Learning-based method for spot addressing in microarray images

Addressing spots in microarray images and deriving expression values for corresponding genes are fundamental tasks in microarray image analysis. Reliable expression values can be obtained only if the spot locations are accurately known. Here, a novel approach for spot addressing in microarray images based oil supervised learning is proposed. The aim is to locate each spot through classifying the image based oil local features into spot centers and background using support vector machine classifier. The resulting spot location information is complemented through image processing methods in the post-processing phase. Our method, through searching locations for individual spots, enables accurate segmentation and extraction of expression values. The benefit of searching individual spots becomes clear in case of misaligned spots or spot rows.