219 research outputs found
Retinal network characterization through fundus image processing: Significant point identification on vessel centerline
[EN] This paper describes a new approach for significant point identification on vessel centerline. Significant points such as bifurcations and crossovers are able to define and characterize the retinal vascular network. In particular, hit-or-miss transformation is used to detect terminal, bifurcation and simple crossing points but a post-processing stage is needed to identify complex intersections. This stage focuses on the idea that the intersection of two vessels creates a sort of close loop formed by the vessels and this effect can be used to differentiate a bifurcation from a crossover. Experimental results show quantitative improvements by increasing the number of true positives and reducing the false positives and negatives in the significant point detection when the proposed method is compared with another state-of-the-art work. A sensitivity equal to 1 and a predictive positive value of 0.908 was achieved in the analyzed cases. Hit-or-miss transformation must be applied on a binary skeleton image. Therefore, a method to extract the vessel skeleton in a direct way is also proposed. Although the identification of the significant points of the retinal tree can be useful by itself for multiple applications such as biometrics and image registration, this paper presents an algorithm that makes use of the significant points to measure the bifurcation angles of the retinal network which can be related to cardiovascular risk determination.This work was supported by the Ministerio de Economia y Conipetitividad of Spain, Project ACRIMA (TIN2013-46751-R). The authors would like to thank people who provide the public databases used in this work (DRIVE, STARE and VARIA).Morales, S.; Naranjo Ornedo, V.; Angulo, J.; Legaz-Aparicio, A.; Verdu-Monedero, R. (2017). Retinal network characterization through fundus image processing: Significant point identification on vessel centerline. Signal Processing: Image Communication. 59:50-64. https://doi.org/10.1016/j.image.2017.03.013S50645
Tracking and diameter estimation of retinal vessels using Gaussian process and Radon transform
Extraction of blood vessels in retinal images is an important step for computer-aided diagnosis of
ophthalmic pathologies. We propose an approach for blood vessel tracking and diameter estimation. We hypothesize
that the curvature and the diameter of blood vessels are Gaussian processes (GPs). Local Radon transform,
which is robust against noise, is subsequently used to compute the features and train the GPs. By learning
the kernelized covariance matrix from training data, vessel direction and its diameter are estimated. In order to
detect bifurcations, multiple GPs are used and the difference between their corresponding predicted directions is
quantified. The combination of Radon features and GP results in a good performance in the presence of noise.
The proposed method successfully deals with typically difficult cases such as bifurcations and central arterial
reflex, and also tracks thin vessels with high accuracy. Experiments are conducted on the publicly available
DRIVE, STARE, CHASEDB1, and high-resolution fundus databases evaluating sensitivity, specificity, and
Matthew’s correlation coefficient (MCC). Experimental results on these datasets show that the proposed method
reaches an average sensitivity of 75.67%, specificity of 97.46%, and MCC of 72.18% which is comparable to the
state-of-the-art
Biometrics
Biometrics uses methods for unique recognition of humans based upon one or more intrinsic physical or behavioral traits. In computer science, particularly, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance. The book consists of 13 chapters, each focusing on a certain aspect of the problem. The book chapters are divided into three sections: physical biometrics, behavioral biometrics and medical biometrics. The key objective of the book is to provide comprehensive reference and text on human authentication and people identity verification from both physiological, behavioural and other points of view. It aims to publish new insights into current innovations in computer systems and technology for biometrics development and its applications. The book was reviewed by the editor Dr. Jucheng Yang, and many of the guest editors, such as Dr. Girija Chetty, Dr. Norman Poh, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park, Dr. Sook Yoon and so on, who also made a significant contribution to the book
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
REVIEW ALGORITMA SEGMENTASI PEMBULUH DARAH PADA CITRA FUNDUS RETINA MATA UNTUK MEMBANTU DIAGNOSIS DIABETIC RETINOPATHY
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Diabetic retinopathy merupakan salah satu penyakit retina mata yang diakibatkan oleh komplikasi dari penyakit diabetes mellitus. Salah satu faktor yang penting dalam pendeteksian diabetic retinopathy adalah pembuluh darah pada retina mata. Pendeteksian pembuluh darah pada retina mata merupakan langkah awal dalam proses diagnosis penyakit diabetic retinopathy. Oleh karena itu suatu algoritma untuk segmentasi pembuluh darah yang tepat dan baik sangatlah diperlukan. Makalah ini membahas dan menganalisa berbagai algoritma dan teknik segmentasi pembuluh darah pada citra fundus retina mata. Makalah ini diharapkan dapat digunakan sebagai bahan referensi dalam memilih metode yang akan digunakan peneliti dalam melakukan segmentasi pembuluh darah yang sangat berguna dalam proses diagnosis penyakit diabetic retinopathy.</p
Generalizable automated pixel-level structural segmentation of medical and biological data
Over the years, the rapid expansion in imaging techniques and equipments has driven the demand for more automation in handling large medical and biological data sets. A wealth of approaches have been suggested as optimal solutions for their respective imaging types. These
solutions span various image resolutions, modalities and contrast (staining) mechanisms. Few approaches generalise well across multiple image types, contrasts or resolution.
