Multiresolution analysis using wavelet, ridgelet, and curvelet transforms for medical image segmentation

Copyright @ 2011 Shadi AlZubi et al. This article has been made available through the Brunel Open Access Publishing Fund.The experimental study presented in this paper is aimed at the development of an automatic image segmentation system for classifying region of interest (ROI) in medical images which are obtained from different medical scanners such as PET, CT, or MRI. Multiresolution analysis (MRA) using wavelet, ridgelet, and curvelet transforms has been used in the proposed segmentation system. It is particularly a challenging task to classify cancers in human organs in scanners output using shape or gray-level information; organs shape changes throw different slices in medical stack and the gray-level intensity overlap in soft tissues. Curvelet transform is a new extension of wavelet and ridgelet transforms which aims to deal with interesting phenomena occurring along curves. Curvelet transforms has been tested on medical data sets, and results are compared with those obtained from the other transforms. Tests indicate that using curvelet significantly improves the classification of abnormal tissues in the scans and reduce the surrounding noise

Mueller matrix polarimetry of plasmon resonant silver nano-rods: biomedical prospects

Fundamental understanding of the light-matter interaction in the context of nano-particles is immensely bene- fited by the study of geometry dependent tunable Localized Surface Plasmon Resonance (LSPR) and has been demonstrated to have potential applications in various areas of science. The polarization characteristics of LSPR in addition to spectroscopic tuning can be suitably exploited in such systems as contrast enhancement mech- anisms and control parameters. Such polarization characteristics like diattenuation and retardance have been studied here using a novel combination of Muller-matrix polarimetry with the T-matrix matrix approach for silver nano-rods to show unprecedented control and sensitivity to local refractive index variations. The study carried out over various aspect ratios for a constant equal volume sphere radius shows the presence of longitu- dinal (dipolar and quadrupolar) and transverse (dipolar) resonances; arising due to differential contribution of polarizabilities in two directions. The overlap regions of these resonances and the resonances themselves exhibit enhanced retardance and diattenuation respectively. The spectral and amplitude tunability of these polarimetric parameters through the aspect ratios to span from the minimum to maximum ([0, 1] in the case of diattenuation and [0, {\pi}] in the case of retardance) presents a novel result that could be used to tailor systems for study of biological media. On the other hand, the high sensitivity of diattenuation dip (caused by equal contribution of polarizabilities) could be possibly used for medium characterization and bio-sensing or bio-imaging studies.Comment: 8 pages, 6 figures, Proceedings of the Saratov Fall Meeting, 201

A FPGA system for QRS complex detection based on Integer Wavelet Transform

Due to complexity of their mathematical computation, many QRS detectors are implemented in software and cannot operate in real time. The paper presents a real-time hardware based solution for this task. To filter ECG signal and to extract QRS complex it employs the Integer Wavelet Transform. The system includes several components and is incorporated in a single FPGA chip what makes it suitable for direct embedding in medical instruments or wearable health care devices. It has sufficient accuracy (about 95%), showing remarkable noise immunity and low cost. Additionally, each system component is composed of several identical blocks/cells what makes the design highly generic. The capacity of today existing FPGAs allows even dozens of detectors to be placed in a single chip. After the theoretical introduction of wavelets and the review of their application in QRS detection, it will be shown how some basic wavelets can be optimized for easy hardware implementation. For this purpose the migration to the integer arithmetic and additional simplifications in calculations has to be done. Further, the system architecture will be presented with the demonstrations in both, software simulation and real testing. At the end, the working performances and preliminary results will be outlined and discussed. The same principle can be applied with other signals where the hardware implementation of wavelet transform can be of benefit

Sparse And Low Rank Decomposition Based Batch Image Alignment for Speckle Reduction of retinal OCT Images

Optical Coherence Tomography (OCT) is an emerging technique in the field of biomedical imaging, with applications in ophthalmology, dermatology, coronary imaging etc. Due to the underlying physics, OCT images usually suffer from a granular pattern, called speckle noise, which restricts the process of interpretation. Here, a sparse and low rank decomposition based method is used for speckle reduction in retinal OCT images. This technique works on input data that consists of several B-scans of the same location. The next step is the batch alignment of the images using a sparse and low-rank decomposition based technique. Finally the denoised image is created by median filtering of the low-rank component of the processed data. Simultaneous decomposition and alignment of the images result in better performance in comparison to simple registration-based methods that are used in the literature for noise reduction of OCT images.Comment: Accepted for presentation at ISBI'1