Paul W. Jones

Image Compression Techniques for Medical Diagnostic Imaging Systems

As a result of recent technological advances, a significant (and increasing) number of the images in a typical radiology department are represented in digital form. This includes images that have been captured using digital imaging modalities such as computed tomography and magnetic resonance imaging as well as images that have been digitally converted from film originals as in computed radiography. To provide economical storage of such images and to allow for their efficient transmission over various networks, it becomes necessary to consider digital image compression techniques. In this article, the fundamental concepts of several popular reversible and nonreversible image compression schemes are reviewed. In addition, the performance of the schemes in terms of compression ratio and reconstructed image quality as applied to medical diagnostic images is investigated.

Multiresolution unsharp masking technique for mammogram image enhancement

A multi-resolution unsharp masking (USM) technique is developed for image feature enhancement in digital mammogram images. This technique includes four processing phases: (1) determination of parameters of multi-resolution analysis (MRA) based on the properties of images; (2) multi-resolution decomposition of original images into sub-band images via wavelet transformation with perfect reconstruction filters; (3) modification of sub-band images with adaptive unsharp masking technique; and (4) reconstruction of image from modified sub- band images via inverse wavelet transformation. An adaptive unsharp masking technique is applied to the sub-band images in order to modify the pixel values based on the edge components at various frequency scales. Smoothing and gain factor parameters, employed in the unsharp masking, are determined according to the resolution, frequency, and energy content of the sub-band images. Experimental results show that this technique is able to enhance the contrast of region of interest (microcalcification clusters) in mammogram image.

Comparative study of wavelet and discrete cosine transform (DCT) decompositions with equivalent quantization and encoding strategies for medical images

Wavelet-based image compression is receiving significant attention, largely because of its potential for good image quality at low bit rates. In medical applications, low bit rate coding may not be the primary concern, and it is not obvious that wavelet techniques are significantly superior to more established techniques at higher quality levels. In this work we present a straightforward comparison between a wavelet decomposition and the well-known discrete cosine transform decomposition (as used in the JPEG compression standard), using comparable quantization and encoding strategies to isolate fundamental differences between the two methods. Our focus is on the compression of single-frame, monochrome images taken from several common modalities (chest and bone x-rays and mammograms).

Assessment of image quality in digital cinema using the motion quality ruler method

This paper describes a subjective image quality assessment method called the motion quality ruler (MQR) that can be used to determine the perceived quality of motion sequences. This method is an extension of a similar method that has been used to assess still image quality in photography and was standardized as ISO 20462. The MQR method provides a calibrated numerical scale in units of just noticeable differences (JNDs), and the results of different experiments using the MQR can be compared and combined, even if they were done at different times or locations. It can be used to quantify overall image quality, as well as other image attributes, in a very efficient manner over a wide range of JNDs. Results are presented from an experiment that used the MQR method to evaluate the overall quality of JPEG 2000-compressed sequences in a digital cinema environment.

Aliasing and Reconstruction Distortion in Digital Intermediates

This paper addresses two types of artifacts associated with the image sampling and reconstruction process, namely, aliasing and reconstruction distortion. Aliasing is an artifact that results from sampling a continuous signal at too low of a spatial rate relative to the input frequency content. Shannons sampling theorem states that discrete sampling of a signal at a uniform rate higher than twice the highest frequency in the signal, called the Nyquist rate, will allow a perfect reconstruction of the original continuous signal. However, image displays do not reconstruct images according to the ideal reconstruction equation, and, in many cases, the display uses nothing more than a sample-and-hold reconstruction. It has long been known that nonideal reconstruction can lead to distortion of the image data at frequencies below the Nyquist rate. Proper recognition of the distinction between aliasing and reconstruction errors can mean the difference between accepting and avoiding artifacts.

