Interphase fluorescence in situ hybridization (FISH) technology is a potential and promising molecular imaging
tool, which can be applied to screen and detect cervical cancer. However, manual FISH detection method is a subjective,
tedious, and time-consuming process that results in a large inter-reader variability and possible detection error (in
particular for heterogeneous cases). Automatic FISH image analysis aims to potentially improve detection efficiency and
also produce more accurate and consistent results. In this preliminary study, a new computerized scheme is developed to
automatically segment analyzable interaphase cells and detect FISH signals using digital fluorescence microscopic
images acquired from Pap-smear specimens. First, due to the large intensity variations of the acquired interphase cells
and overlapping cells, an iterative (multiple) threshold method and a feature-based classifier are applied to detect and
segment all potentially analyzable interphase nuclei depicted on a single image frame. Second, a region labeling
algorithm followed up a knowledge-based classifier is implemented to identify splitting and diffused FISH signals.
Finally, each detected analyzable cell is classified as normal or abnormal based on the automatically counted number of
FISH signals. To test the performance of this scheme, an image dataset involving 250 Pap-smear FISH image frames
was collected and used in this study. The overall accuracy rate for segmenting analyzable interphase nuclei is 86.6%
(360/424). The sensitivity and specificity for classifying abnormal and normal cells are 88.5% and 86.6%, respectively.
The overall cell classification agreement rate between our scheme and a cytogeneticist is 86.6%. The testing results
demonstrate the feasibility of applying this automated scheme in FISH image analysis.