2023
Aswath, Anusha; Alsahaf, Ahmad; Westenbrink, B. Daan; Giepmans, Ben N. G.; Azzopardi, George
COFI - Coarse-semantic to fine-instance unsupervised mitochondria segmentation in EM Inproceedings
Links | BibTeX | Altmetric | Tags: brain-inspired, contour detection, convnets, deep learning, segmentation
@inproceedings{Anusha2023,
title = {COFI - Coarse-semantic to fine-instance unsupervised mitochondria segmentation in EM},
author = {Anusha Aswath and Ahmad Alsahaf and B. Daan Westenbrink and Ben N. G. Giepmans and George Azzopardi
},
doi = {https://doi.org/10.1007/978-3-031-44240-7_9},
year = {2023},
date = {2023-09-20},
urldate = {2023-07-01},
booktitle = {Computer Analysis of Images and Patterns. CAIP 2023. Lecture Notes in Computer Science},
volume = {14185},
publisher = {Springer},
keywords = {brain-inspired, contour detection, convnets, deep learning, segmentation},
pubstate = {published},
tppubtype = {inproceedings}
}
2019
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}
}
2015
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}
}
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}
}