This thesis proposes an automated pixel-level framework that addresses 2D, 2D+t and 3D
structural segmentation in a more generalizable manner, yet has enough adaptability to address
a number of specific image modalities, spanning retinal funduscopy, sequential
fluorescein angiography and two-photon microscopy.
The pixel-level segmentation scheme involves: i ) constructing a phase-invariant orientation field of the local spatial neighbourhood; ii ) combining local feature maps with intensity-based
measures in a structural patch context; iii ) using a complex supervised learning process to interpret the combination of all the elements in the patch in order to reach a classification decision. This has the advantage of transferability from retinal blood vessels in 2D to neural structures in 3D.
To process the temporal components in non-standard 2D+t retinal angiography sequences, we first introduce a co-registration procedure: at the pairwise level, we combine projective
RANSAC with a quadratic homography transformation to map the coordinate systems between any two frames. At the joint level, we construct a hierarchical approach in order for each individual frame to be registered to the global reference intra- and inter- sequence(s). We then take a non-training approach that searches in both the spatial neighbourhood of each pixel and the filter output across varying scales to locate and link microvascular centrelines to (sub-)
pixel accuracy. In essence, this \link while extract" piece-wise segmentation approach combines the local phase-invariant orientation field information with additional local phase estimates to obtain a soft classification of the centreline (sub-) pixel locations.
Unlike retinal segmentation problems where vasculature is the main focus, 3D neural segmentation requires additional
exibility, allowing a variety of structures of anatomical importance yet with different geometric properties to be differentiated both from the background and against other structures. Notably, cellular structures, such as Purkinje cells, neural dendrites and interneurons, all display certain elongation along their medial axes, yet each class has a characteristic shape captured by an orientation field that distinguishes it from other structures. To take this
into consideration, we introduce a 5D orientation mapping to capture these orientation properties.
This mapping is incorporated into the local feature map description prior to a learning
machine. Extensive performance evaluations and validation of each of the techniques presented in this thesis is carried out. For retinal fundus images, we compute Receiver Operating Characteristic (ROC) curves on existing public databases (DRIVE & STARE) to assess and compare our algorithms with other benchmark methods. For 2D+t retinal angiography sequences, we compute the error metrics ("Centreline Error") of our scheme with other benchmark methods.
