26,818 research outputs found
Unsupervised delineation of the vessel tree in retinal fundus images
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.
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.peer-reviewe
Delineation of line patterns in images using B-COSFIRE filters
Delineation of line patterns in images is a basic step required in various
applications such as blood vessel detection in medical images, segmentation of
rivers or roads in aerial images, detection of cracks in walls or pavements,
etc. In this paper we present trainable B-COSFIRE filters, which are a model of
some neurons in area V1 of the primary visual cortex, and apply it to the
delineation of line patterns in different kinds of images. B-COSFIRE filters
are trainable as their selectivity is determined in an automatic configuration
process given a prototype pattern of interest. They are configurable to detect
any preferred line structure (e.g. segments, corners, cross-overs, etc.), so
usable for automatic data representation learning. We carried out experiments
on two data sets, namely a line-network data set from INRIA and a data set of
retinal fundus images named IOSTAR. The results that we achieved confirm the
robustness of the proposed approach and its effectiveness in the delineation of
line structures in different kinds of images.Comment: International Work Conference on Bioinspired Intelligence, July
10-13, 201
Trainable COSFIRE filters for vessel delineation with application to retinal images
Retinal imaging provides a non-invasive opportunity for the diagnosis of several medical pathologies. The automatic segmentation of the vessel tree is an important pre-processing step which facilitates subsequent automatic processes that contribute to such diagnosis. We introduce a novel method for the automatic segmentation of vessel trees in retinal fundus images. We propose a filter that selectively responds to vessels and that we call B-COSFIRE with B standing for bar which is an abstraction for a vessel. It is based on the existing COSFIRE (Combination Of Shifted Filter Responses) approach. A B-COSFIRE filter achieves orientation selectivity by computing the weighted geometric mean of the output of a pool of Difference-of-Gaussians filters, whose supports are aligned in a collinear manner. It achieves rotation invariance efficiently by simple shifting operations. The proposed filter is versatile as its selectivity is determined from any given vessel-like prototype pattern in an automatic configuration process. We configure two B-COSFIRE filters, namely symmetric and asymmetric, that are selective for bars and bar-endings, respectively. We achieve vessel segmentation by summing up the responses of the two rotation-invariant B-COSFIRE filters followed by thresholding. The results that we achieve on three publicly available data sets (DRIVE: Se = 0.7655, Sp = 0.9704; STARE: Se = 0.7716, Sp = 0.9701; CHASE_DB1: Se = 0.7585, Sp = 0.9587) are higher than many of the state-of-the-art methods. The proposed segmentation approach is also very efficient with a time complexity that is significantly lower than existing methods.peer-reviewe
Enhanced delineation of degradation in aortic walls through OCT
Degradation of the wall of human ascending thoracic aorta has been assessed through Optical Coherence Tomography (OCT). OCT images of the media layer of the aortic wall exhibit micro-structure degradation in case of diseased aortas from aneurysmal vessels or in aortas prone to aortic dissections. The degeneration in vessel walls appears as low-reflectivity areas due to the invasive appearance of acidic polysaccharides and mucopolysaccharides within a typical ordered microstructure of parallel lamellae of smooth muscle cells, elastin and collagen fibers. An OCT indicator of wall degradation can be generated upon the spatial quantification of the extension of degraded areas in a similar way as conventional histopathology. This proposed OCT marker offers a real-time clinical insight of the vessel status to help cardiovascular surgeons in vessel repair interventions. However, the delineation of degraded areas on the B-scan image from OCT is sometimes difficult due to presence of speckle noise, variable SNR conditions on the measurement process, etc. Degraded areas could be outlined by basic thresholding techniques taking advantage of disorders evidences in B-scan images, but this delineation is not always optimum and requires complex additional processing stages. This work proposes an optimized delineation of degraded spots in vessel walls, robust to noisy environments, based on the analysis of the second order variation of image intensity of backreflection to determine the type of local structure. Results improve the delineation of wall anomalies providing a deeper physiological perception of the vessel wall conditions. Achievements could be also transferred to other clinical scenarios: carotid arteries, aorto-iliac or ilio-femoral sections, intracranial, etc.This work has been supported by the Spanish Government through the CYCIT projects DA2TOI (FIS2010-19860) and FOS4 (TEC2013-47264-C2-1-R)
Detection of curved lines with B-COSFIRE filters: A case study on crack delineation
The detection of curvilinear structures is an important step for various
computer vision applications, ranging from medical image analysis for
segmentation of blood vessels, to remote sensing for the identification of
roads and rivers, and to biometrics and robotics, among others. %The visual
system of the brain has remarkable abilities to detect curvilinear structures
in noisy images. This is a nontrivial task especially for the detection of thin
or incomplete curvilinear structures surrounded with noise. We propose a
general purpose curvilinear structure detector that uses the brain-inspired
trainable B-COSFIRE filters. It consists of four main steps, namely nonlinear
filtering with B-COSFIRE, thinning with non-maximum suppression, hysteresis
thresholding and morphological closing. We demonstrate its effectiveness on a
data set of noisy images with cracked pavements, where we achieve
state-of-the-art results (F-measure=0.865). The proposed method can be employed
in any computer vision methodology that requires the delineation of curvilinear
and elongated structures.Comment: Accepted at Computer Analysis of Images and Patterns (CAIP) 201
Multiscale blood vessel delineation using B-COSFIRE filters
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.peer-reviewe
A method for delineation of bone surfaces in photoacoustic computed tomography of the finger
Photoacoustic imaging of interphalangeal peripheral joints is of interest in
the context of using the synovial membrane as a surrogate marker of rheumatoid
arthritis. Previous work has shown that ultrasound produced by absorption of
light at the epidermis reflects on the bone surfaces within the finger. When
the reflected signals are backprojected in the region of interest, artifacts
are produced, confounding interpretation of the images. In this work, we
present an approach where the photoacoustic signals known to originate from the
epidermis, are treated as virtual ultrasound transmitters, and a separate
reconstruction is performed as in ultrasound reflection imaging. This allows us
to identify the bone surfaces. Further, the identification of the joint space
is important as this provides a landmark to localize a region-of-interest in
seeking the inflamed synovial membrane. The ability to delineate bone surfaces
allows us not only to identify the artifacts, but also to identify the
interphalangeal joint space without recourse to new US hardware or a new
measurement. We test the approach on phantoms and on a healthy human finger
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