Textural Difference Enhancement based on Image Component Analysis

In this thesis, we propose a novel image enhancement method to magnify the textural differences in the images with respect to human visual characteristics. The method is intended to be a preprocessing step to improve the performance of the texture-based image segmentation algorithms. We propose to calculate the six Tamura's texture features (coarseness, contrast, directionality, line-likeness, regularity and roughness) in novel measurements. Each feature follows its original understanding of the certain texture characteristic, but is measured by some local low-level features, e.g., direction of the local edges, dynamic range of the local pixel intensities, kurtosis and skewness of the local image histogram. A discriminant texture feature selection method based on principal component analysis (PCA) is then proposed to find the most representative characteristics in describing textual differences in the image. We decompose the image into pairwise components representing the texture characteristics strongly and weakly, respectively. A set of wavelet-based soft thresholding methods are proposed as the dictionaries of morphological component analysis (MCA) to sparsely highlight the characteristics strongly and weakly from the image. The wavelet-based thresholding methods are proposed in pair, therefore each of the resulted pairwise components can exhibit one certain characteristic either strongly or weakly. We propose various wavelet-based manipulation methods to enhance the components separately. For each component representing a certain texture characteristic, a non-linear function is proposed to manipulate the wavelet coefficients of the component so that the component is enhanced with the corresponding characteristic accentuated independently while having little effect on other characteristics. Furthermore, the above three methods are combined into a uniform framework of image enhancement. Firstly, the texture characteristics differentiating different textures in the image are found. Secondly, the image is decomposed into components exhibiting these texture characteristics respectively. Thirdly, each component is manipulated to accentuate the corresponding texture characteristics exhibited there. After re-combining these manipulated components, the image is enhanced with the textural differences magnified with respect to the selected texture characteristics. The proposed textural differences enhancement method is used prior to both grayscale and colour image segmentation algorithms. The convincing results of improving the performance of different segmentation algorithms prove the potential of the proposed textural difference enhancement method

Textural Difference Enhancement based on Image Component Analysis

In this thesis, we propose a novel image enhancement method to magnify the textural differences in the images with respect to human visual characteristics. The method is intended to be a preprocessing step to improve the performance of the texture-based image segmentation algorithms. We propose to calculate the six Tamura's texture features (coarseness, contrast, directionality, line-likeness, regularity and roughness) in novel measurements. Each feature follows its original understanding of the certain texture characteristic, but is measured by some local low-level features, e.g., direction of the local edges, dynamic range of the local pixel intensities, kurtosis and skewness of the local image histogram. A discriminant texture feature selection method based on principal component analysis (PCA) is then proposed to find the most representative characteristics in describing textual differences in the image. We decompose the image into pairwise components representing the texture characteristics strongly and weakly, respectively. A set of wavelet-based soft thresholding methods are proposed as the dictionaries of morphological component analysis (MCA) to sparsely highlight the characteristics strongly and weakly from the image. The wavelet-based thresholding methods are proposed in pair, therefore each of the resulted pairwise components can exhibit one certain characteristic either strongly or weakly. We propose various wavelet-based manipulation methods to enhance the components separately. For each component representing a certain texture characteristic, a non-linear function is proposed to manipulate the wavelet coefficients of the component so that the component is enhanced with the corresponding characteristic accentuated independently while having little effect on other characteristics. Furthermore, the above three methods are combined into a uniform framework of image enhancement. Firstly, the texture characteristics differentiating different textures in the image are found. Secondly, the image is decomposed into components exhibiting these texture characteristics respectively. Thirdly, each component is manipulated to accentuate the corresponding texture characteristics exhibited there. After re-combining these manipulated components, the image is enhanced with the textural differences magnified with respect to the selected texture characteristics. The proposed textural differences enhancement method is used prior to both grayscale and colour image segmentation algorithms. The convincing results of improving the performance of different segmentation algorithms prove the potential of the proposed textural difference enhancement method

Use of remote sensing techniques for geological hazard surveys in vegetated urban regions

