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Related Experiment Video

Updated: Jul 14, 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

Removing non-nuclei information from histopathological images: A preprocessing step towards improving nuclei

Ricardo Moncayo¹, Anne L Martel^2,3, Eduardo Romero¹

¹Computer Imaging and Medical Applications Laboratory (CIM@LAB), Universidad Nacional de Colombia, Bogotá, Colombia.

Journal of Pathology Informatics

|October 9, 2023

Summary

This study introduces a novel method to enhance nuclei detection and segmentation in digital pathology images. By removing background noise using noiselet features and K-means clustering, the system significantly improves accuracy for computer-aided diagnosis.

Keywords:

Cancer disease Histopathology Noiselet transformation Nuclei detection

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Author Spotlight: Enhancing Nuclei Isolation for Multiome Sequencing in Challenging Tumor Microenvironments

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Published on: October 13, 2023

Area of Science:

Digital Pathology
Medical Image Analysis
Computational Biology

Background:

Accurate nuclei detection and segmentation are crucial for computer-aided diagnosis in digital pathology.
Hematoxylin and eosin (HE) images present challenges due to variable nuclei and background appearances.
Existing methods struggle with background noise and inconsistent cellular structures.

Purpose of the Study:

To develop and evaluate a method for improving nuclei detection and segmentation in HE images.
To enhance the reliability of computer-aided diagnosis systems by refining image analysis.
To specifically address the challenge of variable appearances in digital pathology images.

Main Methods:

Image preprocessing using noiselet transform to capture spatial features.
K-means clustering to create a 'noiselet code-book' for feature representation.
Tile-based classification using histograms of noiselet-projected patches to differentiate nuclei from background.
Integration with a watershed-based segmentation algorithm.

Main Results:

The denoising-plus-watershed method demonstrated improved nuclei detection with an average F-score increase from 0.830 to 0.86.
Nuclei segmentation accuracy was enhanced, with the Dice score rising from 0.701 to 0.723.
The method showed effectiveness across 8 different tissue types in an open database.

Conclusions:

The proposed method effectively removes background information, leading to better nuclei detection and segmentation in HE images.
This approach offers a significant improvement over traditional methods for digital pathology image analysis.
The enhanced accuracy supports the development of more robust computer-aided diagnosis systems.