Robust cDNA microarray image segmentation and analysis technique based on Hough circle transform
This addresses the challenge of accurately segmenting spots in microarray images for bioinformatics researchers, but it is incremental as it adapts an existing technique to a specific domain.
The paper tackled the problem of spot segmentation in cDNA microarray images by applying the Circular Hough Transform (CHT) to improve accuracy and efficiency in localization and segmentation, showing superiority over K-means and SVM methods in experiments on the UNC database.
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