MEDICAL IMAGES COMPRESSION BASED ON SPIHT AND BAT INSPIRED ALGORITHMS

S. Jasim
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引用次数: 1

Abstract

There is a significant necessity to compress the medical images for the purposes of communication and storage.Most currently available compression techniques produce an extremely high compression ratio with a high-quality loss. Inmedical applications, the diagnostically significant regions (interest region) should have a high image quality. Therefore, it ispreferable to compress the interest regions by utilizing the Lossless compression techniques, whilst the diagnostically lessersignificant regions (non-interest region) can be compressed by utilizing the Lossy compression techniques. In this paper, a hybridtechnique of Set Partition in Hierarchical Tree (SPIHT) and Bat inspired algorithms have been utilized for Lossless compressionthe interest region, and the non-interest region is loosely compressed with the Discrete Cosine Transform (DCT) technique.The experimental results present that the proposed hybrid technique enhances the compression performance and ratio. Also,the utilization of DCT increases compression performance with low computational complexity.
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基于spiht和bat启发算法的医学图像压缩
为了通信和存储的目的,有必要对医学图像进行压缩。目前大多数可用的压缩技术产生极高的压缩比和高质量的损失。在医学应用中,诊断意义区域(兴趣区域)应具有较高的图像质量。因此,最好利用无损压缩技术来压缩感兴趣的区域,而诊断上不太重要的区域(非感兴趣区域)可以利用有损压缩技术来压缩。本文利用分层树集合分割(SPIHT)和Bat算法的混合技术对感兴趣区域进行无损压缩,对非感兴趣区域采用离散余弦变换(DCT)技术进行松散压缩。实验结果表明,该混合技术提高了压缩性能和压缩比。此外,DCT的利用在降低计算复杂度的同时提高了压缩性能。
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