For microscopic cortical data stacks, we present segmentation results on both surrogate data with known ground-truth and experimental rat cerebellar cortex two-photon microscopic tissue stacks.Open Acces
REVIEW ALGORITMA SEGMENTASI PEMBULUH DARAH PADA CITRA FUNDUS RETINA MATA UNTUK MEMBANTU DIAGNOSIS DIABETIC RETINOPATHY
Diabetic retinopathy merupakan salah satu penyakit retina mata yang diakibatkan oleh komplikasi dari penyakit diabetes mellitus. Salah satu faktor yang penting dalam pendeteksian diabetic retinopathy adalah pembuluh darah pada retina mata. Pendeteksian pembuluh darah pada retina mata merupakan langkah awal dalam proses diagnosis penyakit diabetic retinopathy. Oleh karena itu suatu algoritma untuk segmentasi pembuluh darah yang tepat dan baik sangatlah diperlukan. Makalah ini membahas dan menganalisa berbagai algoritma dan teknik segmentasi pembuluh darah pada citra fundus retina mata. Makalah ini diharapkan dapat digunakan sebagai bahan referensi dalam memilih metode yang akan digunakan peneliti dalam melakukan segmentasi pembuluh darah yang sangat berguna dalam proses diagnosis penyakit diabetic retinopathy
Retinal vessel segmentation using textons
Segmenting vessels from retinal images, like segmentation in many other medical image domains, is a challenging task, as there is no unified way that can be adopted to extract the vessels accurately. However, it is the most critical stage in automatic assessment of various forms of diseases (e.g. Glaucoma, Age-related macular degeneration, diabetic retinopathy and cardiovascular diseases etc.). Our research aims to investigate retinal image segmentation approaches based on textons as they provide a compact description of texture that can be learnt from a training set. This thesis presents a brief review of those diseases and also includes their current situations, future trends and techniques used for their automatic diagnosis in routine clinical applications. The importance of retinal vessel segmentation is
particularly emphasized in such applications. An extensive review of previous work on retinal vessel segmentation and salient texture analysis methods is presented. Five automatic retinal vessel segmentation methods are proposed in this thesis. The first method focuses on addressing the problem of removing pathological anomalies (Drusen, exudates) for retinal vessel segmentation, which have been identified by other researchers as a problem and a common source of error. The results show that the modified method shows some
improvement compared to a previously published method. The second novel supervised segmentation method employs textons. We propose a new filter bank (MR11) that includes bar detectors for vascular feature extraction and other kernels to detect edges and photometric variations in the image. The k-means clustering algorithm is adopted for texton generation based on the vessel and non-vessel elements which are identified by ground truth. The third improved supervised method is developed based on the second one, in which textons are generated by k-means clustering and texton maps representing vessels are derived by back projecting pixel clusters onto hand labelled ground truth. A further step is implemented to ensure that the best combinations of textons are represented in the map and subsequently used to identify vessels in the test set. The experimental results on two benchmark datasets show that our proposed method performs well compared to other published work and the results of human experts. A further test of our system on an independent set of optical fundus images verified its consistent performance. The statistical analysis on experimental results also reveals that it is possible to train unified textons for retinal vessel segmentation. In the fourth method a novel scheme using Gabor filter bank for vessel feature extraction is proposed. The ii method is inspired by the human visual system. Machine learning is used to optimize the
Gabor filter parameters. The experimental results demonstrate that our method significantly enhances the true positive rate while maintaining a level of specificity that is comparable with other approaches. Finally, we proposed a new unsupervised texton based retinal vessel
segmentation method using derivative of SIFT and multi-scale Gabor filers. The lack of sufficient quantities of hand labelled ground truth and the high level of variability in ground truth labels amongst experts provides the motivation for this approach. The evaluation results
reveal that our unsupervised segmentation method is comparable with the best other supervised methods and other best state of the art methods
Tubular Curvature Filter: Implicit Pointwise Curvature Calculation Method for Tubular Objects
Curvature estimation methods are important as they capture salient features
for various applications in image processing, especially within medical domains
where tortuosity of vascular structures is of significant interest. Existing
methods based on centerline or skeleton curvature fail to capture curvature
gradients across a rotating tubular structure. This paper presents a Tubular
Curvature Filter method that locally calculates the acceleration of bundles of
curves that traverse along the tubular object parallel to the centerline. This
is achieved by examining the directional rate of change in the eigenvectors of
the Hessian matrix of a tubular intensity function in space. This method
implicitly calculates the local tubular curvature without the need to
explicitly segment the tubular object. Experimental results demonstrate that
the Tubular Curvature Filter method provides accurate estimates of local
curvature at any point inside tubular structures.Comment: 11 pages, 5 figure
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