The adaptive integration (ADI) method for two-dimensional (2-D) phase unwrapping is presented. The method uses an algorithm for noise robust estimation of partial derivatives, followed by a noise robust adaptive integration process. The ADI method can easily unwrap phase images with moderate noise levels, and the resulting images are congruent modulo 2pi with the observed, wrapped, input images. In a quantitative evaluation, both the ADI and the BLS methods (Strand et al.) were better than the least-squares methods of Ghiglia and Romero (GR), and of Marroquin and Rivera (MRM). In a qualitative evaluation, the ADI, the BLS, and a conjugate gradient version of the MRM method (MRMCG), were all compared using a synthetic image with shear, using 115 magnetic resonance images, and using 22 fiber-optic interferometry images. For the synthetic image and the interferometry images, the ADI method gave consistently visually better results than the other methods. For the MR images, the MRMCG method was best, and the ADI method second best. The ADI method was less sensitive to the mask definition and the block size than the BLS method, and successfully unwrapped images with shears that were not marked in the masks. The computational requirements of the ADI method for images of nonrectangular objects were comparable to only two iterations of many least-squares-based methods (e.g., GR). We believe the ADI method provides a powerful addition to the ensemble of tools available for 2-D phase unwrapping.

Magnetic resonance imaging (MRI) of the brain, followed by automated segmentation of the corpus callosum (CC) in midsagittal sections has important applications in neurology and neurocognitive research since the size and shape of the CC are shown to be correlated to sex, age, neurodegenerative diseases and various lateralized behavior in man. Moreover, whole head, multispectral 3D MRI recordings enable voxel-based tissue classification and estimation of total brain volumes, in addition to CC morphometric parameters. We propose a new algorithm that uses both multispectral MRI measurements (intensity values) and prior information about shape (CC template) to segment CC in midsagittal slices with very little user interaction. The algorithm has been successfully tested on a sample of 10 subjects scanned with multispectral 3D MRI, collected for a study of dyslexia. We conclude that the proposed method for CC segmentation is promising for clinical use when multispectral MR images are recorded.

We present a block least-squares (BLS) method for two-dimensional (2-D) phase unwrapping. The method works by tessellating the input image into small square blocks with only one phase wrap. These blocks are unwrapped using a simple procedure, and the unwrapped blocks are merged together using one of two proposed block merging algorithms. By specifying a suitable mask, the method can easily handle objects of any shape. This approach is compared with the Ghiglia-Romero method and the Marroquin-Rivera method. On synthetic images with different noise levels, the BLS method is shown to be superior, both with respect to the resulting gray values in the unwrapped image as well as visual inspection. The method is also shown to successfully unwrap synthetic and real images with shears, fiber-optic interferometry images, and medical magnetic resonance images. We believe the new method has the potential to improve the present quality of phase unwrapped images of several different image modalities.

Starts by giving the medical imaging modalities that are in practical use and lists several of the new medical imaging modalities under development. The remainder of the paper is concentrated on progress in MR imaging, ultrasound imaging and X-ray CT imaging. These modalities are major radiological imaging tools, which will have growing significance in the next decade. They are surpassed only by ordinary X-ray projection imaging, which is much more static in its development. Particular attention is given to applications where image processing and image analysis tasks are needed.

Compares the performance of topographic analysis and binary analysis for recognition of digits in hydrographic maps. The performance of each method was measured by the correct classification rate of the final symbol recognition step when processing a complete hydrographic map of size 0.45/spl times/0.6 m/sup 2/ with about 35000 digits. The experimental results indicated that binary analysis had a better performance than topographic analysis. Overall, the performance of the binary analysis was acceptable.

Compares the performance of seven different cepstrum based methods for radial blind deconvolution of medical ultrasound images. The first is the generalized cepstrum method and the second is the spectral root cepstrum method. All the last five methods calculate the complex cepstrum, but different computational techniques are employed to reduce or eliminate aliasing, phase unwrapping or effects of noise. The third method simply computes the real cepstrum. The fourth method computes the complex cepstrum using phase unwrapping, the fifth substitutes differentiation for phase unwrapping, the sixth uses polynomial rooting in the spatial domain, and the seventh computes the complex cepstrum from the bispectrum. Using in vivo radio frequency data from a clinical scanner, the generalized cepstrum method gave the best images. This result is an important guideline for selecting a specific cepstrum based radial deconvolution method for real time implementation in ultrasound scanners.

