Flow cytometry makes use of reflected laser light to analyze and classify biological cells.The laser light strikes the cell, is reflected, and sensed by a photomultiplier tube, resulting in a 1-D signature profile of the cell. We developed a digital signal processing system that augments the commercial, analog flow cytometer. The 1-D analog signal is digitized, and waveform features are digitallycomputed.

A prototype of a second-generation digital data acquisition system (DDAPS-2), a mixed-signal design operating at 40 MHz, was built to interface to a commercial flow cytometer. The DDAPS-2 intercepts the analog signal from the photomultiplier tube and preamp, performs analog-to-digital conversion, extracts various features and then feeds these extracted features into one of the several pattern classification algorithms. The novelty of the DDAPS-2 is the use of dual-buffering FIFO memories to acquire digital samples of the pulse voltage signal.

Through experimental results using a variety of cell types, we demonstrated that the digital system provides improved performance compared to the analog system since a much richer set of features can be used to discriminate cell populations. Experimental results also demonstrated the improvement in the pulse capture performance of DDAPS-2 over DDAPS-1, which used a single-buffering FIFO memory.

Publications:

1. Mahesh Godavarti and Jeffrey J. Rodriguez, "An Improved Technique for Automated Image Histogram Thresholding," in Proc. 8th Intl. Conf. on Signal Processing Applications \& Technology, San Diego, CA, Sept. 14-17, 1997, pp. 1165-1170.
   
   
2. Mahesh Godavarti, Jeffrey J. Rodriguez, Timothy A. Yopp, Georgina M. Lambert, and David W. Galbraith, "Automated Particle Classification Based on Digital Acquisition and Analysis of Flow Cytometric Pulse Waveforms," Cytometry, vol. 24, no. 4, 1996, pp. 330-339. [ PDF ]
   
   
3. Mahesh Godavarti, Jeffrey J. Rodriguez, Timothy A. Yopp, Georgina M. Lambert, and David W. Galbraith, "Neural Network Analysis of Digital Flow Cytometric Data," in Proc. IEEE Intl. Conf. on Neural Networks, vol. 5, Perth, Australia, Nov. 27 - Dec. 1, 1995, pp. 2211-2216. [ PDF ]