scholarly journals Retina blood vessel extraction based on kirsch’s template method

2020 ◽  
Vol 18 (1) ◽  
pp. 318
Author(s):  
Nur Syazlin Zolkifli ◽  
Ain Nazari ◽  
Mohd Marzuki Mustafa ◽  
Wan NurShazwani Wan Zakaria ◽  
Nor Surayahani Suriani ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Blood Vessel ◽  
Blood Vessels ◽  
Early Stage ◽  
Medical Community ◽  
Computer Assisted ◽  
Early Screening ◽  
Online Databases ◽  
Assisted Diagnosis ◽  
Vessel Extraction

<p class="IJASEITAbtract">Analysis on the retina blood vessels from fundus images have been widely used in the medical community to detect the disorder condition in the blood vessels. An automated tracing of retina blood vessel can help to provide valuable computer-assisted diagnosis for the ophthalmic disorders. Thus, it helps to reduce the time for the ophthalmologist to analyses and diagnose the result of the fundus image of patient. The purpose of this research is to build an algorithm to trace the retina blood vessels. The method to be used in this research consist of two parts which are the pre-processing part and the feature extraction by using the Kirsch’s template. Combining the pre-processing at the early stage and feature extraction at the next stage is applied to extract the edges of the blood vessels.  The proposed algorithm was verified by using two online databases, DRIVE and HRF to validate the performance measures. Hence, proposed method is capable to extract the retina blood vessel and give the accuracy of 0.7917, the sensitivity of 0.9077 and the specificity of 0.7215. In conclusion, the extraction of the blood vessels is highly recommended as the early screening stage for the eye diseases beneficially.</p>

Morphological Contour Based Blood Vessel Segmentation in Retinal Images Using Otsu Thresholding

2018 ◽  
Vol 9 (4) ◽  
pp. 48-63 ◽  
Author(s):  
S. Saranya Rubini ◽  
A. Kunthavai ◽  
M.B. Sachin ◽  
S. Deepak Venkatesh
Keyword(s):  
Blood Vessel ◽  
Blood Vessels ◽  
Early Stage ◽  
Histogram Equalization ◽  
Vessel Segmentation ◽  
Contour Detection ◽  
Experimental Result ◽  
Blood Vessel Segmentation ◽  
Otsu Thresholding ◽  
Sensitivity Specificity

Retinal image analysis plays an important part in identifying various eye related diseases such as diabetic retinopathy (DR), glaucoma and many others. Accurate segmentation of blood vessels plays an important part in identifying the retinal diseases at an early stage. In this article, an unsupervised approach based on contour detection has been proposed for effective segmentation of retinal blood vessels. The proposed morphological contour-based blood vessel segmentation (MCBVS) method performs preprocessing using contrast limited adaptive histogram equalization followed by alternate sequential filtering to generate a noise-free image. The resultant image undergoes Otsu thresholding for candidate extraction followed by contour detection to properly segment the blood vessels. The MCBVS method has been tested on the DRIVE dataset and the experimental result shows that the proposed method achieved a sensitivity, specificity and accuracy of 58.79%, 90.77% and 86.7%, respectively. The MCBVS method performs better than the existing methods Sobel, Prewitt and Modified U-Net in terms of accuracy.

SEGMENTATION OF RETINAL BLOOD VESSELS BY TOP-HAT MULTI-SCALE DETECTION FOR OPTIC DISC REMOVAL

2015 ◽  
Vol 77 (6) ◽  
Author(s):  
Ain Nazari ◽  
Mohd Marzuki Mustafa ◽  
Mohd Asyraf Zulkifley
Keyword(s):  
Blood Vessels ◽  
Optic Disc ◽  
System Development ◽  
Computer Assisted ◽  
Fundus Image ◽  
Multi Scale ◽  
Analysis Process ◽  
Online Databases ◽  
Retinal Blood Vessels ◽  
Scale Detection

