Recently, medical imaging and machine learning gained significant attention in the early detection of brain tumor. Compound structure and tumor variations, such as change of size, make brain tumor segmentation and classification a challenging task. In this review, we survey existing work on brain tumor, their stages, survival rate of patients after each stage, and computerized diagnosis methods. We discuss existing image processing techniques with a special focus on preprocessing techniques and their importance for tumor enhancement, tumor segmentation, feature extraction and features reduction techniques. We also provide the corresponding mathematical modeling, classification, performance matrices, and finally important datasets. Last but not least, a detailed analysis of existing techniques is provided which is followed by future directions in this domain.

In recent years, a considerable number of approaches have been proposed by the researchers to evaluate infectious diseases by examining the digital images of peripheral blood cell (PBC) recorded using microscopes. In this chapter, a semi-automated approach is proposed by integrating the Shannon's entropy (SE) thresholding and DRLS-based segmentation procedure to extract the stained blood cell from digital PBC pictures. This work implements a two-step practice with cuckoo search (CS) and SE-based pre-processing and DRLS-based post-processing procedure to examine the PBC pictures. During the experimentation, the PBC pictures are adopted from the database leukocyte images for segmentation and classification (LISC). The proposed approach is implemented by considering the RGB scale and gray scale version of the PBC pictures, and the performance of the proposed approach is confirmed by computing the picture similarity and statistical measures computed with the extracted stained blood cell with the ground truth image.

Gamma Calculus is an inherently parallel, high-level programming model, which allows simple programming molecules to interact, creating a complex system with minimum of coding. Gamma calculus modeled programs were written on top of IBM’s TSpaces middleware, which is Java-based and uses a “Tuple Space” based model for communication, similar to that in Gamma. A parser was written in C++ to translate the Gamma syntax. This was implemented on UHD’s grid cluster (grid.uhd.edu), and in an effort to increase performance and scalability, existing Gamma programs are being transferred to Nvidia’s CUDA architecture. General Purpose GPU computing is well suited to run Gamma programs, as GPU’s excel at running the same operation on a large data set, potentially offering a large speedup.