3-D medical image compression using 3-D wavelet coders

Medical image compression using 3-D Hartley transform

In this paper a low complexity algorithm is proposed for near lossless compression of images. The reconstructed near lossless image can differ from the original one within a pixelwise error tolerance. This property is used to convert the histogram of the original image, by the proposed algorithm, to a new histogram which is proved to have minimum entropy. Hence, a new image is formed which has minimum entropy and high spatial correlation among its pixels and can efficiently be compressed. Simulation results show the effectiveness of this compression algorithm.

Near lossless image compression by local packing of histogram

In this paper, 3-D discrete Hartley, cosine and Fourier transforms are used for the compression of magnetic resonance images and X-ray angiograms. The performance results are then compared and evaluated. The transforms are applied on image blocks of sizes 8times8timesM where M represents the number of slices. The resultant transform coefficients are quantized and then encoded using a combination of run length and Huffman coding schemes to achieve maximum compression. The performances of the transforms are evaluated in terms of peak signal to noise ratio and bit rate. It is found from the experimental results, that 3-D discrete Hartley transform yields the best results for magnetic resonance brain images whereas for X-ray angiograms the 3-D discrete cosine transform is found to be superior to the other two transforms

Performance evaluation of 3-D transforms for medical image compression

In this paper an algorithm is proposed which performs near-lossless image compression. For each pixel in a row of the image a group of value-states are considered, which have values close to that of the pixel. A trellis is constructed for every row of the image where the nodes of the trellis are the states of the pixels of that row. The goal of the algorithm is to find a path on this trellis that creates a sequence which can be efficiently coded using run length encoding (RLE). For sections of the pixels of the row that suitable RLE cannot be achieved then minimization of the entropy is employed to complete a path on the trellis. The application of the algorithm to a wide range of standard images shows that the scheme, while having low computational complexity, is competitive with other near-lossless image compression methods.

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Near-Lossless Image Compression Based on Maximization of Run Length Sequences

It is shown that the discrete Hartley transform (DHT) of length N = 4 can be used to perform an integer-to-integer transformation. This behavior of the DHT can be used to compute a 2-D separable lossless Hartley like transform (L-HLT). The performance of the L-HLT is found to be better than the DCT for near lossless image compression and wavelet-based filter bank approach for lossless compression.

http://ieeexplore.ieee.org/iel5/9074/28788/01292445.pdf

J. Gupta

Papers

Near lossless image compression using lossless Hartley like transform