Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

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

Reducing False-Positives Due to Urinary Stagnation in the Prostatic Urethra on 18 F-DCFPyL PSMA PET/CT With MRI.

Clinical nuclear medicine·2024

Same author

PRECISE Version 2: Updated Recommendations for Reporting Prostate Magnetic Resonance Imaging in Patients on Active Surveillance for Prostate Cancer.

European urology·2024

Same author

Automated prostate gland segmentation in challenging clinical cases: comparison of three artificial intelligence methods.

Abdominal radiology (New York)·2024

Same author

Evaluating Diagnostic Accuracy and Inter-reader Agreement of the Prostate Imaging After Focal Ablation Scoring System.

European urology open science·2024

Same author

A Phase 1 Trial of Salvage Stereotactic Body Radiation Therapy for Radiorecurrent Prostate Cancer After Brachytherapy.

International journal of radiation oncology, biology, physics·2024

Same author

Localized high-risk prostate cancer harbors an androgen receptor low subpopulation susceptible to HER2 inhibition.

medRxiv : the preprint server for health sciences·2024

Search research articles

Related Experiment Video

Updated: May 16, 2026

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

Prostate segmentation in MR images using discriminant boundary features.

Meijuan Yang¹, Xuelong Li, Baris Turkbey

¹Center for OPTical IMagery Analysis and Learning, State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, Shaanxi, China. meijuan.yang@opt.ac.cn

IEEE Transactions on Bio-Medical Engineering

|November 30, 2012

Summary

This summary is machine-generated.

This study introduces a novel method for prostate magnetic resonance image segmentation using adaptive Scale Invariant Feature Transform (SIFT) and discriminant analysis. The approach enhances segmentation accuracy for prostate carcinoma diagnosis and surgical planning.

More Related Videos

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

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

Published on: March 21, 2025

Manual Segmentation of the Human Choroid Plexus Using Brain MRI

Manual Segmentation of the Human Choroid Plexus Using Brain MRI

Published on: December 15, 2023

Related Experiment Videos

Last Updated: May 16, 2026

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

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

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

Published on: March 21, 2025

Manual Segmentation of the Human Choroid Plexus Using Brain MRI

Manual Segmentation of the Human Choroid Plexus Using Brain MRI

Published on: December 15, 2023

Area of Science:

Medical Imaging
Computer Vision
Machine Learning

Background:

Prostate segmentation in MRI is crucial for diagnosing and planning surgery for prostate carcinoma.
Statistical shape models are widely used but require robust local features for accuracy.
Scale Invariant Feature Transform (SIFT) captures local boundary information but lacks location-specific scale and variance.

Purpose of the Study:

To develop an enhanced segmentation method for prostate MRI.
To improve the accuracy of statistical shape models in medical image segmentation.
To make SIFT features adaptive to landmark locations for more precise prostate segmentation.

Main Methods:

Introduced discriminant analysis to measure SIFT feature distinctiveness and adapt scale/variance to landmark locations.
Developed separate, optimized appearance descriptors for each landmark due to significant gray value and gradient variations.
Implemented a two-stage coarse-to-fine segmentation approach incorporating local shape variations.

Main Results:

The proposed algorithms demonstrated efficiency in prostate segmentation from MR images.
The adaptive SIFT features and discriminant analysis improved the distinctiveness and robustness of local features.
The two-stage segmentation approach effectively utilized local shape variations for accurate results.

Conclusions:

The study presents an effective algorithm for prostate MRI segmentation.
The integration of adaptive SIFT features and discriminant analysis enhances segmentation accuracy.
This method shows promise for improving computer-assisted diagnosis and surgical planning in prostate cancer treatment.