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An Efficient ELM Approach for Blood Vessel Segmentation in Retinal Images  [PDF]
X. Merlin Sheeba
Bonfring International Journal of Man Machine Interface , 2011, DOI: 10.9756/bijmmi.1004
Abstract: Diabetic Retinopathy (DR) is one of the most important ophthalmic pathological reasons of blindness among people of working age. Previous techniques for blood vessel detection in retinal images can be categorized into rule-based and supervised methods. This research presents a new supervised technique for blood vessel detection in digital retinal images. This novel approach uses an Extreme Learning Machine (ELM) approach for pixel classification and calculates a 7-D vector comprises of gray-level and moment invariants-based features for pixel representation. The approach is based on pixel classification using a 7-D feature vector obtained from preprocessed retinal images and given as input to a ELM. Classification results (real values between 0 and 1) are thresholded to categorize each pixel into two classes namely vessel and nonvessel. Ultimately, a post processing fills pixel gaps in detected blood vessels and eliminates falsely-detected isolated vessel pixels. The technique was assessed on the publicly available DRIVE and STARE databases, widely used for this purpose, as they comprises of retinal images where the vascular structure has been precisely marked by experts. Method performance on both sets of test images is better than other existing solutions in literature. The approach proves particularly accurate for vessel detection in STARE images. Its efficiency and strength with different image conditions, along with its simplicity and fast implementation, make this blood vessel segmentation proposal appropriate for retinal image computer analyses such as automated screening for early diabetic retinopathy detection.
Blood Vessel Segmentation for Retinal Images Based on Am-fm Method  [cached]
S. Dhanalakshmi,T. Ravichandran
Research Journal of Applied Sciences, Engineering and Technology , 2012,
Abstract: This system proposes a new supervised approach for the blood vessel segmentation method in retina image. This proposed system overcomes the problem of segmenting thin vessels. This method uses a Fuzzy Neural Network (FNN) scheme for pixel classification and computes a 7-D vector composed of gray-level, moment invariants-based features for pixel representation and AM-FM method for composition of the images. The method was evaluated on the publicly available DRIVE and STARE databases, widely used for this purpose, since they contain retinal images where the vascular structure has been precisely marked by experts. Method performance on both sets of test images is better than other existing solutions in literature. The method proves especially accurate for vessel detection in STARE images. Its effectiveness and robustness with different image conditions together with its simplicity and fast implementation make this blood vessel segmentation proposal suitable for retinal image computer analyses such as automated screening for early diabetic retinopathy detection.
Blood Vessel Segmentation For High Resolution Retinal Images
J. Benadict Raja,C. G. Ravichandran
International Journal of Computer Science Issues , 2011,
Abstract: Segmentation of blood vessels in retinal images used for the early diagnosis of retinal diseases such as hypertension, diabetes and glaucoma. The high resolution, variability in vessel width, brightness and low contrast make vessel segmentation as difficult task. There exist several methods for segmenting blood vessels from retinal images. However, most of these methods fail to segment high resolution (large in size) images, very few methods provide solution for such a high resolution images but it require lengthy elapsed time and the accuracy of these methods is not completely satisfactory. Parallel method have emerged to overcome these limitations by offering parallel environment and parallel algorithm to segment such an high resolution images in an acceptable time. The planned research enhances the speed and accuracy of segmentation for high resolution retinal images by involving a new data partition scheme and suitable segmentation algorithm for parallel environment.
FCM Based Blood Vessel Segmentation Method for Retinal Images  [PDF]
Nilanjan Dey,Anamitra Bardhan Roy,Moumita Pal,Achintya Das
Computer Science , 2012,
Abstract: Segmentation of blood vessels in retinal images provides early diagnosis of diseases like glaucoma, diabetic retinopathy and macular degeneration. Among these diseases occurrence of Glaucoma is most frequent and has serious ocular consequences that can even lead to blindness, if it is not detected early. The clinical criteria for the diagnosis of glaucoma include intraocular pressure measurement, optic nerve head evaluation, retinal nerve fiber layer and visual field defects. This form of blood vessel segmentation helps in early detection for ophthalmic diseases, and potentially reduces the risk of blindness. The low-contrast images at the retina owing to narrow blood vessels of the retina are difficult to extract. These low contrast images are, however useful in revealing certain systemic diseases. Motivated by the goals of improving detection of such vessels, this present work proposes an algorithm for segmentation of blood vessels and compares the results between expert ophthalmologist hand-drawn ground-truths and segmented image(i.e. the output of the present work).Sensitivity, specificity, positive predictive value (PPV), positive likelihood ratio (PLR) and accuracy are used to evaluate overall performance.It is found that this work segments blood vessels successfully with sensitivity, specificity, PPV, PLR and accuracy of 99.62%, 54.66%, 95.08%, 219.72 and 95.03%, respectively.