Nowadays, an automatic retinal vessels segmentation is important component in computer assisted system to detect numerous eye abnormalities. There are various sizes of the retinal blood vessels captured from fundus image modality, which can be detected by using multi-scale approach. However, the main limitation of the current multi-scale approaches is the inability to remove the optic disc from the detected blood vessels. In this paper, a hybrid of multi-scale detection with pre-processing approach is proposed so that clearer vessel segmentation can be obtained. The proposed method embedded with a pre-processing phase that includes four series of processes that include Top-hat transformation as the main part. This technique will reduce the influence of the structure of optic disc and enhance the contrast of the vessel from the background. Then, the result from the pre-processing phase will be fed to the multi-scale detection to perform the segmentation. The proposed method is evaluated on two publicly available online databases: HRF and DRIVE. On HRF database, the best obtained precision and specificity values are 0.9689 and 0.9989, respectively. Meanwhile, for DRIVE database, the system performs well in all performance measures: precision, specificity, accuracy and error with the best values of 0.7541, 0.9739, 0.9510 and 0.0490, respectively. In conclusion, the proposed method is able to filter the unwanted optical disc from the fundus image effectively. Thus, retinal blood vessel image can be used for further analysis process and beneficial for pre-screening system development.  

Analogizing the Thinning Algorithm and Elicitation of Vascular Landmark in Retinal Images

2016 ◽  
Vol 10 (3) ◽  
pp. 29-37
Author(s):  
Shiny Priyadarshini J. ◽  
Gladis D.
Keyword(s):  
Feature Extraction ◽  
Blood Vessel ◽  
Blood Vessels ◽  
Personal Identification ◽  
Retinal Images ◽  
Feature Points ◽  
Retinal Blood Vessel ◽  
Thinning Algorithm ◽  
Retinal Tissue ◽  
Unique Identity

The retinal tissue is composed of network of blood vessels forming a unique biometric pattern. Feature extraction in retinal blood vessel is becoming an emerging trend in the field of personal identification. Because of its unique identity and less vulnerability to noise and distortion it has become one of the most secured biometric identities. The paper highlights the segmentation of blood vessel and the extraction of feature points such as termination and bifurcation points using Zhang Suen's thinning algorithm in retinal images. A comparison has been made and results are analyzed and tabulated between Zhang Suen and Morphological thinning. The count has been taken for both termination and bifurcation markings as spurious and non- spurious minutiae. The spurious minutiae are removed by using the crossing number method. The results clearly depict that the Zhang Suen's thinning algorithm gives better result when compared to morphological thinning.

FUZZY INFORMATION GRANULATION ON BLOOD VESSEL EXTRACTION FROM 3D TOF MRA IMAGE

2000 ◽  
Vol 14 (04) ◽  
pp. 409-425 ◽  
Author(s):  
SYOJI KOBASHI ◽  
NAOTAKE KAMIURA ◽  
YUTAKA HATA ◽  
FUJIO MIYAWAKI
Keyword(s):  
Image Segmentation ◽  
Blood Vessel ◽  
Blood Vessels ◽  
Medical Image ◽  
Three Dimensional ◽  
Medical Image Segmentation ◽  
Volume Data ◽  
Fuzzy Information ◽  
Vessel Extraction ◽  
Fuzzy Information Granulation

This paper shows an application of fuzzy information granulation (fuzzy IG) to medical image segmentation. Fuzzy IG is to derive fuzzy granules from information. In the case of medical image segmentation, information and fuzzy granules correspond to an image taken from a medical scanner, and anatomical parts, namely region of interests (ROIs), respectively. The proposed method to granulate information is composed of volume quantization and fuzzy merging. Volume quantization is to gather similar neighboring voxels. The generated quanta are selectively merged according to degrees for pre-defined fuzzy models that represent anatomical knowledge of medical images. The proposed method was applied to blood vessel extraction from three-dimensional time-of-flight (TOF) magnetic resonance angiography (MRA) images of the brain. The volume data studied in this work is composed of about 100 contiguous and volumetric MRA images. According to the fuzzy IG concept, information correspond to the volume data, fuzzy granules corresponds to the blood vessels and fat. The qualitative evaluation by a physician was done for two- and three-dimensional images generated from the obtained blood vessels. The evaluation shows that the method can segment MRA volume data, and that fuzzy IG is applicable to, and suitable for medical image segmentation.

