Medical images performance analysis and observations with SPIHT and wavelet techniques

Miya, Javed; Ansari, M. A.

doi:10.1080/02522667.2020.1721616

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…Recursion was also tested in the study, although it primarily focused on MRI images of kidneys. The recursion aspect of the algorithm was examined by manually adjusting the recursion limiter number (N) in each iteration, but this was only tested within a range from n=0 to n=15 [22].…”

Section: Background Studymentioning

confidence: 99%

Advancements in Lossless and Reversible Compression of Digital Pathology Images via Auto-Recursive Set Partitioning in Hierarchical Trees and Wavelet Decomposition

Yuan,

Adam,

Hasan

et al. 2023

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Set Partitioning in Hierarchical Trees (SPIHT) represents a leading-edge algorithm in nearlossless image compression, leveraging the Discrete Wavelet Transform. However, its effectiveness diminishes when applied to high-resolution images such as Digital Pathology Images (DPIs). This research aims to enhance the SPIHT algorithm specifically for DPIs by investigating the impact of applying various wavelets in the wavelet decomposition process and the introduction of auto-recursion in the SPIHT algorithm. An extensive selection of wavelet types were tested within the wavelet decomposition process integral to the SPIHT algorithm. The ultimate goal was to identify the wavelet that yields the highest compression ratio and the one that maintains the highest data consistency. The proposed auto-recursion was also examined against the original n-recursive algorithm to discern differences in compression performance. The results indicated that the BIOR 5.5 wavelet is more apt for achieving a high compression ratio, while the BIOR 3.9 wavelet is more suitable for securing high compression quality in the compression of high-resolution DPIs. The newly introduced auto-recursion feature contributes significantly to optimizing the quality of the compressed image. Visual verification of the compressed image's quality, for any potential loss of detail, was carried out through expert validation in a clinical setting. This expert validation confirmed that the proposed algorithm can produce higher quality compressed images with negligible loss of quality. Thus, this research offers a partial solution to current challenges in digital pathology related to storage, transfer, and archiving of high-resolution DPIs, by providing a more effective compression algorithm.

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Section: Background Studymentioning

confidence: 99%

Advancements in Lossless and Reversible Compression of Digital Pathology Images via Auto-Recursive Set Partitioning in Hierarchical Trees and Wavelet Decomposition

Yuan,

Adam,

Hasan

et al. 2023

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show abstract

“…Several image compression algorithms commonly use arithmetic coding schemes because of its ability to use fractional bits for generation of codes to obtain the optimal performance [2]. When compared to image compression techniques like JPEG, vector quantization and vector quantization with wavelets, Set Partitioning in Hierarchical Trees (SPIHT) coding provides superior performance in terms of peak signal-to-noise ratio (PSNR), performance, speed and various other factors [3]. Scalable bit stream may be generated continuously for encoding still images using the wavelet based SPIHT algorithm.…”

Section: Introductionmentioning

confidence: 99%

Implementation of FPGA hardware architecture for optimized SPIHT coder using 2DWT-A

2022

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During multimedia communication and in Health care IOT, we transmit medical images and other information to health care experts, caretakers and relatives with faster speed. Remote monitoring gains periodical data and can be transported to the cloud due to its massive nature. This huge data can accommodate larger bandwidth and reduces the speed of operation. Hence, image compression is crucial for multimedia data communication. The proposed work develops an integrated architecture for both image transform and SPIHT based encoding process. We have selected the SPIHT algorithm for image compression because of its several expedient features. Parallel pipelined poly-phase two dimensional discrete wavelet transform based image transform architecture has been designed and implemented with reduced hardware and latency. The SPIHT encoder receives the low frequency components from the 2DWT-A image transformer. We removed other high frequency components to reduce the number of coefficients for transmission. In SPIHT encoder architecture, the hardware efficient binary arithmetic encoder with position control unit is used to avoid prefix zeros during arithmetic computation which replaces the hardware expensive arithmetic coders. This increases the speed of operation of the image codec. This brings effective hardware utilization into focus. Xilinx FPGA device with Microblaze core processor and SPARTAN 3EDK kit is used for realization of the hardware architecture. We use image codec architecture enactment principle for examination. We compare the performance of the proposed architecture with the existing schemes. The requirements, power and delay for the proposed method are optimized considerably when compared to the existing techniques. It reduces the area of the designed system while increasing the frequency by a factor of 2.1MHz. We use 180nm CMOS technology in Cadence tool for estimating the performance at cell level.

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Analyzing Most Popular Object Detection Models for Deep Neural Networks

Sharma,

Dandeva

2023

Lecture Notes in Electrical Engineering

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Medical images performance analysis and observations with SPIHT and wavelet techniques

Cited by 6 publications

References 13 publications

Advancements in Lossless and Reversible Compression of Digital Pathology Images via Auto-Recursive Set Partitioning in Hierarchical Trees and Wavelet Decomposition

Advancements in Lossless and Reversible Compression of Digital Pathology Images via Auto-Recursive Set Partitioning in Hierarchical Trees and Wavelet Decomposition

Implementation of FPGA hardware architecture for optimized SPIHT coder using 2DWT-A

Analyzing Most Popular Object Detection Models for Deep Neural Networks

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