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

Computer discrimination between benign and malignant urothelial cells.

L G Koss, P H Bartels, M Bibbo

    Acta Cytologica
    |July 1, 1975
    PubMed
    Summary

    Computer analysis accurately distinguishes between benign and malignant cells, with less than ten percent error. Nuclear texture analysis is a key feature for this diagnostic advancement in urinary tract cytology.

    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

    A PC-based system for the objective analysis of histologic specimens through quantitative contextual karyometry.

    Applied optics·2010
    Same author

    Knowledge engineering in quantitative histopathology.

    Applied optics·2010
    Same author

    Computers or cytotechnologists?

    Acta cytologica·1999
    Same author

    Automated screening for cervical cancer: diagnostic decision procedures.

    Acta cytologica·1997
    Same author

    Computer-assisted quality assurance.

    Acta cytologica·1996
    Same author

    Industrial developments in automated cytology as submitted by their developers.

    Analytical and quantitative cytology and histology·1993

    Area of Science:

    • Uropathology
    • Computational Pathology
    • Medical Imaging Analysis

    Background:

    • Accurate cell classification is crucial for diagnosing urinary tract conditions.
    • Traditional methods of cell analysis can be time-consuming and prone to human error.
    • Advancements in computational methods offer potential for improved diagnostic accuracy and efficiency.

    Purpose of the Study:

    • To evaluate the efficacy of TICAS (Total Integrated Classification and Analysis System) routines and subroutines for computer-aided discrimination between benign and malignant cells.
    • To identify key cellular features, such as nuclear texture, that are significant for accurate classification.
    • To assess the potential of computerized cytology for improving the diagnosis of urinary tract abnormalities.

    Main Methods:

    Related Experiment Videos

  • Utilized TICAS routines and subroutines for automated cell analysis.
  • Focused on nuclear texture as a primary feature for distinguishing cell types.
  • Quantified classification error rates to assess performance.

    • Main Results:

    • Achieved computer discrimination between benign and malignant cells with a classification error rate not exceeding ten percent.
    • Identified nuclear texture as a feature of major diagnostic significance.
    • Demonstrated the feasibility of automated analysis for urinary tract cytology.

    Conclusions:

    • Computerized analysis using TICAS demonstrates high accuracy in differentiating benign and malignant cells.
    • Nuclear texture is a critical parameter for reliable computer-aided diagnosis in cytology.
    • This approach shows significant promise for the future of automated urinary tract cytology, potentially enhancing diagnostic capabilities.