As medical image data sets are digitized and the number of data sets is increasing exponentially, there is a need for
automated image processing and analysis technique. Most medical imaging methods require human visual inspection
and manual measurement which are labor intensive and often produce inconsistent results. In this paper, we propose an
automated image segmentation and classification method that identifies tumor cell nuclei in medical images and
classifies these nuclei into two categories, stained and unstained tumor cell nuclei. The proposed method segments and
labels individual tumor cell nuclei, separates nuclei clusters, and produces stained and unstained tumor cell nuclei
counts. The representative fields of view have been chosen by a pathologist from a known diagnosis (clear cell renal cell
carcinoma), and the automated results are compared with the hand-counted results by a pathologist.
We analyze challenges in the current approaches to digital video surveillance solutions, both technically and financially.
We propose a Cell Processor based digital video surveillance platform to overcome those challenges and address ever
growing needs in enterprise class surveillance solutions capable of addressing multiple thousands camera installations.
To improve the compression efficiency we have chosen H.264 video compression algorithm which outperforms all
standard video compression schemes as of today.