FCM Based Blood Vessel Segmentation Method for Retinal Images
Nilanjan Dey,Anamitra Bardhan Roy,Moumita Pal,Achintya Das
International Journal of Computer Science and Network , 2012,
Abstract: Segmentation of blood vessels in retinal images providesearly diagnosis of diseases like glaucoma, diabeticretinopathy and macular degeneration. Among thesediseases occurrence of Glaucoma is most frequent and hasserious ocular consequences that can even lead toblindness, if it is not detected early. The clinical criteria forthe diagnosis of glaucoma include intraocular pressuremeasurement, optic nerve head evaluation, retinal nervefiber layer and visual field defects. This form of bloodvessel segmentation helps in early detection for ophthalmicdiseases, and potentially reduces the risk of blindness.The low-contrast images at the retina owing to narrowblood vessels of the retina are difficult to extract. Theselow contrast images are, however useful in revealingcertain systemic diseases. Motivated by the goals ofimproving detection of such vessels, this present workproposes an algorithm for segmentation of blood vessels,and compares the results between expert ophthalmologists’hand-drawn ground-truths and segmented image (i.e. theoutput of the present work). Sensitivity, specificity, positivepredictive value (PPV), positive likelihood ratio (PLR) andaccuracy are used to evaluate overall performance. It isfound that this work segments blood vessels successfullywith sensitivity, specificity, PPV, PLR and accuracy of99.62%, 54.66%, 95.08%, 219.72 and 95.03%,respectively.
Automatic detection of multiple oriented blood vessels in retinal images  [PDF]
P. C. Siddalingaswamy, K. Gopalakrishna Prabhu
Journal of Biomedical Science and Engineering (JBiSE) , 2010, DOI: 10.4236/jbise.2010.31015
Abstract: Automatic segmentation of the vasculature in retinal images is important in the detection of diabetic retinopathy that affects the morphology of the blood vessel tree. In this paper, a hybrid method for efficient segmentation of multiple oriented blood vessels in colour retinal images is proposed. Initially, the appearance of the blood vessels are enhanced and background noise is suppressed with the set of real component of a complex Gabor filters. Then the vessel pixels are detected in the vessel enhanced image using entropic thresholding based on gray level co-occurrence matrix as it takes into account the spatial distribution of gray levels and preserving the spatial structures. The performance of the method is illustrated on two sets of retinal images from publicly available DRIVE (Digital Retinal Images for Vessel Extraction) and Hoover’s databases. For DRIVE database, the blood vessels are detected with sensitivity of 86.47±3.6 (Mean±SD) and specificity of 96±1.01.
Retinal Blood Vessel Segmentation for Assessment of Diabetic Retinopathy Using a Two-Dimensional Model
C. Jayakumari,T. Santhanam
Asian Journal of Information Technology , 2012,
Abstract: Retinal blood vessels are very imperative structures in ophthalmologic images. Automated image processing has the immense potential to assist the physicians in the early detection of diabetes, by observing the changes in blood vessel patterns in the retina. This study details a novel vessel tracking algorithm as a part of the set of tools exercised for the automated diagnosis of diabetic retinopathy. To begin with, the papilla is detected by utilizing Canny s edge detection algorithm. Then, the blood vessels are traced by using the second-order derivative Gaussian filter. The projected algorithm has achieved acceptable results to spot the small veins/thin vessels that play a mighty contribution in the clinical arena.
Extraction of Blood Vessels in Retinal Images Using Four Different Techniques  [PDF]
Asloob Ahmad Mudassar,Saira Butt
Journal of Medical Engineering , 2013, DOI: 10.1155/2013/408120
Abstract: A variety of blood vessel extraction (BVE) techniques exist in the literature, but they do not always lead to acceptable solutions especially in the presence of anomalies where the reported work is limited. Four techniques are presented for BVE: (1) BVE using Image Line Cross-Sections (ILCS), (2) BVE using Edge Enhancement and Edge Detection (EEED), (3) BVE using Modified Matched Filtering (MMF), and (4) BVE using Continuation Algorithm (CA). These four techniques have been designed especially for abnormal retinal images containing low vessel contrasts, drusen, exudates, and other artifacts. The four techniques were applied to 30 abnormal retinal images, and the success rate was found to be (95 to 99%) for CA, (88–91%) for EEED, (80–85%) for MMF, and (74–78%) for ILCS. Application of these four techniques to 105 normal retinal images gave improved results: (99-100%) for CA, (96–98%) for EEED, (94-95%) for MMF, and (88–93%) for ILCS. Investigations revealed that the four techniques in the order of increasing performance could be arranged as ILCS, MMF, EEED, and CA. Here we demonstrate these four techniques for abnormal retinal images only. ILCS, EEED, and CA are novel additions whereas MMF is an improved and modified version of an existing matched filtering technique. CA is a promising technique. 1. Introduction Accurate and automatic assessment of retinal images has been considered as a powerful tool for the diagnosis of retinal disorders such as diabetic retinopathy, hypertension, and arteriosclerosis. Blood vessels have varying contrast due to which the darker vessels (thick vessels) can be extracted easily using standard techniques mentioned in the literature while it is difficult to extract the vessels having poor contrast (thin vessels). Segmentation of blood vessels in retinal images is a field of interest for scientists since last two decades [1–4]. Various kinds of eye abnormalities are indicated by changes in vessel tree structure [5, 6]. A true vessel tree structure should contain information about precise thickness of blood vessels in the retinal images. Optic disc and fovea can be located by tracking the vessel tree [7]. Central retinal artery occlusion produces dilated tortuous veins, age related macular degeneration and diabetes can generate new blood vessels (neovascularization), and the study of retinopathy of prematurity in premature infants is not possible without the knowledge of vessel tree structure. Progression of such eye diseases can only be tracked by noticing the changes in the vessel tree structure with the passage of time.
