KEYWORDS: Decision support systems, Databases, Pattern recognition, Data storage, Surgery, Electrocardiography, Heart, Data centers, Medicine, Data modeling
A physician's decision support system consists of three components: (1) a comprehensive patient record and medical knowledge database, (2) information infrastructure for data storage, transfer, and (3) an analytical inference engine, accompanied by business operation database. Medical knowledge database provides the guideline for the selection of powerful clinical features or tests to be observed so that an accurate diagnosis as well as effective treatment can be quickly reached. With a tremendous amount of information stored in multiple data centers, it takes an effective information infrastructure to provide streamlined flow of information to the physician in a timely fashion. A real-time analytical inference engine mimics the physician's reasoning process. However due to incomplete, imperfect data and medical knowledge, a realistic output from this engine will be a list of options with associated confidence level, expected risk, so that the physician can make a well-informed final decision. Physicians are challenged to pursue the objective of ensuring an acceptable quality of care in an economically restrained environment. Therefore, business operation data have to be factored into the calculation of overall loss. Follow-up of diagnosis and treatment provides retrospective assessment of the accuracy and effectiveness of the existing inference engine.
Currently early detection of breast cancer is primarily accomplished by mammography and suspicious findings may lead to a decision for performing a biopsy. Digital enhancement and pattern recognition techniques may aid in early detection of some patterns such as microcalcification clusters indicating onset of DCIS (ductal carcinoma in situ) that accounts for 20% of all mammographically detected breast cancers and could be treated when detected early. These individual calcifications are hard to detect due to size and shape variability and inhomogeneous background texture. Our study addresses only early detection of microcalcifications that allows the radiologist to interpret the x-ray findings in computer-aided enhanced form easier than evaluating the x-ray film directly. We present an algorithm which locates microcalcifications based on local grayscale variability and of tissue structures and image statistics. Threshold filters with lower and upper bounds computed from the image statistics of the entire image and selected subimages were designed to enhance the entire image. This enhanced image was used as the initial image for identifying the micro-calcifications based on the variable box threshold filters at different resolutions. The test images came from the Texas Tech University Health Sciences Center and the MIAS mammographic database, which are classified into various categories including microcalcifications. Classification of other types of abnormalities in mammograms based on their characteristic features is addressed in later studies.
Due to coherent imaging techniques employed in medical imaging such as ultrasound imaging, signal dependent speckle noise appears as granular noise that is difficult to remove without blurring the image details. Considerable efforts have been spent to design systems with improved resolution and reduced speckle noise. This study investigates the effect of recently developed nonlinear and linear filtering techniques in restoring medical images corrupted with speckle noise. Preliminary results demonstrate that a class of nonlinear filters based on mathematical morphology outperforms linear wavelet filters and a simple nonlinear median filter in removing speckle noise.
Despite the proven superiority of vector quantization (VQ) over scalar quantization (SQ) in terms of rate distortion theory, currently existing vector quantization algorithms, still, suffer from several practical drawbacks, such as codebook initialization, long search-process, and optimization of the distortion measure. We present a new adaptive vector quantization algorithm that uses a fuzzy distortion measure to find a globally optimum codebook. The generation of codebooks is facilitated by a self-organizing neural network-based clustering that eliminates adhoc assignment of the codebook size as required by standard statistical clustering. In addition, a multiresolution wavelet decomposition of the original image enhances the process of codebook generation. Preliminary results using standard monochrome images demonstrate excellent convergence of the algorithm, significant bit rate reduction, and yield reconstructed images with high visual quality and good PSNR and MSE. Extension of this adaptive VQ to color image compression is currently under investigation.
Segmentation of medical images poses a critical problem in image analysis. Segmenting a scene into different regions in the absence of sufficient apriori information is a challenging problem. A multiresolution image representation approach is presented here which makes use of a fuzzy neural network to segment a reconstructed image from wavelet decomposition into regions of interest. The multiresolution wavelets provide a basis for analyzing the information content of the image with global as well as local perspectives. The higher resolution levels contain information pertaining to the finer details while the lower resolutions capture the global features. A neuro-fuzzy algorithm facilitates the segmentation of the wavelet reconstructed image into different regions based on image intensity. The proposed algorithm has been applied to images of different kinds and has yielded promising results. The concept of using multiresolution wavelets and a neuro-fuzzy classification scheme has the added advantage of flexibility in the level of segmentation achieved.
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