Adaptive noise reduction and edge enhancement in medical images by using ICA

Abstract

This chapter focuses on the development of novel image enhancement and robust edge detection methods for practical medical image processing. It is known that the popular transformation-domain shrinkage approach for image enhancement applies a fixed mathematical basis to transform all images to be processed for noise or artifact reduction. However it is not adaptable to processed images, and then easily leads to blurring in the enhanced images. On the other hand, the techniques that are commonly used for edge detection are known as gradient and Laplacian operators (or mask), and smoothed gradient masks are typically used for edge detection in noisy images. However, these methods share a common major drawback wherein the associated masks are always fixed irrespective of the noise level in the images. In this study, we propose a novel learning-based method to adaptively deduce the transforming basis or masks from the processing data for medical image enhancement and robust edge detection. By using independent component analysis (ICA), the proposed learning-based method can extract suitable basis functions or masks for image transformation for processing data, which are adaptable to both the processed image and related noise in the image. The efficiency of the proposed learning-based method for medical image enhancement and edge detection is demonstrated experimentally using positron emission tomography (PET) and magnetic resonance imaging (MRI) medical images.