabetic patients have a high risk of developing diabetic retinopathy (DR), which is one of the major causes of blindness. With early detection and the right treatment patients may be spared from losing their vision. We propose a computer-aided detection system, which uses retinal fundus images as input and it detects all types of lesions that define diabetic retinopathy. The aim of our system is to assist eye specialists by automatically detecting the healthy retinas and referring the images of the unhealthy ones. For the latter cases, the system offers an interactive tool where the doctor can examine the local lesions that our system marks as suspicious. The final decision remains in the hands of the ophthalmologists. Our approach consists of a multi-class detector, that is able to locate and recognize all candidate DR-defining lesions. If the system detects at least one lesion, then the image is marked as unhealthy. The lesion detector is built on the faster R-CNN ResNet 101 architecture, which we train by transfer learning. We evaluate our approach on three benchmark data sets, namely Messidor-2, IDRiD, and E-Ophtha by measuring the sensitivity (SE) and specificity (SP) based on the binary classification of healthy and unhealthy images. The results that we obtain for Messidor-2 and IDRiD are (SE: 0.965, SP: 0.843), and (SE: 0.83, SP: 0.94), respectively. For the E-Ophtha data set we follow the literature and perform two experiments, one where we detect only lesions of the type micro aneurysms (SE: 0.939, SP: 0.82) and the other when we detect only exudates (SE: 0.851, SP: 0.971). Besides the high effectiveness that we achieve, the other important contribution of our work is the interactive tool, which we offer to the medical experts, highlighting all suspicious lesions detected by the proposed system.
2017
3.
Rodríguez-Sánchez, Antonio; Chea, Daly; Azzopardi, George; Stabinger, Sebastian
A deep learning approach for detecting and correcting highlights in endoscopic images Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: convnets, deep learning, medical image analysis
@inproceedings{8310082,
title = {A deep learning approach for detecting and correcting highlights in endoscopic images},
author = {Rodr\'{i}guez-S\'{a}nchez, Antonio and Chea, Daly and Azzopardi, George and Stabinger, Sebastian},
doi = {10.1109/IPTA.2017.8310082},
year = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
booktitle = {2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)},
pages = {1-6},
abstract = {The image of an object changes dramatically depending on the lightning conditions surrounding that object. Shadows, reflections and highlights can make the object very difficult to be recognized for an automatic system. Additionally, images used in medical applications, such as endoscopic images and videos contain a large amount of such reflective components. This can pose an extra difficulty for experts to analyze such type of videos and images. It can then be useful to detect - and possibly correct - the locations where those highlights happen. In this work we designed a Convolutional Neural Network for that task. We trained such a network using a dataset that contains groundtruth highlights showing that those reflective elements can be learnt and thus located and extracted. We then used that trained network to localize and correct the highlights in endoscopic images from the El Salvador Atlas Gastrointestinal videos obtaining promising results.},
keywords = {convnets, deep learning, medical image analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
The image of an object changes dramatically depending on the lightning conditions surrounding that object. Shadows, reflections and highlights can make the object very difficult to be recognized for an automatic system. Additionally, images used in medical applications, such as endoscopic images and videos contain a large amount of such reflective components. This can pose an extra difficulty for experts to analyze such type of videos and images. It can then be useful to detect - and possibly correct - the locations where those highlights happen. In this work we designed a Convolutional Neural Network for that task. We trained such a network using a dataset that contains groundtruth highlights showing that those reflective elements can be learnt and thus located and extracted. We then used that trained network to localize and correct the highlights in endoscopic images from the El Salvador Atlas Gastrointestinal videos obtaining promising results.
2015
4.
