Compression of Digital Medical Images Based on Multiple Regions of Interest

Firoozbakht, Mohsen; Dehmeshki, Jamshid; Martini, Maria G.; Ebrahimdoost, Yousef; Amin, Hesham H.; Dehkordi, M.E.; Youannic, A.; Qanadli, Salah D.

doi:10.1109/icds.2010.62

Cited by 14 publications

(8 citation statements)

References 5 publications

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“…The algorithm used the Canny operator to extract useful image edge information, combined JPEG2000 to reduce the ROI losslessly, and used a multi-level tree set split in hierarchical tree (SPIHT) to compress the BG. For vascular images, Firoozbakht et al [10] proposed a compression algorithm based on context and multi-ROI coding. The algorithm divided a vascular image into a primary ROI (vascular stenosis region), a secondary ROI (other important areas of blood vessels), and BG.…”

Section: A Roi Codingmentioning

confidence: 99%

A Fast Fractal Based Compression for MRI Images

et al. 2019

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Magnetic resonance imaging (MRI), which assists doctors in determining clinical staging and expected surgical range, has high medical value. A large number of MRI images require a large amount of storage space and the transmission bandwidth of the PACS system in offline storage and remote diagnosis. Therefore, high-quality compression of MRI images is very research-oriented. Current compression methods for MRI images with high compression ratio cause loss of information on lesions, leading to misdiagnosis; compression methods for MRI images with low compression ratio does not achieve the desired effect. Therefore, a fast fractal-based compression algorithm for MRI images is proposed in this paper. First, three-dimensional (3D) MRI images are converted into a two-dimensional (2D) image sequence, which facilitates the image sequence based on the fractal compression method. Then, range and domain blocks are classified according to the inherent spatiotemporal similarity of 3D objects. By using self-similarity, the number of blocks in the matching pool is reduced to improve the matching speed of the proposed method. Finally, a residual compensation mechanism is introduced to achieve compression of MRI images with high decompression quality. The experimental results show that compression speed is improved by 2-3 times, and the PSNR is improved by nearly 10. It indicates the proposed algorithm is effective and solves the contradiction between high compression ratio and high quality of MRI medical images.

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Section: A Roi Codingmentioning

confidence: 99%

A Fast Fractal Based Compression for MRI Images

et al. 2019

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“…In RoI-based image coding, some regions, that are of interest to the viewer, are encoded with a higher fidelity than the rest of the image. There are several 2D applications using the RoI coding such as compression of infrared or digital medical images [12,13], segmentation [14], and accurate objects location [15]. However, for some 3D applications, the areas of interest can be partially or fully dependent on the depth information [16].…”

Section: Introductionmentioning

confidence: 99%

Autofocus on Depth of Interest for 3D Image Coding

Samrouth

Déforges

Liu

et al. 2017

Journal of Electrical and Computer Engineering

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For some 3D applications, one may want to focus on a specific depth zone representing a region of interest in the scene. In this context, we introduce a new functionality called “autofocus” for 3D image coding, exploiting the depth map as an additional semantic information provided by the 3D sequence. The method is based on a joint “Depth of Interest” (DoI) extraction and coding scheme. First, the DoI extraction scheme consists of a precise extraction of objects located within a DoI zone, given by the viewer or deduced from an analysis process. Then, the DoI coding scheme provides a higher quality for the objects in the DoI at the expense of other depth zones. The local quality enhancement supports both higher SNR and finer resolution. The proposed scheme embeds the Locally Adaptive Resolution (LAR) codec, initially designed for 2D images. The proposed DoI scheme is developed without modifying the global coder framework, and the DoI mask is not transmitted, but it is deduced at the decoder. Results showed that our proposed joint DoI extraction and coding scheme provide a high correlation between texture objects and depth. This consistency avoids the distortion along objects contours in depth maps and those of texture images and synthesized views.

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“…In a study investigating the compression of more than one ROI on peripheral arterial CT images, first order ROI was compressed as lossless, second order ROI as lossy and Non-ROI was compressed as lossy with higher ratio compared to second order ROI [6]. An average compression ratio of 3 and 39 dB PSNR were obtained as a result of objective evaluation.…”

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confidence: 99%

Medical Image Compression for Telemedicine Applications

Nasıfoğlu¹,

Eroğul²

2019

ABEB

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High resolution medical images obtained by different imaging modalities stored in PACS needs higher storage space and bandwidth because of requiring much space in memory. In this sense, compression of medical images is important for efficient use of database. The main purpose of image compression is to reduce the number of bits representing the image while preserving the image quality and the intensity level of the pixels as much as possible depending on grayscale or RGB image [1]. Since medical images also contain diagnostic information about a disease or an artifact, less or no loss of detail in terms of quality is desired while compressing the significant areas. Otherwise, there may be difficulties or misdiagnosis in the treatment planning. With the lossless image compression technique, it is possible to preserve the entire pixel data while reducing the image size. The disadvantage of this technique is that it does not gain high memory size due to the low compression performance. On the other hand, higher compression ratios can be obtained by compromising redundant data with the lossy compression technique. Loss of image quality seems to be a risky condition in terms of correct diagnosis, but it is possible to reach acceptable compression rates and control data loss by setting appropriate parameters without losing diagnostic information. Adaptive image compression (AIC) is a hybrid technique that combines both lossless and lossy image compression techniques [2,3]. When applying AIC, it is primarily necessary to detect regions of interest in order to determine which regions will be compressed as lossy or lossless. After determining the focused or noticeable regions of radiologist on graph, it is possible to sort and adaptively compress these regions by importance. If the first order region is considered as the area containing the most information for the diagnosis, lossless compression can be applied here so as to avoid loss of detail. If there are second, third, and continuing order regions of interest, it may be preferable to adaptively compress these areas with little loss. Non-ROI (Non-Region of Interest) parts can be considered as less important or healthy areas in the diagnostic sense. Therefore, higher compression ratios can be achieved by compromising more details for these regions. After applying AIC, reconstructed image should be evaluated as sufficient and acceptable by the physician in terms of diagnostic information.Thus, the compressed images are recommended to be evaluated with subjective criteria in addition to objective criteria.The most common used objective criteria parameters for evaluating the compression performance are Compression Ratio (CR), Bits per Pixel (BPP), Mean Square Error (MSE) and the Peak Signal to Noise Ratio (PSNR). Compression ratio is defined as the

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Compression of Digital Medical Images Based on Multiple Regions of Interest

Cited by 14 publications

References 5 publications

A Fast Fractal Based Compression for MRI Images

A Fast Fractal Based Compression for MRI Images

Autofocus on Depth of Interest for 3D Image Coding

Medical Image Compression for Telemedicine Applications

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