Multivariate image analysis in biomedicine
Journal of Biomedical Informatics
Expert Systems with Applications: An International Journal
An effective automated grading system for HCC in biopsy images
ICCOMP'07 Proceedings of the 11th WSEAS International Conference on Computers
Effective segmentation and classification for HCC biopsy images
Pattern Recognition
A classification scheme for lymphocyte segmentation in H&E stained histology images
ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
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A computer-aided detection system for tissue cell nuclei in histological sections is introduced and validated as part of the Biopsy Analysis Support System (BASS). Cell nuclei are selectively stained with monoclonal antibodies, such as the anti-estrogen receptor antibodies, which are widely applied as part of assessing patient prognosis in breast cancer. The detection system uses a receptive field filter to enhance negatively and positively stained cell nuclei and a squashing function to label each pixel value as belonging to the background or a nucleus. In this study, the detection system assessed all biopsies in an automated fashion. Detection and classification of individual nuclei as well as biopsy grading performance was shown to be promising as compared to that of two experts. Sensitivity and positive predictive value were measured to be 83% and 67.4%, respectively. One major advantage of BASS stems from the fact that the system simulates the assessment procedures routinely employed by human experts; thus it can be used as an additional independent expert. Moreover, the system allows the efficient accumulation of data from large numbers of nuclei in a short time span. Therefore, the potential for accurate quantitative assessments is increased and a platform for more standardized evaluations is provided.