On the use of lossless integer wavelet transforms in medical image segmentation

Recent trends in medical image processing involve computationally intensive processing techniques on large data sets, especially for 3D applications such as segmentation. registration, volume rendering etc. Multi-resolution image processing techniques have been used in order to speed-up these methods. However, all well-known techniques currently used in multi-resolution medical image processing rely on using Gaussain-based or other equivalent floating point representations that are lossy and irreversible. In this paper, we study the use of Integer Wavelet Transforms (IWT) to address the issue of lossless representation and reversible reconstruction for such medical image processing applications while still retaining all the benefits which floating-point transforms offer such as high speed and efficient memory usage. In particular, we consider three low-complexity reversible wavelet transforms namely the - Lazy-wavelet. the Haar wavelet or (1, 1) and the S+P transform as against the Gaussian filter for multi-resolution speed-up of an automatic bone removal algorithm for abdomen CT Angiography.