JPEG2000 ROI coding through component priority for digital mammography

Bartrina-Rapesta, Joan; Serra-Sagristí, Joan; Aulí-Llinís, Francesc

doi:10.1016/j.cviu.2010.09.008

Cited by 21 publications

(10 citation statements)

References 38 publications

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“…Interaction can also be enhanced by selectively compressing the more relevant areas with lossless quality and the rest of the image [42] with some loses. Different works have adapted and implemented wavelet-based [43,44] and JPEG2000 approaches [42,45] to ease the access to specific image areas. In the proposed approach the entire WSI was assumed to be relevant and a free lossless navigation was so possible.…”

Section: Discussionmentioning

confidence: 98%

An adaptable navigation strategy for Virtual Microscopy from mobile platforms

Corredor

Romero

Iregui

2015

Journal of Biomedical Informatics

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Real integration of Virtual Microscopy with the pathologist service workflow requires the design of adaptable strategies for any hospital service to interact with a set of Whole Slide Images. Nowadays, mobile devices have the actual potential of supporting an online pervasive network of specialists working together. However, such devices are still very limited. This article introduces a novel highly adaptable strategy for streaming and visualizing WSI from mobile devices. The presented approach effectively exploits and extends the granularity of the JPEG2000 standard and integrates it with different strategies to achieve a lossless, loosely-coupled, decoder and platform independent implementation, adaptable to any interaction model. The performance was evaluated by two expert pathologists interacting with a set of 20 virtual slides. The method efficiently uses the available device resources: the memory usage did not exceed a 7% of the device capacity while the decoding times were smaller than the 200 ms per Region of Interest, i.e., a window of 256×256 pixels. This model is easily adaptable to other medical imaging scenarios.

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Section: Discussionmentioning

confidence: 98%

An adaptable navigation strategy for Virtual Microscopy from mobile platforms

Corredor

Romero

Iregui

2015

Journal of Biomedical Informatics

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“…In JPEG2K (Bartrina-Rapesta et al, 2011), ROI coding is the scaling method in which the wavelet coefficients belonging to the contextual regions are shifted upward. Moreover this method allows the use of arbitrary scaling value.…”

Section: Roi Based Coding Techniques 121 Jpeg2k (Scaling Method)mentioning

confidence: 99%

Medical Image Compression Technique Using Listless Set Partitioning in Hierarchical Trees and Contextual Vector Quantization for Brain Images

Sridevi¹

2013

Journal of Computer Science

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A hybrid image compression techniques has been developed to compress medical images. Due to the extensive use of medical images like CT and MR scan, these medical imagery are stored for a longer period for the continuous monitoring of the patients and the amount of data associated with images is large and it occupies enormous storage capacity. So, the medical images need to be compressed to reduce the storage cost and for transmission without any loss. In this study, a hybrid method which combines the Listless Set Partitioning in Hierarchical Trees (LSPIHT) and the Contextual Vector Quantization (CVQ) method for the compression of brain images. Here, the region containing the most important information for diagnosis is called Region of Interest (ROI) and this is to be compressed with out any loss in the quality. In this method, the ROI is encoded separately using LSPIHT and the Back Ground region (BG) is encoded using CVQ. Finally, the two regions are merged together to get the reconstructed image. Our results show that the proposed method gives very good image quality for diagnosis without any degradable loss. The performance of the compression technique is evaluated using the parameters (CR, MSE, PSNR) and achieved better result compared to other existing methods. As a result, we strongly believe that using our method, we can overcome the limitations in storage and transmission of medical images that are produced day by day.

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“…Among others, this requirement may appear in devices with memory constrained resources, when images with a very high bit-depth have to be coded, or when the use of ROIspecific techniques that enlarge the dynamic range of the image [43] are in use. 2SDQ is applied in codeblocks that have M x > M max removing R x = M x − M max magnitude bits, with M max denoting the maximum number of magnitude bits allowed.…”

Section: B Limited Bit-depth Codingmentioning

confidence: 99%

2-Step Scalar Deadzone Quantization for Bitplane Image Coding

Aulí-Llinàs

2013

IEEE Trans. on Image Process.

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Abstract-Modern lossy image coding systems generate a quality progressive codestream that, truncated at increasing rates, produces an image with decreasing distortion. Quality progressivity is commonly provided by an embedded quantizer that employs uniform scalar deadzone quantization (USDQ) together with a bitplane coding strategy. This paper introduces a 2-step scalar deadzone quantization (2SDQ) scheme that achieves same coding performance as that of USDQ while reducing the coding passes and the emitted symbols of the bitplane coding engine. This serves to reduce the computational costs of the codec and/or to code high dynamic range images. The main insights behind 2SDQ are the use of two quantization step sizes that approximate wavelet coefficients with more or less precision depending on their density, and a rate-distortion optimization technique that adjusts the distortion decreases produced when coding 2SDQ indices. The integration of 2SDQ in current codecs is straightforward. The applicability and efficiency of 2SDQ is demonstrated within the framework of JPEG2000.Index Terms-2-step scalar deadzone quantization, general embedded quantization, bitplane image coding, JPEG2000.

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JPEG2000 ROI coding through component priority for digital mammography

Cited by 21 publications

References 38 publications

An adaptable navigation strategy for Virtual Microscopy from mobile platforms

An adaptable navigation strategy for Virtual Microscopy from mobile platforms

Medical Image Compression Technique Using Listless Set Partitioning in Hierarchical Trees and Contextual Vector Quantization for Brain Images

2-Step Scalar Deadzone Quantization for Bitplane Image Coding

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