{"title":"使用混合 DCT 和混合胶囊自动编码器对脑部 MR 图像进行有效压缩","authors":"Bindu Puthentharayil Vikraman , Jabeena Afthab","doi":"10.1016/j.jvcir.2024.104296","DOIUrl":null,"url":null,"abstract":"<div><div>Nowadays, image compression is gaining popularity in various fields because of its storage and transmission capability. This work aims to introduce a medical image (MI) compression model in brain magnetic resonance images (MRI) to mitigate issues in bandwidth and storage. Initially, pre-processing is done to neglect the noises in inputs using the Adaptive Linear Smoothing and Histogram Equalization (ALSHE) method. Then, the Region of Interest (ROI) and Non-ROI parts are separately segmented by the Optimized Fuzzy C-Means (OFCM) approach for reducing high complexity issues. Finally, a novel Hybrid Discrete Cosine Transform-Improved Zero Wavelet (DCT-IZW) is proposed for lossless compression and Hybrid Equilibrium Optimization-Capsule Auto Encoder (EO-CAE) for lossy compression. Then, the compressed ROI and Non-ROI images are added together, and the inverse operation of the compression process is performed to obtain the reconstructed image. This study used BRATS (2015, 2018) datasets for simulation and attained better performance than other existing methods.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"104 ","pages":"Article 104296"},"PeriodicalIF":2.6000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Effective image compression using hybrid DCT and hybrid capsule auto encoder for brain MR images\",\"authors\":\"Bindu Puthentharayil Vikraman , Jabeena Afthab\",\"doi\":\"10.1016/j.jvcir.2024.104296\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Nowadays, image compression is gaining popularity in various fields because of its storage and transmission capability. This work aims to introduce a medical image (MI) compression model in brain magnetic resonance images (MRI) to mitigate issues in bandwidth and storage. Initially, pre-processing is done to neglect the noises in inputs using the Adaptive Linear Smoothing and Histogram Equalization (ALSHE) method. Then, the Region of Interest (ROI) and Non-ROI parts are separately segmented by the Optimized Fuzzy C-Means (OFCM) approach for reducing high complexity issues. Finally, a novel Hybrid Discrete Cosine Transform-Improved Zero Wavelet (DCT-IZW) is proposed for lossless compression and Hybrid Equilibrium Optimization-Capsule Auto Encoder (EO-CAE) for lossy compression. Then, the compressed ROI and Non-ROI images are added together, and the inverse operation of the compression process is performed to obtain the reconstructed image. This study used BRATS (2015, 2018) datasets for simulation and attained better performance than other existing methods.</div></div>\",\"PeriodicalId\":54755,\"journal\":{\"name\":\"Journal of Visual Communication and Image Representation\",\"volume\":\"104 \",\"pages\":\"Article 104296\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2024-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Visual Communication and Image Representation\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1047320324002529\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Visual Communication and Image Representation","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1047320324002529","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
摘要
如今，图像压缩因其存储和传输能力强而在各个领域越来越受欢迎。这项工作旨在引入脑磁共振图像（MRI）中的医学图像压缩模型，以缓解带宽和存储问题。首先，使用自适应线性平滑和直方图均衡（ALSHE）方法进行预处理，以忽略输入中的噪声。然后，使用优化模糊 C-Means (OFCM) 方法分别分割感兴趣区域 (ROI) 和非感兴趣区域 (ROI) 部分，以减少高复杂性问题。最后，提出了用于无损压缩的新型混合离散余弦变换-改进零小波（DCT-IZW）和用于有损压缩的混合平衡优化-胶囊自动编码器（EO-CAE）。然后，将压缩后的 ROI 和非 ROI 图像相加，并对压缩过程进行逆运算，得到重建图像。该研究使用 BRATS（2015、2018）数据集进行模拟，取得了比其他现有方法更好的性能。
Effective image compression using hybrid DCT and hybrid capsule auto encoder for brain MR images
Nowadays, image compression is gaining popularity in various fields because of its storage and transmission capability. This work aims to introduce a medical image (MI) compression model in brain magnetic resonance images (MRI) to mitigate issues in bandwidth and storage. Initially, pre-processing is done to neglect the noises in inputs using the Adaptive Linear Smoothing and Histogram Equalization (ALSHE) method. Then, the Region of Interest (ROI) and Non-ROI parts are separately segmented by the Optimized Fuzzy C-Means (OFCM) approach for reducing high complexity issues. Finally, a novel Hybrid Discrete Cosine Transform-Improved Zero Wavelet (DCT-IZW) is proposed for lossless compression and Hybrid Equilibrium Optimization-Capsule Auto Encoder (EO-CAE) for lossy compression. Then, the compressed ROI and Non-ROI images are added together, and the inverse operation of the compression process is performed to obtain the reconstructed image. This study used BRATS (2015, 2018) datasets for simulation and attained better performance than other existing methods.
期刊介绍：
The Journal of Visual Communication and Image Representation publishes papers on state-of-the-art visual communication and image representation, with emphasis on novel technologies and theoretical work in this multidisciplinary area of pure and applied research. The field of visual communication and image representation is considered in its broadest sense and covers both digital and analog aspects as well as processing and communication in biological visual systems.
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