Community-based case–control study of depression in older people

1999 ◽  
Vol 175 (4) ◽  
pp. 340-347 ◽  
Author(s):  
J. R. M. Copeland ◽  
Ruoling Chen ◽  
Michael Dewey ◽  
C. F. M. McCracken ◽  
Chris Gilmore ◽  
...  
Keyword(s):  
Risk Factors ◽  
Older People ◽  
Early Stage ◽  
Statistical Significance ◽  
Later Life ◽  
Alcohol Problems ◽  
Computer Assisted ◽  
Data Set ◽  
Mental Problems ◽  
Assisted Diagnosis

BackgroundRisk factors of depression in later life, particularly for sub-cases and for psychotic and neurotic types of depression, are unclear.AimsTo identify such risk factors.MethodOver 5200 older people ($65 years), randomly selected from Liverpool, were interviewed using the Geriatric Mental State (GMS)and the Minimum Data Set (MDS). The computer-assisted diagnosis AGECAT identified 483 cases and 575 sub-cases of depression and 2451 with no mental problems. Logistic regression was employed to examine factors relevant to caseness.ResultsIn multiple logistical regression, odds ratios (ORs) were significantly high for being female (2.04, 95% CI 1.56–2.69), widowed (2.00, 1.18–3.39), having alcohol problems (4.37, 1.40–2.94), physical disablement (2.03, 1.40–2.94), physical illness (1.98,.1.25–3.15), taking medications to calm down (10.04, 6.41 −15.71), and dissatisfaction with life (moderate 4.54, 3.50–5.90; more severe 29.00, 16.00–52.59). Good social networks reduced the ORs. If sub-cases were included as controls, the statistical significance was reduced.ConclusionsAge was not associated with depression in later life whereas gender, physical disablement and dissatisfaction with life were. The sub-cases shared many risk factors with cases, suggesting that prevention may need to be attempted at an early stage.

scholarly journals Analogizing the Thinning Algorithm and Elicitation of Vascular Landmark in Retinal Images

Ophthalmology ◽  
2018 ◽  
pp. 69-77
Author(s):  
Shiny Priyadarshini J. ◽  
Gladis D.
Keyword(s):  
Feature Extraction ◽  
Blood Vessel ◽  
Blood Vessels ◽  
Personal Identification ◽  
Retinal Images ◽  
Feature Points ◽  
Retinal Blood Vessel ◽  
Thinning Algorithm ◽  
Retinal Tissue ◽  
Unique Identity

The retinal tissue is composed of network of blood vessels forming a unique biometric pattern. Feature extraction in retinal blood vessel is becoming an emerging trend in the field of personal identification. Because of its unique identity and less vulnerability to noise and distortion it has become one of the most secured biometric identities. The paper highlights the segmentation of blood vessel and the extraction of feature points such as termination and bifurcation points using Zhang Suen's thinning algorithm in retinal images. A comparison has been made and results are analyzed and tabulated between Zhang Suen and Morphological thinning. The count has been taken for both termination and bifurcation markings as spurious and non- spurious minutiae. The spurious minutiae are removed by using the crossing number method. The results clearly depict that the Zhang Suen's thinning algorithm gives better result when compared to morphological thinning.

scholarly journals Prediction of diabetic retinopathy using machine learning techniques

2021 ◽  
Author(s):  
T Jemima Jebaseeli ◽  
◽  
C. Anand Deva Durai ◽  
Salem Alelyani ◽  
Mohammed Saleh Alsaqer ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Blood Vessel ◽  
Early Stage ◽  
Image Data ◽  
Histogram Equalization ◽  
Machine Learning Techniques ◽  
Computer Assisted ◽  
Diabetic Patients ◽  
Resonance Theory ◽  
Adaptive Resonance

