Application of Fractal and Wavelets in Microcalcification Detection

Breast cancer has been recognized as one or the most frequent, malignant tumors in women, clustered microcalcifications in mammogram images has been widely recognized as an early sign of breast cancer. This work is devote to review the application of Fractal and Wavelets in microcalcifications detection

Enhancement of Single and Composite Images Based on Contourlet Transform Approach

Image enhancement is an imperative step in almost every image processing algorithms. Numerous image enhancement algorithms have been developed for gray scale images despite their absence in many applications lately. This thesis proposes hew image enhancement techniques of 8-bit single and composite digital color images. Recently, it has become evident that wavelet transforms are not necessarily best suited for images. Therefore, the enhancement approaches are based on a new 'true' two-dimensional transform called contourlet transform. The proposed enhancement techniques discussed in this thesis are developed based on the understanding of the working mechanisms of the new multiresolution property of contourlet transform. This research also investigates the effects of using different color space representations for color image enhancement applications. Based on this investigation an optimal color space is selected for both single image and composite image enhancement approaches. The objective evaluation steps show that the new method of enhancement not only superior to the commonly used transformation method (e.g. wavelet transform) but also to various spatial models (e.g. histogram equalizations). The results found are encouraging and the enhancement algorithms have proved to be more robust and reliable

Multiscale wavelet representations for mammographic feature analysis

This paper introduces a novel approach for accomplishing mammographic feature analysis through multiresolution representations. We show that efficient (nonredundant) representations may be identified from digital mammography and used to enhance specific mammographic features within a continuum of scale space. The multiresolution decomposition of wavelet transforms provides a natural hierarchy in which to embed an interactive paradigm for accomplishing scale space feature analysis. Choosing wavelets (or analyzing functions) that are simultaneously localized in both space and frequency, results in a powerful methodology for image analysis. Multiresolution and orientation selectivity, known biological mechanisms in primate vision, are ingrained in wavelet representations and inspire the techniques presented in this paper. Our approach includes local analysis of complete multiscale representations. Mammograms are reconstructed from wavelet coefficients, enhanced by linear, exponential and constant weight functions localized in scale space. By improving the visualization of breast pathology we can improve the changes of early detection of breast cancers (improve quality) while requiring less time to evaluate mammograms for most patients (lower costs)

Improvement of BM3D Algorithm and Employment to Satellite and CFA Images Denoising

This paper proposes a new procedure in order to improve the performance of block matching and 3-D filtering (BM3D) image denoising algorithm. It is demonstrated that it is possible to achieve a better performance than that of BM3D algorithm in a variety of noise levels. This method changes BM3D algorithm parameter values according to noise level, removes prefiltering, which is used in high noise level; therefore Peak Signal-to-Noise Ratio (PSNR) and visual quality get improved, and BM3D complexities and processing time are reduced. This improved BM3D algorithm is extended and used to denoise satellite and color filter array (CFA) images. Output results show that the performance has upgraded in comparison with current methods of denoising satellite and CFA images. In this regard this algorithm is compared with Adaptive PCA algorithm, that has led to superior performance for denoising CFA images, on the subject of PSNR and visual quality. Also the processing time has decreased significantly.Comment: 11 pages, 7 figur

Wavelets and Imaging Informatics: A Review of the Literature

AbstractModern medicine is a field that has been revolutionized by the emergence of computer and imaging technology. It is increasingly difficult, however, to manage the ever-growing enormous amount of medical imaging information available in digital formats. Numerous techniques have been developed to make the imaging information more easily accessible and to perform analysis automatically. Among these techniques, wavelet transforms have proven prominently useful not only for biomedical imaging but also for signal and image processing in general. Wavelet transforms decompose a signal into frequency bands, the width of which are determined by a dyadic scheme. This particular way of dividing frequency bands matches the statistical properties of most images very well. During the past decade, there has been active research in applying wavelets to various aspects of imaging informatics, including compression, enhancements, analysis, classification, and retrieval. This review represents a survey of the most significant practical and theoretical advances in the field of wavelet-based imaging informatics

