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Related Experiment Video

Updated: May 21, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Microarray core detection by geometric restoration.

Jimmy C Azar¹, Christer Busch, Ingrid B Carlbom

¹Centre for Image Analysis, Uppsala University, Uppsala, Sweden. jimmy.azar@cb.uu.se

Analytical Cellular Pathology (Amsterdam)

|June 12, 2012

Summary

This study introduces an automated method for detecting tissue cores in whole-slide images. The technique accurately locates tissue cores for high-throughput analysis in histopathology, improving cancer diagnosis.

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Area of Science:

Digital Pathology
Computational Histopathology
Medical Image Analysis

Background:

Whole-slide imaging of tissue microarrays (TMAs) enables large-scale histopathological sample analysis.
Automated image processing is crucial for high-throughput analysis in cancer diagnosis.

Purpose of the Study:

To present a fully automated method for accurate detection and localization of tissue cores in TMAs.
To develop an efficient algorithm for high-throughput analysis of histopathological images.

Main Methods:

A novel method based on geometric restoration of core shapes, without assumptions on grid geometry.
Utilizes hierarchical clustering and Davies-Bouldin index for core number estimation.
Employs morphological granulometry for radius refinement and reconstructs circular discs for localization.

Main Results:

The proposed method accurately reconstructs tissue core locations without localization errors.
Demonstrates superior efficiency compared to Hough transform-based circle detection methods.
Achieves accurate and efficient automated high-throughput analysis of microarray images.

Conclusions:

The developed algorithm provides a simple, accurate, and computationally efficient solution for TMA image analysis.
Facilitates automated high-throughput histopathological analysis for improved diagnostic capabilities.
Advances digital pathology by enabling robust automated detection and localization of tissue cores.