Increasing transmission of medical images across multiple user systems raises concerns for image security. Hiding watermark information in medical image data files is one solution for enhancing security and privacy protection of data. Medical image watermarking however is not a widely studied area, due partially to speculations on loss in viewer performance caused by degradation of image information. Such concerns are addressed if the amount of information lost due to watermarking can be kept at minimal levels and below visual perception thresholds. This paper describes experiments where three alternative visual quality metrics were used to assess the degradation caused by watermarking medical images. Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) medical images were watermarked using different methods: Block based Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) with various embedding strengths. The visual degradation of each watermarking parameter setting was assessed using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Measure (SSIM) and Steerable Visual Difference Predictor (SVDP) numerical metrics. The suitability of each of the three numerical metrics for medical image watermarking visual quality assessment is noted. In addition, subjective test results from human observers are used to suggest visual degradation thresholds.
The growth of internet communications, multimedia storage capacity, and software sophistication triggered the need to protect intellectual property in digital media. Digital watermark can be inserted into images for copyright protection, copy protection, tamper detection and authentication. Unfortunately, geometrical robustness in digital image watermarking remains a challenging issue because consumer software enables rotational, scaling and translational attacks on the watermark with little image quality degradation. To balance robustness requirements and computation simplicity, we propose a method to re-synchronize watermark information for its effective detection. The method uses scale normalization and flowline curvature in embedding and detection processes. Scale normalization with unit aspect ratio and predefined area offers scale invariance and translation invariance. Rotational robustness is achieved using the flowline curvature properties of extracted robust corners. The watermark is embedded in Discrete Fourier Transform (DFT) domain of the normalized image using fixed strength additive embedding. Geometric properties recovery is simplified using flowline curvature properties and robust corners as reference points prior to watermark detection. Despite the non-blind nature and vulnerability to local transformations of this approach, experimental results indicate its potential application in robust image watermarking.
Visual inspection of the tongue has been an important diagnostic method of Traditional Chinese Medicine (TCM). Clinic data have shown significant connections between various viscera cancers and abnormalities in the tongue and the tongue coating. Visual inspection of the tongue is simple and inexpensive, but the current practice in TCM is mainly experience-based and the quality of the visual inspection varies between individuals. The computerized inspection method provides quantitative models to evaluate color, texture and surface features on the tongue. In this paper, we investigate visualization techniques and processes to allow interactive data analysis with the aim to merge computerized measurements with human expert's diagnostic variables based on five-scale diagnostic conditions: Healthy (H), History Cancers (HC), History of Polyps (HP), Polyps (P) and Colon Cancer (C).
The development of data warehouses for the storage and analysis of very large corpora of medical image data represents a significant trend in health care and research. Amongst other benefits, the trend toward warehousing enables the use of techniques for automatically discovering knowledge from large and distributed databases. In this paper, we present an application design for knowledge discovery from databases (KDD) techniques that enhance the performance of the problem solving strategy known as case- based reasoning (CBR) for the diagnosis of radiological images. The problem of diagnosing the abnormality of the cervical spine is used to illustrate the method. The design of a case-based medical image diagnostic support system has three essential characteristics. The first is a case representation that comprises textual descriptions of the image, visual features that are known to be useful for indexing images, and additional visual features to be discovered by data mining many existing images. The second characteristic of the approach presented here involves the development of a case base that comprises an optimal number and distribution of cases. The third characteristic involves the automatic discovery, using KDD techniques, of adaptation knowledge to enhance the performance of the case based reasoner. Together, the three characteristics of our approach can overcome real time efficiency obstacles that otherwise mitigate against the use of CBR to the domain of medical image analysis.
Case-based reasoning (CBR) which involves the representation of prior experience as cases, provides a natural approach for developing a medical diagnosis support system because medical practitioners usually solve new problems by comparing them to previously seen cases. We propose a general framework for such a system with the aim to assess the normality and abnormality of the cervical spine. Two distinct types of visual features are used for indexing the cases: a small set of basic features that is known to be useful by radiologists for diagnosis and a minimal set of salient generic visual features. The latter is obtained by using knowledge discovery from databases (KDD) techniques. Standard image analysis techniques are used to automatically extract both of these types of features from the images. The efficiency and adaptiveness of the system can be further improved by using KDD techniques to reduce the set of cases to the minimum as well as to extract appropriate adaptation rules.
This paper proposed a systematic approach for exploring the interactions of aesthetic properties and design variables, by integrating knowledge from other fields such as philosophy and arts. Commonly-accepted aesthetic properties and language terms used for evaluation and criticism are first discussed and a common set of nine principles for achieving aesthetic products in a number of creative disciplines is identified. We then analyze the way these principles influence product characteristics and extract concrete and computable properties of products that may be varied to induce different aesthetic judgments and responses.
Feature detectors in the early preattentive stage of the Human Visual System (HVS) are believed to cause region of the viewing field to be identified as perceptually salient, attracting the attention of the viewer. It is anticipated that this characteristic of the HVS can be incorporated into a feature based fuzzy scene decomposition model, which will assist an image rendering system in the allocation of the highest levels of detail to the most conspicuous objects. Efficiency gains should occur, with minimal loss of perceptual image quality. This paper describes the early stages of the development of this fuzzy model, for a small subset of commonly accepted visual features: color, size, location, edges and depth cues. Previous researchers have used arbitrary feature relationship models in image processing systems, with some success. Our aim is to improve on these models by integrating present knowledge of visual feature relationships, with experimental results of our own, and to apply this model to the area of image synthesis. Preliminary results from experiments with size features are presented, along with planned experimentation for other visual features. This work will have applications in the areas of scientific visualization, vision simulation and entertainment.
A wavelet-based multiscale scheme for segmenting MR images is presented, which aims to extract structures of different sizes by performing segmentation from coarse to fine scales. This scheme alleviates some common difficulties encountered by region-based segmentation to avoid over-segmentation as well as to prevent small regions from being missed. It also allows users more effective control over the segmentation process in order to extract features suitable for their own purposes.
Ligand-receptor protein binding is an important process in drug design. This paper discusses the development of a visual tool for studying the binding of ligand-receptor pairs, to help in identifying active sites of the molecules. The tool can be used to explore many possible variations in binding pairs without doing expensive laboratory experiments. The traditional view of the binding process has been limited to a static lock and key model. It is now regarded that the ligand and receptor change dynamically during the interaction. Traditional experimental methods only determine the shape of static chemical structures. Our tool improves on the previous methods by dynamically simulating the entire binding process of isolated ligand-receptor pairs. The open design of our dynamic interaction model allows its extension with further constraints and heuristic rules. This is needed when the existing forces do not provide a sufficiently complete system description. For example, more detailed simulation constraints can increase the probability of convergence of a ligand-receptor pair. New constraints can limit the degree of freedom of rotation about bonds, to take into account molecular affinity. The intermolecular rules may be changed to include the effects of hydrogen bonding, and other forces.
In previous work, an hierarchical system for shape classification based on morphological techniques was developed. A major concern, however, was the lack of a fast and accurate morphological algorithm for calculating the convex hull of an object, an important step in the classification process. Presented here are an overview of the classification system to date and several different convex hull algorithms designed to overcome these problems.
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