Medical images require compression, before transmission or storage, due to constrained bandwidth and storage capacity. An ideal image compression system must yield high-quality compressed image with high compression ratio. In this paper, Haar wavelet transform and discrete cosine transform are considered and a neural network is trained to relate the X-ray image contents to their ideal compression method and their optimum compression ratio.

/pdf/backpropagation-neural-network-implementation-for-medical-17gdv0scvq.pdf

Backpropagation Neural Network Implementation for Medical Image Compression

Image compression plays a crucial role in medical imaging allowing efficient storage and transmissions by reducing the amount of data required to represent the digital image. The main goal is to achieve higher compression ratios and minimum degradation in quality. To decrease the storage space, the use of different compression techniques is justified by some medical imaging modalities generate the volume that of data which will be increasing. Different medical images like X-ray angiograms, magnetic resonance images, Ultrasound and computed Tomography are used in the medical image compression techniques. In medical applications it is required to conserve the diagnostic validity of the image requires the use of lossless compression methods, producing low compression factors. For medical data, lossless compression is preferred to the greater gains of lossy compression, in the interest of accuracy. . A set of experiment has been performed for the analysis of the proposed work on the several DICOM medical images and it has been observed that the DWT, DCT and Huffman coding has higher compression ratio than the hybrid model. The proposed method gives better quality of image that includes high PSNR and CR as well as low MSE. The proposed medical "DICOM images compression scheme" is based on Hybrid DWT, DCT and Huffman coding techniques.

/pdf/medical-image-compression-using-hybrid-techniques-of-dwt-dct-1izeswfykp.pdf

Medical Image Compression Using Hybrid Techniques of DWT, DCT and Huffman Coding

Advanced medical imaging requires storage of large quantities of digitized clinical data. Due to the constrained bandwidth and storage capacity, a medical image must be compressed before transmission and storage. However, the compression will reduce the image fidelity, especially when the images are compressed at lower bit rates. The reconstructed images suffer from blocking artifacts and the image quality will be severely degraded under the circumstance of high compression ratios. Medical imaging poses the great challenge of having compression algorithms that reduce the loss of fidelity as much as possible so as not to contribute to diagnostic errors and yet have high compression rates for reduced storage and transmission time. To meet this challenge several hybrid compression schemes exclusively for medical images are developed in the recent years. This paper presents overview of various compression techniques based on DCT, DWT, ROI and Neural Networks for two dimensional (2D) still medical images.

Two Dimensional Medical Image Compression Techniques-A Survey

In this paper a technique is proposed for medical image compression using neural network based vector quantizers. There exist hundreds of modalities of medical images and each modality has hundreds of sub classes for different organs and different sizes, in such a situation it is difficult to generalize a neural network for all modalities. To tackle this problem and having a prior knowledge about similar nature and size of acquisition for a single type of medical image, we propose a flag byte which is automatically set by the image size and some features. This flag byte is then used to select the size of the net codebook to be used. Proposed method not only leads to dynamic architectures of neural nets used but also towards an adaptive selection of codebooks. This method yields high compression ratios with much better quality than existing standards.

Adaptive Architecture Neural Nets for Medical Image Compression

Medical images are 
			very important for diagnostics and therapy However, digital imaging 
			generates large amounts of data which need to be compressed, without 
			loss of relevant information, to economize storage space and allow 
			speedy transfer In this research three techniques are implemented 
			for medical image compression, which provide high compression ratios 
			with no loss of diagnostic quality Different image modalities are 
			employed for experiments in which X-rays, MRI, CT scans, Ultrasounds 
			and Angiograms are included The proposed schemes are evaluated by 
			comparing with existing standard compression techniques like JPEG, 
			lossless JPEG2000, LOCOI and Huffman Coding
			In a medical image only a small region is diagnostically relevant 
			while the remaining image is much less important This is called 
			Region of Interest (ROI) The first approach compresses the ROI 
			strictly losslessly and the remaining regions of the image with some 
			loss In the second approach an image is first compressed at a high 
			compression ratio but with loss, and the difference image is then 
			compressed losslessly Difference image contain less data and is 
			compressed more compactly than original Third approach exploits 
			inter-image redundancy for similar modality and same part of human 
			body More similarity means less entropy which leads to higher 
			compression performance The overall compression ratio is 
			combination of lossy and lossless compression ratios The resulting 
			compression is not only strictly lossless, but also expected to 
			yield a high compression ratio
			These techniques are based on self designed Neural Network Vector 
			Quantizer (NNVQ) and Huffman coding Their clever combination is 
			used to get lossless effect These are spatial domain techniques and 
			do not require frequency domain transformation An overall 
			compression ratio of 6-14 is obtained for images with proposed 
			methods Whereas, by compressing same images by a lossless JPEG2K 
			and Huffman, compression ratio of 2 is obtained at most The main 
			contribution of the research is higher compression ratios than 
			standard techniques in lossless scenario This result will be of 
			great importance for data management in a hospital and for 
			teleradiology

Diagnostically lossless compression of medical images-2

A method is proposed for compression of medical images. The approach is called DLC2 (Diagnostically Lossless Compression-2). To get lossless effect, we use lossy coding followed by error image coding. An NNVQ (Neural Network Vector Quantizer) is used for lossy compression and Huffman coding is used to code the difference image losslessly. It is an spatial domain technique. No frequency domain transformations are required, this makes the proposed scheme simple and computationally economical. Proposed method can achieve higher compression ratios than existing standard lossless compression techniques and also meets the legal requirement of medical image archiving

Diagnostically Lossless Compression-2 of Medical Images

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Diagnostically Lossless Compression-2 of Medical Images

Adaptive Architecture Neural Nets for Medical Image Compression