title: Image analysis for neuroblastoma classification: segmentation of cell nuclei.
abstract: Neuroblastoma is a childhood cancer of the nervous system. Current prognostic classification of this disease partly relies on morphological characteristics of the cells from H&E-stained images. In this work, an automated cell nuclei segmentation method is developed. This method employs morphological top-hat by reconstruction algorithm coupled with hysteresis thresholding to both detect and segment the cell nuclei. Accuracy of the automated cell nuclei segmentation algorithm is measured by comparing its outputs to manual segmentation. The average segmentation accuracy is 90.24+/-5.14%
authors.affiliation: Biomed. Informatics Dept., Ohio State Univ., Columbus, OH 43210, USA. gurcan.1@osu.edu
authors.forename: Metin N
authors.initials: MN
authors.lastname: Gurcan
authors.forename: Tony
authors.initials: T
authors.lastname: Pan
authors.forename: Hiro
authors.initials: H
authors.lastname: Shimada
authors.forename: Joel
authors.initials: J
authors.lastname: Saltz
mesh_terms: D000465:Algorithms; D001331:Automation; D001932:Brain Neoplasms / Q000175:diagnosis* / Q000473:pathology*; D002467:Cell Nucleus / Q000378:metabolism*; D003937:Diagnosis, Differential; D004867:Equipment Design; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000295:instrumentation; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation* / Q000379:methods*; D008495:Medical Oncology / Q000295:instrumentation* / Q000379:methods; D009447:Neuroblastoma / Q000175:diagnosis* / Q000473:pathology*; D010363:Pattern Recognition, Automated; D011379:Prognosis; D015203:Reproducibility of Results