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 Video

Updated: May 23, 2026

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
06:08

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

Published on: March 21, 2025

A feature-based learning framework for accurate prostate localization in CT images.

Shu Liao, Dinggang Shen

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |April 19, 2012
    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

    Denoising Diffusion-Weighted Images Using Grouped Iterative Hard Thresholding of Multi-Channel Framelets.

    Computational diffusion MRI : MICCAI Workshop·2017
    Same author

    Robust Construction of Diffusion MRI Atlases with Correction for Inter-Subject Fiber Dispersion.

    Computational diffusion MRI : MICCAI Workshop·2017
    Same author

    Robust Fusion of Diffusion MRI Data for Template Construction.

    Scientific reports·2017
    Same author

    Learning-Based Multimodal Image Registration for Prostate Cancer Radiation Therapy.

    Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2017
    Same author

    Segmenting hippocampal subfields from 3T MRI with multi-modality images.

    Medical image analysis·2017
    Same author

    Joint Discriminative and Representative Feature Selection for Alzheimer's Disease Diagnosis.

    Machine learning in medical imaging. MLMI (Workshop)·2017
    Same journal

    Change-Prior-Guided Unsupervised Change Detection of Heterogeneous Remote Sensing Images.

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
    Same journal

    AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
    Same journal

    BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
    Same journal

    GoP-based Quality Enhancement on Video Compression.

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
    Same journal

    Align then Tensorize: Multi-Level Consistent Anchor Graph Learning for Scalable Multi-View Clustering.

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
    Same journal

    Beyond Fidelity: Diverse Image Synthesis via Retrieval-Augmented Diffusion.

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
    See all related articles

    This study introduces a novel feature-based learning framework for precise prostate localization in CT images. The method effectively addresses challenges like low contrast, motion, and bowel gas, achieving superior accuracy in medical image analysis.

    Area of Science:

    • Medical Imaging
    • Radiotherapy
    • Computer-Aided Diagnosis

    Background:

    • Prostate segmentation in CT images is crucial for medical analysis and image-guided radiation therapy.
    • Challenges include low image contrast, prostate motion, and variable image appearance due to bowel gas.
    • Accurate localization is essential for effective treatment planning and delivery.

    Purpose of the Study:

    • To develop and evaluate a feature-based learning framework for accurate prostate localization in CT images.
    • To overcome limitations of existing methods in handling image contrast, motion, and bowel gas artifacts.
    • To improve the precision of prostate localization for radiation therapy applications.

    Main Methods:

    • Extraction and selection of robust anatomical features for voxel signature.

    More Related Videos

    Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
    14:08

    Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

    Published on: April 13, 2013

    Related Experiment Videos

    Last Updated: May 23, 2026

    A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
    06:08

    A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

    Published on: March 21, 2025

    Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
    14:08

    Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

    Published on: April 13, 2013

  • Automatic filtering of irrelevant regions, such as those containing bowel gas.
  • An online update mechanism to integrate population and patient-specific information for adaptive localization.

    • Main Results:

    • The proposed method demonstrated high localization accuracy on a CT prostate dataset of 24 patients.
    • Experimental results showed superior performance compared to several state-of-the-art prostate localization algorithms.
    • The framework effectively handled variations caused by prostate motion and bowel gas.

    Conclusions:

    • The feature-based learning framework offers a robust solution for accurate prostate localization in CT images.
    • The method's ability to adapt to image variations enhances its clinical applicability in radiation therapy.
    • This approach represents a significant advancement in automated medical image analysis for prostate cancer treatment.