Finding out user identity information from emails is one of the important research topics in email mining. Most approaches extract an email user’s name only from the header of an email, but there are often many name information appearing in the body of emails, and those names are usually more suitable for representing the sender’s or recipient’s identity. This paper focuses on the problem of extracting email users’ name aliases in the body of plain-text emails. After locating and extracting salutation and signature blocks from email bodies, we can identify the potential aliases in the salutation and signature lines, which can be directly associated with the corresponding email address in email headers, by using named entity recognition(NER) tools. However the identified aliases may be half-baked or there are still some potential aliases that can’t be correctly identified. So we propose a novel approach to efficiently and accurately extract aliases in the salutation and signature lines based on name boundary word template built on the characteristics of alias neighboring words. Results on the public subset of the Enron corpus indicate that the approaches presented in this paper can efficiently extract user’s aliases from email bodies.

Fuzzy enhancement is applied in computer aided diagnosis of liver cancer from B mode ultrasound images as a pre-processing procedure in this paper. It was evaluated with three classifiers including K means, back propagation neural network and support vector machine using 25 features from first order statistic (FOS), gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), Grey level dependant matrix (GLDM) and LAWS. In the analysis of 166 normal liver tissue, 30 hemangioma and 60 malignant tumor, our method improved the classification accuracy of three classifiers (K means, BP neural network and support machine vector) in distinguishing liver cancer, hemangioma and normal liver cancer from B mode ultrasound images. It is proved that fuzzy enhancement as an efficient preprocessing procedure could be used in the computer aided diagnosis system of liver cancer.

Speckle noise is widely existence in coherent imaging systems, such as synthetic aperture radar, sonar, ultrasound and laser imaging, and is commonly described as signal correlated. In this paper, we focus on speckle reduction problem in real ultrasound image. Unlike traditional anisotropic diffusion methods usually taking image gradient as a diffusion index, in this paper, we present a new texture based anisotropic diffusion method for speckle reduction in real ultrasound image. The results comparing our new method with other well known methods on both synthetic images and real ultrasound images are reported to show the superiority of our method in keeping important features of real ultrasound images.

Directed surface extraction from CT images is the first task in the design of medical equipment. In this paper a new approach based on level set method is proposed to extract the directed surface from CT images. Two level set functions with corresponding speed functions are involved in this study. One is used to cut the desired bone from the input CT model in which the directed surface, usually the outermost surface, and the complex inner surface are both contained. The other is used to remove the complex inner surface. The experimental results show the feasible of the proposed method.

Blending is now a popular technology for large realtime texture synthesis .Nevertheless, creating blend map during rendering is time and computation consuming work. In this paper, we exploited a method to create a kind of blend tile which can be tile together seamlessly. Note that blend map is in fact a kind of image, which is Markov Random Field, contains multiresolution signals, while wavelet is a powerful way to process multiresolution signals, we use wavelet to process the traditional blend tile. After our processing steps, the result blend tile become smooth and suitable for tiling, with no important features lost. Using this kind blend tile, many computation resources for computing blend map during texture synthesizing is saved. The experimental results shows that our method may successfully process many traditional blend tiles.

Self-assembly reveals the essence of DNA computing, DNA self-assembly is thought to be the best way to make DNA computing transform into computer chip. This paper introduce a method of DNA 3D self-assembly algorithm to solve the Maximum Clique Problem. Firstly, we introduce a non-deterministic algorithm. Then, according to the algorithm we design the types of DNA tiles which the computation needs. Lastly, we demonstrate the self-assembly process and the experimental methods which could get the final result. The computation time is linear, and the number of the distinctive tile types is constant.

Local thermal enhancement in target tissue is of great interest in tumor hyperthermia. In this study, we proposed a brand-new thermal enhancement protocol for tumor hyperthermia using heat generated from thermoge-nesis evoked by capsaicin, which can safely deliver a totally localized heating to target tissue. A healthy male volunteer was recruited, whose partial areas of the dorsum of hand and posterior aspect of forearm were smeared with 1% (w/w) capsaicin solution, to determine the increase of ther-mogenesis in human body. In addition, animal experiments on healthy Kunming (KM) mice (20-22g) were performed to test the feasibility and efficacy of capsaicin-induced thermal enhancement. These KM mice were first locally smeared with, subcutaneous or intraperitoneal injected of the same capsaicin solution, and then heated by near infrared laser. Preliminary experiments on the volunteer showed an effec-tive temperature increase in the skin area. Animal experi-ments indicated that distinct thermal enhancement in heat-ing effect, and that the thermal enhancement induced by intraperitoneal injection of capsaicin is more obvious than the other two ways. Thus capsaicin can be used as a poten-tial therapeutic adjuvant to locally enhance heating effects in target tissue during tumor hyperthermia.

Microscopic image mosaic stitches several adjacent images into an integrated seamless picture, and is of significant practical value to remote medicine applications, especially in remote diagnosis. However, due to limitation in image acquisition method, a mismatch could occur as a result of variance in adjacent image stitching data and accumulation of errors. The current image stitching method still has room for improvement regarding processing speed and effectiveness, particularly in precision. In this paper, we proposed a new image mosaic revising algorithms based on the relativity of adjacent images and expounding the principal and equations on image mosaic error revising, as well as achieving automatic intelligent calculation with the revised algorithm. Through experiment, inaccurate pathological mosaic images from 20 groups were revised rapidly and accurately with error controlled within one pixel. It was proved that the approach is effective in revising the error matching in microscopic images mosaic. Moreover, it is easy to operate and effective for more accurate image stitching.