Volumetric medical image compression using 3D listless embedded block partitioning

Senapati et al. SpringerPlus (2016) 5:2100 DOI 10.1186/s40064-016-3784-y Open Access RESEARCH Volumetric medical image compression using 3D listless embedded block partitioning Ranjan K. Senapati1*, P. M. K Prasad2, Gandharba Swain3 and T. N. Shankar3 *Correspondence: 1 Department of ECE, K L University, Vaddeswaram, Guntur, Andhra Pradesh 522502, India Full list of author information is available at the end of the article Abstract This paper presents a listless variant of a modified three-dimensional (3D)-block coding algorithm suitable for medical image compression. A higher degree of correlation is achieved by using a 3D hybrid transform. The 3D hybrid transform is performed by a wavelet transform in the spatial dimension and a Karhunen–Loueve transform in the spectral dimension. The 3D transformed coefficients are arranged in a one-dimensional (1D) fashion, as in the hierarchical nature of the wavelet-coefficient distribution strategy. A novel listless block coding algorithm is applied to the mapped 1D coefficients which encode in an ordered-bit-plane fashion. The algorithm originates from the most significant bit plane and terminates at the least significant bit plane to generate an embedded bit stream, as in 3D-SPIHT. The proposed algorithm is called 3D hierarchical listless block (3D-HLCK), which exhibits better compression performance than that exhibited by 3D-SPIHT. Further, it is highly competitive with some of the state-of-theart 3D wavelet coders for a wide range of bit rates for magnetic resonance, digital imaging and communication in medicine and angiogram images. 3D-HLCK provides rate and resolution scalability similar to those provided by 3D-SPIHT and 3D-SPECK. In addition, a significant memory reduction is achieved owing to the listless nature of 3D-HLCK. Keywords: 3D hierarchical listless embedded block, Set partitioning in hierarchical trees, Volumetric compression, Embedded coder, Peak-signal-to-noise-ratio Background As the amount of patient data increases, compression techniques for the digital storage and transmission of medical images become mandatory. Imaging modalities such as ultrasonography (US), computer tomography (CT), magnetic resonance imaging (MRI) and X-rays provide flexible means of viewing anatomical cross sections for diagnosis. Three dimensional (3D) medical images can be viewed as a time sequence of radiographic images, the tomographic slices (images) of a dynamic object, or a volume of a tomographic slice images of a static object (Udupa and Herman 2000). In this paper, a 3D medical image corresponds to a volume of tomographic slices, which is a rectangular array of voxels with certain intensity values. Progressive lossy to lossless compression from a unified bit string is highly desirable in medical imaging. Lossy compression is tolerated as long as the required diagnostic quality is preserved. Lossless to lossy compression techniques are primarily used in telemedicine, teleradiology and the wireless monitoring of capsule endoscopy. © The Author(s) 2016. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Senapati et al. SpringerPlus (2016) 5:2100 A compression technique using wavelets provides better image quality compared to joint photographic experts group compression (JPEG) (Pennebaker and Mitchell 1993; Santa-cruz et al. 2000). It also provides a rich set of features such as a progressive in quality and resolution, the region of interest (ROI) and optimal rate-distortion performance with a modest increase in computational complexity. The JPEG standard uses an 8 × 8 discrete cosine transform (DCT) and the JPEG2000 standard uses two dimensional discrete wavelet transform (2D-DWT). The Karhunen–Loueve transform (KLT) is an optimal method for encoding images in the mean squared error (MSE) sense. The compression performance of 2D cosine, Fourier, and Hartley transforms was compared using positron emission tomography (PET) and magnetic resonance (MR) images in Shyam Sunder et al. (2006). The authors claimed that the discrete Hartley transform (DHT) and the discrete Fourier transform (DFT) perform better than the DCT. Several techniques based on the three-dimensional discrete cosine transform (3D-DCT) have been proposed for volumetric data coding (Tai et al. 2000). Nevertheless, these techniques fail to provide lossless coding coupled with quality and resolution scalability, which is a significant drawback for teleradiology and telemedicine applications. Several works on wavelet-based 3D medical image compression have been reported in the literature (Schelkens et al. 2003; Xiong et al. 2003; Chao et al. 2003; Gibson et al. 2004; Xiaolin and Tang 2005; Sriram and Shyamsunder 2011; Ramakrishnan and Sriram 2006; Srikanth and Ramakrishnan 2005; He et al. 2003). A method based on block-based quad-tree compression, layered zero-coding, and context-based arithmetic coding was proposed by Schelkens et al. (2003). They claimed that the method gives an excellent result for lossless compression and a comparable result for lossy compression. Modified 3D-SPIHT and 3D-EBCOT schemes for the compression of medical data were proposed by Xiong et al. (2003). Their method gives a comparable result for lossy and lossless compression. An optimal 3D coefficient tree structure for 3D zero-tree coding was proposed by Chao et al. (2003). They suggested that an asymmetrical tree can produce a higher compression ratio than a symmetrical one. Gibson et al. (2004) incorporated an ROI and texture modelling stage into the 3D-SPIHT coder for the compression of angiogram video sequences based on bit allocation criteria. Xiaolin and Tang (2005) presented a 3D scalable coding scheme which aimed to improve the productivity of a radiologist by providing a high decoder throughput, random access to the coded data volume, progressive transmission, and coding gain in a balanced design approach. Sriram and Shyamsunder (2011) proposed an optimal coder by making use of wavelets db4, db6, cdf9/7, and cdf5/3 with 3D-SPIHT, 3D-SPECK, and 3D-BISK. They found that cdf 9/7 with 3D-SPIHT yields the best compression performance. Ramakrishnan and Sriram (2006) proposed a wavelet-based SPIHT coder for DICOM images for teleradiology applications. Similarly, many works based on 3D-SPECK, 3D-BISK, and 3D-SPIHT used for the compression of hyperspectral images have been reported (Tang et al. 2003; Fowler and Rucker 2007; Lu and Pearlman 2001). 3D-SPIHT and 3D-SPECK use auxiliary lists [e.g., a list of insignificant pixels (LIP), a list of insignificant sets (LIS), and a list of significant pixels (LSP)] for tree/block partitioning. The auxili (...truncated)