%0 Journal Article
%T Denoising of Medical Ultrasound Images Using Spatial Filtering and Multiscale Transforms
%A V N Prudhvi Raj
%A T Venkateswarlu
%J International Journal of Computer Science & Information Technology
%D 2013
%I Academy & Industry Research Collaboration Center (AIRCC)
%X Medical imaging became the integral part in health care where all the critical diagnosis such as blocks inthe veins, plaques in the carotid arteries, minute fractures in the bones, blood flow in the brain etc arecarried out without opening the patient¡¯s body. There are various imaging modalities for differentapplications to observe the anatomical and physiological conditions of the patient. These modalities willintroduce noise and artifacts during medical image acquisition. If the noise and artifacts are not minimiseddiagnosis will become difficult. One of the non-invasive modality widely used is ultrasound Imaging whereno question of radiation but suffers from speckle noise produced by the small particles in the tissues who¡¯ssize is less than the wavelength of the ultrasound. The presence of the speckle noise will cause the lowcontrast images because of this the low contrast lesions and tumours can¡¯t be detected in the diagnosticphase. So there is a strong need in developing the despeckling techniques to improve the quality ofultrasound images. Here in this paper we are presenting the denoising techniques for speckle reduction inultrasound imaging. First we presented the various spatial filters and their suitability for reducing thespeckle. Then we developed the denoising methods using multiscale transforms such as Discrete WaveletTransform (DWT), Undecimated Discrete Wavelet Transform (UDWT), dual tree complex wavelettransform (DTCDWT) and Double density dual tree complex wavelet transform (DDDTCDWT). Theperformance of the filters was evaluated using various metrics based on pixel based, correlation based,edge based and Human visual system (HVS) based and we found that denoising using double density dualtree complex discrete wavelet transform is outperformed with best edge preserving feature.
%K Discrete Wavelet Transform
%K Dual Tree Complex Wavelet Transform
%K Double density wavelet transform
%K double density dual tree complex wavelet transform.
%U http://airccse.org/journal/jcsit/4612ijcsit13.pdf