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A multidimensional approach to medical image processing

Medical Imaging 1997 Symposium - image processing

作者： Battle, XL Bizais, Y Los Alamos National Laboratory MS D454 Los Alamos NM 87545 United States Laboratoire de Biophysique Université de Bretagne Occidentale 29285 Brest Cedex BP 815 France

ISBN: (纸本)0819424455

This paper addresses the issue of writing image processing algorithms and programs that are independent of the dimension of the dataset. Such an approach aims at writing libraries and tool-boxes that will be smaller (because one routine works on all dimensions) as well as easier to debug (the more the routine is used, the easier it is debugged). The data to be processed is stored in a multi-dimensional, self-documented format describing, not only the content of the image, but also its context and the conditions of its acquisition. The work presented in this paper is based on the image kernel of the MIMOSA standard. We propose a recursive programming scheme that allows one to write general algorithms for such multi-dimensional images. Oddly enough, the design of such algorithms is easy and intuitive, thanks to the recursion. Moreover, the computational cost;remains comparable to the one of dimension-specific algorithms. The cost of the recursion is indeed negligible compared to the cost of non trivial processings. We present an implementation of a reduced version of the MIMOSA image kernel, show how elementary processing such as convolution and filtering can be easily implemented. Finally we propose an algorithm for the no Fast Fourier Transform operating on real data.

关键词： multidimensional image processing recursive algorithms nD fast Fourier transform

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A task-specific evaluation of three-dimensional image interpolation techniques

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IEEE TRANSACTIONS ON MEDICAL IMAGING 1999年第2期18卷 137-143页

作者： Grevera, GJ Udupa, JK Miki, Y Univ Penn Hlth Syst Dept Radiol Med Image Proc Grp Philadelphia PA 19104 USA Kyoto Univ Hosp Dept Nucl Med & Diagnost Imaging Kyoto 606 Japan

image interpolation is an important operation that is widely used in medical imaging, image processing, and computer graphics. A variety of interpolation methods are available in the literature, However, their systematic evaluation is lacking. In a previous paper, we presented a framework for the task-independent comparison of interpolation methods based on certain image-derived figures of merit using a variety of medical image data pertaining to different parts of the human body taken from different modalities. In this work, we present an objective task-specific framework for evaluating interpolation techniques. The task considered is how the interpolation methods influence the accuracy of quantification of the total volume of lesions in the brain of multiple sclerosis (MS) patients. Sixty lesion-detection experiments coming from ten patient studies, two subsampling techniques and the original data, and three interpolation methods are carried out, along with a statistical analysis of the results.

关键词： computer-aided diagnosis image interpolation multidimensional image processing three-dimensional imaging

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Cue-based segmentation of 4D cardiac image sequences

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COMPUTER VISION AND image UNDERSTANDING 2000年第2期77卷 251-262页

作者： Reinhardt, JM Wang, AJ Weldon, TP Higgins, WE Univ Iowa Dept Biomed Engn Iowa City IA 52242 USA GTE Informat Technol Irving TX 75038 USA Univ N Carolina Dept Elect Engn Charlotte NC 28223 USA Penn State Univ Dept Elect Engn University Pk PA 16802 USA Penn State Univ Dept Comp Sci & Engn University Pk PA 16802 USA Penn State Univ Dept Bioengn University Pk PA 16802 USA

Medical imaging scanners now exist that can generate 4D cardiac images, Since the heart moves, cardiac anatomy and physiology can be studied using 4D image sequences. Interactive manual 4D image analysis Can be time-consuming and error prone-automatic and semi-automatic methods have many advantages over manual segmentation. This paper describes a procedure for performing semi-automatic image segmentation on 4D image sequences, Our procedure is based on a small set of user defined image-segmentation cues specified at certain time points in the sequence. These cues are then automatically interpolated or extrapolated for the remaining time points, The complete set of cues is interpreted and used to generate a sequence of image processing operations (such as operators for image enhancement, morphological processing, and region segmentation) that can subsequently segment the 4D image, This procedure permits 4D cardiac image segmentation with only a small amount of user interaction. The proposed approach compares favorably to results generated by defining cues on each individual volume and to results generated completely manually. The 4D approach also requires significantly less interaction time than pure manual analysis. (C) 2000 Academic Press.