Efficient JPEG 2000 VBR Compression with True Constant Perceived Quality

This paper describes a novel JPEG 2000 variable bit rate (VBR) encoding system for digital cinema that uses visually lossless viewing distance as the image quality metric to provide true constant perceived quality. In this approach, the wavelet coefficients are quantized using a human visual system model so that there are no detectable compression errors when a movie is viewed at the specified viewing distance (or at any greater distance). The compressed codestream includes only the minimum information that is needed to ensure visually lossless quality, so coding efficiency is high. Compression times are also significantly reduced, as compared to typical VBR systems, which results in a more efficient workflow. These benefits are achieved while maintaining full compatibility with the Digital Cinema Initiatives (DCI) compression specifications.

Applications Of Vector Quantization To Progressive Compression And Transmission Of Images

In this paper, we develop a technique based on tree-searched mean residual vector quantization (MRVQ) for progressive compression and transmission of images. In the first stage, averages over image subblocks of a certain size are transmitted. If the receiver decides to retain the image, the residual image generated by subtracting the block averages from the original is progressively transmitted using the tree-searched vector quantization (VQ) hierarchy. In an attempt to reduce the bit-rate of the initial transmission, Knowltons scheme is used to transmit the block averages progressively. Using a (4x4) block size, we obtain high quality images at 1.4 bits/pixel.

Multiple JPEG compression cycles in medical images

This paper presents a study on quantifying and understanding the impact of multiple lossy JPEG compression cycles on medical images. In medical imaging applications, an image is compressed using a specific technique and parameters and then transmitted to a user (i.e., physician) where it is decompressed for viewing. The user may then wish to retransmit the decompressed image (or a portion of the image) to another user for further review. This second transmission might use the same compression parameters as the first transmission, or they might change because of system constraints. This cycle of compression/decompression might be repeated several times. When performing multiple JPEG compression cycles, several scenarios are likely to occur between cycles: (1) changes in the compression ratio; (2) changes in the quantization table; and (3) cropping of the image. In this study, we considered combinations of high (40:1), medium (20:1), and low (10:1) compression ratios and visually-based q-tables designed for viewing distances of 2, 3, and 4-picture heights. The compression ratio and/or q-table were varied between compression cycles. We also consider cropping of the image by one-pixel increments between compression cycles. Root-mean- squared error (RMSE) between the original image and the decompressed image was used to quantify the impact of these parameters. This study examined these issues for the specific case of computed radiography images.

Wavelet quad-tree compression of medical images using JPEG quantization and encoding strategies

Wavelet-based image compression is receiving significant attention because of its potential for good image quality at low bit rates. In this paper, we describe and analyze a lossy wavelet compression scheme that uses direct extensions of the JPEG quantization and Huffman encoding strategies to provide high compression efficiency with reasonable complexity. The focus is on the compression of 12-bit medical images obtained from computed radiography and mammography, but the general methods and conclusions presented in this paper are applicable to a wide range of image types.

Digital Images and Image Compression

The demand for handling images in digital form has increased dramatically in recent years. Thanks to performance improvements and significant reductions in the cost of image scanners, photographs, printed text, and other media can now be easily converted into digital form (image digitization). Direct acquisition of digital images (scene digitization) is also becoming more common as sensors and associated electronics improve; the use of satellite imaging, e.g., LANDSAT, in remote sensing of the earth and the advent of electronic still-cameras in the consumer market are good examples. In addition, many different imaging modalities in medicine, such as computed tomography (CT) or magnetic resonance imaging (MRI), generate images directly in digital form. Computer-generated images (synthetic images) are also becoming a major source of digital data. The use of computer graphics in advertising and entertainment is widespread, and its use in scientific visualization and engineering applications is growing at a rapid pace. The reason for this interest in digital images is clear: representing images in digital form allows visual information to be easily manipulated in useful and novel ways. This fact, combined with the exponential growth in computing power over the past decade, has resulted in the use of digital imaging systems in such diverse fields as astronomy, remote sensing, medicine, photojournalism, graphic arts, law enforcement, advertisement, and manufacturing. Despite the advantages, there is one potential problem with digital images, namely, the large number of bits required to represent them. Fortunately, digital images, in their canonical representation, generally contain a significant amount of redundancy. Image compression, which is the art/science of efficient coding of picture data, aims at taking advantage of this redundancy to reduce the number of bits required to represent an image.