On a hybrid lossless compression technique for three‐dimensional medical images

Subramanian, Boopathiraja; Palanisamy, Kalavathi; Prasath, V. B. Surya

doi:10.1002/acm2.12960

Cited by 6 publications

(3 citation statements)

References 41 publications

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“…An object based hybrid lossless three dimensional medical image compression algorithm proposed in [17], this method compresses a selected portion i.e VOI on the medical image which produces a better compression ratio and bit rate is also reduced. To extract the VOI they used a selective bounding volume method because it reduces the reconstruction complexity of 3D images and it needs only fewer amounts of reconstruction details.…”

Section: Related Workmentioning

confidence: 99%

Subband Thresholding for Near-Lossless Medical Image Compression

Viswanthan,

Palanisamy

2023

IJCDS

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Medical images play a prominent role in diagnostic and treatment planning in the medical arena. Medical images are acquired using several medical imaging modalities like Computed Tomography (CT), Magnetic Resonance Imaging (MRI), UltraSound (US), Positron Emission Tomography (PET), and X-Ray. These are used as an image acquisition tool to capture the medical images. Numerous volumes of medical data are produced for processing, storing and transmission over the network for telemedicine services every day. Those images are in the form of multidimensional data with high resolution. Hence, it certainly yields higher bandwidth and storage. Thus, it is very essential for medical image applications to reduce storage and to solve transmission problems. Every information it holds is a matter of human lives. The fast and safe transmission of these data will decrease the mortality rate. Lossy type of image compression technique achieves better compression rate than image quality where lossless compression can only retain the image quality. Therefore, there is an existing need for near-lossless compression which compromises both the compression rate and image quality. In this paper, we have proposed a near-lossless medical image compression technique that thresholds the sub-bands to increase the number of zero coefficients. And as an entropy encoder run-length encoding is applied to compress the medical image to retain the diagnostic information with high compression efficiency with better image quality. Compression Ratio (CR), Peak Signal to Noise Ratio (PSNR), Time Complexity and Bits Per Pixel (BPP) were used as assessment parameters to examine the performance of our proposed method. We achieved an average of 2 BPP and PSNR of 42.43 dB which is higher than the existing methods.

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Section: Related Workmentioning

confidence: 99%

Subband Thresholding for Near-Lossless Medical Image Compression

Viswanthan,

Palanisamy

2023

IJCDS

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“…For this reason, lossless or bit-preserving techniques are the methods of choice for compressing-decompressing medical images for the purpose of transmission [ 12 – 14 ]. Since compression performance and quality are inversely related, lossless compression approaches preserve the high-quality images with a low level of compression performance [ 15 , 16 ]. Due to this unique inverse relation, either the image quality or compression performance must be compromised.…”

Section: Introductionmentioning

confidence: 99%

Near Lossless Compression for 3D Radiological Images Using Optimal Multilinear Singular Value Decomposition (3D-VOI-OMLSVD)

et al. 2022

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Storage and transmission of high-compression 3D radiological images that create high-quality reconstruction upon decompression are critical necessities for effective and efficient teleradiology. To cater to this need, we propose a near lossless 3D image volume compression method based on optimal multilinear singular value decomposition called “3D-VOI-OMLSVD.” The proposed strategy first eliminates any blank 2D image slices from the 3D image volume and uses the selective bounding volume (SBV) to identify and extract the volume of Interest (VOI). Following this, the VOI is decomposed with an optimal multilinear singular value decomposition (OMLSVD) to obtain the corresponding core tensor, factor matrices, and singular values that are compressed with adaptive binary range coder (ABRC), integrated as an entropy encoder. The compressed file can be transferred or transmitted and then decompressed in order to reconstruct the original image. The resultant decompressed VOI is acquired by reversing the above process and then fusing it with the background, using the bound volume coordinates associated with the compressed 3D image. The proposed method performance was tested on a variety of 3D radiological images with different imaging modalities and dimensions using quantitative evaluation metrics such as the compression rate (CR), bit rate (BR), peak signal to noise ratio (PSNR), and structural similarity index (SSIM). Furthermore, we also investigate the impact of VOI extraction on the model performance, before comparing it with two popular compression methods, namely JPEG and JPEG2000. Our proposed method, 3D-VOI-OMLSVD, displayed a high CR value, with a maximum of 37.31, and a low BR, with the lowest reported to be 0.21. The SSIM score was consistently high, with an average performance of 0.9868, while using < 1 second for decoding the image. We observe that with VOI extraction, the compression rate increases manifold, and bit rate drops significantly, and thus reduces the encoding and decoding time to a great extent. Compared to JPEG and JPEG2000, our method consistently performs better in terms of higher CR and lower BR. The results indicate that the proposed compression methodology performs consistently to create high-quality image compressions, and overall gives a better outcome when compared against two state-of-the-art and widely used methods, JPEG and JPEG2000.

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“…Every image contains some redundant data that can be exploited in image compression [21], [22]. By eliminating such redundant data, the amount of data required to represent the image is reduced.…”

Section: Introductionmentioning

confidence: 99%

A spatial image compression algorithm based on run length encoding

2021

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Image compression is vital for many areas such as communication and storage of data that is rapidly growing nowadays. In this paper, a spatial lossy compression algorithm for gray scale images is presented. It exploits the inter-pixel and the psycho-visual data redundancies in images. The proposed technique finds paths of connected pixels that fluctuate in value within some small threshold. The path is calculated by looking at the 4-neighbors of a pixel then choosing the best one based on two conditions; the first is that the selected pixel must not be included in another path and the second is that the difference between the first pixel in the path and the selected pixel is within the specified threshold value. A path starts with a given pixel and consists of the locations of the subsequently selected pixels. Run-length encoding scheme is applied on paths to harvest the inter-pixel redundancy. After applying the proposed algorithm on several test images, a promising quality vs. compression ratio results have been achieved.

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On a hybrid lossless compression technique for three‐dimensional medical images

Cited by 6 publications

References 41 publications

Subband Thresholding for Near-Lossless Medical Image Compression

Subband Thresholding for Near-Lossless Medical Image Compression

Near Lossless Compression for 3D Radiological Images Using Optimal Multilinear Singular Value Decomposition (3D-VOI-OMLSVD)

A spatial image compression algorithm based on run length encoding

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