55 research outputs found
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
Development of retinal blood vessel segmentation methodology using wavelet transforms for assessment of diabetic retinopathy
Automated image processing has the potential to assist in the early detection of diabetes, by detecting changes in blood vessel diameter and patterns in the retina. This paper describes the development of segmentation methodology in the processing of retinal blood vessel images obtained using non-mydriatic colour photography. The methods used include wavelet analysis, supervised classifier probabilities and adaptive threshold procedures, as well as morphology-based techniques. We show highly accurate identification of blood vessels for the purpose of studying changes in the vessel network that can be utilized for detecting blood vessel diameter changes associated with the pathophysiology of diabetes. In conjunction with suitable feature extraction and automated classification methods, our segmentation method could form the basis of a quick and accurate test for diabetic retinopathy, which would have huge benefits in terms of improved access to screening people for risk or presence of diabetes
Comparison of various methods to delineate blood vessels in retinal images
The blood vessels in the human retina are easily visualisable via digital fundus photography and provide an excellent window to the health of a patient affected by diseases of blood circulation such as diabetes. Diabetic retinopathy is identifiable through lesions of the vessels such as narrowing of the arteriole walls, beading of venules into sausage like structures and new vessel growth as an attempt to reperfuse ischaemic regions. Automated quantification of these lesions would be beneficial to diabetes research and to clinical practice, particularly for eye-screening programmes for the detection of eye-disease amongst diabetic persons
Curvelet Transform based Retinal Image Analysis
Edge detection is an important assignment in image processing, as it is used as a primary tool for pattern recognition, image segmentation and scene analysis. An edge detector is a high-pass filter that can be applied for extracting the edge points within an image. Edge detection in the spatial domain is accomplished through convolution with a set of directional derivative masks in this domain. On the other hand, working in the frequency domain has many advantages, starting from introducing an alternative description to the spatial representation and providing more efficient and faster computational schemes with less sensitivity to noise through high filtering, de-noising and compression algorithms. Fourier transforms, wavelet and curvelet transform are among the most widely used frequency-domain edge detection from satellite images. However, the Fourier transform is global and poorly adapted to local singularities. Some of these draw backs are solved by the wavelet transforms especially for singularities detection and computation. In this paper, the relatively new multi-resolution technique, curvelet transform, is assessed and introduced to overcome the wavelet transform limitation in directionality and scaling. In this research paper, the assessment of second generation curvelet transforms as an edge detection tool will be introduced and compared with first generation cuevelet transform.DOI:http://dx.doi.org/10.11591/ijece.v3i3.245
Automatic Optic Disc Center and Boundary Detection in Color Fundus Images
Accurately detection of retinal landmarks, like optic disc, is an important step in the computer aided diagnosis frameworks. This paper presents an efficient method for automatic detection of the optic disc’s center and estimating its boundary. The center and initial diameter of optic disc are estimated by employing an ANN classifier. The ANN classifier employs visual features of vessels and their background tissue to classify extracted main vessels of retina into two groups: the vessels inside the optic disc and the vessels outside the optic disc. To this end, average intensity values and standard deviation of RGB channels, average width and orientation of the vessels and density of the detected vessels their junction points in a window around each central pixel of main vessels are employed. The center of detected vessels, which are belonging to the inside of the optic disc region, is adopted as the optic disc center and the average length of them in vertical and horizontal directions is selected as initial diameter of the optic disc circle. Then exact boundary of the optic disc is extracted using radial analysis of the initial circle. The performance of the proposed method is measured on the publicly available DRIONS, DRIVE and DIARETDB1 databases and compared with several state-of-the-art methods. The proposed method shows much higher mean overlap (70.6%) in the same range of detection accuracy (97.7%) and center distance (12 pixels). The average sensitivity and predictive values of the proposed optic disc detection method are 80.3% and 84.6% respectively
Invariant Scattering Transform for Medical Imaging
Over the years, the Invariant Scattering Transform (IST) technique has become
popular for medical image analysis, including using wavelet transform
computation using Convolutional Neural Networks (CNN) to capture patterns'
scale and orientation in the input signal. IST aims to be invariant to
transformations that are common in medical images, such as translation,
rotation, scaling, and deformation, used to improve the performance in medical
imaging applications such as segmentation, classification, and registration,
which can be integrated into machine learning algorithms for disease detection,
diagnosis, and treatment planning. Additionally, combining IST with deep
learning approaches has the potential to leverage their strengths and enhance
medical image analysis outcomes. This study provides an overview of IST in
medical imaging by considering the types of IST, their application,
limitations, and potential scopes for future researchers and practitioners
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