D. Nedumaran

Comparative study of Speckle Noise Reduction of Ultrasound B-scan Images in Matrix Laboratory Environment

ABSTRACT In the field of biomedical imaging, the ultrasound (US) B-Scan images are used for tissue characterization. These images are obtained with a simple linear or sector scan US probe, which show a granular appearance called speckle. Speckle is modeled as a signal dependent noise, which tends to reduce the image resolution and contrast, thereby reducing the diagnostic values of the US imaging modality. Over a period, various speckle reduction techniques have been developed by researchers did not represent a comprehensive method that takes all the constraints into consideration. This work addressed the Wiener filtering in wavelet domain with soft thresholding as a comprehensive technique. Also, this paper compares the efficiency of the wavelet-based thresholding (VisuShrink, BayesShrink and SureShrink) technique in despeckling the medical US images with five other classical speckle reduction filters. The performance of these filters are determined by the statistical quantity measures such as Peak Signal-to-Noise Ratio (PSNR) and Root Mean Square Error (RMSE).The results obtained are presented in the form of filtered images, statistical tables and diagrams. Based on the statistical measures and visual quality of the US B-scan images the Wiener filtering with BayesShrink thresholding technique in the wavelet-domian performed well over the other filter techniques.

Speckle filtering of ultrasound B-Scan Images - a comparative study between spatial and diffusion filters

Speckle noise is the inherent property of ultrasound B-Scan images which has been filtered using well-established speckle reduction techniques. In this work, six spatial filters namely Frost, Median, Lee, Kuan, Wiener, and Homomorphic filters, and two diffusion filters viz., Speckle Reduction Anisotropic Diffusion (SRAD) filter, and Anisotropic Diffusion (AD) filter have been attempted over 200 different digital ultrasound B-scan images of kidney, abdomen, liver and choroids. A comparative study has been made on these filters in preserving the edges of the images with effective denoising by calculating fourteen established performance metrics along with the execution time in order to determine the effective and optimum despeckling algorithm for real time implementation. To do this, a cumulative speckle reduction (CSR) algorithm has been developed using MATLAB 7.1, which performs all despeckle filtering functions as well as performance metrics calculation in a single iteration. This study reveals that most of the despeckle filters performed well and gave optimum performance, but SRAD is the outperformed filtering technique for B-scan ultrasound image as far as the performance metrics, execution time and visual inspection are concerned.

Implementation of ECG signal processing and analysis techniques in digital signal processor based system

This work describes the implementation of wavelet-based de noising algorithm on electrocardiogram (ECG) signal and detection of important parameter such as heart rate, amplitude, timings of the ECG, etc. The algorithm is implemented in DSP (TI TMS320C67x) based starter kit (DSK) with a two-electrode ECG preamplifier. The signal from the ECG preamplifier is acquired through the Codec input of the DSP starter kit. The acquired data is subjected to signal processing techniques such as removal of power line frequencies and high frequency component removal using wavelet-denoising technique. ECG component analysis such as QRS peak detection, heart rate calculation, etc is performed using nonlinear filter technique called first order derivative and moving average filter. The performance of the algorithm (such as speed, calculation efficiency, accuracy, etc) is studied in the DSP environment as well as MATLAB environment for comparison. The results of this study reveal the potentiality of the DSP system for routine clinical use.

Design and Development of Digital Signal Processor Based Phonocardiogram System

This paper presents a real-time and cost effective system for the heart auscultation monitoring and hearing. The system design comprises of a Phonocardiographic pre-amplifier circuit with a TMS320C6711 Digital Signal Processor Starter kit (DSK) and its associated software. The Phonocardiogram signal from the pre-amplifier circuit is acquired through the CODEC input of the DSK and subjected to various signal processing techniques. Frequency analysis and component analysis are performed to identify the normal and pathological heart sound patterns using Short Time Fourier Transform (STFT) and Wavelet transform techniques respectively. To study the performance of the system, the analysis of heart sound patterns for various diseases were conducted. Finally the computational efficiency of the system was calculated by comparing the execution time of the algorithms in the proposed DSPPCG (Digital Signal Processor based Phonocardiogram) system with the PCPCG (PC based Phonocardiogram) system.

Synthesis and characterisation of magnetic Fe2O3/Au core/shell nanoparticles for biomedical applications

The present work details the preparation of core-shell Fe2O3@Au nanoparticles via iterative hydroxylamine seeding, which results in nanoparticles of controllable sizes ranging from 14.8 to 15.6 nm diameter. The properties of the synthesised nanoparticles were investigated by several techniques. XRD studies show that the nanoparticles have mixed two crystalline phases of α-Fe2O3 and γ-Fe2O3. The elemental analysis of the synthesised nanoparticles consists of Fe2O3 core and Au shell. VSM measurements ascertain that the synthesised nanoparticles having diameter d ≤ 16 nm show superparamagnetic behaviour at room temperature whereas with diameter d > 16 nm show hysteresis behaviour. Mossbauer studies exhibit the super-paramagnetic behaviour of sextet, doublet and Fe3+ charge state of the synthesised sample. Impedance studies of the sample exhibit a high resistance (≥ 105 Ω), which confirms the mixed phases. Thus, the synthesised nanoparticles show controllable grain size, shape, texture and magnetic properties, which finds as a suitable candidate for many biomedical applications.

