Imaging flow cytometry and high speed microscopy have shown immense promise for clinical diagnostics, biological research, and drug discovery. They enable high throughput screening and sorting using biological, chemical, or mechanical properties of cells. These techniques can separate mature cells from immature ones, determine the presence of cancerous cells, classify stem cells during differentiation, and screen drugs based upon how they affect cellular architecture. The process works by imaging cells at a high rate, extracting features of the cell (e.g., size, location, circularity, deformation), and using those features to classify the cell. Modern systems have a target throughput of thousands of cells per second, which requires imaging at rates of more than 60,000 frames per second. The cellular features must be calculated in less than a millisecond to enable real-time sorting. This creates challenging computing performance constraints in terms of both throughput and latency. In this paper, we present a hardware accelerated system for high throughput cellular image analysis. We carefully developed algorithms and their corresponding hardware implementations to meet the strict computational demands. Our algorithm analyzes and extracts cellular morphological features from low resolution microscopic images. Our hardware accelerated system operates at over 60,000 frames per second with 0.068 ms latency. This is almost 1400× faster in throughput than similar software based analysis and 335× better in terms of latency.