Medical image compression using region-of-interest vector quantization

Cziho, A.; Cazuguel, Guy; Solaiman, Basel; Roux, Christian

doi:10.1109/iembs.1998.747110

Cited by 10 publications

(4 citation statements)

References 6 publications

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“…Nevertheless, 3-D data are out of the scope of the baseline JPEG2000. Even though some authors have addressed the task of object-based coding for medical images (see, for example, [24]- [26]), such an approach still deserves some investigation.…”

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confidence: 99%

Lossy to lossless object-based coding of 3-D MRI data

Menegaz

Thiran

2002

IEEE Trans. on Image Process.

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Abstract-We propose a fully three-dimensional (3-D) object-based coding system exploiting the diagnostic relevance of the different regions of the volumetric data for rate allocation. The data are first decorrelated via a 3-D discrete wavelet transform. The implementation via the lifting steps scheme allows to map integer-to-integer values, enabling lossless coding, and facilitates the definition of the object-based inverse transform. The coding process assigns disjoint segments of the bitstream to the different objects, which can be independently accessed and reconstructed at any up-to-lossless quality. Two fully 3-D coding strategies are considered: embedded zerotree coding (EZW-3D) and multidimensional layered zero coding (MLZC), both generalized for region of interest (ROI)-based processing. In order to avoid artifacts along region boundaries, some extra coefficients must be encoded for each object. This gives rise to an overheading of the bitstream with respect to the case where the volume is encoded as a whole. The amount of such extra information depends on both the filter length and the decomposition depth. The system is characterized on a set of head magnetic resonance images. Results show that MLZC and EZW-3D have competitive performances. In particular, the best MLZC mode outperforms the others state-of-the-art techniques on one of the datasets for which results are available in the literature.

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confidence: 99%

Lossy to lossless object-based coding of 3-D MRI data

Menegaz

Thiran

2002

IEEE Trans. on Image Process.

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“…Vector quantization (VQ) is another ROI coding technique [20]. In this the image is divided into small nonoverlapping blocks.…”

Section: Review Of Related Researchmentioning

confidence: 99%

Review on Region of Interest Coding Techniques for Medical Image Compression

Jangbari¹,

Patel²

2016

IJCA

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Telemedicine is a field of medicine characterized by telecommunication for clinical health care and transmission of medical images and videos. For transmission, a huge bandwidth is required over the internet. The size of the images that belongs to a single patient is very large which contains resolution factor and diagnostic images. So there is a need for efficient compression techniques for compressing these medical images.The regions which are considered to be more important than others in medical images is known as a Region of Interest (ROI) e.g. tumor is ROI in brain MRI. In this work, the ROI is detected by the saliency map technique, after that targets or ROIs be coded at available bits while the remainder of the background or non-ROI part is coded using fewer bits.By this method, the target regions within the video frame or image will be well preserved while the number of bits needed to code the video sequence or images is reduced.Thus, the transmission bandwidth and storage requirements are reduced.

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“…In Ref. 13, vector quantization with different codebooks is applied to different segmented areas. A larger codebook is created for an important area, and vice versa.…”

Section: Introductionmentioning

confidence: 99%

Compression of 3-D echocardiographic images using a modified 3-D set-partitioning-in-hierarchical-trees algorithm based on a 3-D wavelet packet transform

Hang

Greenberg

Zheng

et al. 2006

J. Electron. Imaging

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Abstract. An efficient compression strategy is indispensable for 3-D digital echocardiography, which can provide more accurate diagnostic information than 2-D echocardiography without geometric assumption, but unfortunately requires a huge storage space. We describe a new set partitioning in hierarchical trees (SPIHT) algorithm that is based on the wavelet packet transform and use it to compress 3-D echocardiographic images. The new algorithm originates from the well-known SPIHT algorithm that is based on a wavelet transform but outperforms it for the compression of 3-D echocardiographic images. Experimental results are presented to verify the effectiveness of the new SPIHT algorithm. Results show that the new algorithm achieves a very high compression ratio while retaining good quality 3-D echocardiographic images. © 2006 SPIEand IS&T.

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Medical image compression using region-of-interest vector quantization

Cited by 10 publications

References 6 publications

Lossy to lossless object-based coding of 3-D MRI data

Lossy to lossless object-based coding of 3-D MRI data

Review on Region of Interest Coding Techniques for Medical Image Compression

Compression of 3-D echocardiographic images using a modified 3-D set-partitioning-in-hierarchical-trees algorithm based on a 3-D wavelet packet transform

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