Computer-aided prognosis of neuroblastoma on whole-slide images: Classification of stromal development

Authors:
O. Sertel;J. Kong;H. Shimada;U. V. Catalyurek;J. H. Saltz;M. N. Gurcan
Affiliations:
Department of Electrical and Computer Engineering, The Ohio State University, 2015 Neil Ave, Columbus, OH 43210, USA and Department of Biomedical Informatics, The Ohio State University, 3190 Grave ...;Department of Electrical and Computer Engineering, The Ohio State University, 2015 Neil Ave, Columbus, OH 43210, USA and Department of Biomedical Informatics, The Ohio State University, 3190 Grave ...;Department of Pathology and Laboratory Medicine, Childrens Hospital Los Angeles and The University of Southern California Keck School of Medicine, 4650 Sunset Boulevard, Los Angeles, CA 90027, USA;Department of Biomedical Informatics, The Ohio State University, 3190 Graves Hall, Columbus, OH 43210, USA and Department of Electrical and Computer Engineering, The Ohio State University, 2015 Ne ...;Department of Biomedical Informatics, The Ohio State University, 3190 Graves Hall, Columbus, OH 43210, USA;Department of Biomedical Informatics, The Ohio State University, 3190 Graves Hall, Columbus, OH 43210, USA
Venue:
Pattern Recognition
Year:
2009

Citing 11
Cited 8

Introduction to statistical pattern recognition (2nd ed.)

Introduction to statistical pattern recognition (2nd ed.)
Texture analysis

Handbook of pattern recognition & computer vision
Floating search methods in feature selection

Pattern Recognition Letters
Feature Selection: Evaluation, Application, and Small Sample Performance

IEEE Transactions on Pattern Analysis and Machine Intelligence
Image-guided decision support system for pathology

Machine Vision and Applications
Statistical Pattern Recognition: A Review

IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns

IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient processing of pathological images using the grid: computer-aided prognosis of neuroblastoma

Proceedings of the 5th IEEE workshop on Challenges of large applications in distributed environments
Pathological Image Analysis Using the GPU: Stroma Classification for Neuroblastoma

BIBM '07 Proceedings of the 2007 IEEE International Conference on Bioinformatics and Biomedicine
Stroma classification for neuroblastoma on graphics processors

International Journal of Data Mining and Bioinformatics
Perceptually uniform color spaces for color texture analysis: an empirical evaluation

IEEE Transactions on Image Processing

Texture measures combination for improved meningioma classification of histopathological images

Pattern Recognition
Wavelet-based multiscale texture segmentation: Application to stromal compartment characterization on virtual slides

Signal Processing
Run-time optimizations for replicated dataflows on heterogeneous environments

Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
Unsupervised segmentation for inflammation detection in histopathology images

ICISP'10 Proceedings of the 4th international conference on Image and signal processing
Tissue object patterns for segmentation in histopathological images

Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
Improving performance of adaptive component-based dataflow middleware

Parallel Computing
Optimizing dataflow applications on heterogeneous environments

Cluster Computing
A survey of pipelined workflow scheduling: Models and algorithms

ACM Computing Surveys (CSUR)

Quantified Score

Hi-index	0.01

Visualization

Abstract

We are developing a computer-aided prognosis system for neuroblastoma (NB), a cancer of the nervous system and one of the most malignant tumors affecting children. Histopathological examination is an important stage for further treatment planning in routine clinical diagnosis of NB. According to the International Neuroblastoma Pathology Classification (the Shimada system), NB patients are classified into favorable and unfavorable histology based on the tissue morphology. In this study, we propose an image analysis system that operates on digitized H&E stained whole-slide NB tissue samples and classifies each slide as either stroma-rich or stroma-poor based on the degree of Schwannian stromal development. Our statistical framework performs the classification based on texture features extracted using co-occurrence statistics and local binary patterns. Due to the high resolution of digitized whole-slide images, we propose a multi-resolution approach that mimics the evaluation of a pathologist such that the image analysis starts from the lowest resolution and switches to higher resolutions when necessary. We employ an offline feature selection step, which determines the most discriminative features at each resolution level during the training step. A modified k-nearest neighbor classifier is used to determine the confidence level of the classification to make the decision at a particular resolution level. The proposed approach was independently tested on 43 whole-slide samples and provided an overall classification accuracy of 88.4%.