(1)Imaging Research Department
Siemens Corporate Research
Princeton, NJ 08540
(2)ECE Department
Rutgers University
Piscataway, NJ 08854
The colors associated with a digitized specimen representing peripheral blood smear are typ
ically characterized by only a few, non Gaussian clusters, whose shapes have to be discerned
solely from the image being processed. Nonparametric methods such as mode based analysis
[10], are particularly suitable for the segmentation of this type of data since they do not
constrain the cluster shapes. This chapter reviews an efficient cell segmentation algorithm
that detects clusters in the L u v color space and delineates their borders by employing
the gradient ascent mean shift procedure [8, 9]. The color space is randomly tessellated
with search windows that are moved till convergence to the nearest mode of the underlying
probability distribution. After the pruning of the mode candidates, the colors are classified
using the basins of attraction. The segmented image is derived by mapping the color vectors
in the image domain and enforcing spatial constraints.
The segmenter is the core module of the Image Guided Decision Support (IGDS) system [14, 13] which is discussed next. The IGDS architecture supports decision making in
clinical pathology and provides components for remote microscope control and multiuser
visualization. The primary and long term goal of the IGDS related research is to reduce the
number of false negatives during routine specimen screening by medical technologists. The
DecisionSupport component of the system searches remote databases, retrieves and displays
cases which exhibit visual features consistent to the case in question, and suggests the most
likely diagnosis according to majority logic. Based on the MicroController component the
primary user can command a robotic microscope from the distance, obtain highquality im
ages for the diagnosis, and authorize other users to visualize the same images. The system
has a natural manmachine interface that contains engines for speech recognition and voice
feedback.
Advanced Algorithmic Approaches to Medical Image Segmentation:
State-Of-The-Art Applications in Cardiology, Neurology,
Mammography and Pathology .
J. Suri, S. Singh and K. Setarehdan (Eds.), Springer, 2001, 541-558.
