Image pattern classification is considered a significant step for image and video processing.Although various image pattern algorithms have been proposed so far that achieved adequate classification,achieving higher accuracy while reducing the computation time remains challenging to date. A robust imagepattern classification method is essential to obtain the desired accuracy. This method can be accuratelyclassify image blocks into plain, edge, and texture (PET) using an efficient feature extraction mechanism.Moreover, to date, most of the existing studies are focused on evaluating their methods based on specificorthogonal moments, which limits the understanding of their potential application to various DiscreteOrthogonal Moments (DOMs). The

In this research a proposed technique is used to enhance the frame difference technique performance for extracting moving objects in video file. One of the most effective factors in performance dropping is noise existence, which may cause incorrect moving objects identification. Therefore it was necessary to find a way to diminish this noise effect. Traditional Average and Median spatial filters can be used to handle such situations. But here in this work the focus is on utilizing spectral domain through using Fourier and Wavelet transformations in order to decrease this noise effect. Experiments and statistical features (Entropy, Standard deviation) proved that these transformations can stand to overcome such problems in an elegant way.

Image pattern classification is considered a significant step for image and video processing. Although various image pattern algorithms have been proposed so far that achieved adequate classification, achieving higher accuracy while reducing the computation time remains challenging to date. A robust image pattern classification method is essential to obtain the desired accuracy. This method can be accurately classify image blocks into plain, edge, and texture (PET) using an efficient feature extraction mechanism. Moreover, to date, most of the existing studies are focused on evaluating their methods based on specific orthogonal moments, which limits the understanding of their potential application to various Discrete Orthogonal Moments (DOM

We report herein an innovative approach to prostate tumor therapy using tumor specific radioactive gold nanoparticles (198Au) functionalized with Mangiferin (MGF). Production and full characterization of MGF-198AuNPs are described. In vivo therapeutic efficacy of MGF-198AuNPs, through intratumoral delivery, in SCID mice bearing prostate tumor xenografts are described. Singular doses of the nano-radiopharmaceutical (MGF-198AuNPs) resulted in over 85% reduction of tumor volume as compared to untreated control groups. The excellent anti-tumor efficacy of MGF-198AuNPs are attributed to the retention of over 90% of the injected dose within tumors for long periods of time. The retention of MGF-198AuNPs is also rationalized in terms of the higher