1. FISH Image Analysis Using a Modified Radial Basis Function Network
- Author
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Andrea Cavazzana, Ioannis Kasampalidis, Michele Menicagli, Generoso Bevilacqua, Ioannis Pitas, Ioannis Kostopoulos, Paolo Aretini, Antonina Starita, Georgia Karayannopoulou, and Kleoniki Lyroudia
- Subjects
Radial basis function network ,business.industry ,Computer science ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Pattern recognition ,Image segmentation ,Image (mathematics) ,Data set ,Medical imaging ,%22">Fish ,Radial basis function ,Artificial intelligence ,business ,Nuclei segmentation ,Biomedical engineering - Abstract
Fluorescent in situ hybridization (FISH) is a valuable method for determining Her-2/neu status in breast carcinoma samples, an important prognostic indicator. Visual evaluation of FISH images is a difficult task which involves manual counting of dots in multiple images, a procedure which is both time consuming and prone to human error. A number of algorithms have recently been developed dealing with (semi)-automated analysis of FISH images. These algorithms are quite promising but further improvement is required in improving their accuracy. Here, we present a novel method for analyzing FISH images based on the statistical properties of Radial Basis Functions. Our method was evaluated on a data set of 100 breast carcinoma cases provided by the Aristotle University of Thessaloniki and the University of Pisa, with promising results.
- Published
- 2007