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Proceedings Paper

Feature measures for the segmentation of neuronal membrane using a machine learning algorithm
Author(s): Saadia Iftikhar; Afzal Godil
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Paper Abstract

In this paper, we present a Support Vector Machine (SVM) based pixel classifier for a semi-automated segmentation algorithm to detect neuronal membrane structures in stacks of electron microscopy images of brain tissue samples. This algorithm uses high-dimensional feature spaces extracted from center-surrounded patches, and some distinct edge sensitive features for each pixel in the image, and a training dataset for the segmentation of neuronal membrane structures and background. Some threshold conditions are later applied to remove small regions, which are below a certain threshold criteria, and morphological operations, such as the filling of the detected objects, are done to get compactness in the objects. The performance of the segmentation method is calculated on the unseen data by using three distinct error measures: pixel error, wrapping error, and rand error, and also a pixel by pixel accuracy measure with their respective ground-truth. The trained SVM classifier achieves the best precision level in these three distinct errors at 0.23, 0.016 and 0.15, respectively; while the best accuracy using pixel by pixel measure reaches 77% on the given dataset. The results presented here are one step further towards exploring possible ways to solve these hard problems, such as segmentation in medical image analysis. In the future, we plan to extend it as a 3D segmentation approach for 3D datasets to not only retain the topological structures in the dataset but also for the ease of further analysis.

Paper Details

Date Published: 24 December 2013
PDF: 5 pages
Proc. SPIE 9067, Sixth International Conference on Machine Vision (ICMV 2013), 90670V (24 December 2013); doi: 10.1117/12.2050933
Show Author Affiliations
Saadia Iftikhar, National Institute of Standards and Technology (United States)
Afzal Godil, National Institute of Standards and Technology (United States)

Published in SPIE Proceedings Vol. 9067:
Sixth International Conference on Machine Vision (ICMV 2013)
Branislav Vuksanovic; Antanas Verikas; Jianhong Zhou, Editor(s)

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