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Feature Measures for the Segmentation of Neuronal Membrane using Machine Learning Algorithm



Afzal A. Godil, Saadia S. Iftikhar


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, is 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 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, like 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.
Proceedings Title
2013 the 6th International Conference on Machine Vision (ICMV 2013)
Conference Dates
November 15-17, 2013
Conference Location


Neuronal membrane segmentation, machine learning, image morphology, feature selection.


Godil, A. and Iftikhar, S. (2014), Feature Measures for the Segmentation of Neuronal Membrane using Machine Learning Algorithm, 2013 the 6th International Conference on Machine Vision (ICMV 2013), London, -1, [online], (Accessed April 17, 2024)
Created January 6, 2014, Updated February 19, 2017