There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

The 11th edition of Harrison's Principles of Internal Medicine welcomes Anthony Fauci to its editorial staff, in addition to more than 85 new contributors. While the organization of the book is similar to previous editions, major emphasis has been placed on disorders that affect multiple organ systems. Important advances in genetics, immunology, and oncology are emphasized. Many chapters of the book have been rewritten and describe major advances in internal medicine. Subjects that received only a paragraph or two of attention in previous editions are now covered in entire chapters. Among the chapters that have been extensively revised are the chapters on infections in the compromised host, on skin rashes in infections, on many of the viral infections, including cytomegalovirus and Epstein-Barr virus, on sexually transmitted diseases, on diabetes mellitus, on disorders of bone and mineral metabolism, and on lymphadenopathy and splenomegaly. The major revisions in these chapters and many

Harrison's Principles of Internal Medicine

The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

We explore the effect of dimensionality on the nearest neighbor problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance to the farthest data point. To provide a practical perspective, we present empirical results on both real and synthetic data sets that demonstrate that this effect can occur for as few as 10-15 dimensions. These results should not be interpreted to mean that high-dimensional indexing is never meaningful; we illustrate this point by identifying some high-dimensional workloads for which this effect does not occur. However, our results do emphasize that the methodology used almost universally in the database literature to evaluate high-dimensional indexing techniques is flawed, and should be modified. In particular, most such techniques proposed in the literature are not evaluated versus simple linear scan, and are evaluated over workloads for which nearest neighbor is not meaningful. Often, even the reported experiments, when analyzed carefully, show that linear scan would outperform the techniques being proposed on the workloads studied in high (10-15) dimensionality!.

When is nearest neighbor meaningful

The medical profession has changed dramatically in the past decade due to the advances in engineering innovations. Traditionally, medical doctors relied solely on their training and experience for diagnosis and interpretation of medical conditions captured through a variety of imaging techniques. Typically, these images are produced from CAT scan machines, MRI, or X-rays.

Today, computer aided techniques are designed to enhance the visual quality of biomedical images to better help humans discriminate regions of interest, such as the presence of cancer. Since these enhanced images are intended to assist the medical professional in making a diagnosis or to track the progress of a treatment, it is important that abnormalities presented in the images are actual objects and not artifacts resulting from the enhancement process that could lead to an inaccurate diagnosis.

Too much information presented can be distracting for the viewer and too little information presented could be disastrous for the patient, should a life-threatening abnormality be missed or filtered out. Therefore, the dilemma lies in answering the question: “What is the best image to use?”

The answer has always been subjective due to the perspective of the human observer and what constitutes the desired target object to be detected. For instance, some doctors looking at a scan may be interested in masses; another doctor may want to focus on calcifications or, even perhaps, the blood vessels.

In this special issue, articles are presented that address this question for several different biomedical image processing applications. Several novel quantifiable evaluation methods that can rank the quality of a processed image in accordance with human visual perception are introduced. These metrics are rigorously compared to evaluations obtained from human evaluators using the “Mean Opinion Score” (MOS), along with other standard benchmarking techniques.

The innovative approaches presented in this issue are sure to lead the biomedical image processing field beyond one of an “assistive technology” to one that creates a reliable, accurate autonomous technology for use in distance and robot vision applications. Achieving these goals will forge new groundbreaking paths that will reach into and serve remote populations of the world.

/pdf/image-processing-algorithms-and-measures-for-the-analysis-of-4tkk42x3d5.pdf

Image processing algorithms and measures for the analysis of biomedical imaging systems applications

In this paper we present a novel image quality measure for color images. This measure is based on the correlation of information between color planes and principal component analysis conversion of a color image into grayscale. Furthermore, a scheme for boundary detection based on this principle is developed and presented. This innovative design utilizes partial derivatives of Boolean functions for edge detection and is then analyzed using our new measure. Results testing the scheme on a database of natural and synthetic images show the performance to be quantitatively competitive with existing measures.

Detection and comparison of color edges via median based PCA

In recent years, active research has mainly concentrated on authenticating a signature; tracking a document in a digital library, and tamper detection of a scanned document or secured communication using binary images. Binary image steganographical systems provide a solution for the above discussed issues. The two color constraint of the image limits the extension of various LSB embedding techniques to the binary case. In this paper, we present a new data hiding system for binary images and scanned documents. The system initially identifies embeddable blocks and enforces specific block statistics to hide sensitive information. The distribution of th e flippable pixels in these blocks is highly uneven over the image. A variable block embedding threshold is employed for capitalizing on this uneven distribution of pixels. In addition, we also present a measure to find the best the cover given a specific file of sensitive information. The simulation was performed over 50 vari ous binary images such the scanned documents, cartoons, threshold color images. Simulation results shows that 1) The amount of data embedded is comparatively higher than the existing algorithms (such as K.H. Hwang et.al [5], J. Chen et.al [10], M.Y.Wu et.al [9] ).2) The visual distortion in cover imag e is minimal when compared with the existing algorithms (such as J. Chen[10], M.Y.Wu et.al [9] ) will be presented. Key words: Steganography, data hiding, binary image, scanned documents, variable embedding rate

Secure and robust steganographic algorithm for binary images

Biometric evidence plays a crucial role in criminal scene analysis. Forensic prints can be extracted from any solid surface such as firearms, doorknobs, carpets and mugs. Prints such as fingerprints, palm prints, footprints and lip-prints can be classified into patent, latent, and three-dimensional plastic prints. Traditionally, law enforcement officers capture these forensic traits using an electronic device or extract them manually, and save the data electronically using special scanners. The reliability and accuracy of the method depends on the ability of the officer or the electronic device to extract and analyze the data. Furthermore, the 2-D acquisition and processing system is laborious and cumbersome. This can lead to the increase in false positive and true negative rates in print matching. In this paper, a method and system to extract forensic prints from any surface, irrespective of its shape, is presented. First, a suitable 3-D camera is used to capture images of the forensic print, and then the 3-D image is processed and unwrapped to obtain 2-D equivalent biometric prints. Computer simulations demonstrate the effectiveness of using 3-D technology for biometric matching of fingerprints, palm prints, and lip-prints. This system can be further extended to other biometric and non-biometric modalities.

Sos S. Agaian

Papers

Image processing algorithms and measures for the analysis of biomedical imaging systems applications

Detection and comparison of color edges via median based PCA

Secure and robust steganographic algorithm for binary images

Forensic print extraction using 3D technology and its processing

Enhancement of Underwater Color Images by Two-side 2-D Quaternion Discrete Fourier Transform.