101 research outputs found

    Segmentation and Characterization of Small Retinal Vessels in Fundus Images Using the Tensor Voting Approach

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    RÉSUMÉ La rĂ©tine permet de visualiser facilement une partie du rĂ©seau vasculaire humain. Elle offre ainsi un aperçu direct sur le dĂ©veloppement et le rĂ©sultat de certaines maladies liĂ©es au rĂ©seau vasculaire dans son entier. Chaque complication visible sur la rĂ©tine peut avoir un impact sur la capacitĂ© visuelle du patient. Les plus petits vaisseaux sanguins sont parmi les premiĂšres structures anatomiques affectĂ©es par la progression d’une maladie, ĂȘtre capable de les analyser est donc crucial. Les changements dans l’état, l’aspect, la morphologie, la fonctionnalitĂ©, ou mĂȘme la croissance des petits vaisseaux indiquent la gravitĂ© des maladies. Le diabĂšte est une maladie mĂ©tabolique qui affecte des millions de personnes autour du monde. Cette maladie affecte le taux de glucose dans le sang et cause des changements pathologiques dans diffĂ©rents organes du corps humain. La rĂ©tinopathie diabĂ©tique dĂ©crit l’en- semble des conditions et consĂ©quences du diabĂšte au niveau de la rĂ©tine. Les petits vaisseaux jouent un rĂŽle dans le dĂ©clenchement, le dĂ©veloppement et les consĂ©quences de la rĂ©tinopa- thie. Dans les derniĂšres Ă©tapes de cette maladie, la croissance des nouveaux petits vaisseaux, appelĂ©e nĂ©ovascularisation, prĂ©sente un risque important de provoquer la cĂ©citĂ©. Il est donc crucial de dĂ©tecter tous les changements qui ont lieu dans les petits vaisseaux de la rĂ©tine dans le but de caractĂ©riser les vaisseaux sains et les vaisseaux anormaux. La caractĂ©risation en elle-mĂȘme peut faciliter la dĂ©tection locale d’une rĂ©tinopathie spĂ©cifique. La segmentation automatique des structures anatomiques comme le rĂ©seau vasculaire est une Ă©tape cruciale. Ces informations peuvent ĂȘtre fournies Ă  un mĂ©decin pour qu’elles soient considĂ©rĂ©es lors de son diagnostic. Dans les systĂšmes automatiques d’aide au diagnostic, le rĂŽle des petits vaisseaux est significatif. Ne pas rĂ©ussir Ă  les dĂ©tecter automatiquement peut conduire Ă  une sur-segmentation du taux de faux positifs des lĂ©sions rouges dans les Ă©tapes ultĂ©rieures. Les efforts de recherche se sont concentrĂ©s jusqu’à prĂ©sent sur la localisation prĂ©cise des vaisseaux de taille moyenne. Les modĂšles existants ont beaucoup plus de difficultĂ©s Ă  extraire les petits vaisseaux sanguins. Les modĂšles existants ne sont pas robustes Ă  la grande variance d’apparence des vaisseaux ainsi qu’à l’interfĂ©rence avec l’arriĂšre-plan. Les modĂšles de la littĂ©rature existante supposent une forme gĂ©nĂ©rale qui n’est pas suffisante pour s’adapter Ă  la largeur Ă©troite et la courbure qui caractĂ©risent les petits vaisseaux sanguins. De plus, le contraste avec l’arriĂšre-plan dans les rĂ©gions des petits vaisseaux est trĂšs faible. Les mĂ©thodes de segmentation ou de suivi produisent des rĂ©sultats fragmentĂ©s ou discontinus. Par ailleurs, la segmentation des petits vaisseaux est gĂ©nĂ©ralement faite aux dĂ©pends de l’amplification du bruit. Les modĂšles dĂ©formables sont inadĂ©quats pour segmenter les petits vaisseaux. Les forces utilisĂ©es ne sont pas assez flexibles pour compenser le faible contraste, la largeur, et vii la variance des vaisseaux. Enfin, les approches de type apprentissage machine nĂ©cessitent un entraĂźnement avec une base de donnĂ©es Ă©tiquetĂ©e. Il est trĂšs difficile d’obtenir ces bases de donnĂ©es dans le cas des petits vaisseaux. Cette thĂšse Ă©tend les travaux de recherche antĂ©rieurs en fournissant une nouvelle mĂ©- thode de segmentation des petits vaisseaux rĂ©tiniens. La dĂ©tection de ligne Ă  Ă©chelles multiples (MSLD) est une mĂ©thode rĂ©cente qui dĂ©montre une bonne performance de segmentation dans les images de la rĂ©tine, tandis que le vote tensoriel est une mĂ©thode proposĂ©e pour reconnecter les pixels. Une approche combinant un algorithme de dĂ©tection de ligne et de vote tensoriel est proposĂ©e. L’application des dĂ©tecteurs de lignes a prouvĂ© son efficacitĂ© Ă  segmenter les vais- seaux de tailles moyennes. De plus, les approches d’organisation perceptuelle comme le vote tensoriel ont dĂ©montrĂ© une meilleure robustesse en combinant les informations voisines d’une maniĂšre hiĂ©rarchique. La mĂ©thode de vote tensoriel est plus proche de la perception humain que d’autres modĂšles standards. Comme dĂ©montrĂ© dans ce manuscrit, c’est un outil pour segmenter les petits vaisseaux plus puissant que les mĂ©thodes existantes. Cette combinaison spĂ©cifique nous permet de surmonter les dĂ©fis de fragmentation Ă©prouvĂ©s par les mĂ©thodes de type modĂšle dĂ©formable au niveau des petits vaisseaux. Nous proposons Ă©galement d’utiliser un seuil adaptatif sur la rĂ©ponse de l’algorithme de dĂ©tection de ligne pour ĂȘtre plus robuste aux images non-uniformes. Nous illustrons Ă©galement comment une combinaison des deux mĂ©thodes individuelles, Ă  plusieurs Ă©chelles, est capable de reconnecter les vaisseaux sur des distances variables. Un algorithme de reconstruction des vaisseaux est Ă©galement proposĂ©. Cette derniĂšre Ă©tape est nĂ©cessaire car l’information gĂ©omĂ©trique complĂšte est requise pour pouvoir utiliser la segmentation dans un systĂšme d’aide au diagnostic. La segmentation a Ă©tĂ© validĂ©e sur