This paper investigates how gray scale information can be used in a hydrographic map understanding system to improve the system performance. To process gray scale scanned map images, we have implemented a topographic analysis method and a binary analysis method. In addition, deconvolution of the gray scale map image was used as an optional preprocessing step for both the methods. Both the methods process the input image by extracting binary print components, recognizing long lines, splitting touching digits and recognizing the digits. The topographic analysis extracts the information by computing topographic labels for each pixel, while the binary analysis is based on locally adaptive thresholding of the gray scale image. The performance of each method was evaluated by measuring the recognition performance of the digit recognition module. Experimental results indicate that the computationally intensive deconvolution and topographic analysis does not improve system performance. The same high performance is achieved by binary analysis, provided a high quality locally adaptive binary method is used.

A general model for multisource classification of remotely sensed data based on Markov random fields (MRF) is proposed. A specific model for fusion of optical images, synthetic aperture radar (SAR) images, and GIS (geographic information systems) ground cover data is presented in detail and tested. The MRF model exploits spatial class dependencies (spatial context) between neighboring pixels in an image, and temporal class dependencies between different images of the same scene. By including the temporal aspect of the data, the proposed model is suitable for detection of class changes between the acquisition dates of different images. The performance of the proposed model is investigated by fusing Landsat TM images, multitemporal ERS-1 SAR images, and GIS ground-cover maps for land-use classification, and on agricultural crop classification based on Landsat TM images, multipolarization SAR images, and GIS crop field border maps. The performance of the MRF model is compared to a simpler reference fusion model. On an average, the MRF model results in slightly higher (2%) classification accuracy when the same data is used as input to the two models. When GIS field border data is included in the MRF model, the classification accuracy of the MRF model improves by 8%. For change detection in agricultural areas, 75% of the actual class changes are detected by the MRF model, compared to 62% for the reference model. Based on the well-founded theoretical basis of Markov random field models for classification tasks and the encouraging experimental results in our small-scale study, the authors conclude that the proposed MRF model is useful for classification of multisource satellite imagery.

There is a large number of documents, including hand-printed maps, where useful information is lost if binarization is performed on the scanned image before further processing. For such documents, methods which utilize the gray scale values must be used in order to extract as much of the available information as possible. Topographic analysis has been used in the literature to recognize characters directly in the gray scale images. The authors extend the topographic analysis method, so that characters and lines can be extracted from gray scale map images where methods using only the information in the binary image fail.

This paper presents an evaluation of eleven locally adaptive binarization methods for gray scale images with low contrast, variable background intensity and noise. Niblack's method (1986) with the addition of the postprocessing step of Yanowitz and Bruckstein's method (1989) added performed the best and was also one of the fastest binarization methods.>

This paper presents an evaluation of locally adaptive binarization methods for gray scale images with low contrast, variable background intensity and noise. Such low quality occurs frequently in utility maps and excludes the use of global binarization methods. Only robust locally adaptive binarization methods with no need for on-line tuning of the parameters were considered since the gray scale images of utility maps often consist of a billion (10/sup 9/) pixels or more. Eight locally adaptive binarization methods were tested on five different images. The postprocessing step (PS) of Yanowitz and Bruckstein's (1989) method improved all the other best binarization methods. Niblack's (1986) method with PS gave the best performance. Eikvil, Taxt and Moen's (1991) method with PS, and Yanowitz and Bruckstein's method did almost as well. Comparison was also made on the CPU requirement.>

Describes how homomorphic deconvolution can be used to improve the radial resolution of in vitro and in vivo medical ultrasound images. Each of the recorded radiofrequency ultrasound beams used to form the image was considered as a finite depth sequence of length N, and was weighted with the same exponential depth sequence to create at least some minimum phase sequences. The mean value at each depth sample of the complex cepstrum sequences was computed, and the low depth portion of this mean sequence was taken as the complex cepstrum representation of the ultrasound pulse. It was transformed back to the Fourier frequency domain, and was used to compute the deconvolved echo depth sequence. The method gave substantial improvement in the radial resolution of B-scan images of a tissue mimicking phantom and of human tissues in vivo without significant amplification of the image noise.

