1. Project Overview To separate or classify the cells by their given conditions automatically, one can extract the characteristics of cells in biomolecule fluids based on image processing analysis. These processes need to deal with a huge amount of image data in a high speed as fluid flowing, and must be done in very short time to control the separator. The purpose of this project is to implement a realtime system to compute them and get the cellular analysis by using Graphic Processing Unit(GPU) 2. Project Approach – Technical details In cell biology, the image processing can be adapted the automatic analysis of cell characteristics and this requires the complex computation with high resolution of image data. Computational complexity makes analyzing system hardly implemented in real time, and the repetition in each pixel causes a heavy burden in utilization of hardware system. Parallelization of image processing algorithm allows reduce computational time. GPU contains a large number of microprocessors and its architecture is very suitable for parallelization. Specialized hardware, such as FPGA or DSP, may work perfectly in the purpose of real time system. However, they needs specialty and expertize to be used as well as developed. Therefore, GPU processing can be considered as an appropriate solution for the realtime image processing system 3. Project Objectives, Milestones, and Major Deliverables > Project goals in short term  To optimize the algorithm for to get the most efficiency
 To modify the process for parallelization
 To implement the real time system using GPU platform
> Project goals in long term  To compare the efficiency of the implementation of GPU system to FPGA
 To characterize the property of the parallelizable module
 To make a function for image processing analysis in parallel system
> Milestones and Major Deliverables 4. Constraints, Risk, and Feasibility > Constraints  The limitation of parallelization, because of datadependency
 The computational complexity needing time consumption
> Risk  To work on only given test images
 To generate a code with redundancy
5. Project Development  Items  Cost  Hardware  Desktop computer w/ Graphic Processing Unit, Graphic Card by NVIDIA    Software  CUDA toolkit  Free 
6. Project Schedule  Algorithm analysis based on MatLab code : To comprehend the basic algorithm of cellular image analysis based on MatLab code and literature survey
 Conversion to C code and optimization of the algorithm : To convert MatLab code to C and modify some parts to be optimized for computational efficiency and parallelization
 Parallelization in CUDA : To implement GPU code for realtime system in each module
 Optimization in GPU : To optimize the result of parallelization to maximize the utilization rate of GPU hardware and minimize the redundant complex computations.