une base de donnĂ©es d’images de fond d’oeil Ă  haute rĂ©solution. Cette base contient des images manifestant une rĂ©tinopathie diabĂ©tique. La seg- mentation emploie des mesures de dĂ©saccord standards et aussi des mesures basĂ©es sur la perception. En considĂ©rant juste les petits vaisseaux dans les images de la base de donnĂ©es, l’amĂ©lioration dans le taux de sensibilitĂ© que notre mĂ©thode apporte par rapport Ă  la mĂ©thode standard de dĂ©tection multi-niveaux de lignes est de 6.47%. En utilisant les mesures basĂ©es sur la perception, l’amĂ©lioration est de 7.8%. Dans une seconde partie du manuscrit, nous proposons Ă©galement une mĂ©thode pour caractĂ©riser les rĂ©tines saines ou anormales. Certaines images contiennent de la nĂ©ovascula- risation. La caractĂ©risation des vaisseaux en bonne santĂ© ou anormale constitue une Ă©tape essentielle pour le dĂ©veloppement d’un systĂšme d’aide au diagnostic. En plus des dĂ©fis que posent les petits vaisseaux sains, les nĂ©ovaisseaux dĂ©montrent eux un degrĂ© de complexitĂ© encore plus Ă©levĂ©. Ceux-ci forment en effet des rĂ©seaux de vaisseaux Ă  la morphologie com- plexe et inhabituelle, souvent minces et Ă  fortes courbures. Les travaux existants se limitent viii Ă  l’utilisation de caractĂ©ristiques de premier ordre extraites des petits vaisseaux segmentĂ©s. Notre contribution est d’utiliser le vote tensoriel pour isoler les jonctions vasculaires et d’uti- liser ces jonctions comme points d’intĂ©rĂȘts. Nous utilisons ensuite une statistique spatiale de second ordre calculĂ©e sur les jonctions pour caractĂ©riser les vaisseaux comme Ă©tant sains ou pathologiques. Notre mĂ©thode amĂ©liore la sensibilitĂ© de la caractĂ©risation de 9.09% par rapport Ă  une mĂ©thode de l’état de l’art. La mĂ©thode dĂ©veloppĂ©e s’est rĂ©vĂ©lĂ©e efficace pour la segmentation des vaisseaux rĂ©ti- niens. Des tenseurs d’ordre supĂ©rieur ainsi que la mise en Ɠuvre d’un vote par tenseur via un filtrage orientable pourraient ĂȘtre Ă©tudiĂ©s pour rĂ©duire davantage le temps d’exĂ©cution et rĂ©soudre les dĂ©fis encore prĂ©sents au niveau des jonctions vasculaires. De plus, la caractĂ©ri- sation pourrait ĂȘtre amĂ©liorĂ©e pour la dĂ©tection de la rĂ©tinopathie prolifĂ©rative en utilisant un apprentissage supervisĂ© incluant des cas de rĂ©tinopathie diabĂ©tique non prolifĂ©rative ou d’autres pathologies. Finalement, l’incorporation des mĂ©thodes proposĂ©es dans des systĂšmes d’aide au diagnostic pourrait favoriser le dĂ©pistage rĂ©gulier pour une dĂ©tection prĂ©coce des rĂ©tinopathies et d’autres pathologies oculaires dans le but de rĂ©duire la cessitĂ© au sein de la population.----------ABSTRACT As an easily accessible site for the direct observation of the circulation system, human retina can offer a unique insight into diseases development or outcome. Retinal vessels are repre- sentative of the general condition of the whole systematic circulation, and thus can act as a "window" to the status of the vascular network in the whole body. Each complication on the retina can have an adverse impact on the patient’s sight. In this direction, small vessels’ relevance is very high as they are among the first anatomical structures that get affected as diseases progress. Moreover, changes in the small vessels’ state, appearance, morphology, functionality, or even growth indicate the severity of the diseases. This thesis will focus on the retinal lesions due to diabetes, a serious metabolic disease affecting millions of people around the world. This disorder disturbs the natural blood glucose levels causing various pathophysiological changes in different systems across the human body. Diabetic retinopathy is the medical term that describes the condition when the fundus and the retinal vessels are affected by diabetes. As in other diseases, small vessels play a crucial role in the onset, the development, and the outcome of the retinopathy. More importantly, at the latest stage, new small vessels, or neovascularizations, growth constitutes a factor of significant risk for blindness. Therefore, there is a need to detect all the changes that occur in the small retinal vessels with the aim of characterizing the vessels to healthy or abnormal. The characterization, in turn, can facilitate the detection of a specific retinopathy locally, like the sight-threatening proliferative diabetic retinopathy. Segmentation techniques can automatically isolate important anatomical structures like the vessels, and provide this information to the physician to assist him in the final decision. In comprehensive systems for the automatization of DR detection, small vessels role is significant as missing them early in a CAD pipeline might lead to an increase in the false positive rate of red lesions in subsequent steps. So far, the efforts have been concentrated mostly on the accurate localization of the medium range vessels. In contrast, the existing models are weak in case of the small vessels. The required generalization to adapt an existing model does not allow the approaches to be flexible, yet robust to compensate for the increased variability in the appearance as well as the interference with the background. So far, the current template models (matched filtering, line detection, and morphological processing) assume a general shape for the vessels that is not enough to approximate the narrow, curved, characteristics