The properties of the elliptic Fourier descriptors of Kuhl and Giardina (1981) in statistical classification of single, vectorized handwritten symbols were studied. These descriptors usually give rise to unimodal class-specific distributions in feature space and allow reconstruction of a symbol based on the measured features alone. A complication of these descriptors applied to vectorized symbols is the need for subclasses in the statistical classification scheme. The recognition rates obtained using elliptic Fourier descriptors were higher than what we obtained using other established descriptors. We conclude that elliptic Fourier descriptors have promising properties in statistical classification schemes for single, vectorized handwritten symbols.

Proposes a new method for statistical classification of multisource data. The method is suited for land-use classification based on the fusion of remotely sensed images of the same scene captured at different dates from multiple sources. It incorporates a priori information about the likelihood of changes between the acquisition of the different images to be fused. A framework for the fusion of remotely sensed data based on a Bayesian formulation is presented. First, a simple fusion model is given, and then the basic model is extended to take into account the temporal attribute if the different data sources are acquired at different dates. The performance of the model is evaluated by fusing Landsat TM images and ERS-1-SAR images for land-use classification. The fusion model gives significant improvements in the classification error rates compared to the conventional single-source classifiers.>

The authors demonstrate an improved differentiation of the most common tissue types in the human brain and surrounding structures by quantitative validation using multispectral analysis of magnetic resonance images. This is made possible by a combination of a special training technique and an increase in the number of magnetic resonance channel images with different pulse acquisition parameters. The authors give a description of the tissue-specific multivariate statistical distributions of the pixel intensity values and discuss how their properties may be explored to improve the statistical modeling further. A statistical method to estimate the tissue-specific longitudinal and transverse relaxation times is also given. It is concluded that multispectral analysis of magnetic resonance images is a valuable tool to recognize the most common normal tissue types in the brain and surrounding structures.

The ability of three different methods to separate nonrotated symbols of fonts with different slope angles is examined. The authors compare the properties of the elliptic Fourier descriptors of Kuhl and Giardina with the Fourier descriptors of Zahn and Roskies and the grid method.>

The analogy between a multispectral image and a 2-dimensional solid in a thermodynamic quasi-equilibrium state is used to design a maximum entropy image restoration method. The solid is described by a dynamic quantum field model. Closed form solutions for the Gibbs partition function, the associated local Markov transitional probability functions and the resulting entropy function in equilibrium and quasi-equilibrium states of the 2-dimensional solid are given. Using these functions the entropy of the whole solid is maximized by maximizing the entropy of each of the single particles in the solid. The model is simple even for multispectral images because the calculations take place in the 2-dimensional solid model instead of in the multispectral image. The performance of the new method is illustrated by the restoration of simulated piecewise constant images with known noise characteristics. Well defined physical models of thermodynamics and statistical quantum field theory can be used to design powerful and simple models for image restoration.>

Steps in a procedure for the automatic measurement of the cardiac ejection fraction in time-varying two-dimensional ultrasound images are presented. Two statistical contextual noise reduction methods are generalized to handle noisy three-dimensional gray-level images (the third dimension being time). The improved gray-level images are thresholded by an adaptive thresholding algorithm. The concept of connected components in, three-dimensional binary images is introduced and used to segment structures from noise according to their extension in time. It is concluded that the automatic determination of the cardiac ejection fraction in ultrasound images will be possible.>

Several methods for segmentation of document images are explored. The authors pose the segmentation operation as a statistical classification task with two pattern classes: print and background. A number of classification strategies are available. All require some prior information about the distribution of gray levels for the two classes. Learning (either supervised or unsupervised) and automatic updating of the class-conditional densities are performed within image subregions to adapt global density estimates to the local area. After local densities have been obtained, each pixel within the window is classified; several techniques for this are considered. Results on four test images indicate that the commonly used contextual models are not suitable to all document images.>

Several methods for segmentation of document images (maps, drawings, etc.) are explored. The segmentation operation is posed as a statistical classification task with two pattern classes: print and background. A number of classification strategies are available. All require some prior information about the distribution of gray levels for the two classes. Training (either supervised or unsupervised) is employed to form these initial density estimates. Automatic updating of the class-conditional densities is performed within subregions in the image to adapt these global density estimates to the local image area. After local class-conditional densities have been obtained, each pixel is classified within the window using several techniques: a noncontextual Bayes classifier, Besag's classifier, relaxation, Owen and Switzer's classifier, and Haslett's classifier. Four test images were processed. In two of these, the relaxation method performed best, and in the other two, the noncontextual method performed best. Automatic updating improved the results for both classifiers.>