Immediate PBM therapy is more effective than delayed PBM therapy in minimizing the complications associated with mandibular third molar removal surgery.

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Ultrasound (US) imaging application in medicine and other fields is enormous. It has several advantages over other medical imaging modalities. The use of US in diagnosis is well established because of its noninvasive nature, portable, accurate, low-cost imaging modality, capable of forming real-time imaging and continuing improvement in image quality. Medical images and satellite images are usually degraded by noise during image acquisition and transmission process. It is estimated that 1 out of every form medical diagnostic image studies in the world involves ultrasonic techniques. The objectives of this article are to give an overview of the types of speckle reduction techniques in US imaging, present a new technique of speckle reduction, and carry out a comparative evaluation of despeckle filtering based on image quality metrics. A new speckle suppression method and coherence enhancement of medical US image are proposed: 9 despeckled filtering techniques and 5 edge detection operators, with the result evaluated by image quality metrics. The best edge detection hybrid with best filter, quality evaluation metrics has been found that the proposed method performance better than all other methods, while the structural details and result preserving of small and important image feature that contain diagnostic information in a better way than other despeckling filter.

Ultrasound imaging is a widely used and safe medical diagnostic technique, due to its noninvasive nature, low cost, capability of forming real time imaging, and the continuing improvements in image quality. However, the usefulness of ultrasound imaging is degraded by the presence of signal dependent noise known as speckle. In this paper, we propose a new method for speckle reduction and coherence enhancement of ultrasound images based on a hybrid of total variation (TV) method and wavelet thresholding. In this model, a noisy image is decomposed into four subbands in wavelet domain. The low frequency subband contains the low frequency coefficients with less noise that can be easily eliminated using TV-based method. More edges and other detailed information like textures are contained in the other three subbands the wavelet based soft thresholding is applied on these three subbands. In the last step we use TV method to get the final denoised image since the TV is the ability of preserving edge is smoothening by wavelet thresholding. The proposed method is compared with previous methods as applied to simulated and real data using quantitative quality evaluation metrics to show the advantage of the new method.