Robust cDNA microarray image segmentation and analysis technique based on Hough circle transform

TL;DR

This paper introduces a robust cDNA microarray image segmentation method using Hough circle transform to improve spot localization accuracy and efficiency over traditional techniques.

Contribution

The paper presents a novel application of Hough circle transform for microarray spot segmentation, enhancing accuracy and robustness in spot detection.

Findings

Outperforms K-means in spot segmentation accuracy.

Achieves higher efficiency in spot localization.

Demonstrates superiority over SVM in experiments.

Abstract

One of the most challenging tasks in microarray image analysis is spot segmentation. A solution to this problem is to provide an algorithm than can be used to find any spot within the microarray image. Circular Hough Transformation (CHT) is a powerful feature extraction technique used in image analysis, computer vision, and digital image processing. CHT algorithm is applied on the cDNA microarray images to develop the accuracy and the efficiency of the spots localization, addressing and segmentation process. The purpose of the applied technique is to find imperfect instances of spots within a certain class of circles by applying a voting procedure on the cDNA microarray images for spots localization, addressing and characterizing the pixels of each spot into foreground pixels and background simultaneously. Intensive experiments on the University of North Carolina (UNC) microarray database indicate that the proposed method is superior to the K-means method and the Support vector machine (SVM). Keywords: Hough circle transformation, cDNA microarray image analysis, cDNA microarray image segmentation, spots localization and addressing, spots segmentation