This paper proposes a method for progressive lossy-to-lossless compression of four-dimensional (4-D) medical images (sequences of volumetric images over time) by using a combination of three-dimensional (3-D) integer wavelet transform (IWT) and 3-D motion compensation. A 3-D extension of the set-partitioning in hierarchical trees (SPIHT) algorithm is employed for coding the wavelet coefficients. To effectively exploit the redundancy between consecutive 3-D images, the concepts of key and residual frames from video coding is used. A fast 3-D cube matching algorithm is employed to do motion estimation. The key and the residual volumes are then coded using 3-D IWT and the modified 3-D SPIHT. The experimental results presented in this paper show that our proposed compression scheme achieves better lossy and lossless compression performance on 4-D medical images when compared with JPEG-2000 and volumetric compression based on 3-D SPIHT.

We propose a coding system featuring 3D encoding/2D decoding object-based functionalities. Any object of any 2D image of the dataset can be recovered at a finely graded up to lossless quality. Compression is improved by exploiting the full correlation among data samples by means of 3D DWT. A swift access to 2D images is obtained by enabling 2D decoding. Given the index of the image of interest along the axis, only the concerned portion of the bitstream is decoded, at the desired quality. The selective access to data can be improved by splitting the image in regions corresponding to the different objects. Then, a suitable ordering of the encoded information within the bitstream enables random access to any object at the desired rate. This enables a pseudo-lossless regime, where the diagnostically relevant parts of the image are represented without loss, while a lower quality is assumed to be acceptable for the others. Results show that the proposed system is a good compromise between the gain in compression efficiency provided by 3D systems and the fast access to the data of 2D ones. 1.

Abstract—We propose and evaluate a number of novel improvements to the mesh-based coding scheme for 3-D brain magnetic resonance images. This includes: 1) elimination of the clinically irrelevant background leading to meshing of only the brain part of the image; 2) content-based (adaptive) mesh generation using spatial edges and optical flow between two consecutive slices; 3) a simple solution for the aperture problem at the edges, where an accurate estimation of motion vectors is not possible; and 4) contextbased entropy coding of the residues after motion compensation using affine transformations. We address only lossless coding of the images, and compare the performance of uniform and adaptive mesh-based schemes. The bit rates achieved (about 2 bits per voxel) by these schemes are comparable to those of the state-of-the-art three-dimensional (3-D) wavelet-based schemes. The mesh-based schemes have been shown to be effective for the compression of 3-D brain computed tomography data also. Adaptive mesh-based schemes perform marginally better than the uniform mesh-based methods, at the expense of increased complexity. Index Terms—3-D coding, content-based mesh, context-based modeling, medical image coding, volumetric image compression. I.

We propose a fully three-dimensional (3-D) wavelet-based coding system featuring 3-D encoding/two-dimensional (2-D) decoding functionalities. A fully 3-D transform is combined with context adaptive arithmetic coding; 2-D decoding is enabled by encoding every 2-D subband image independently. The system allows a finely graded up to lossless quality scalability on any 2-D image of the dataset. Fast access to 2-D images is obtained by decoding only the corresponding information thus avoiding the reconstruction of the entire volume. The performance has been evaluated on a set of volumetric data and compared to that provided by other 3-D as well as 2-D coding systems. Results show a substantial improvement in coding efficiency (up to 33%) on volumes featuring good correlation properties along the axis. Even though we did not address the complexity issue, we expect a decoding time of the order of one second/image after optimization. In summary, the proposed 3-D/2-D multidimensional layered zero coding system provides the improvement in compression efficiency attainable with 3-D systems without sacrificing the effectiveness in accessing the single images characteristic of 2-D ones.

We propose a fully three-dimensional wavelet-based coding system featuring 3D encoding/2D decoding functionalities. A fully threedimensional transform is combined with context adaptive arithmetic coding; 2D decoding is enabled by encoding every 2D subband image independently. The system allows a finely graded up to lossless quality scalability on any 2D image of the dataset. Fast access to 2D images is obtained by decoding only the corresponding information thus avoiding the reconstruction of the entire volume. The performance has been evaluated on a set of volumetric data and compared to that provided by other 3D as well as 2D coding systems. Results show a substantial improvement in coding efficiency (up to 33%) on volumes featuring good correlation properties along the z axis. Even though we did not address the complexity issue, we expect a decoding time of the order of one second/image after optimization. In summary, the proposed 3D/2D Multidimensional Layered Zero Coding System (3D/2D MLZC) provides the improvement in compression efficiency attainable with 3D systems without sacrificing the effectiveness in accessing the single images characteristic of 2D ones.

Abstract:- The MR images play a major role in the diagnosis of vital organs of the human body. Huge amount of medical image data is generated on a daily basis. This data needs to be stored for future study and follow up. This requires a large amount of storage space which is especially true for three- dimensional (3D) medical data formed by image sequences. This has resulted in image compression being an important issue in reducing the cost of data storage and transmission time. In this paper we evaluate the performance of wavelet based coding algorithm 3D SPIHT using MATLAB. For this purpose 8 MRI head scan data test sets of size 128×128×8 voxels are used and we have evaluated PSNR as the error metric for spatial levels 3 and 5 after forming adaptive mesh and the simulation results show the comparison of performance of PSNR values for various bits per pixels. Keywords — Content-based mesh, Image compression, Wavelet coding, JPEG coding, Simulation. 1.

This paper proposes an improved version of lifting based 3D Discrete Wavelet Transform (DWT) VLSI architecture which uses bi-orthogonal 9/7 filter processing. The whole architecture was optimized in efficient pipeline and parallel design way to speed up and achieve higher hardware utilization. The Discrete Wavelet Transform (DWT) was based on time-scale representation, which provides efficient multi-resolution. The lifting based DWT architecture has the advantage of lower computational complexities transforming signals with extension and regular data flow. This is suitable for VLSI implementation. It uses a cascade combination of three 1-D wavelet transform along with a set of in-chip memory buffers between the stages. The discrete wavelet transform (DWT) is being increasingly used for image coding. This is due to the fact that DWT supports features like progressive image transmission (by quality, by resolution), ease of compressed image manipulation, region of interest coding, etc. DWT has traditionally been implemented by convolution. Such an implementation demands both a large number of computations and a large storage features that are not desirable for either high-speed or low-power applications. Recently, a lifting-based scheme that often requires far fewer computations has been proposed for the DWT. The main feature of the lifting based DWT scheme is to break up the high pass and low pass filters into a sequence of upper and lower triangular matrices and convert the filter implementation into banded matrix multiplications. Such a scheme has several advantages, including “in-place ” computation of the DWT, integer-to-integer wavelet transform (IWT), symmetric forward and inverse transform, etc. Therefore, it comes as no surprise that lifting has been chosen in the upcoming.

An evaluation of the impact of lossy compression on rigid registration algorithms for medical images is proposed. Due to the lack of gold standard for many clinical problems, the framework relies on a statistical procedure that estimates a reference from a large set of uncompressed images. The robustness, repeatability and accuracy of registration algorithms can then be derived for each compression ratio. Results are obtained thanks to a grid technology handling the computation cost of the method. Experiments reveal that the impact of compression is quite negligible below a significant compression ratio if the registration algorithm has a good multi-scale handling. Beyond this threshold, feature-based methods are highly penalized. 1.