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首页> 外文期刊>Arabian Journal for Science and Engineering >Feed-Forward Neural Network-Based Predictive Image Coding for Medical Image Compression
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Feed-Forward Neural Network-Based Predictive Image Coding for Medical Image Compression

机译：基于前馈神经网络的医学图像压缩预测图像编码

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摘要

The generation of high volume of medical images in recent years has increased the demand for more efficient compression methods to cope up with the storage and transmission problems. In the case of medical images, it is important to ensure that the compression process does not affect the image quality adversely. In this paper, a predictive image coding method is proposed which preserves the quality of the medical image in the diagnostically important region (DIR) even after compression. In this method, the image is initially segmented into two portions, namely DIR and non-DIR portions, using a graph-based segmentation procedure. The prediction process is implemented using two identical feed-forward neural networks (FF-NNs) at the compression and decompression stages. Gravitational search and particle swarm algorithms are used for training the FF-NNs. Prediction is performed in both a lossless (LLP) and near-lossless (NLLP) manner for evaluating the performances of the two FF-NN training algorithms. The prediction error sequence which is the difference between the actual and predicted pixel values is further compressed using a Markov model-based arithmetic coding. The proposed method is tested using CLEF med 2009 database. The experimental results demonstrate that the proposed method is equipped for compressing the medical images with minimum degradation in the image quality. It is found that the gravitational search method achieves higher PSNR values compared to the particle swarm and backpropagation methods.
机译：近年来，大量医学图像的产生增加了对更有效的压缩方法以应对存储和传输问题的需求。对于医学图像，重要的是要确保压缩过程不会对图像质量产生不利影响。在本文中，提出了一种预测图像编码方法，即使在压缩之后，该方法仍可保留重要诊断区域（DIR）中的医学图像质量。在这种方法中，首先使用基于图形的分割程序将图像分割为两个部分，即DIR和非DIR部分。在压缩和解压缩阶段，使用两个相同的前馈神经网络（FF-NN）实施预测过程。引力搜索和粒子群算法用于训练FF-NN。以无损（LLP）和接近无损（NLLP）的方式执行预测，以评估两种FF-NN训练算法的性能。使用基于马尔可夫模型的算术编码进一步压缩作为实际像素值与预测像素值之差的预测误差序列。使用CLEF med 2009数据库对提出的方法进行了测试。实验结果表明，所提出的方法能够以最小的图像质量下降来压缩医学图像。发现与粒子群和反向传播方法相比，重力搜索方法获得了更高的PSNR值。

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