Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Cell image segmentation with kernel-based dynamic clustering and an ellipsoidal cell shape model.

F Yang1, T Jiang

  • 1National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, People's Republic of China. fgyang@nlpr.ia.ac.cn

Journal of Biomedical Informatics
|August 23, 2001
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Genome-wide comparative chromosome map between human and the Forrest's pika (Ochotona forresti) established by cross-species chromosome painting: further support for the Glires hypothesis.

Cytogenetic and genome research·2010
Same author

Independent influence of gait speed and step length on stability and fall risk.

Gait & posture·2010
Same author

Control of center of mass motion state through cuing and decoupling of spontaneous gait parameters in level walking.

Journal of biomechanics·2010
Same author

Use of exchanging media in ATR configurations for determination of thickness and optical constants of thin metallic films.

Applied optics·2010
Same author

The fate of Cu, Zn, Pb and Cd during the pyrolysis of sewage sludge at different temperatures.

Environmental technology·2010
Same author

A database of thermodynamic quantities for the reactions of glycolysis and the tricarboxylic acid cycle.

The journal of physical chemistry. B·2010
Same journal

CoAff-DTI: Fine-grained drug-target interaction prediction using pre-trained language models and affinity-guided mechanisms.

Journal of biomedical informatics·2026
Same journal

Evaluation of temporal preservation in synthetic longitudinal patient data.

Journal of biomedical informatics·2026
Same journal

ARKE: An ontology-driven framework for automated mapping of local radiology procedure terms to the LOINC-RadLex playbook using large language model.

Journal of biomedical informatics·2026
Same journal

A validation-driven training controller for cross-lingual biomedical NER via reinforcement learning-based adaptive loss weighting.

Journal of biomedical informatics·2026
Same journal

ASP-HR: An Adaptive Spatial Perception and Hierarchical Reasoning mechanism for document-level biomedical relation extraction.

Journal of biomedical informatics·2026
Same journal

Beyond Accuracy: Safety-Centered guidelines for the evaluation of LLM-based therapy recommendation systems for chronic multimorbidity patients.

Journal of biomedical informatics·2026
See all related articles

This study introduces a new method for cell image segmentation using dynamic clustering and genetic algorithms to accurately identify cell boundaries, even with significant image noise. The approach effectively segments noisy cell images by modeling cell shapes with an ellipse.

Area of Science:

  • Biomedical image analysis
  • Computational biology
  • Computer vision

Background:

  • Accurate cell image segmentation is crucial for biological research.
  • Severe noise in microscopy images presents a significant challenge for traditional segmentation methods.
  • Existing techniques often struggle to delineate cell boundaries effectively under noisy conditions.

Purpose of the Study:

  • To develop a robust cell image segmentation approach for noisy images.
  • To integrate prior knowledge of cell shape into the segmentation process.
  • To improve the accuracy and reliability of cell boundary detection.

Main Methods:

  • A novel approach combining kernel-based dynamic clustering and a genetic algorithm.
  • Incorporation of an elliptical cell contour model to represent cell boundaries.

Related Experiment Videos

  • Utilizing gradient images to identify potential cell boundary points.
  • Employing a genetic algorithm to optimize elliptical model parameters for contour matching.

    • Main Results:

    • The proposed method demonstrates effective cell image segmentation under severe noise conditions.
    • Accurate delineation of cell contours was achieved using the elliptical model and genetic algorithm optimization.
    • Successful application on noisy images of human thyroid and small intestine cells.

    Conclusions:

    • The combined approach of dynamic clustering and genetic algorithms offers a powerful solution for noisy cell image segmentation.
    • Integrating prior knowledge of cell shape significantly enhances segmentation accuracy.
    • This method provides a reliable tool for quantitative analysis of cellular structures in challenging imaging scenarios.