Retinal vessel extraction using Lattice Neural Networks with dendritic processing

Vega, Roberto; Sánchez-Ante, Gildardo; Falcón-Morales, Luis E.; Sossa, Humberto; Guevara, Elizabeth

doi:10.1016/j.compbiomed.2014.12.016

Cited by 118 publications

(44 citation statements)

References 35 publications

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“…The vessel mask is obtained by thresholding the sigmoid output. A further improvement is introduced in [55], where intensitybased and moment-invariant features are used to segment the retinal vasculature through Lattice Neural Network with Dendritic processing (LNND). As a matter of fact, LNND architecture does not require to set the number of hidden layers in the network, allowing for a simple network training and consequently for a reduction of the computational cost.…”

Section: B Supervisedmentioning

confidence: 99%

“…V-A) Oliveira et al [22] 2011 Liver CT Goceri et al [23] 2017 Liver MRI Bruyninckx et al [24] 2010 Liver CT Bruyninckx et al [25] 2009 Lung CT Asad et al [26] 2017 Retina CFP Mapayi et al [27] 2015 Retina CFP Sreejini et al [28] 2015 Retina CFP Cinsdikici et al [29] 2009 Retina CFP Al-Rawi et al [30] 2007 Retina CFP Hanaoka et al [31] 2015 Brain MRA Supervised machine learning Sironi et al [32] 2014 Brain Microscopy (Sec. V-B) Merkow et al [33] 2016 Cardiovascular and Lung CT and MRI Sankaran et al [34] 2016 Coronary CTA Schaap et al [35] 2011 Coronary CTA Zheng et al [36] 2011 Coronary CT Nekovei et al [37] 1995 Coronary CT Smistad et al [38] 2016 Femoral region, Carotid US Chu et al [39] 2016 Liver X-ray fluoroscopic Orlando et al [40] 2017 Retina CFP Dasgupta et al [41] 2017 Retina CFP Mo et al [42] 2017 Retina CFP Lahiri et al [43] 2017 Retina CFP Annunziata et al [44] 2016 Retina Microscopy Fu et al [45] 2016 Retina CFP Luo et al [46] 2016 Retina CFP Liskowski et al [47] 2016 Retina CFP Li et al [48] 2016 Retina CFP Javidi et al [49] 2016 Retina CFP Maninis et al [50] 2016 Retina CFP Prentasvic et al [51] 2016 Retina CT Wu et al [52] 2016 Retina CFP Annunziata et al [53] 2015 Retina Microscopy Annunziata et al [54] 2015 Retina Microscopy Vega et al [55] 2015 Retina CFP Wang et al [56] 2015 Retina CFP Fraz et al [57] 2014 Retina CFP Ganin et al [58] 2014 Retina CFP...…”

Section: Introductionmentioning

confidence: 99%

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Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics

Moccia

Momi

Hadji

et al. 2018

Computer Methods and Programs in Biomedicine

453

263

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Abstract-Background: Blood vessel segmentation is a topic of high interest in medical image analysis since the analysis of vessels is crucial for diagnosis, treatment planning and execution, and evaluation of clinical outcomes in different fields, including laryngology, neurosurgery and ophthalmology. Automatic or semiautomatic vessel segmentation can support clinicians in performing these tasks. Different medical imaging techniques are currently used in clinical practice and an appropriate choice of the segmentation algorithm is mandatory to deal with the adopted imaging technique characteristics (e.g. resolution, noise and vessel contrast).Objective: This paper aims at reviewing the most recent and innovative blood vessel segmentation algorithms. Among the algorithms and approaches considered, we deeply investigated the most novel blood vessel segmentation including machine learning, deformable model, and tracking-based approaches.Method: This paper analyzes more than 100 articles focused on blood vessel segmentation methods. For each analyzed approach, summary tables are presented reporting imaging technique used, anatomical region and performance measures employed. Benefits and disadvantages of each method are highlighted.Discussion: Despite the constant progress and efforts addressed in the field, several issues still need to be overcome. A relevant limitation consists in the segmentation of pathological vessels. Unfortunately, not consistent research effort has been addressed to this issue yet. Research is needed since some of the main assumptions made for healthy vessels (such as linearity and circular cross-section) do not hold in pathological tissues, which on the other hand require new vessel model formulations. Moreover, image intensity drops, noise and low contrast still represent an important obstacle for the achievement of a high-quality enhancement. This is particularly true for optical imaging, where the image quality is usually lower in terms of noise and contrast with respect to magnetic resonance and computer tomography angiography. Conclusion:No single segmentation approach is suitable for all the different anatomical region or imaging modalities, thus the primary goal of this review was to provide an up to date source of information about the state of the art of the vessel segmentation algorithms so that the most suitable methods can be chosen according to the specific task.