of the small vessels. Additionally, due to the weak contrast in the small vessel regions, the current segmentation and the tracking methods produce fragmented or discontinued results. Alternatively, the small vessel segmentation can be accomplished at the expense of x background noise magnification, in the case of using thresholding or the image derivatives methods. Furthermore, the proposed deformable models are not able to propagate a contour to the full extent of the vasculature in order to enclose all the small vessels. The deformable model external forces are ineffective to compensate for the low contrast, the low width, the high variability in the small vessel appearance, as well as the discontinuities. Internal forces, also, are not able to impose a global shape constraint to the contour that could be able to approximate the variability in the appearance of the vasculature in different categories of vessels. Finally, machine learning approaches require the training of a classifier on a labelled set. Those sets are difficult to be obtained, especially in the case of the smallest vessels. In the case of the unsupervised methods, the user has to predefine the number of clusters and perform an effective initialization of the cluster centers in order to converge to the global minimum. This dissertation expanded the previous research work and provides a new segmentation method for the smallest retinal vessels. Multi-scale line detection (MSLD) is a recent method that demonstrates good segmentation performance in the retinal images, while tensor voting is a method first proposed for reconnecting pixels. For the first time, we combined the line detection with the tensor voting framework. The application of the line detectors has been proved an effective way to segment medium-sized vessels. Additionally, perceptual organization approaches like tensor voting, demonstrate increased robustness by combining information coming from the neighborhood in a hierarchical way. Tensor voting is closer than standard models to the way human perception functions. As we show, it is a more powerful tool to segment small vessels than the existing methods. This specific combination allows us to overcome the apparent fragmentation challenge of the template methods at the smallest vessels. Moreover, we thresholded the line detection response adaptively to compensate for non-uniform images. We also combined the two individual methods in a multi-scale scheme in order to reconnect vessels at variable distances. Finally, we reconstructed the vessels from their extracted centerlines based on pixel painting as complete geometric information is required to be able to utilize the segmentation in a CAD system. The segmentation was validated on a high-resolution fundus image database that in- cludes diabetic retinopathy images of varying stages, using standard discrepancy as well as perceptual-based measures. When only the smallest vessels are considered, the improve- ments in the sensitivity rate for the database against the standard multi-scale line detection method is 6.47%. For the perceptual-based measure, the improvement is 7.8% against the basic method. The second objective of the thesis was to implement a method for the characterization of isolated retinal areas into healthy or abnormal cases. Some of the original images, from which xi these patches are extracted, contain neovascularizations. Investigation of image features for the vessels characterization to healthy or abnormal constitutes an essential step in the direction of developing CAD system for the automatization of DR screening. Given that the amount of data will significantly increase under CAD systems, the focus on this category of vessels can facilitate the referral of sight-threatening cases to early treatment. In addition to the challenges that small healthy vessels pose, neovessels demonstrate an even higher degree of complexity as they form networks of convolved, twisted, looped thin vessels. The existing work is limited to the use of first-order characteristics extracted from the small segmented vessels that limits the study of patterns. Our contribution is in using the tensor voting framework to isolate the retinal vascular junctions and in turn using those junctions as points of interests. Second, we exploited second-order statistics computed on the junction spatial distribution to characterize the vessels as healthy or neovascularizations. In fact, the second-order spatial statistics extracted from the junction distribution are combined with widely used features to improve the characterization sensitivity by 9.09% over the state of art. The developed method proved effective for the segmentation of the retinal vessels. Higher order tensors along with the implementation of tensor voting via steerable filtering could be employed to further reduce the execution time, and resolve the challenges at vascular junctions. Moreover, the characterization could be advanced to the detection of prolifera- tive retinopathy by extending the supervised learning to include non-proliferative diabetic retinopathy cases or other pathologies. Ultimately, the incorporation of the methods into CAD systems could facilitate screening for the effective reduction of the vision-threatening diabetic retinopathy rates, or the early detection of other than ocular pathologies

