| Paper: | WA-P8.8 |
| Session: | Biomedical Imaging IV: Segmentation and Quantitative Analysis |
| Time: | Wednesday, September 19, 09:50 - 12:30 |
| Presentation: |
Poster
|
| Title: |
COMPUTER-AIDED GRADING OF NEUROBLASTIC DIFFERENTIATION: MULTI-RESOLUTION AND MULTI-CLASSIFIER APPROACH |
| Authors: |
Jun Kong; The Ohio State University | | |
| | Olcay Sertel; The Ohio State University | | |
| | Hiroyuki Shimada; University of Southern California | | |
| | Kim Boyer; The Ohio State University | | |
| | Joel Saltz; The Ohio State University | | |
| | Metin Gurcan; The Ohio State University | | |
| Abstract: |
In this paper, the development of a computer-aided system for the classification of grade of neuroblastic differentiation is presented. This automated process is carried out within a multi-resolution framework that follows a coarse-to-fine strategy. Additionally, a novel segmentation approach using the Fisher-Rao criterion, embedded in the generic Expectation-Maximization algorithm, is employed. Multiple decisions from a classifier group are aggregated using a two-step classifier combiner that consists of a majority voting process and a weighted sum rule using priori classifier accuracies. The developed system, when tested on 14,616 image tiles, had the best overall accuracy of 96.89%. Furthermore, multi-resolution scheme combined with automated feature selection process resulted in 34% savings in computational costs on average when compared to a previously developed single-resolution system. Therefore, the performance of this system shows good promise for the computer-aided pathological assessment of the neuroblastic differentiation in clinical practice. |
