A novel algorithm for segmentation of lung images

Abstract

Several image segmentation techniques have been presented in the literature applied in the medical domain. However, there are few multiscale segmentation methods that can segment the medical image so that various components within the image could be separated at multiple scales. In this paper, we present a new segmentation method based on an optical transfer function implemented in the Frequency domain. With this new segmentation technique, we demonstrate that it is possible to segment the High Resolution Computed Tomographic (HRCT) images into its various components at multiple scales hence separating the information available in HRCT image. We show that the HRCT image can be segmented such that we get separate images for bones, tissues, lungs and anatomical structures within lungs. The processing is done in frequency domain using the Fast Fourier Transform and Discrete Cosine Transform. Further, we propose an algorithm for extraction of anatomical structures from the segmented image. © Springer-Verlag Berlin Heidelberg 2006.