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Quantification of Cell-Substrate Adhesion Area and Cell Shape Distributions in MCF7 Cell Monolayers

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Published on: June 24, 2020

HEp-2 cell classification using shape index histograms with donut-shaped spatial pooling.

Anders Boesen Lindbo Larsen, Jacob Schack Vestergaard, Rasmus Larsen

IEEE Transactions on Medical Imaging

|April 25, 2014

Summary

This summary is machine-generated.

A novel texture analysis method using shape index histograms offers superior automatic classification of HEp-2 cell images. This approach provides a competitive and efficient solution for indirect immunofluorescence image analysis.

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Area of Science:

Medical Imaging
Computer Vision
Biomedical Image Analysis

Background:

Accurate classification of HEp-2 cell staining patterns is crucial for diagnosing autoimmune diseases.
Existing automated methods for indirect immunofluorescence image analysis face challenges in capturing complex texture features.

Purpose of the Study:

To introduce a novel texture measure, shape index histograms, for enhanced automatic classification of HEp-2 cell images.
To develop a radially symmetric spatial decomposition scheme suitable for cell image analysis.

Main Methods:

Utilizing shape index histograms to capture second-order image structure at multiple scales.
Implementing a spatial decomposition scheme with donut-shaped pooling regions for histogram contributions.
Evaluating the method on ICIP 2013 and ICPR 2012 competition datasets.

Main Results:

Shape index histograms demonstrate superiority over other texture descriptors for HEp-2 cell classification.
The proposed method is highly competitive compared to existing automated systems.
The method achieves strong performance with relatively low computational complexity.

Conclusions:

Shape index histograms represent a powerful new tool for texture analysis in biomedical images.
The developed spatial decomposition enhances the effectiveness of texture-based classification for HEp-2 cells.
This method offers an efficient and accurate approach for automated indirect immunofluorescence image analysis.