High throughput screening has been used to rapidly screen for chemical compounds in a biological assay. Until recently, many of the biological assays utilized simple biochemical techniques, the result of which could be interpreted in single or at most a few numerical values. That made it easy to evaluate, without bias, any unique chemical entities screened. However, with biological cells or tissue images, the information was qualitative or at best limited to simplified algorithms. Recently, it is now becoming possible to perform standardized assays and utilize complex image data to derive reproducible information which could be utilized to precisely quantify the efficacy of compounds. Much of this is possible due to the precise mathematical algorithms that are used to compute image data to derive information. This review will discuss some of the basic algorithms involving kernel operations that are commonly used and how they can be applied for any image or picture data.