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Section: B Supervisedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics

Moccia

Momi

Hadji

et al. 2018

Computer Methods and Programs in Biomedicine

453

263

View full text Add to dashboard Cite

show abstract

“…This methodology showed average ACC 0.9503, SEN 0.697, and SPE 0.983 for DRIVE database, respectively. Vega, Sanchez‐Ante, Falcon‐Morales, Sossa, and Guevara () proposed a methodology which was based on lattice neural network with dendritic processing (LNNDP). The methodology consisted of six successive steps.…”

Section: Supervised Retinal Vessels Segmentation Methodsmentioning

confidence: 99%

Automated techniques for blood vessels segmentation through fundus retinal images: A review

Akbar

Sharif

Akram

et al. 2019

Microscopy Res & Technique

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Retina is the interior part of human's eye, has a vital role in vision. The digital image captured by fundus camera is very useful to analyze the abnormalities in retina especially in retinal blood vessels. To get information of blood vessels through fundus retinal image, a precise and accurate vessels segmentation image is required. This segmented blood vessel image is most beneficial to detect retinal diseases. Many automated techniques are widely used for retinal vessels segmentation which is a primary element of computerized diagnostic systems for retinal diseases. The automatic vessels segmentation may lead to more challenging task in the presence of lesions and abnormalities. This paper briefly describes the various publicly available retinal image databases and various machine learning techniques. State of the art exhibited that researchers have proposed several vessel segmentation methods based on supervised and supervised techniques and evaluated their results mostly on publicly datasets such as digital retinal images for vessel extraction and structured analysis of the retina. A comprehensive review of existing supervised and unsupervised vessel segmentation techniques or algorithms is presented which describes the philosophy of each algorithm. This review will be useful for readers in their future research. K E Y W O R D S retinal blood vessels, retinal diseases, retinal image databases, supervised techniques, unsupervised techniques

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“…It can be useful to reduce computational complexity of the algorithm. In Vega et al a lattice neural network with dendritic processing is used for retinal blood vessel extraction. This process includes preprocessing, feature computation, classification, and postprocessing.…”

Section: Introductionmentioning

confidence: 99%

Retinal blood vessel segmentation for macula detachment surgery monitoring instruments

Hajabdollahi

Karimi

Soroushmehr

et al. 2018

Circuit Theory & Apps

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In recent years image processing has improved detection and diagnosis in medical application. Image processing applications are now embedded in medical instruments such as MRI and CT. In the case of retinopathy, fast extraction of blood vessels can allow the physician to view injury regions during surgery. Macula detachment surgeries, or computer-aided intraocular surgeries, require precise and real-time knowledge of the vasculature during the operation. Use of artificial neural network has produced good results in image processing applications, but its implementation may not be suitable for real-time applications in small, embedded hardware. Because of error resiliency of the neural network, its structure can be pruned and simplified. In this paper an efficient hardware implementation of neural network for retinal vessel segmentation is proposed. We simplify the neural network structure in such a way that the accuracy of the results is not altered significantly. Simulation results and FPGA implementation show that our proposed network has low complexity and can be applied for segmentation of retinal vessels with acceptable accuracy. This makes the proposed method a good candidate to be implemented in any device such as a binocular indirect ophthalmoscope. KEYWORDS artificial neural networks (ANN), error compensation, hardware implementation, retinal vessel segmentation, retinopathy

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Retinal vessel extraction using Lattice Neural Networks with dendritic processing

Cited by 118 publications

References 35 publications

Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics

Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics

Automated techniques for blood vessels segmentation through fundus retinal images: A review

Retinal blood vessel segmentation for macula detachment surgery monitoring instruments

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