A General System for Automatic Biomedical Image Segmentation Using Intensity Neighborhoods

Author:

Chen Cheng1,Ozolek John A.2,Wang Wei1,Rohde Gustavo K.134

Affiliation:

1. Department of Biomedical Engineering, Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, PA 15213, USA

2. Department of Pathology, Children’s Hospital of Pittsburgh, Pittsburgh, PA 15224, USA

3. Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA

4. Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA

Abstract

Image segmentation is important with applications to several problems in biology and medicine. While extensively researched, generally, current segmentation methods perform adequately in the applications for which they were designed, but often require extensive modifications or calibrations before being used in a different application. We describe an approach that, with few modifications, can be used in a variety of image segmentation problems. The approach is based on a supervised learning strategy that utilizes intensity neighborhoods to assign each pixel in a test image its correct class based on training data. We describe methods for modeling rotations and variations in scales as well as a subset selection for training the classifiers. We show that the performance of our approach in tissue segmentation tasks in magnetic resonance and histopathology microscopy images, as well as nuclei segmentation from fluorescence microscopy images, is similar to or better than several algorithms specifically designed for each of these applications.

Funder

National Institutes of Health

Publisher

Hindawi Limited

Subject

Radiology Nuclear Medicine and imaging

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