We propose resolution progressive Three-Dimensional Set Partitioned
Embedded bloCK (3D-SPECK), an embedded wavelet based algorithm for
hyperspectral image compression. The proposed algorithm also supports
random
Region-Of-Interest (ROI) access. For a hyperspectral image sequence,
integer wavelet transform is applied on all three dimensions. The
transformed image sequence exhibits a hierarchical pyramidal
structure. Each subband is treated as a code block. The algorithm
encodes each code block separately to generate embedded
sub-bitstream. The sub-bitstream for each subband is SNR progressive,
and for the whole sequence, the overall bitstream is resolution
progressive. Rate is allocated amongst the sub-bitstreams produced for
each block. We always have the full number of bits possible devoted to
that given scale, and only partial decoding is needed for the lower
than full scales. The overall bitstream can serve the
lossy-to-lossless hyperspectral image compression. Applying resolution
scalable 3D-SPECK independently on each 3D tree can generate embedded
bitstream to support random ROI access. Given the ROI, the
algorithm can identify ROI and reconstruct only the ROI. The
identification of ROI is done at the decoder side. Therefore, we only
need to encode one embedded bitstream at the encoder side, and
different users at the decoder side or the transmission end could
decide their own different regions of interest and access or decode
them. The structure of hyperspectral images reveals spectral
responses that would seem ideal candidates for compression by
3D-SPECK. Results show that the proposed algorithm has excellent
performance on hyperspectral image compression.
We present a computer vision tool to improve the clinical outcome of patients undergoing radiation therapy for prostate cancer by improving irradiation technique. While intensity modulated radiotherapy (IMRT) allows one to irradiate a specific region in
the body with high accuracy, it is still difficult to know exactly where to aim the radiation beam on every day of the 30~40 treatments that are necessary. This paper presents a geometric model-based technique to accurately segment the prostate and other surrounding structures in a daily serial CT image, compensating for daily motion and shape variation. We first acquire a collection of serial CT scans of patients undergoing external beam radiotherapy, and manual segmentation of the prostate and other nearby structures by radiation oncologists. Then we train shape and local appearance models for the structures of interest. When new images are available, an iterative algorithm is applied to locate the prostate and surrounding structures
automatically. Our experimental results show that excellent matches can be given to the prostate and surrounding structure. Convergence is declared after 10 iterations. For 256 x 256 images, the mean distance between the hand-segmented contour and the automatically estimated contour is about 1.5 pixels (2.44 mm), with variance about 0.6 pixel (1.24 mm).
We investigate and compare the performance of several three-dimensional (3D) embedded wavelet algorithms on lossless 3D
image compression. The algorithms are Asymmetric Tree Three-Dimensional Set Partitioning In Hierarchical Trees (AT-3DSPIHT), Three-Dimensional Set Partitioned Embedded bloCK (3D-SPECK), Three-Dimensional Context-Based Embedded Zerotrees of Wavelet coefficients (3D-CB-EZW), and JPEG2000 Part II for multi-component images. Two kinds of images are investigated in our study -- 8-bit CT and MR medical images and 16-bit AVIRIS hyperspectral images. First, the performances by using different size of coding units are compared. It shows that increasing the size of coding unit improves the performance somewhat. Second, the performances by using different integer wavelet transforms are compared for AT-3DSPIHT, 3D-SPECK and
3D-CB-EZW. None of the considered filters always performs the best for
all data sets and algorithms. At last, we compare the different lossless compression algorithms by applying integer wavelet transform on the entire image volumes. For 8-bit medical image volumes, AT-3DSPIHT performs the best almost all the time, achieving average of 12% decreases in file size compared with JPEG2000 multi-component, the second performer. For 16-bit hyperspectral images, AT-3DSPIHT always performs the best, yielding average 5.8% and 8.9% decreases in file size compared with 3D-SPECK and JPEG2000 multi-component, respectively. Two 2D compression algorithms, JPEG2000 and UNIX zip, are also included for reference, and all 3D algorithms perform much better than 2D algorithms.
A Hyperspectral image is a sequence of images generated by collecting contiguously spaced spectral bands of data. One can view such an image sequence as a three-dimensional array of intensity values (pixels) within a rectangular prism. We present a Three-Dimensional Set Partitioned Embedded bloCK (3DSPECK) algorithm based on the observation that hyperspectral images are contiguous in the spectrum axis (this implies large inter-band correlations) and there is no motion between bands. Therefore, the three-dimensional discrete wavelet transform can fully exploit the inter-band correlations. A SPECK partitioning algorithm extended to three-dimensions is used to sort significant pixels. Rate distortion (Peak Signal-to-Noise Ratio (PSNR) vs. bit rate) performances were plotted by comparing 3DSPECK against 3DSPIHT on several sets of hyperspectral images. Results show that 3DSPECK is comparable to 3DSPIHT in hyperspectral image compression. 3DSPECK can achieve compression ratios in the approximate range of 16 to 27 while providing very high quality reconstructed images. It guarantees over 3 dB PSNR improvement at all rates or rate saving at least a factor of 2 over 2D coding of separate spectral bands without axial transformation.
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