COMPUTER AIDED SYSTEM FOR BREAST CANCER DIAGNOSIS USING CURVELET TRANSFORM

Breast cancer is a leading cause of death among women worldwide. Early detection is the key for improving breast cancer prognosis. Digital mammography remains one of the most suitable tools for early detection of breast cancer. Hence, there are strong needs for the development of computer aided diagnosis (CAD) systems which have the capability to help radiologists in decision making. The main goal is to increase the diagnostic accuracy rate. In this thesis we developed a computer aided system for the diagnosis and detection of breast cancer using curvelet transform. Curvelet is a multiscale transform which possess directionality and anisotropy, and it breaks some inherent limitations of wavelet in representing edges in images. We started this study by developing a diagnosis system. Five feature extraction methods were developed with curvelet and wavelet coefficients to differentiate between different breast cancer classes. The results with curvelet and wavelet were compared. The experimental results show a high performance of the proposed methods and classification accuracy rate achieved 97.30%. The thesis then provides an automatic system for breast cancer detection. An automatic thresholding algorithm was used to separate the area composed of the breast and the pectoral muscle from the background of the image. Subsequently, a region growing algorithm was used to locate the pectoral muscle and suppress it from the breast. Then, the work concentrates on the segmentation of region of interest (ROI). Two methods are suggested to accomplish the segmentation stage: an adaptive thresholding method and a pattern matching method. Once the ROI has been identified, an automatic cropping is performed to extract it from the original mammogram. Subsequently, the suggested feature extraction methods were applied to the segmented ROIs. Finally, the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) classifiers were used to determine whether the region is abnormal or normal. At this level, the study focuses on two abnormality types (mammographic masses and architectural distortion). Experimental results show that the introduced methods have very high detection accuracies. The effectiveness of the proposed methods has been tested with Mammographic Image Analysis Society (MIAS) dataset. Throughout the thesis all proposed methods and algorithms have been applied with both curvelet and wavelet for comparison and statistical tests were also performed. The overall results show that curvelet transform performs better than wavelet and the difference is statistically significant

Adaptive multiscale processing for contrast enhancement

This paper introduces a novel approach for accomplishing mammographic feature analysis through overcomplete multiresolution representations. We show that efficient representations may be identified from digital mammograms within a continuum of scale space and used to enhance features of importance to mammography. Choosing analyzing functions that are well localized in both space and frequency, results in a powerful methodology for image analysis. We describe methods of contrast enhancement based on two overcomplete (redundant) multiscale representations: (1) Dyadic wavelet transform (2) (phi) -transform. Mammograms are reconstructed from transform coefficients modified at one or more levels by non-linear, logarithmic and constant scale-space weight functions. Multiscale edges identified within distinct levels of transform space provide a local support for enhancement throughout each decomposition. We demonstrate that features extracted from wavelet spaces can provide an adaptive mechanism for accomplishing local contrast enhancement. We suggest that multiscale detection and local enhancement of singularities may be effectively employed for the visualization of breast pathology without excessive noise amplification

Hexagonal wavelet processing of digital mammography

This paper introduces a novel approach for accomplishing mammographic feature analysis through overcomplete multiresolution representations. We show that efficient representations may be identified from digital mammograms and used to enhance features of importance to mammography within a continuum of scale-space. We present a method of contrast enhancement based on an overcomplete, non-separable multiscale representation: the hexagonal wavelet transform. Mammograms are reconstructed from transform coefficients modified at one or more levels by local and global non-linear operators. Multiscale edges identified within distinct levels of transform space provide local support for enhancement. We demonstrate that features extracted from multiresolution representations can provide an adaptive mechanism for accomplishing local contrast enhancement. We suggest that multiscale detection and local enhancement of singularities may be effectively employed for the visualization of breast pathology without excessive noise amplification