Abstract
The number of medical images being stored and communicated daily is rapidly increasing, according to the need for these images in medical diagnoses. Hence, the storage space and bandwidths required to store and communicate these images are exponentially increasing, which has brought attention toward compressing these images. In this study, a new compression method is proposed for medical images based on convolutional neural networks. The proposed neural network consists of two main stages: a segmentation stage and an autoencoder. The segmentation stage is used to recognize the Region of Interest (ROI) in the image and provide it to the autoencoder stage, so more emphasis on the information of the ROI is applied. The autoencoder part of the neural network contains a bottleneck layer that has one-eighth of the dimensions of the input image. The values in this layer are used to represent the image, while the following layers are used to decompress the images, after training the neural network. The proposed method is evaluated using the CLEF MED 2009 dataset, where the evaluation results show that the method has significantly better performance, compared to the existing state-of-the-art methods, by providing more visually similar images using less data.
Highlights
With the rapidly growing use of medical images in diagnosis procedures, the storage space and bandwidth required to store and communicate these images are exponentially increasing [1]
The CR has still been able to maintain higher levels at ∝ 0.8, compared to the BP based method from Ahamed et al [16], with values of 6.73 and 5.2, respectively. This compression rate is lower than that produced by the Gravitational Search (GS)/Particle Swarm (PS) method from Ahamed et al.[16], but, by comparing the performance measures of this approach to the performance of the proposed method in Table 2, it is clear that the proposed method has achieved significantly better performance with 9.68 average CR, compared to 8.85 achieved by the GS/PS method from Ahamed et al [16]. is improvement in the CR is accompanied by the ability of the proposed method to produce images with high average Peak Signal-to-Noise Ratio (PSNR) rates, 49.07 dB for the Region of Interest (ROI) and 47.79 for the entire image, compared to 41.46 dB achieved by the GS/PS method from Ahamed et al [16]
According to the importance of compressing medical images, a new method is proposed in this paper based on a convolutional neural network. e neural network used in the proposed method achieves two main tasks: segmenting the image and compressing it
Summary
With the rapidly growing use of medical images in diagnosis procedures, the storage space and bandwidth required to store and communicate these images are exponentially increasing [1]. Significant emphasis has been attracted by image compression techniques, according to their ability to reduce the size of the data required to represent the image, by eliminating redundant information [2]. Lossy compression techniques can significantly reduce the size of the produced compressed image and have the ability to adjust or balance the tradeoff between the size of the produced data and the quality of the retrieved image, compared to the original one [6]. Ese methods attempt to maintain the information in the Region of Interest (ROI) similar to the original image while applying more lossy compression to the remaining part of the image, to significantly reduce its size. In addition to the improved segmentation performance, the use of ANNs for this task provides the compression method with better flexibility, regarding the type of the objects in the medical images being compressed. The proposed training approach for the neural network allows training the different parts separately, so that the segmentation part can be updated for any type of medical images
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