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Related Concept Videos

Structure of Blood Vessels01:15

Structure of Blood Vessels

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Blood is circulated throughout the human body through a network of blood vessels called the circulatory system. This system includes arteries that transport blood from the heart to various body parts. These arterial pathways divide into smaller vessels until they reach the arterioles, which further split into capillaries. It is within these minuscule capillaries that the exchange of nutrients and waste products takes place. After this exchange, the blood is collected by venules, which fuse to...
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The vascular system, an integral part of the circulatory system, comprises various blood vessels that play crucial roles in maintaining the body's homeostasis. These blood vessels form a complex and efficient circulatory network. The three primary categories of blood vessels are the arteries, veins, and capillaries.
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The development of the vascular system in a fetus is a complex and intricate process that begins as early as 15 to 16 days post-conception. This process starts outside the embryo, specifically in the mesoderm of the yolk sac, chorion, and connecting stalk. Approximately two days later, the formation of blood vessels occurs within the embryo itself.
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The human cardiovascular system comprises five primary types of blood vessels: arteries, arterioles, veins, venules, and capillaries, each serving unique functions.
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Passive filters are utilized to shape the frequency spectrum of signals across a diverse array of applications. These filters, using only passive elements like resistors (R), inductors (L), and capacitors (C), are capable of selectively allowing or blocking certain frequency ranges without the need for external power sources.
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Active filters are electronic circuits that use operational amplifiers (op-amps), resistors, and capacitors to filter out unwanted frequency components from a signal. A first-order low-pass active filter is designed to pass signals with a frequency lower than a certain cutoff frequency and attenuate frequencies higher than that cutoff frequency. The transfer function for a first-order low-pass active filter is:
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Related Experiment Video

Updated: Feb 2, 2026

Optimized Protocol for Retinal Wholemount Preparation for Imaging and Immunohistochemistry
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Optimizing the trainable B-COSFIRE filter for retinal blood vessel segmentation.

Sufian A Badawi1, Muhammad Moazam Fraz1

  • 1School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad, Pakistan.

Peerj
|November 28, 2018
PubMed
Summary

Optimized blood vessel segmentation in retinal images using an enhanced B-COSFIRE filter improves diagnostic accuracy. This method achieves high accuracy on public datasets, outperforming existing techniques for automated retinal image analysis.

Keywords:
B-COSFIREBCOSFIREComputer Aided Diagnosis (CAD)Retinal blood vesselsRetinal images

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Biomedical Engineering

Background:

  • Automated retinal blood vessel segmentation is crucial for diagnosing eye diseases.
  • Existing methods often struggle with accuracy and artifact handling.

Purpose of the Study:

  • To optimize blood vessel segmentation in retinal images.
  • To enhance the trainable B-COSFIRE filter with improved parameter identification.

Main Methods:

  • Extended the trainable B-COSFIRE filter with an optimization procedure.
  • Applied the optimized filter to three public datasets: STARE, DRIVE, and CHASE-DB1.
  • Incorporated thresholding parameter selection and background artifact removal techniques.

Main Results:

  • Achieved high vessel segmentation accuracy: 95.47% (DRIVE), 95.30% (STARE), and 95.30% (CHASE-DB1).
  • The proposed approach outperformed other state-of-the-art methods.
  • ANOVA analysis identified significant parameters impacting performance (p-value < 0.05).

Conclusions:

  • The enhanced B-COSFIRE filter significantly improves retinal blood vessel segmentation accuracy.
  • This optimized approach offers a more robust solution for automated retinal image analysis.
  • The method demonstrates superior performance across multiple public datasets.