Diabetic Retinopathy (DR) is the complicatedness of diabetes that happens due to macular degeneration among Type II diabetic patients. The early symptom of this disease is predicted through annual eye checkups. Hence, one can save their vision at an early stage. Later on, it prompts retinal detachment. There is a requirement for awareness among diabetic patients about this disease to prevent their life from vision misfortune. Along these lines, there is a need for a computer-assisted method to analyze the disease. The proposed system used Adaptive Histogram Equalization (AHE) technique for image enhancement, Hop Field Neural Network for blood vessel segmentation, and Adaptive Resonance Theory (ART) for blood vessel classification. The proposed system analyzes the disease and classifies the disease level effectively with high accuracy. Also, the system notifies the users about the stages of the disease. The proposed system is evaluated with the clinical as well as open fundus image data sets like DRIVE, STARE, MESSIDOR, HRF, DRIONS, and REVIEW for diabetic retinopathy prediction. Also, physicians evaluated the system and concluded that the proposed system does not deviate from the quality of disease analysis and grading. The proposed techniques accomplished 99.99% accuracy. The system is evaluated by the ophthalmologists and witnesses that the proposed system has not veered off as far as quality.

A REVIEW ON ARTERY WALL SEGMENTATION TECHNIQUES AND INTIMA-MEDIA THICKNESS MEASUREMENT FOR CAROTID ULTRASOUND IMAGES

2012 ◽  
Vol 05 (01) ◽  
pp. 1230001 ◽  
Author(s):  
XIN YANG ◽  
WANJI HE ◽  
KAITONG LI ◽  
JIAOYING JIN ◽  
XUMING ZHANG ◽  
...  
Keyword(s):  
Early Stage ◽  
Imaging Techniques ◽  
Atherosclerotic Lesion ◽  
Intima Media Thickness ◽  
Thickness Measurement ◽  
Medical Intervention ◽  
Computer Assisted ◽  
Ultrasound Images ◽  
Media Thickness ◽  
Assisted Diagnosis

Stroke and heart attack, which could be led by a kind of cerebrovascular and cardiovascular disease named as atherosclerosis, would seriously cause human morbidity and mortality. It is important for the early stage diagnosis and monitoring medical intervention of the atherosclerosis. Carotid stenosis is a classical atherosclerotic lesion with vessel wall narrowing down and accumulating plaques burden. The carotid artery of intima-media thickness (IMT) is a key indicator to the disease. With the development of computer assisted diagnosis technology, the imaging techniques, segmentation algorithms, measurement methods, and evaluation tools have made considerable progress. Ultrasound imaging, being real-time, economic, reliable, and safe, now seems to become a standard in vascular assessment methodology especially for the measurement of IMT. This review firstly attempts to discuss the clinical relevance of measurements in clinical practice at first, and then followed by the challenges that one has to face when approaching the segmentation of ultrasound images. Secondly, the commonly used methods for the IMT segmentation and measurement are presented. Thirdly, discussion and evaluation of different segmentation techniques are performed. An overview of summary and future perspectives is given finally.

scholarly journals Algorithm of Pulmonary Vascular Segment and Centerline Extraction

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shi Qiu ◽  
Jie Lian ◽  
Yan Ding ◽  
Tao Zhou ◽  
Ting Liang
Keyword(s):  
Blood Vessels ◽  
Correct Diagnosis ◽  
Hessian Matrix ◽  
Vascular Lesions ◽  
Computer Assisted ◽  
Centerline Extraction ◽  
Vascular Segment ◽  
Ct Data ◽  
Assisted Diagnosis ◽  
Expansion Model

Because pulmonary vascular lesions are harmful to the human body and difficult to detect, computer-assisted diagnosis of pulmonary blood vessels has become the focus and difficulty of the current research. An algorithm of pulmonary vascular segment and centerline extraction which is consistent with the physician’s diagnosis process is proposed for the first time. We construct the projection of maximum density, restore the vascular space information, and correct random walk algorithm to satisfy automatic and accurate segmentation of blood vessels. Construct a local 3D model to restrain Hessian matrix when extracting centerline. In order to assist the physician to make a correct diagnosis and verify the effectiveness of the algorithm, we proposed a visual expansion model. According to the 420 high-resolution CT data of lung blood vessels labeled by physicians, the accuracy of segmentation algorithm AOM reached 93%, and the processing speed was 0.05 s/frame, which achieved the clinical application standards.

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