Automatic detection of blood vessels from CTA images employing morphological operation

Diabetes occurs due to the excess of glucose in the blood that may affect many organs of the body. The increase in blood sugar in the body causes many problems. One of the most prominent of these problems is Diabetic Retinopathy (DR). DR occurs due to the mutilation of the blood vessels in a retina. The detection of DR is complicated and time-consuming due to its features for the ophthalmologists. Therefore, automatic detection is required, recently different machine and deep learning techniques are being applied to detect and classify DR. In this paper, we conducted a study of the various techniques available in the literature for the identification/classification of DR, the datasets used, strengths and weaknesses of each method and provides the future directions. Moreover, we also discussed the different steps for the detection that are segmentation of blood vessels in a retina, detecting lesions and other abnormalities of DR in binary and multiclass classification.

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Automatic Detection of Tumor Cell in Brain MRI Using Rough-Fuzzy Feature Selection with Support Vector Machine and Morphological Operation

Intelligent Systems Reference Library - Advanced Machine Learning Approaches in Cancer Prognosis ◽

10.1007/978-3-030-71975-3_8 ◽

2021 ◽

pp. 221-246

Author(s):

Amiya Halder ◽

Apurba Sarkar

Keyword(s):

Support Vector Machine ◽

Feature Selection ◽

Tumor Cell ◽

Brain Mri ◽

Automatic Detection ◽

Support Vector ◽

Morphological Operation ◽

Fuzzy Feature Selection

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Automatic Detection of Blood Vessels in Retinal OCT Images

Biomedical Applications Based on Natural and Artificial Computing - Lecture Notes in Computer Science ◽

10.1007/978-3-319-59773-7_1 ◽

2017 ◽

pp. 3-10 ◽

Cited By ~ 1

Author(s):

Joaquim de Moura ◽

Jorge Novo ◽

José Rouco ◽

M. G. Penedo ◽

Marcos Ortega

Keyword(s):

Blood Vessels ◽

Automatic Detection

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Automated Detection of Red Lesions Using Superpixel Multichannel Multifeature

Computational and Mathematical Methods in Medicine ◽

10.1155/2017/9854825 ◽

2017 ◽

Vol 2017 ◽

pp. 1-13 ◽

Cited By ~ 3

Author(s):

Wei Zhou ◽

Chengdong Wu ◽

Dali Chen ◽

Zhenzhu Wang ◽

Yugen Yi ◽

...

Keyword(s):

Diabetic Retinopathy ◽

Blood Vessels ◽

Extraction Method ◽

Critical Role ◽

Automatic Detection ◽

Automated Detection ◽

Lesion Detection ◽

Classification Approach ◽

Contextual Feature

Red lesions can be regarded as one of the earliest lesions in diabetic retinopathy (DR) and automatic detection of red lesions plays a critical role in diabetic retinopathy diagnosis. In this paper, a novel superpixel Multichannel Multifeature (MCMF) classification approach is proposed for red lesion detection. In this paper, firstly, a new candidate extraction method based on superpixel is proposed. Then, these candidates are characterized by multichannel features, as well as the contextual feature. Next, FDA classifier is introduced to classify the red lesions among the candidates. Finally, a postprocessing technique based on multiscale blood vessels detection is modified for removing nonlesions appearing as red. Experiments on publicly available DiaretDB1 database are conducted to verify the effectiveness of our proposed method.

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Automatic Detection of Blood Vessels in Retinal Images Using FC-DenseNet Neural Networks

10.1109/icstcc52150.2021.9607051 ◽

2021 ◽

Author(s):

Mihaela Deaconu ◽

Dan Popescu ◽

Loretta Ichim

Keyword(s):

Neural Networks ◽

Blood Vessels ◽

Automatic Detection ◽

Retinal Images

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Automatic Detection of Building in Medium Density Image Using Morphological Operation

10.1109/igarss47720.2021.9553401 ◽

2021 ◽

Author(s):

Karuna Sitaram Kirwale ◽

Seema S. Kawathekar ◽

Ratnadeep R. Deshmukh

Keyword(s):

Automatic Detection ◽

Medium Density ◽

Morphological Operation ◽

Density Image

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Automatic Detection of Blood Vessels in Retinal Images for Diabetic Retinopathy Diagnosis

Computational and Mathematical Methods in Medicine ◽

10.1155/2015/419279 ◽

2015 ◽

Vol 2015 ◽

pp. 1-12 ◽

Cited By ~ 15

Author(s):

D. Siva Sundhara Raja ◽

S. Vasuki

Keyword(s):

Diabetic Retinopathy ◽

Blood Vessels ◽

Anisotropic Diffusion ◽

Vision Loss ◽

Automatic Detection ◽

Diabetic Patients ◽

Morphological Operations ◽

Anisotropic Diffusion Filter ◽

Retinal Blood Vessels ◽

Detection And Diagnosis

Diabetic retinopathy (DR) is a leading cause of vision loss in diabetic patients. DR is mainly caused due to the damage of retinal blood vessels in the diabetic patients. It is essential to detect and segment the retinal blood vessels for DR detection and diagnosis, which prevents earlier vision loss in diabetic patients. The computer aided automatic detection and segmentation of blood vessels through the elimination of optic disc (OD) region in retina are proposed in this paper. The OD region is segmented using anisotropic diffusion filter and subsequentially the retinal blood vessels are detected using mathematical binary morphological operations. The proposed methodology is tested on two different publicly available datasets and achieved 93.99% sensitivity, 98.37% specificity, 98.08% accuracy in DRIVE dataset and 93.6% sensitivity, 98.96% specificity, and 95.94% accuracy in STARE dataset, respectively.

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