Three‐dimensional fusion of clustered and classified features for enhancement of liver and lesions from abdominal radiology images

Abstract

Medical images usually are of low contrast in nature and have poor visual perception capability. Image enhancement techniques can improve significant features such as edge, texture and contrast which are helpful for further processing. This study discusses an image fusion-based enhancement scheme suitable for enhancing liver and lesions from abdominal radiology images. Apart from other fusion techniques, feature-based fusion is employed. The pixel-wise features selected are intensity values, gradient magnitude and local homogeneity. These pixel-wise features are clustered and classified using fuzzy C means (FCMs) and support vector machine (SVM), respectively. FCM clusters pixel-wise features into foreground and background, edge and non-edge as well as homogeneous and non-homogeneous regions. These two classes are applied for training and testing the SVM. The classifier output is transformed into images and the pixel-wise features of these images are fused to form a new image. Another important aspect of this scheme is the fusion of pixel-wise features in three dimensions to form a new image. The resulting image is an RGB image having better visual perception capacity having both enhancement in edge and texture. Pixel level multi-dimensional fusion is capable of enhancing the maximum relevant information.