Extracting Blood Vessels in Retinal Images by Adaptive Thresholding
一种视网膜血管自适应提取方法

PAN Li-feng,WANG Li-sheng,
潘立丰
,王利生

中国图象图形学报 , 2006,
Abstract: In terms of the special gray distribution in retinal images, a novel blood vessel extraction method based on adaptive thresholding is proposed in this paper. The whole image is divided into many small sub-images with identical dimension, and u threshold is calculated respectively in each sub-image for segmenting local blood vessels. Because both vessels and background are locally uniform in retinal images, there must be a threshold which is able to segment vessels precisely in a certain sub-image. The method employed for determining the local threshold not only allows sub-images to be very small, but also ensures the threshold to be optimal in the sense of least square error. A new edge tracking algorithm based on zero-crossing edge detection technique is applied in the process of threshold computing. Further more, a feature synthesis method based on region growing is presented, which is used to clear fragments in results of adaptive thresholding. The experiments on many retinal images indicate that this blood vessel extraction method is computational efficient and can extract most blood vessels including very small blood vessels.
Automatic Segmentation and Measurement of Vasculature in Retinal Fundus Images Using Probabilistic Formulation  [PDF]
Yi Yin,Mouloud Adel,Salah Bourennane
Computational and Mathematical Methods in Medicine , 2013, DOI: 10.1155/2013/260410
Abstract: The automatic analysis of retinal blood vessels plays an important role in the computer-aided diagnosis. In this paper, we introduce a probabilistic tracking-based method for automatic vessel segmentation in retinal images. We take into account vessel edge detection on the whole retinal image and handle different vessel structures. During the tracking process, a Bayesian method with maximum a posteriori (MAP) as criterion is used to detect vessel edge points. Experimental evaluations of the tracking algorithm are performed on real retinal images from three publicly available databases: STARE (Hoover et al., 2000), DRIVE (Staal et al., 2004), and REVIEW (Al-Diri et al., 2008 and 2009). We got high accuracy in vessel segmentation, width measurements, and vessel structure identification. The sensitivity and specificity on STARE are 0.7248 and 0.9666, respectively. On DRIVE, the sensitivity is 0.6522 and the specificity is up to 0.9710. 1. Introduction Automatic vessel segmentation in medical images is a very important task in many clinical investigations. In ophthalmology, the early diagnosis of several pathologies such as arterial hypertension, arteriosclerosis, diabetic retinopathy, cardiovascular disease, and stroke [1, 2] could be achieved by analyzing changes in blood vessel patterns such as tortuosity, bifurcation, and variation of vessel width on retinal images. Early detection and characterization of retinal blood vessels are needed for a better and effective treatment of diseases. Hence, computer-aided detection and analysis of retinal images could help doctors, allowing them to use a quantitative tool for a better diagnosis, especially when analyzing a huge amount of retinal images in screening programs. Many methods for blood vessel detection on retinal images have been reported in the literature [3–5]. These techniques can be roughly classified into pixel-based methods [6–14], model-based methods [15–21], and tracking-based approaches [22–29], respectively. Pixel-based approaches consist in convolving the image with a spatial filter and then assigning each pixel to background or vessel region, according to the result of a second processing step such as thresholding or morphological operation. Chaudhuri et al. [8] used 2D Gaussian kernels with 12 orientations, retaining the maximum response. Hoover et al. [6] improved this technique by computing local features to assign regions to vessel or background. A multithreshold scheme was used by Jiang and Mojon [9], whereas Sofka and Stewart [10] presented a multiscale matched filter. Zana and Klein
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