关键词： image segmentation cue-based image analysis multidimensional image processing medical imaging 3D imaging cardiac imaging interpolation 4D image processing

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Error prediction of the Gaussian ML classifier in remotely sensed data

Error prediction of the Gaussian ML classifier in remotely s...

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Conference on Algorithms, Devices, and Systems for Optical Information processing

作者： Lee, C Yonsei Univ. (South Korea)

ISBN: (纸本)0819425818

In this paper, a new method to predict the classification error in the Gaussian ML classifier is proposed. The Gaussian ML classifier is one of the most widely classifiers in pattern classification and remote sensing because of its speed and performance. Several methods have been proposed to estimate error of the Gaussian ML classifier. In particular, the Bhattacharyya distance gives theoretical upper and lower bounds of the classification error. However, in many cases, the bounds are not tight enough to be useful. In this paper, we propose a different approach to predict error of the Gaussian ML classifier using the Bhattacharyya distance. We generate two classes with normal distribution and calculate the Bhattacharyya distance and the classification accuracy. The class statistics used to generate data are obtained from real remotely sensed data. We repeat the experiment about 100 million times with different class statistics and try to find the relationship between the classification error and the Bhattacharyya distance empirically. The range of the dimension of the generated data is from 1 to 17. From the experiments, we are able to obtain a formula that gives a much better error estimation of the Gaussian ML classifier. Apparently, it is possible to predict the classification error within 1-2% margin.

关键词： Gaussian ML classifier error prediction remote sensing classification multidimensional image processing

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Comparison of different classification algorithms for NOAA AVHRR images

Comparison of different classification algorithms for NOAA A...

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Conference on Applications of Digital image processing XXIII

作者： Piazza, E NAVIA AVIAT AS Div NOVA N-3194 Horten Norway

ISBN: (纸本)0819437603

In this work four different algorithms for image classification have been applied to AHVRR images coming from the NOAA-14 polar orbiter satellite. The goal is to assign each pixel to one of the following classes: Sea, Land, Cloud, Cloud Edge or Out of the scene. The last class are needed since the images are resampled with always the same geographical limit, thus, sometime, due to satellite being low on the horizon, some pixel can be out of the radiometer view. The first method is based on thresholds on the value of the NDVI (Normalized Differential Vegetation Index) alone. The second method is based on thresholds on 4 of the 5 bands of the AVHRR. The third method is a simple Isodata algorithm on the same 4 bands of the second method and on the same 2 bands of the first method, the ones used to extract the NDVI value. The fourth method is a fuzzy C-Means algorithm on the same set of bands of first and second method. Finally some improvement are proposed and discussed with focus an the actual NOAA-14 images. The results, in terms of computational time, and classification behaviour are also discussed. For comparison purpouses, the second method, thresholds on 4 of the 5 bands of the AVHRR, is assumed to be the thruth and all the results are given with reference to it.

关键词： remote sensing multidimensional image processing

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A task-specific evaluation of 3D image interpolation techniques

A task-specific evaluation of 3D image interpolation techniq...

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Conference on image Display - Medical Imaging 1998

作者： Grevera, GJ Udupa, JK Miki, Y Univ Penn Dept Radiol Hlth Syst Philadelphia PA 19104 USA

ISBN: (纸本)0819427802

image interpolation is an important operation that is widely used in medical imaging, image processing, and computer graphics. A variety of interpolation methods are available in the literature. However, their systematic evaluation is lacking. At a previous meeting, we presented a framework for the task independent comparison of interpolation methods based on a variety of medical image data pertaining to different parts of the human body taken from different modalities. In this new work, we present an objective, task-specific framework for evaluating interpolation techniques. The task considered is how the interpolation methods influence the accuracy of quantification of the total volume of lesions in the brain of Multiple Sclerosis (MS) patients. Sixty lesion detection experiments coming from ten patient studies, two subsampling techniques and the original data, and 3 interpolation methods is presented along with a statistical analysis of the results. This work comprises a systematic framework for the task-specific comparison of interpolation methods. Specifically, the influence of three interpolation methods in MS lesion quantification is compared.