    Stability, Structure and Scale: Improvements in Multi-modal Vessel Extraction for SEEG Trajectory Planning

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    Purpose Brain vessels are among the most critical landmarks that need to be assessed for mitigating surgical risks in stereo-electroencephalography (SEEG) implantation. Intracranial haemorrhage is the most common complication associated with implantation, carrying signi cant associated morbidity. SEEG planning is done pre-operatively to identify avascular trajectories for the electrodes. In current practice, neurosurgeons have no assistance in the planning of electrode trajectories. There is great interest in developing computer assisted planning systems that can optimise the safety pro le of electrode trajectories, maximising the distance to critical structures. This paper presents a method that integrates the concepts of scale, neighbourhood structure and feature stability with the aim of improving robustness and accuracy of vessel extraction within a SEEG planning system. Methods The developed method accounts for scale and vicinity of a voxel by formulating the problem within a multi-scale tensor voting framework. Feature stability is achieved through a similarity measure that evaluates the multi-modal consistency in vesselness responses. The proposed measurement allows the combination of multiple images modalities into a single image that is used within the planning system to visualise critical vessels. Results Twelve paired datasets from two image modalities available within the planning system were used for evaluation. The mean Dice similarity coe cient was 0.89 ± 0.04, representing a statistically signi cantly improvement when compared to a semi-automated single human rater, single-modality segmentation protocol used in clinical practice (0.80 ±0.03). Conclusions Multi-modal vessel extraction is superior to semi-automated single-modality segmentation, indicating the possibility of safer SEEG planning, with reduced patient morbidity