The feasibility of using aerial photography for lithologic differentiation in a heavily vegetated region is investigated using multispectral imagery obtained from LANDSAT satellite and aircraft-borne photography. Delineating and mapping of localized vegetal zones can be accomplished by the use of remote sensing because a difference in morphology and physiology results in different natural reflectances or signatures. An investigation was made to show that these local plant zones are affected by altitude, topography, weathering, and gullying; but are controlled by lithology. Therefore, maps outlining local plant zones were used as a basis for lithologic map construction

Signal processing algorithms for enhanced image fusion performance and assessment

The dissertation presents several signal processing algorithms for image fusion in noisy multimodal conditions. It introduces a novel image fusion method which performs well for image sets heavily corrupted by noise. As opposed to current image fusion schemes, the method has no requirements for a priori knowledge of the noise component. The image is decomposed with Chebyshev polynomials (CP) being used as basis functions to perform fusion at feature level. The properties of CP, namely fast convergence and smooth approximation, renders it ideal for heuristic and indiscriminate denoising fusion tasks. Quantitative evaluation using objective fusion assessment methods show favourable performance of the proposed scheme compared to previous efforts on image fusion, notably in heavily corrupted images. The approach is further improved by incorporating the advantages of CP with a state-of-the-art fusion technique named independent component analysis (ICA), for joint-fusion processing based on region saliency. Whilst CP fusion is robust under severe noise conditions, it is prone to eliminating high frequency information of the images involved, thereby limiting image sharpness. Fusion using ICA, on the other hand, performs well in transferring edges and other salient features of the input images into the composite output. The combination of both methods, coupled with several mathematical morphological operations in an algorithm fusion framework, is considered a viable solution. Again, according to the quantitative metrics the results of our proposed approach are very encouraging as far as joint fusion and denoising are concerned. Another focus of this dissertation is on a novel metric for image fusion evaluation that is based on texture. The conservation of background textural details is considered important in many fusion applications as they help define the image depth and structure, which may prove crucial in many surveillance and remote sensing applications. Our work aims to evaluate the performance of image fusion algorithms based on their ability to retain textural details from the fusion process. This is done by utilising the gray-level co-occurrence matrix (GLCM) model to extract second-order statistical features for the derivation of an image textural measure, which is then used to replace the edge-based calculations in an objective-based fusion metric. Performance evaluation on established fusion methods verifies that the proposed metric is viable, especially for multimodal scenarios

Liver CT enhancement using Fractional Differentiation and Integration

In this paper, a digital image filter is proposed to enhance the Liver CT image for improving the classification of tumors area in an infected Liver. The enhancement process is based on improving the main features within the image by utilizing the Fractional Differential and Integral in the wavelet sub-bands of an image. After enhancement, different features were extracted such as GLCM, GRLM, and LBP, among others. Then, the areas/cells are classified into tumor or non-tumor, using different models of classifiers to compare our proposed model with the original image and various established filters. Each image is divided into 15x15 non-overlapping blocks, to extract the desired features. The SVM, Random Forest, J48 and Simple Cart were trained on a supplied dataset, different from the test dataset. Finally, the block cells are identified whether they are classified as tumor or not. Our approach is validated on a group of patients’ CT liver tumor datasets. The experiment results demonstrated the efficiency of enhancement in the proposed technique

Color space analysis for iris recognition

This thesis investigates issues related to the processing of multispectral and color infrared images of the iris. When utilizing the color bands of the electromagnetic spectrum, the eye color and the components of texture (luminosity and chromaticity) must be considered. This work examines the effect of eye color on texture-based iris recognition in both the near-IR and visible bands. A novel score level fusion algorithm for multispectral iris recognition is presented in this regard. The fusion algorithm - based on evidence that matching performance of a texture-based encoding scheme is impacted by the quality of texture within the original image - ranks the spectral bands of the image based on texture quality and designs a fusion rule based on these rankings. Color space analysis, to determine an optimal representation scheme, is also examined in this thesis. Color images are transformed from the sRGB color space to the CIE Lab, YCbCr, CMYK and HSV color spaces prior to encoding and matching. Also, enhancement methods to increase the contrast of the texture within the iris, without altering the chromaticity of the image, are discussed. Finally, cross-band matching is performed to illustrate the correlation between eye color and specific bands of the color image

JERS-1 SAR and LANDSAT-5 TM image data fusion: An application approach for lithological mapping