Strisciuglio, Nicola; Vento, Mario; Azzopardi, George; Petkov, Nicolai
Unsupervised delineation of the vessel tree in retinal fundus images Inproceedings
Abstract | Links | BibTeX | Tags: brain-inspired, medical image analysis, segmentation
@inproceedings{strisciuglio2015unsupervised,
title = {Unsupervised delineation of the vessel tree in retinal fundus images},
author = {Nicola Strisciuglio and Mario Vento and George Azzopardi and Nicolai Petkov},
editor = {Joao Manuel R.S. Tavares and R.M. Natal Jorge},
url = {https://research.rug.nl/en/publications/unsupervised-delineation-of-the-vessel-tree-in-retinal-fundus-ima},
isbn = {9781138029262},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
booktitle = {Computational Vision and Medical Image Processing V - Proceedings of 5th Eccomas Thematic Proceedings of the 5th Eccomas Thematic Conference on Computational Vision and Medical Image Processing},
pages = {149-156},
abstract = {Retinal imaging has gained particular popularity as it provides an opportunity to diagnose various medical pathologies in a non-invasive way. One of the basic and very important steps in the analysis of such images is the delineation of the vessel tree from the background. Such segmentation facilitates the investigation of the morphological characteristics of the vessel tree and the analysis of any lesions in the background, which are both indicators for various pathologies. We propose a novel method called B-COSFIRE for the delineation of the vessel tree. It is based on the classic COSFIRE approach, which is a trainable nonlinear filtering method. RE filter to be configured by the automatic analysis of any given vessel-like pattern. The responses of a B-COSFIRE filter is achieved by combining the responses of difference-of-Gaussians filters whose areas of support are determined in an automatic configuration step. We configure two types of B-COSFIRE filters, one that responds selectively along vessels and another that is selective to vessel endings. The segmentation of the vessel tree is achieved by summing up the response maps of both types of filters followed by thresholding. We demonstrate high effectiveness of the proposed approach by performing experiments on four public data sets, namely DRIVE, STARE, CHASE_DB1 and HRF. The delineation approach that we propose also has lower time complexity than existing methods.},
keywords = {brain-inspired, medical image analysis, segmentation},
pubstate = {published},
tppubtype = {inproceedings}
}
Retinal imaging has gained particular popularity as it provides an opportunity to diagnose various medical pathologies in a non-invasive way. One of the basic and very important steps in the analysis of such images is the delineation of the vessel tree from the background. Such segmentation facilitates the investigation of the morphological characteristics of the vessel tree and the analysis of any lesions in the background, which are both indicators for various pathologies. We propose a novel method called B-COSFIRE for the delineation of the vessel tree. It is based on the classic COSFIRE approach, which is a trainable nonlinear filtering method. RE filter to be configured by the automatic analysis of any given vessel-like pattern. The responses of a B-COSFIRE filter is achieved by combining the responses of difference-of-Gaussians filters whose areas of support are determined in an automatic configuration step. We configure two types of B-COSFIRE filters, one that responds selectively along vessels and another that is selective to vessel endings. The segmentation of the vessel tree is achieved by summing up the response maps of both types of filters followed by thresholding. We demonstrate high effectiveness of the proposed approach by performing experiments on four public data sets, namely DRIVE, STARE, CHASE_DB1 and HRF. The delineation approach that we propose also has lower time complexity than existing methods.
5.
