Each image acquired from a medical imaging system is often part of a two-dimensional (2-D) image set whose total presents a three-dimensional (3-D) object for diagnosis. Unfortunately, sometimes these images are of poor quality. These distortions cause an inadequate object-of-interest presentation, which can result in inaccurate image analysis. Blurring is considered a serious problem. Therefore, "deblurring" an image to obtain better quality is an important issue in medical image processing. In our research, the image is initially decomposed. Contrast improvement is achieved by modifying the coefficients obtained from the decomposed image. Small coefficient values represent subtle details and are amplified to
improve the visibility of the corresponding details. The stronger image density variations make a major contribution to the overall dynamic range, and have large coefficient values. These values can be reduced without much information loss.
Lossless compression techniques are essential in archival and communication of large amounts of homogeneous data in radiological image databases. This paper exploits dependencies that exist between the pixel intensities in three dimensions to improve compression for a set of similar medical images. These 3-D dependencies are systematically presented as histograms, plots of wavelet decomposition coefficients, feature vectors of wavelet decomposition coefficients, entropy and correlation. This 3-D dependency is called set redundancy for medical image sets. Predictive coding is adapted to set redundancy and combined with integer wavelet transformations to improve compression. This set compression improvement is demonstrated with 3-D sets of magnetic resonance (MR) brain images.
There is a great amount of similarity in a set of medical images. Set Redundancy Compression (SRC) has shown that compression of a similar set of images can provide better compression than the compression obtained from compressing the individual images of the set. SRC is based on the prediction of the other images in the set from a smaller subset (this subset can be as small as one image). This paper presents a new wavelet based prediction method for prediction of the intermediate images in a similar set of medical images. The technique uses the correlation between coefficients in the wavelet transforms of the image set to produce a better image prediction compared to direct image prediction.
Diagnostically lossless compression techniques are essential in archival and communication of medical images. In this paper, an automated wavelet-based background noise removal method, i.e. diagnostically lossless compression method, is proposed. First, the wavelet transform modulus maxima procedure products the modulus maxima image which contains sharp changes in intensity that are used to locate the edges of the images. Then the Graham Scan algorithm is used to determine the convex hull of the wavelet modulus maxima image and extract the foreground of the image, which contains the entire diagnostic region of the image. Histogram analyses are applied to the non-diagnostic region, which is approximated by the image that is outside the convex hull. After setting all pixels in the non-diagnostic region to zero intensity, a higher compression ratio, without introducing loss of any data used for the diagnosis, is achieved with UNIX utilities compress and pack, and with lossless JPEG. Furthermore, an image of smaller rectangular region containing all the diagnostic region is constructed to further improve the compression ratio achieved.
A major problem associated with a `film-less hospital' is the amount of digital image data that is generated and stored. Image compression must be used to reduce the storage size. Most current image compression methods were developed for the compression of single images. A new compression that uses similar image set redundancy and minimal numbers of orthogonal features can be used to efficiently compress medical images. As presented in this paper, wavelet analysis, principal component analysis, and statistical recrimination can be successfully used to optimally denote image differences and achieve efficient similar set image compression for medical images.
This paper addresses efficient parallel compression and classification for sets of similar images that are normally generated from satellite imagery, medical imaging (CT and MR scans) or aerial surveillance. From our experiments it was observed that image similarities for each class of images can be more efficiently expressed in the domain of image compressing transforms. In particular, the paper shows that only one predictive compressing model can be constructed for the entire class of similar images of the same nature, and then used for nearly optimal compression of any image of the class. The extraction of the optimal class-compressing model still remains a computationally intensive process, which can be considerably improved on parallel computers. The paper demonstrates how a similar database compressing model can be extracted in parallel, and how this can be used for parallel similar database compression and classification of new images into appropriate similarity classes. The results of the parallel similar image analysis are demonstrated with MR and CT brain images obtained from the M.D. Anderson Cancer Center.
The paper demonstrates the existence of a common autoregressive (CAR) compressing transform for a class of similar images, and examines the model sensitivity with respect to translations and rotations of the similar images.
Registration is the process of mapping one image onto another image. This is done quite often in the area of Medical Imaging for clinical as well as diagnostic reasons. Registration of images is useful from a medical perspective to detect growth of tumors, locate tumors with respect to bone structure, determine how well a bone graft is taking. Registration is also the first step in object recognition algorithms and has applications in the areas of aerial surveillance, automatic target recognition, etc.
Compression is based on the removal of redundancy inherent in most images. Most of the work in the area of image compression has been directed at removing the redundancy within the same image. However, if we examine similar images from MRI scans, or CT scans, or aerial images, from surveillance photos; there is often similarity between these images. Removing this inter-image redundancy is the focus of 'set compression' techniques. However, when similar images are registered their compression ratios are much higher, using set compression techniques. In order to register images we consider the Wavelet Modulus Maxima to determine the important control points which provide shift invariant structures. Using these structures and control points form different images in a similar set of images, we register the images using the homologous set of points. By suing the difference image we are able to exploit the inter-image redundancy. The difference image can be further compressed by any other available compression technique to further increase the compression ratio. Since we are dealing with sets of images, these applications are well suited for using parallel architectures. For example in similar sets of medical images, there is a great potential to exploit the inherent parallelism is the process, by working on several images simultaneously. We make use of several nodes on each image as well as process several images in parallel. This paper discusses the issues related to the parallel implementation of our image registration and compression algorithm. This paper also discussed the results and details the gains obtained in parallel implementation using several nodes.
Registration of images is of great importance in the fields of aerial surveillance, automatic target recognition, machine vision and medical imaging. Wavelets are tools that are being used to analyze signals and images. Wavelets in some sense are alternatives to Fourier transforms. Wavelets provide excellent time and frequency localization properties when compared to Fourier transforms. Singularities and irregular structures carry very important information about edges. Wavelets are excellent tools for detecting these singularities and characterizing the regularity of the function using Lipschitz exponents as shown by mallat et. al. In this paper we use the wavelet modulus maxima which is the strict local maxima of the modulus of the wavelet of the modulus of the wavelet transform to locate the singularities at each scale. homologous points from two similar images are then used to register the images using a best fit criteria. The objective function being that the difference image is a minimum image. The technique exploits inter image redundancy in addition to the intra image redundancy in sets of similar images after they have been registered. This lead to higher compression ratios. It also permits the use of any existing compression technique to exploit intra image redundancy.