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Immunocytochemistry and Immunohistochemistry

Immunocytochemistry and Immunohistochemistry

Immunocytochemistry (ICC) and immunohistochemistry (IHC) are techniques that use antibodies to check for specific proteins or antigens in a sample. The technique was first published by Albert Coons in 1941 to detect the presence of pneumococcal antigen in tissue sections from mice infected with Pneumococcus. Immunocytochemistry helps localization of proteins or antigens in individual cells like blood cells, stem cells, etc., while immunohistochemistry does the same for tissue samples.
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Updated: May 5, 2026

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Published on: April 8, 2015

Automatic cell classification and quantification with machine learning in immunohistochemistry images.

Pikting Cheung¹, Wei Zhang², Muhammad Shehzad Khan^1,3

¹Department of Physics, City University of Hong Kong, Hong Kong, SAR, China.

Journal of Histotechnology

|July 1, 2025

Summary

This summary is machine-generated.

An innovative mathematical method precisely quantifies lymphoma cells in immunohistochemistry (IHC) images. This automated approach improves diagnostic accuracy for lymphoma classification, reducing human error.

Keywords:

Cell quantification computational pathology immunohistochemistry lymphoma machine learning

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Area of Science:

Oncology
Computational Pathology
Medical Imaging Analysis

Background:

Lymphoma incidence is increasing, necessitating accurate classification methods.
Immunohistochemistry (IHC) is crucial for lymphoma classification.
Manual cell counting in IHC images is time-consuming and prone to error.

Purpose of the Study:

To develop an automated mathematical methodology for precise quantification and spatial analysis of immunopositive and immunonegative cells in CD3-stained lymphoma IHC images.
To reduce human intervention and improve the accuracy of cell counting in lymphoma diagnosis.

Main Methods:

Developed an algorithm using a mathematical color model for cell differentiation.
Employed morphological erosion, algorithmic transformations, and customized histogram equalization for feature enhancement.
Utilized refined local thresholding for improved classification precision.
Applied a customized circular Hough transform for cell counting and spatial data assessment.

Main Results:

Achieved an overall accuracy of 93.98% for automatic cell counts in IHC image samples.
Automated counts and location information were cross-validated by three pathology specialists.
Demonstrated effective and reliable performance of the automated approach.

Conclusions:

The innovative framework enhances lymphoma cell counting accuracy in IHC images.
Combines physics-based color understanding with machine learning for improved diagnosis.
Reduces the risks of human error in lymphoma classification.