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

Identification of substructures in CT-images

A H Mir1, S N Tandon, M Hanmandlu

  • 1Centre for Biomedical Engg. IIT Delhi, India.

Biomedical Sciences Instrumentation
|January 1, 1994
PubMed
Summary

This study introduces an Autoregressive model for describing organ shapes in abdominal CT images. The model effectively identifies substructures, improving automated radiographic image interpretation.

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

Predictive radiomics based ensemble machine learning approach in CT lung nodule diagnosis.

Journal of the Egyptian National Cancer Institute·2025
Same author

Anomalous nucleation of crystals within amorphous germanium nanowires during thermal annealing.

Nanotechnology·2020
Same author

Synchronization of Fractional Order Neurons in Presence of Noise.

IEEE/ACM transactions on computational biology and bioinformatics·2020
Same author

Weighted dimensionality reduction and robust Gaussian mixture model based cancer patient subtyping from gene expression data.

Journal of biomedical informatics·2020
Same author

Effect of aluminium concentration on phase formation and radiation stability of Cr<sub>2</sub>Al <sub>x</sub> C thin film.

Nanotechnology·2020
Same author

A topological approach for cancer subtyping from gene expression data.

Journal of biomedical informatics·2020

Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Image Analysis

Background:

  • Automated interpretation of radiographic images faces challenges due to the vague definition of organ shapes.
  • Accurate identification of substructures is crucial for computer-assisted diagnostic systems.

Purpose of the Study:

  • To propose an Autoregressive model for describing the shape of substructures (organs) from abdominal CT images.
  • To evaluate the effectiveness of the proposed model in identifying these substructures.

Main Methods:

  • An Autoregressive model was developed for shape description.
  • The model utilized outer boundaries of organs extracted from abdominal CT images.
  • Matching results were used to assess identification accuracy.

Main Results:

  • The proposed Autoregressive model demonstrated effectiveness in shape description.
  • Successful identification of substructures within abdominal CT images was achieved.
  • The model's performance indicates its utility in automated image analysis.

Conclusions:

  • The Autoregressive model is a viable approach for substructure identification in abdominal CT images.
  • This method enhances automated systems for radiographic image interpretation.
  • Further research can explore the model's application in diverse medical imaging contexts.

Related Experiment Videos