关键词： image interpolation 3D imaging multidimensional image processing visualization computer-aided diagnosis

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Real time processing of multispectral satellite remote sensing images

Real time processing of multispectral satellite remote sensi...

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Conference on Applications of Digital image processing XXII

作者： Piazza, E NAVIA AVIAT AS Div NOVA N-3194 Horten Norway

ISBN: (纸本)0819432946

This work mainly deal with the processing of Remote Sensed images gathered from a radiometer flown aboard an environmental satellite. In this work some NOAA/AVHRR images have been used with focus on the southern Europe and the Mediterranean Basin. The images are navigated by resolving the satellite motion and the sensor's scanning laws. And calibrated in order to obtain true radiance or temperature values. The first and second band of the AVHRR Radiometer are useful to distinguish between land and sea pixels. While the two thermal infrared channels (4 and 5) proved to be useful to find out the cloud affected pixels and to estimate the value of Sea Surface Temperature on the cloud free pixels. The presented method has been tested on an Intel Pentium 200 MHz based computer. It took 91 sec to perform the following steps: 1. Classify each pixel in one of the following categories: land sea cloud cloud edge coast line outside the swath 2. Estimate on sea, cloudy-free, pixels the Sea Surface Temperature by the means of a low noise split window algorithm on a multispectral image. This performance allows this estimation to be done in real time.

关键词： remote sensing multidimensional image processing

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Optimal feature extraction for normally distributed data

Optimal feature extraction for normally distributed data

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Conference on Algorithms for Multispectral and Hyperspectral imagery IV

作者： Lee, CH Choi, ES Kim, JH Yonsei Univ Dept Elect Engn Seoul 120749 South Korea

ISBN: (纸本)0819428213

In this paper, we propose an optimal feature extraction method for normally distributed data. The feature extraction algorithm is optimal in the sense that we search the whole feature space to find a set of features which give the smallest classification error for the Gaussian ML classifier. Initially, we start with an arbitrary feature vector. Assuming that the feature vector is used for classification, we compute the classification error. Then we move the feature vector slightly in the direction so that the classification error decreases most rapidly. This can be done by taking gradient. We propose two search methods, sequential search and global search. In the sequential search, if more features are needed, we try to find an additional feature which gives the best classification accuracy with the already chosen features. In the global search, we are not restricted to use the already chosen features. Experiment results show that the proposed method outperforms the conventional feature extraction algorithms.

关键词： feature extraction Gaussian ML classifier multiclass remote sensing multidimensional image processing

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Local Adaptive LMS Filtration of multidimensional images 56

Local Adaptive LMS Filtration of Multidimensional Images

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56th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST)

作者： Mironov, Rumen P. Draganov, Ivo R. Tech Univ Sofia Fac Telecommun Bulv Kl Ohridsky 8 Sofia 1000 Bulgaria

ISBN: (纸本)9781665428873

In this paper a new method for local adaptive three dimensional filtration of multidimensional halftone images is presented. The adaptation is based on the local information from the neighbourhood pixels of processed 3D image using generalized 3D LMS filter. An analysis of the quality of the processed multidimensional images is made on the basis of the calculated PSNR, SNR, MSE and the subjective observation. The given experimental results from the simulation in MATLAB environment for operating systems Windows of the developed filtration algorithms, based on a set of real test images are presented. By using the adaptive 3D LMS algorithm the average signal to noise ratio improvement is about 3 dB.

关键词： digital signal processing 3D image processing adaptive filtration LMS adaptation multidimensional image processing

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