This Digital Signal Processing (DSP) study is aimed at real-time capture and analysis of pathological brain images to improve accuracy and efficiency. Simply analyzing cell density statistics and average cell nucleus diameters of a slide image is shown to be useful to determine the abnormality of brain sample. Numerous biopsy samples of various types obtained around the world daily are sent for screening and diagnosis to enable proper treatment, often while patients may be painfully suffering the symptoms for days to weeks anxiously awaiting the biopsy results. In general, pathological image analysis using a computer-based application could demonstrate great precision and efficiency for screening large quantities of cells on one or numerous sample slides, as opposed to the tedious and error-prone human eye counting and measuring hundreds to thousands of cells in one sample slide under a microscope. As a high-level, interactive environment for data visualization/analysis/computation, MATLAB® is currently utilized to perform automatic image analysis and segmentation of brain cells on a computer. By comparing cell concentration and cell nucleus sizes between cancerous and normal image groups, MATLAB® can be programmed to distinguish normal brain cells from questionable ones. Currently, MATLAB® image analysis works on captured/digitized slide images and takes a minute per image to automatically pre-screen abnormalities that require further human expert analysis. With future real-time/parallel/machine-intelligent improvements, we hope that DSP can help physicians, pathologists, and patients everywhere to get immediate diagnosis for timely/effective treatment, and can show accuracy within acceptable levels that are comparable to human pathologists in dealing with cell-overlapping and noncell objects existing in slide images.