Strisciuglio, Nicola; Azzopardi, George; Vento, Mario; Petkov, Nicolai
Multiscale blood vessel delineation using B-COSFIRE filters Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: brain-inspired, medical image analysis, segmentation, trainable filters
@inproceedings{strisciuglio2015multiscale,
title = {Multiscale blood vessel delineation using B-COSFIRE filters},
author = {Nicola Strisciuglio and George Azzopardi and Mario Vento and Nicolai Petkov},
doi = {10.1007/978-3-319-23117-4_26},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
booktitle = {International Conference on Computer Analysis of Images and Patterns},
pages = {300--312},
organization = {Springer, Cham},
abstract = {We propose a delineation algorithm that deals with bar-like structures of different thickness. Detection of linear structures is applicable to several fields ranging from medical images for segmentation of vessels to aerial images for delineation of roads or rivers. The proposed method is suited for any delineation problem and employs a set of B-COSFIRE filters selective for lines and line-endings of different thickness. We determine the most effective filters for the application at hand by Generalized Matrix Learning Vector Quantization (GMLVQ) algorithm. We demonstrate the effectiveness of the proposed method by applying it to the task of vessel segmentation in retinal images. We perform experiments on two benchmark data sets, namely DRIVE and STARE. The experimental results show that the proposed delineation algorithm is highly effective and efficient. It can be considered as a general framework for a delineation task in various applications.},
keywords = {brain-inspired, medical image analysis, segmentation, trainable filters},
pubstate = {published},
tppubtype = {inproceedings}
}
We propose a delineation algorithm that deals with bar-like structures of different thickness. Detection of linear structures is applicable to several fields ranging from medical images for segmentation of vessels to aerial images for delineation of roads or rivers. The proposed method is suited for any delineation problem and employs a set of B-COSFIRE filters selective for lines and line-endings of different thickness. We determine the most effective filters for the application at hand by Generalized Matrix Learning Vector Quantization (GMLVQ) algorithm. We demonstrate the effectiveness of the proposed method by applying it to the task of vessel segmentation in retinal images. We perform experiments on two benchmark data sets, namely DRIVE and STARE. The experimental results show that the proposed delineation algorithm is highly effective and efficient. It can be considered as a general framework for a delineation task in various applications.
2012
6.
Azzopardi, George; Petkov, Nicolai
Detection of retinal vascular bifurcations by rotation-, scale-and reflection-invariant COSFIRE filters Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: brain-inspired, medical image analysis, trainable filters
@inproceedings{azzopardi2012detectionb,
title = {Detection of retinal vascular bifurcations by rotation-, scale-and reflection-invariant COSFIRE filters},
author = {George Azzopardi and Nicolai Petkov},
doi = {10.1109/CBMS.2012.6266338},
year = {2012},
date = {2012-01-01},
urldate = {2012-01-01},
booktitle = {2012 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS)},
pages = {1--4},
organization = {IEEE},
abstract = {We propose trainable filters, which we call COSFIRE (Combination Of Shifted FIlter REsponses), and use to detect vascular bifurcations in retinal images. We configure a COSFIRE filter to be selective for a bifurcation that is specified by a user in a single-step training phase. The automatic configuration comprises the selection of channels of a bank of Gabor filters and the determination of certain blur and shift parameters. A COSFIRE filter response is computed as the geometric mean of the blurred and shifted responses of the selected Gabor filters. The proposed filters share similar properties with some shape-selective neurons in visual cortex. With only five filters we achieve a recall of 98.57% at a precision of 95.37% on the 40 binary retinal images (from DRIVE), containing more than 5000 bifurcations.},
keywords = {brain-inspired, medical image analysis, trainable filters},
pubstate = {published},
tppubtype = {inproceedings}
}
We propose trainable filters, which we call COSFIRE (Combination Of Shifted FIlter REsponses), and use to detect vascular bifurcations in retinal images. We configure a COSFIRE filter to be selective for a bifurcation that is specified by a user in a single-step training phase. The automatic configuration comprises the selection of channels of a bank of Gabor filters and the determination of certain blur and shift parameters. A COSFIRE filter response is computed as the geometric mean of the blurred and shifted responses of the selected Gabor filters. The proposed filters share similar properties with some shape-selective neurons in visual cortex. With only five filters we achieve a recall of 98.57% at a precision of 95.37% on the 40 binary retinal images (from DRIVE), containing more than 5000 bifurcations.
7.
