Analysing and processing medical images with increased performance using fractal geometry

The research relied on the application of a series of steps to analyze medical images, and to basically achieve this goal, a set of techniques were made from both fractal engineering and tissue analysis by improving the studied image and then analyzing the studied image texture in the fractal dimension and propose a hybrid method for segmenting images of complex situations and structures based on the geometric patterns that are repeated and represented by the fractal filter (Hurst), which is one of the modern techniques used in the field of digital image processing. Using fractal methods, that is, a specific application through real fractal structures of medical images and measuring their fractal dimensions and in capturing the exact features based on the scale in dimensional fractions, where the accuracy rate reached )98%( in diagnosing pathological conditions with an error rate close to zero. Also, the coefficients of multiple fractals were calculated (α) ,with a threshold factor of (4.5), the texture is also classified based on the fractal algorithm and Gray-Level Co-Occurrence Matrices (GLCM) and according to the experimental results performed on the medical images, the classification method provides a classification rate of 95%. To increase the accuracy, the lacunarity was calculated in the healthy medical images by applying fractal theorem filters where the gap ratio was close to (1) in the lacunarity size. The results also showed that the decrease in the contrast of the image with the continuation of the smoothing process or the decrease in the intensity levels of the image causes a significant decrease in the contrast of the image, especially in the areas of the edges

Fuzzy Logic Classification of Handwritten Signature Based Computer Access and File Encryption

Often times computer access and file encryption is successful based on how complex a password will be, how often users could change their complex password, the length of the complex password and how creative users are in creating a complex passsword to stand against unauthorized access to computer resources or files. This research proposes a new way of computer access and file encryption based on the fuzzy logic classification of handwritten signatures. Feature extraction of the handwritten signatures, the Fourier transformation algorithm and the k-Nearest Algorithm could be implemented to determine how close the signature is to the signature on file to grant or deny users access to computer resources and encrypted files. lternatively implementing fuzzy logic algorithms and fuzzy k-Nearest Neighbor algorithm to the captured signature could determine how close a signature is to the one on file to grant or deny access to computer resources and files. This research paper accomplishes the feature recognition firstly by extracting the features as users sign their signatures for storage, and secondly by determining the shortest distance between the signatures. On the other hand this research work accomplish the fuzzy logic recognition firstly by classifying the signature into a membership groups based on their degree of membership and secondly by determining what level of closeness the signatures are from each other. The signatures were collected from three selected input devices- the mouse, I-Pen and the IOGear. This research demonstrates which input device users found efficient and flexible to sign their respective names. The research work also demonstrates the security levels of implementing the fuzzy logic, fuzzy k-Nearest Neighbor, Fourier Transform.Master'sCollege of Arts and Sciences: Computer ScienceUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/117719/1/Kwarteng.pd

Artificial Immune Systems - Models, algorithms and applications

Copyright © 2010 Academic Research Publishing Agency.This article has been made available through the Brunel Open Access Publishing Fund.Artificial Immune Systems (AIS) are computational paradigms that belong to the computational intelligence family and are inspired by the biological immune system. During the past decade, they have attracted a lot of interest from researchers aiming to develop immune-based models and techniques to solve complex computational or engineering problems. This work presents a survey of existing AIS models and algorithms with a focus on the last five years.This article is available through the Brunel Open Access Publishing Fun

The Design and Implementation of an Image Segmentation System for Forest Image Analysis

The United States Forest Service (USFS) is developing software systems to evaluate forest resources with respect to qualities such as scenic beauty and vegetation structure. Such evaluations usually involve a large amount of human labor. In this thesis, I will discuss the design and implementation of a digital image segmentation system, and how to apply it to analyze forest images so that automated forest resource evaluation can be achieved. The first major contribution of the thesis is the evaluation of various feature design schemes for segmenting forest images. The other major contribution of this thesis is the development of a pattern recognition-based image segmentation algorithm. The best system performance was a 61.4% block classification error rate, achieved by combining color histograms with entropy. This performance is better than that obtained by an ?intelligent? guess based on prior knowledge about the categories under study, which is 68.0%

Biometric Systems

Biometric authentication has been widely used for access control and security systems over the past few years. The purpose of this book is to provide the readers with life cycle of different biometric authentication systems from their design and development to qualification and final application. The major systems discussed in this book include fingerprint identification, face recognition, iris segmentation and classification, signature verification and other miscellaneous systems which describe management policies of biometrics, reliability measures, pressure based typing and signature verification, bio-chemical systems and behavioral characteristics. In summary, this book provides the students and the researchers with different approaches to develop biometric authentication systems and at the same time includes state-of-the-art approaches in their design and development. The approaches have been thoroughly tested on standard databases and in real world applications

A Review on Block Matching Motion Estimation and Automata Theory based Approaches for Fractal Coding

Fractal compression is the lossy compression technique in the field of gray/color image and video compression. It gives high compression ratio, better image quality with fast decoding time but improvement in encoding time is a challenge. This review paper/article presents the analysis of most significant existing approaches in the field of fractal based gray/color images and video compression, different block matching motion estimation approaches for finding out the motion vectors in a frame based on inter-frame coding and intra-frame coding i.e. individual frame coding and automata theory based coding approaches to represent an image/sequence of images. Though different review papers exist related to fractal coding, this paper is different in many sense. One can develop the new shape pattern for motion estimation and modify the existing block matching motion estimation with automata coding to explore the fractal compression technique with specific focus on reducing the encoding time and achieving better image/video reconstruction quality. This paper is useful for the beginners in the domain of video compression

Exploring the effects of compression via principal components analysis on X-ray image classification

Abstract: Image compression in medical applications implores careful consideration of the effects on data veracity. The inexorable challenge of assessing the volume-veracity trade-off is becoming more prevalent in this critical application area, and particularly when machine learning is used for the purpose of assisted diagnostics. This paper investigates the impact of compressing X-ray images on the accuracy of fracture diagnostics. The accuracy of the classification system is assessed for X-ray images of both healthy and fracture bones when subjected to different levels of compression. Compression is achieved using principal components analysis. Results indicate that accuracy is only marginally affected under a level one compression but begins to deteriorate under level two compression. These results are potentially useful as the level one compression yields gains up to 94% with less than a 2% drop in classification accuracy