Development of new filter bank for detection of nodular patterns and linear patterns in medical images

Abstract

Abnormal shadows in medical images are described as isolated nodular structures or linear structures. However, the size and shape of the shadows usually vary from case to case. Consequently, in order to detect abnormal shadows with high accuracy, we need to use a filter that can deal with various sizes and shapes, rather than a fixed filter assuming a typical shadow shape. The purpose of this study is to detect abnormal shadows with high accuracy, and a new perfect reconstruction filter bank is developed, introducing multiresolution based on wavelet analysis. The filter bank is designed so that the subimages generated the elements of a Hessian matrix at each resolution level. By calculating the small and large eigenvalues, a new filter bank has the following three properties. (1) Nodular patterns of various sizes can be enhanced. (2) Both nodular and linear patterns of various sizes can be enhanced. (3) The original image can be reconstructed with these patterns removed. The filter bank is applied to enhance microcalcifications in mammograms and to enhance/remove bone tissue in chest radiographs. The effectiveness of the method is demonstrated. © 2005 Wiley Periodicals, Inc. Syst Comp Jpn, 36(13): 81–91, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.20171