Optimized Huffman encoding based medical image compression with Improved HDBSCAN.

IF 1.1 3区计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Network-Computation in Neural Systems Pub Date : 2025-06-19 DOI:10.1080/0954898X.2025.2513691

Rajasekhar Butta, Mastan Sharif Shaik

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Abstract

With the development of medical imaging amenities, a rising quantity of data emerges in the present image processing that has led to gradually more burden for data transmission and storage. Image compression is a method of lessening the excess in images and symbolizing it in a short way that could permit more gainful exploitation of storage capacity and network bandwidth. This paper develops a new image compression model with steps like segmentation, encoding, and decoding. Initially, segmentation is carried out using Improved Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). This phase assists in ROI separation. Subsequently, compression occurs using Improved Huffman encoding. Also, in particular, the encoding parameters are optimally chosen via a new algorithm named Snake Updated BES Optimization (SU-BESO). In the last phase, decoding is done, during which, Huffman decoding as well as region fusion are carried out. Finally, the examination is done to prove the potential of the developed SU-BESO model.

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基于改进HDBSCAN的优化Huffman编码医学图像压缩。

随着医学影像设施的发展，当前图像处理中出现的数据量越来越大，数据传输和存储的负担也越来越大。图像压缩是一种减少图像中多余部分的方法，并以一种简短的方式表示它，从而可以更有效地利用存储容量和网络带宽。本文提出了一种新的图像压缩模型，包括分割、编码和解码。首先，使用改进的基于层次密度的带噪声应用空间聚类（HDBSCAN）进行分割。这个阶段有助于ROI分离。随后，使用改进的霍夫曼编码进行压缩。特别地，通过一种名为Snake Updated BES Optimization （SU-BESO）的新算法对编码参数进行了优化选择。最后进行解码，进行霍夫曼解码和区域融合。最后，对所建立的SU-BESO模型进行了验证。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Network-Computation in Neural Systems 工程技术-工程：电子与电气

CiteScore

3.70

自引率

1.30%

发文量

审稿时长

>12 weeks

期刊介绍： Network: Computation in Neural Systems welcomes submissions of research papers that integrate theoretical neuroscience with experimental data, emphasizing the utilization of cutting-edge technologies. We invite authors and researchers to contribute their work in the following areas: Theoretical Neuroscience: This section encompasses neural network modeling approaches that elucidate brain function. Neural Networks in Data Analysis and Pattern Recognition: We encourage submissions exploring the use of neural networks for data analysis and pattern recognition, including but not limited to image analysis and speech processing applications. Neural Networks in Control Systems: This category encompasses the utilization of neural networks in control systems, including robotics, state estimation, fault detection, and diagnosis. Analysis of Neurophysiological Data: We invite submissions focusing on the analysis of neurophysiology data obtained from experimental studies involving animals. Analysis of Experimental Data on the Human Brain: This section includes papers analyzing experimental data from studies on the human brain, utilizing imaging techniques such as MRI, fMRI, EEG, and PET. Neurobiological Foundations of Consciousness: We encourage submissions exploring the neural bases of consciousness in the brain and its simulation in machines.