In the recent decades, polymers are widely used as biomaterials due to their favourable properties such as good biocompatibility, easy design and preparation, structural varieties and interesting bio-mimetic character. The use of biodegradable polymeric nanoparticles (NPs) for controlled drug delivery has shown significant therapeutic potential. Concurrently, targeted delivery technologies are becoming increasingly important as a scientific area of investigation. The current review entails an in-depth discussion of biodegradable polymeric nanoparticles with respect to types, formulation aspects as well as site-specific drug targeting using various ligands modifying the surface of polymeric nanoparticles with special insights to the field of oncology. Ultimately the goal of polymeric nanoparticle drug delivery is the emergence of a nano-fabricated therapeutic drug release device with the capacity to enough hold and release of various active agents on demand.

Retrieval of similar images from large dataset of brain images across patients would help the experts in the decision diagnosis process of diseases. Generally used feature extraction methods are color, texture and shape. In medical images texture and shape features are most efficient. Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) are good descriptor for brain MR image retrieval. But there are many challenges facing in medical application. An empirical study of the impact of increasing bins number in the HOG descriptor concluded that larger the number is more accurate the descriptor is. In fact this is due to the reduction of orientations range that each bin covers. Despite the efficiency of augmenting the bins number, this technique has limited spatial support as the augmentation of the number of bins used leads to increase the histogram dimension. So here proposed a method called Histogram of Fuzzy Oriented Gradients (HFOG), in which a pixel can belong several bins with different degrees. The Local Binary Patterns feature extraction method is widely used for texture analysis; however, the original LBP is based on hard thresholding the neighborhood of each pixel. Therefore, texture representation with LBP is very sensitive to noise and cannot distinguish between a strong and a weak pattern. In this study, Fuzzy Local Binary Patterns was introduced to improve the original LBP.

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