title: Classification of Fourier transform infrared microscopic imaging data of human breast cells by cluster analysis and artificial neural networks.
abstract: Cluster analysis and artificial neural networks (ANNs) are applied to the automated assessment of disease state in Fourier transform infrared microscopic imaging measurements of normal and carcinomatous immortalized human breast cell lines. K-means clustering is used to implement an automated algorithm for the assignment of pixels in the image to cell and non-cell categories. Cell pixels are subsequently classified into carcinoma and normal categories through the use of a feed-forward ANN computed with the Broyden-Fletcher-Goldfarb-Shanno training algorithm. Inputs to the ANN consist of principal component scores computed from Fourier filtered absorbance data. A grid search optimization procedure is used to identify the optimal network architecture and filter frequency response. Data from three images corresponding to normal cells, carcinoma cells, and a mixture of normal and carcinoma cells are used to build and test the classification methodology. A successful classifier is developed through this work, although differences in the spectral backgrounds between the three images are observed to complicate the classification problem. The robustness of the final classifier is improved through the use of a rejection threshold procedure to prevent classification of outlying pixels.
authors: Ohio University, Center for Intelligent Chemical Instrumentation, Department of Chemistry and Biochemistry, Clippinger Laboratories, Athens, Ohio 45701, USA.
authors: Lin
authors: L
authors: Zhang
authors: Gary W
authors: GW
authors: Small
authors: Abigail S
authors: AS
authors: Haka
authors: Linda H
authors: LH
authors: Kidder
authors: E Neil
authors: EN
authors: Lewis
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000737:chemistry / Q000145:classification* / Q000175:diagnosis* / Q000473:pathology; D044584:Carcinoma, Ductal / Q000737:chemistry / Q000145:classification / Q000175:diagnosis / Q000473:pathology; D045744:Cell Line, Tumor; D016000:Cluster Analysis; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D008853:Microscopy / Q000379:methods*; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D012016:Reference Values; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D017550:Spectroscopy, Fourier Transform Infrared / Q000379:methods*