Related Experiment Video
Updated: Oct 12, 2025
08:40
Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging
Published on: April 8, 2016
13.0K
An Optimized Approach for Prostate Image Segmentation Using K-Means Clustering Algorithm with Elbow Method
Rachid Sammouda1, Ali El-Zaart2
1Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia.
Computational Intelligence and Neuroscience
|November 25, 2021
Summary
This study introduces an optimized image segmentation method for prostate cancer analysis. It uses k-means clustering and the elbow method to accurately segment histological and NIR images, aiding in prostate cancer diagnosis.
Area of Science:
- Medical Imaging
- Oncology
- Computational Biology
Background:
- Prostate cancer is a prevalent disease affecting men globally.
- Prostate-specific membrane antigen (PSMA) is a key target for imaging-based prostate cancer diagnosis.
- Photodynamic therapy (PDT) offers a noninvasive treatment option for various cancers.
Purpose of the Study:
- To segment and analyze pixels in histological and near-infrared (NIR) prostate cancer images.
- To utilize PSMA-targeting PDT agents for enhanced image guidance and therapy.
- To develop an optimized image segmentation approach for prostate cancer diagnosis.
Main Methods:
- Acquisition of prostate cancer images using PSMA-targeting PDT low molecular weight agents.
- Application of an optimized image segmentation approach combining k-means clustering with the elbow method.
- Automatic determination of the optimal number of clusters for pixel analysis.
Main Results:
- Successful segmentation and clustering of pixels in prostate cancer images.
- Demonstration of the approach's ability to provide an optimum number of clusters.
- Validation of the method for prostate cancer analysis and diagnosis.
Conclusions:
- The proposed optimized image segmentation approach is effective for prostate cancer analysis.
- This method aids in accurate diagnosis and image-guided treatment planning.
- PSMA-targeting agents combined with advanced segmentation offer a promising avenue for prostate cancer management.
