Akademik Veri Yönetim Sistemi
INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING, cilt.33, sa.11, 2017 (SCI-Expanded)
Traditional diagnostic neuropathology relies on subjective interpretation of visual data obtained from a brightfield microscopy. This approach causes high variability, unsatisfactory reproducibility, and inability for multiplexing even among experts. These problems may affect patient outcomes and confound clinical decision-making. Also, standard histological processing of pathological specimens leads to auto-fluorescence and other artifacts, a reason why fluorescent microscopy is not routinely implemented in diagnostic pathology. To overcome these problems, objective and quantitative methods are required to help neuropathologists in their clinical decision-making. Therefore, we propose a computerized image analysis method to validate anti-PTBP1 antibody for its potential use in diagnostic neuropathology. Images were obtained from standard neuropathological specimens stained with anti-PTBP1 antibody. First, the noise characteristics of the images were modeled and images are de-noised according to the noise model. Next, images are filtered with sigma-adaptive Gaussian filtering for normalization, and cell nuclei are detected and segmented with a k-means-based deterministic approach. Experiments on 29 data sets from 3 cases of brain tumor and reactive gliosis show statistically significant differences between the number of positively stained nuclei in images stained with and without anti-PTBP1 antibody. The experimental analysis of specimens from 3 different brain tumor groups and 1 reactive gliosis group indicates the feasibility of using anti-PTBP1 antibody in diagnostic neuropathology, and computerized image analysis provides a systematic and quantitative approach to explore feasibility.