Denoising of Fingerprint Images using Q-shift Complex Wavelet Transform

In this paper, we present a novel image denoising method using Q-shift with Dual Tree Complex Wavelet Transform (QDTCWT) for denoising fingerprint images. The DTCWT is an over complete wavelet transform with limited redundancy and generates complex coefficients in parallel using a dual tree of wavelet filters. But, low pass delay produces a Hilbert pair relationship between two trees. This is well addressed by Q-shift filters for improving orthogonality and symmetry properties in level 2 and below. QDTCWT have features like linear phase, tight frame, compact spatial support, good frequency domain selectivity with low sidelobe levels, approximate shift invariance, and good directional selectivity in two or more dimensions. This provides the QDTCWT basis mainly useful for de-noising purposes with high degree of shift-invariance and better directionality compared to the other traditional methods. The proposed algorithm has been designed in the MATLAB TM environment and tested in the fingerprint images obtained from the FVC2004 database for denoising. The performance and efficiency of the algorithm are estimated by calculating various quality metrics and compared with the advanced methods already practiced in fingerprint image denoising. The results of this study revealed that the QDTCWT algorithm is capable of producing high quality finger print images with greater fidelity, high robustness and accuracy over the other traditional denoising methods.

Doppler Spectrogram Calculation Using CFFT Algorithm in a Digital Signal Processor Based System

Most of the clinical indices of blood flow are estimated from the spectrograms of Doppler ultrasound (DUS) signals. Any noise may degrade the readability of the spectrogram and the precision of the clinical indices, so the spectral augmentation plays an important role in the calculation of DUS spectrum. The proposed design employed Complex Fast Fourier Transform (CFFT), in a Digital Signal Processor (DSP) (TMS320C6711 DSK) based biomedical signal processing system, which results in reduction of computational time as well as processing time, since conventional methods involve FFT and Hilbert Transform techniques. Also, this system has been designed as open research platform, which can be programmable with a variety of novel algorithms like Gabor and Wavelet techniques for studying improved and resolved spectrograms to obtain accurate diagnostic details in the future.

Implementation of modified polar complex moments-based fingerprint orientation estimation for effective segmentation

Segmentation is an important step in deciding the performance of fingerprint identification systems. In this paper, we present the modified polar complex moments MPCMs fingerprint orientation estimation algorithm, capable of describing the fingerprint flow structures including singular point regions in the fingerprint images effectively. To discard the background region of the low-quality fingerprint images, regularisation was employed. These algorithms are tested on various types of fingerprint images containing low-quality unrecoverable region and the results obtained from the proposed method were compared with those obtained from well-known gradient-based and PCMs methods. The proposed method was also used to study the contrast enhancement process with our previously developed modified histogram equalisation MHE based on adaptive inverse hyperbolic tangent AIHT method. The MPCMs method exhibits better segmentation than the traditional methods in both normal and low-contrast image segmentations, as evident from the estimated matching scores as well as ROC graph.

Performance Evaluation of Gabor Filter in Removing Rician Noise in MR Images

Magnetic Resonance images are contaminated by Rician distributed noise due to the presence of contrast-diminishing signal-dependent bias. In this paper, Gabor filter approach for bias removal in MR images is attempted. The filter was tested in four different brain MR images and the results obtained were compared qualitatively and quantitatively with the other four established filtering techniques. This study exhibits that the Gabor bias removal technique improved the contrast of the MR image that is found from the moderate increase in PSNR value and visual inspection by trained radiologist.

Synthesis and Characterization of Nanocrystalline MgO for Optical Applications Using Sol-Gel Method

This paper describes the preparation and characterization of nanocrystalline Magnesium Oxide (MgO) using sol-gel technique for optical applications. The prepared nanocrystalline MgO was chemically homogeneous, very pure and specifically hydroxylated and was characterized by standard techniques. The size of the prepared nanoparticle was found to be 27.38 nm ± 0.65 nm and exhibited a face centered–cubic structure and exhibited two lifetimes viz., uf0741 = 0.24 ns and uf0742 = 8.9 ns. Its binding energy was found to be 50.9 eV, which showed the formation of single phase MgO on the surface. It behaved as semiconductor over the temperature range of 500 °C to 660 °C and as perfect insulator in the temperature range 100 °K to 300 °K.