1,061 research outputs found
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
Pathological Evidence Exploration in Deep Retinal Image Diagnosis
Though deep learning has shown successful performance in classifying the
label and severity stage of certain disease, most of them give few evidence on
how to make prediction. Here, we propose to exploit the interpretability of
deep learning application in medical diagnosis. Inspired by Koch's Postulates,
a well-known strategy in medical research to identify the property of pathogen,
we define a pathological descriptor that can be extracted from the activated
neurons of a diabetic retinopathy detector. To visualize the symptom and
feature encoded in this descriptor, we propose a GAN based method to synthesize
pathological retinal image given the descriptor and a binary vessel
segmentation. Besides, with this descriptor, we can arbitrarily manipulate the
position and quantity of lesions. As verified by a panel of 5 licensed
ophthalmologists, our synthesized images carry the symptoms that are directly
related to diabetic retinopathy diagnosis. The panel survey also shows that our
generated images is both qualitatively and quantitatively superior to existing
methods.Comment: to appear in AAAI (2019). The first two authors contributed equally
to the paper. Corresponding Author: Feng L
Retinal Vessel Segmentation Using the 2-D Morlet Wavelet and Supervised Classification
We present a method for automated segmentation of the vasculature in retinal
images. The method produces segmentations by classifying each image pixel as
vessel or non-vessel, based on the pixel's feature vector. Feature vectors are
composed of the pixel's intensity and continuous two-dimensional Morlet wavelet
transform responses taken at multiple scales. The Morlet wavelet is capable of
tuning to specific frequencies, thus allowing noise filtering and vessel
enhancement in a single step. We use a Bayesian classifier with
class-conditional probability density functions (likelihoods) described as
Gaussian mixtures, yielding a fast classification, while being able to model
complex decision surfaces and compare its performance with the linear minimum
squared error classifier. The probability distributions are estimated based on
a training set of labeled pixels obtained from manual segmentations. The
method's performance is evaluated on publicly available DRIVE and STARE
databases of manually labeled non-mydriatic images. On the DRIVE database, it
achieves an area under the receiver operating characteristic (ROC) curve of
0.9598, being slightly superior than that presented by the method of Staal et
al.Comment: 9 pages, 7 figures and 1 table. Accepted for publication in IEEE
Trans Med Imag; added copyright notic
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