Face image matching using fractal dimension

A new method is presented in this paper for calculating the correspondence between two face images on a pixel by pixel basis. The concept of fractal dimension is used to develop the proposed non-parametric area-based image matching method which achieves a higher proportion of matched pixels for face images than some well-known methods

Multi-texture image segmentation

Visual perception of images is closely related to the recognition of the different texture areas within an image. Identifying the boundaries of these regions is an important step in image analysis and image understanding. This thesis presents supervised and unsupervised methods which allow an efficient segmentation of the texture regions within multi-texture images. The features used by the methods are based on a measure of the fractal dimension of surfaces in several directions, which allows the transformation of the image into a set of feature images, however no direct measurement of the fractal dimension is made. Using this set of features, supervised and unsupervised, statistical processing schemes are presented which produce low classification error rates. Natural texture images are examined with particular application to the analysis of sonar images of the seabed. A number of processes based on fractal models for texture synthesis are also presented. These are used to produce realistic images of natural textures, again with particular reference to sonar images of the seabed, and which show the importance of phase and directionality in our perception of texture. A further extension is shown to give possible uses for image coding and object identification

Tumour grading and discrimination based on class assignment and quantitative texture analysis techniques

Medical imaging represents the utilisation of technology in biology for the purpose of noninvasively revealing the internal structure of the organs of the human body. It is a way to improve the quality of the patient's life through a more precise and rapid diagnosis, and with limited side-effects, leading to an effective overall treatment procedure. The main objective of this thesis is to propose novel tumour discrimination techniques that cover both micro and macro-scale textures encountered in computed tomography (CI') and digital microscopy (DM) modalities, respectively. Image texture can provide significant information on the (ab)normality of tissue, and this thesis expands this idea to tumour texture grading and classification. The fractal dimension (FO) as a texture measure was applied to contrast enhanced CT lung tumour images in an aim to improve tumour grading accuracy from conventional CI' modality, and quantitative performance analysis showed an accuracy of 83.30% in distinguishing between advanced (aggressive) and early stage (non-aggressive) malignant tumours. A different approach was adopted for subtype discrimination of brain tumour OM images via a set of statistical and model-based texture analysis algorithms. The combined Gaussian Markov random field and run-length matrix texture measures outperformed all other combinations, achieving an overall class assignment classification accuracy of 92.50%. Also two new histopathological multi resolution approaches based on applying the FO as the best bases selection for discrete wavelet packet transform, and when fused with the Gabor filters' energy output improved the accuracy to 91.25% and 95.00%, respectively. While noise is quite common in all medical imaging modalities, the impact of noise on the applied texture measures was assessed as well. The developed lung and brain texture analysis techniques can improve the physician's ability to detect and analyse pathologies leading for a more reliable diagnosis and treatment of disease

Binary Local Fractal Dimension: a Precise Structure Parameter for 3D High Resolution Computed Tomography Images of the Human Spongiosa

We present the Binary Local Fractal Dimension (LFD) to analyze osteoporosis induced fracture risk with clinical 3D high resolution quantitative computed tomographic (HRCT) images of human vertebrae. We test if LFD parameters provide precise additional information besides bone mineral density (BMD) and standard descriptors of bone quality, for example bone surface ratio (BS/BV). We define a weighted LFD (wLFD) using the ¯R2 of the H¨older exponents. We compare the LFD with standard methods (distance transform, direct secant method and run-length method) on 5 vertebrae × 8 volumes of interest and 5 repeated scans. The wLFD contains the highest direct and BMD-independent precision (R2 = 0.985 and R2 = 0.949), followed by BS/BV (R2 = 0.977 and R2 = 0.920) including low correlation with BMD (wLFD: R2 = 0.704, BS/BV: R2 = 0.814). LFD improves the translation from reference μCT- to clinical HRCT-resolution. In conclusion, LFD provides a strong diagnostic tool to characterize bone quality to predict osteoporosis induced fracture risk.Sociedad Argentina de Informática e Investigación Operativ

Modeling and multiresolution characterization of micro/nano surface for novel tailored nanostructures