Azzopardi, George; Petkov, Nicolai
Detection of retinal vascular bifurcations by rotation-and scale-invariant COSFIRE filters Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: brain-inspired, medical image analysis, trainable filters
@inproceedings{azzopardi2012detection,
title = {Detection of retinal vascular bifurcations by rotation-and scale-invariant COSFIRE filters},
author = {George Azzopardi and Nicolai Petkov},
doi = {10.1007/978-3-642-31298-4_43},
year = {2012},
date = {2012-01-01},
urldate = {2012-01-01},
booktitle = {International Conference Image Analysis and Recognition},
pages = {363--371},
organization = {Springer, Berlin, Heidelberg},
abstract = {The analysis of the vascular tree in retinal fundus images is important for identifying risks of various cardiovascular diseases. We propose trainable COSFIRE (Combination Of Shifted FIlter REsponses) filters to detect vascular bifurcations. A COSFIRE filter is automatically configured to be selective for a bifurcation that is specified by a user from a training image. The configuration selects given channels of a bank of Gabor filters and determines certain blur and shift parameters. A COSFIRE filter response is computed as the product of the blurred and shifted responses of the selected Gabor filters. The filter responds to bifurcations that are similar to the one used for its configuration. The proposed filters achieve invariance to rotation and scale. With only five COSFIRE filters we achieve a recall of 98.77% at a precision of 95.32% on a data set of 40 binary fundus images (from DRIVE), containing more than 5000 bifurcations.},
keywords = {brain-inspired, medical image analysis, trainable filters},
pubstate = {published},
tppubtype = {inproceedings}
}
The analysis of the vascular tree in retinal fundus images is important for identifying risks of various cardiovascular diseases. We propose trainable COSFIRE (Combination Of Shifted FIlter REsponses) filters to detect vascular bifurcations. A COSFIRE filter is automatically configured to be selective for a bifurcation that is specified by a user from a training image. The configuration selects given channels of a bank of Gabor filters and determines certain blur and shift parameters. A COSFIRE filter response is computed as the product of the blurred and shifted responses of the selected Gabor filters. The filter responds to bifurcations that are similar to the one used for its configuration. The proposed filters achieve invariance to rotation and scale. With only five COSFIRE filters we achieve a recall of 98.77% at a precision of 95.32% on a data set of 40 binary fundus images (from DRIVE), containing more than 5000 bifurcations.
2011
8.
Azzopardi, George; Petkov, Nicolai
Detection of retinal vascular bifurcations by trainable V4-like filters Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: brain-inspired, medical image analysis, trainable filters
@inproceedings{azzopardi2011detection,
title = {Detection of retinal vascular bifurcations by trainable V4-like filters},
author = {George Azzopardi and Nicolai Petkov},
doi = {10.1007/978-3-642-23672-3_55},
year = {2011},
date = {2011-01-01},
urldate = {2011-01-01},
booktitle = {International Conference on Computer Analysis of Images and Patterns},
pages = {451--459},
organization = {Springer, Berlin, Heidelberg},
abstract = {The detection of vascular bifurcations in retinal fundus images is important for finding signs of various cardiovascular diseases. We propose a novel method to detect such bifurcations. Our method is implemented in trainable filters that mimic the properties of shape-selective neurons in area V4 of visual cortex. Such a filter is configured by combining given channels of a bank of Gabor filters in an AND-gate-like operation. Their selection is determined by the automatic analysis of a bifurcation feature that is specified by the user from a training image. Consequently, the filter responds to the same and similar bifurcations. With only 25 filters we achieved a correct detection rate of 98.52% at a precision rate of 95.19% on a set of 40 binary fundus images, containing more than 5000 bifurcations. In principle, all vascular bifurcations can be detected if a sufficient number of filters are configured and used.},
keywords = {brain-inspired, medical image analysis, trainable filters},
pubstate = {published},
tppubtype = {inproceedings}
}
The detection of vascular bifurcations in retinal fundus images is important for finding signs of various cardiovascular diseases. We propose a novel method to detect such bifurcations. Our method is implemented in trainable filters that mimic the properties of shape-selective neurons in area V4 of visual cortex. Such a filter is configured by combining given channels of a bank of Gabor filters in an AND-gate-like operation. Their selection is determined by the automatic analysis of a bifurcation feature that is specified by the user from a training image. Consequently, the filter responds to the same and similar bifurcations. With only 25 filters we achieved a correct detection rate of 98.52% at a precision rate of 95.19% on a set of 40 binary fundus images, containing more than 5000 bifurcations. In principle, all vascular bifurcations can be detected if a sufficient number of filters are configured and used.