    Multi-Population Differential Evolution for Retinal Blood Vessel Segmentation

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    The retinal blood vessel segmentation plays a significant role in the automatic or computer-assisted diagnosis of retinopathy. Manual blood vessel segmentation is very time-consuming and requires a great amount of domain knowledge. In addition, the blood vessels are only a few pixels wide and cover the entire fundus image. This further hinders the recent systems from automating the retinal blood vessel segmentation efficiently. In this paper, we propose a modified differential evolution (DE) algorithm to carry out automatic retinal blood vessel segmentation. The modified DE employs cross-communication among multiple populations to select three types of features i.e. thick blood vessels, thin blood vessels and non-blood vessels. Multiple classifiers such as neural networks (NN), Support vector machines (SVM), NN based and SVM based ensembles are used to further measure the performance of segmentation. The proposed algorithm is evaluated on three publicly available retinal image datasets like DRIVE, STARE and HRF. It outperformed the state-of-the-art with a high average accuracy of 98.5% along with high sensitivity and specificity

    Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: a review

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    Glaucoma is a group of eye diseases that have common traits such as, high eye pressure, damage to the Optic Nerve Head and gradual vision loss. It affects peripheral vision and eventually leads to blindness if left untreated. The current common methods of pre-diagnosis of Glaucoma include measurement of Intra-Ocular Pressure (IOP) using Tonometer, Pachymetry, Gonioscopy; which are performed manually by the clinicians. These tests are usually followed by Optic Nerve Head (ONH) Appearance examination for the confirmed diagnosis of Glaucoma. The diagnoses require regular monitoring, which is costly and time consuming. The accuracy and reliability of diagnosis is limited by the domain knowledge of different ophthalmologists. Therefore automatic diagnosis of Glaucoma attracts a lot of attention.This paper surveys the state-of-the-art of automatic extraction of anatomical features from retinal images to assist early diagnosis of the Glaucoma. We have conducted critical evaluation of the existing automatic extraction methods based on features including Optic Cup to Disc Ratio (CDR), Retinal Nerve Fibre Layer (RNFL), Peripapillary Atrophy (PPA), Neuroretinal Rim Notching, Vasculature Shift, etc., which adds value on efficient feature extraction related to Glaucoma diagnosis. © 2013 Elsevier Ltd