Satellite image data fusion is an image processing set of procedures utilise either for image optimisation for visual photointerpretation, or for automated thematic classification with low error rate and high accuracy. Lithological mapping using remote sensing image data relies on the spectral and textural information of the rock units of the area to be mapped. These pieces of information can be derived from Landsat optical TM and JERS-1 SAR images respectively. Prior to extracting such information (spectral and textural) and fusing them together, geometric image co-registration between TM and the SAR, atmospheric correction of the TM, and SAR despeckling are required. In this thesis, an appropriate atmospheric model is developed and implemented utilising the dark pixel subtraction method for atmospheric correction. For SAR despeckling, an efficient new method is also developed to test whether the SAR filter used remove the textural information or not. For image optimisation for visual photointerpretation, a new method of spectral coding of the six bands of the optical TM data is developed. The new spectral coding method is used to produce efficient colour composite with high separability between the spectral classes similar to that if the whole six optical TM bands are used together. This spectral coded colour composite is used as a spectral component, which is then fused with the textural component represented by the despeckled JERS-1 SAR using the fusion tools, including the colour transform and the PCT. The Grey Level Cooccurrence Matrix (GLCM) technique is used to build the textural data set using the speckle filtered JERS-1 SAR data making seven textural GLCM measures. For automated thematic mapping and by the use of both the six TM spectral data and the seven textural GLCM measures, a new method of classification has been developed using the Maximum Likelihood Classifier (MLC). The method is named the sequential maximum likelihood classification and works efficiently by comparison the classified textural pixels, the classified spectral pixels, and the classified textural-spectral pixels, and gives the means of utilising the textural and spectral information for automated lithological mapping

Morphological quantitation software in breast MRI: application to neoadjuvant chemotherapy patients

The work in this thesis examines the use of texture analysis techniques and shape descriptors to analyse MR images of the breast and their application as a potential quantitative tool for prognostic indication.Textural information is undoubtedly very heavily used in a radiologist’s decision making process. However, subtle variations in texture are often missed, thus by quantitatively analysing MR images the textural properties that would otherwise be impossible to discern by simply visually inspecting the image can be obtained. Texture analysis is commonly used in image classification of aerial and satellite photography, studies have also focussed on utilising texture in MRI especially in the brain. Recent research has focussed on other organs such as the breast wherein lesion morphology is known to be an important diagnostic and prognostic indicator. Recent work suggests benefits in assessing lesion texture in dynamic contrast-enhanced (DCE) images, especially with regards to changes during the initial enhancement and subsequent washout phases. The commonest form of analysis is the spatial grey-level dependence matrix method, but there is no direct evidence concerning the most appropriate pixel separation and number of grey levels to utilise in the required co-occurrence matrix calculations. The aim of this work is to systematically assess the efficacy of DCE-MRI based textural analysis in predicting response to chemotherapy in a cohort of breast cancer patients. In addition an attempt was made to use shape parameters in order to assess tumour surface irregularity, and as a predictor of response to chemotherapy.In further work this study aimed to texture map DCE MR images of breast patients utilising the co-occurrence method but on a pixel by pixel basis in order to determine threshold values for normal, benign and malignant tissue and ultimately creating functionality within the in house developed software to highlight hotspots outlining areas of interest (possible lesions). Benign and normal data was taken from MRI screening data and malignant data from patients referred with known malignancies.This work has highlighted that textural differences between groups (based on response, nodal status, triple negative and biopsy grade groupings) are apparent and appear to be most evident 1-3 minutes post-contrast administration. Whilst the large number of statistical tests undertaken necessitates a degree of caution in interpreting the results, the fact that significant differences for certain texture parameters and groupings are consistently observed is encouraging.With regards to shape analysis this thesis has highlighted that some differences between groups were seen in shape descriptors but that shape may be limited as a prognostic indicator. Using textural analysis gave a higher proportion of significant differences whilst shape analysis results showed inconsistency across time points.With regards to the mapping this work successfully analysed the texture maps for each case and established lesion detection is possible. The study successfully highlighted hotspots in the breast patients data post texture mapping, and has demonstrated the relationship between sensitivity and false positive rate via hotspot thresholding