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CIALAB Neuroblastoma research to be published in the Pattern Recognition Journal

The article entitled, "Computer-aided prognosis of neuroblastoma onwhole-slide images: Classification of stromal development" has been accepted for publication in the Special Issue on Digital Image Processing and Pattern Recognition Techniques for the Detection of Cancer in Pattern Recognition, one of the most prestigious journal of the Pattern Recognition Society. This study is a part of the computerized neuroblastoma prognosis research being conducted in the CIALAB since 2007. The goal is to develop an image analysis system that will be used in clinical practice to aid pathologists. Subscribers can access the full text online at http://dx.doi.org/10.1016/j.patcog.2008.08.027. The publication abstract appears below.

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

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%.

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