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Related Experiment Video

Updated: Oct 12, 2025

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

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 summary is machine-generated.

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.

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

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.