Nanofabrication is state of the art technology. Various chemical, mechanical, biochemical and semiconductor products have characteristics controlled by the nanostructures of the surface and interphase. Surface microscopic imaging is generally used to capture different surface features. By properly analyzing the surface image, valuable information regarding manufacturing process and product performance can be extracted. While microscopy measurements can offer very accurate qualitative information about surface features, for many applications, it is critical to obtain a quantitative description of the surface morphology. Various statistical features can be used to characterize the surface in quantitative way. Such an analysis can be done by the multi-resolution capabilities of wavelet transforms (WT). A multi-scale molecular simulation can help to investigate the physical and chemical mechanism in manufacturing process. Multiresolution characterization was performed on the model structure to compare with image analysis. In our research, we have used a soft polymeric surface used in microfabrication application and a hard surface used for catalysis, and applied multiresolution characterization for surface feature extraction and multiscale modeling for optimizing system variables to get desired surface characteristics. In microfabrication, the efficiency of the product reduced by line-edge roughness (LER) created on the polymer surface. Off-line LER characterization is usually based on the top-down SEM image. We have shown a wavelet based segmentation method for edge searching region. There was no external decision involved in the wavelet based edge detection and characterization. Ab-initio atomistic based simulations are generally used for polymer material design in atomic scale. For mesoscale modeling we use the coarse graining of the molecules and use the Flory-Huggins mean field interaction parameters of the clusters of atoms or molecules obtained from ab-initio simulations. In our research we have used coarse grained lattice based important sampling Monte Carlo (MC) and kinetic Monte Carlo (kMC) methods for mesoscale simulation. We have identified the phase separation by spinodal decomposition resulting in the formation of LER. The kinetics of the process is found and the process variables are identified that can reduce the roughness. Surface of a transition metal have been analyzed in a similar way for enhanced catalytic performance

Binary Local Fractal Dimension: a Precise Structure Parameter for 3D High Resolution Computed Tomography Images of the Human Spongiosa

We present the Binary Local Fractal Dimension (LFD) to analyze osteoporosis induced fracture risk with clinical 3D high resolution quantitative computed tomographic (HRCT) images of human vertebrae. We test if LFD parameters provide precise additional information besides bone mineral density (BMD) and standard descriptors of bone quality, for example bone surface ratio (BS/BV). We define a weighted LFD (wLFD) using the ¯R2 of the H¨older exponents. We compare the LFD with standard methods (distance transform, direct secant method and run-length method) on 5 vertebrae × 8 volumes of interest and 5 repeated scans. The wLFD contains the highest direct and BMD-independent precision (R2 = 0.985 and R2 = 0.949), followed by BS/BV (R2 = 0.977 and R2 = 0.920) including low correlation with BMD (wLFD: R2 = 0.704, BS/BV: R2 = 0.814). LFD improves the translation from reference μCT- to clinical HRCT-resolution. In conclusion, LFD provides a strong diagnostic tool to characterize bone quality to predict osteoporosis induced fracture risk.Sociedad Argentina de Informática e Investigación Operativ

Binary Local Fractal Dimension: a Precise Structure Parameter for 3D High Resolution Computed Tomography Images of the Human Spongiosa

We present the Binary Local Fractal Dimension (LFD) to analyze osteoporosis induced fracture risk with clinical 3D high resolution quantitative computed tomographic (HRCT) images of human vertebrae. We test if LFD parameters provide precise additional information besides bone mineral density (BMD) and standard descriptors of bone quality, for example bone surface ratio (BS/BV). We define a weighted LFD (wLFD) using the ¯R2 of the H¨older exponents. We compare the LFD with standard methods (distance transform, direct secant method and run-length method) on 5 vertebrae × 8 volumes of interest and 5 repeated scans. The wLFD contains the highest direct and BMD-independent precision (R2 = 0.985 and R2 = 0.949), followed by BS/BV (R2 = 0.977 and R2 = 0.920) including low correlation with BMD (wLFD: R2 = 0.704, BS/BV: R2 = 0.814). LFD improves the translation from reference μCT- to clinical HRCT-resolution. In conclusion, LFD provides a strong diagnostic tool to characterize bone quality to predict osteoporosis induced fracture risk.Sociedad Argentina de Informática e Investigación Operativ