    Retinal Vessels Segmentation Techniques and Algorithms: A Survey

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    Retinal vessels identification and localization aim to separate the different retinal vasculature structure tissues, either wide or narrow ones, from the fundus image background and other retinal anatomical structures such as optic disc, macula, and abnormal lesions. Retinal vessels identification studies are attracting more and more attention in recent years due to non-invasive fundus imaging and the crucial information contained in vasculature structure which is helpful for the detection and diagnosis of a variety of retinal pathologies included but not limited to: Diabetic Retinopathy (DR), glaucoma, hypertension, and Age-related Macular Degeneration (AMD). With the development of almost two decades, the innovative approaches applying computer-aided techniques for segmenting retinal vessels are becoming more and more crucial and coming closer to routine clinical applications. The purpose of this paper is to provide a comprehensive overview for retinal vessels segmentation techniques. Firstly, a brief introduction to retinal fundus photography and imaging modalities of retinal images is given. Then, the preprocessing operations and the state of the art methods of retinal vessels identification are introduced. Moreover, the evaluation and validation of the results of retinal vessels segmentation are discussed. Finally, an objective assessment is presented and future developments and trends are addressed for retinal vessels identification techniques.https://doi.org/10.3390/app802015

    Mathematical Morphology for Quantification in Biological & Medical Image Analysis

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    Mathematical morphology is an established field of image processing first introduced as an application of set and lattice theories. Originally used to characterise particle distributions, mathematical morphology has gone on to be a core tool required for such important analysis methods as skeletonisation and the watershed transform. In this thesis, I introduce a selection of new image analysis techniques based on mathematical morphology. Utilising assumptions of shape, I propose a new approach for the enhancement of vessel-like objects in images: the bowler-hat transform. Built upon morphological operations, this approach is successful at challenges such as junctions and robust against noise. The bowler-hat transform is shown to give better results than competitor methods on challenging data such as retinal/fundus imagery. Building further on morphological operations, I introduce two novel methods for particle and blob detection. The first of which is developed in the context of colocalisation, a standard biological assay, and the second, which is based on Hilbert-Edge Detection And Ranging (HEDAR), with regard to nuclei detection and counting in fluorescent microscopy. These methods are shown to produce accurate and informative results for sub-pixel and supra-pixel object counting in complex and noisy biological scenarios. I propose a new approach for the automated extraction and measurement of object thickness for intricate and complicated vessels, such as brain vascular in medical images. This pipeline depends on two key technologies: semi-automated segmentation by advanced level-set methods and automatic thickness calculation based on morphological operations. This approach is validated and results demonstrating the broad range of challenges posed by these images and the possible limitations of this pipeline are shown. This thesis represents a significant contribution to the field of image processing using mathematical morphology and the methods within are transferable to a range of complex challenges present across biomedical image analysis

    A Multi-Anatomical Retinal Structure Segmentation System For Automatic Eye Screening Using Morphological Adaptive Fuzzy Thresholding

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    Eye exam can be as efficacious as physical one in determining health concerns. Retina screening can be the very first clue to detecting a variety of hidden health issues including pre-diabetes and diabetes. Through the process of clinical diagnosis and prognosis; ophthalmologists rely heavily on the binary segmented version of retina fundus image; where the accuracy of segmented vessels, optic disc and abnormal lesions extremely affects the diagnosis accuracy which in turn affect the subsequent clinical treatment steps. This thesis proposes an automated retinal fundus image segmentation system composed of three segmentation subsystems follow same core segmentation algorithm. Despite of broad difference in features and characteristics; retinal vessels, optic disc and exudate lesions are extracted by each subsystem without the need for texture analysis or synthesis. For sake of compact diagnosis and complete clinical insight, our proposed system can detect these anatomical structures in one session with high accuracy even in pathological retina images. The proposed system uses a robust hybrid segmentation algorithm combines adaptive fuzzy thresholding and mathematical morphology. The proposed system is validated using four benchmark datasets: DRIVE and STARE (vessels), DRISHTI-GS (optic disc), and DIARETDB1 (exudates lesions). Competitive segmentation performance is achieved, outperforming a variety of up-to-date systems and demonstrating the capacity to deal with other heterogenous anatomical structures
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