imsegm package

Module contents

# Image segmentation - general superpixel segmentation & region growing

This package is aiming at (un/semi)supervised segmentation on superpixels with
computing some basic colour and texture features. This general segmentation can be followed by an object centre detection and proximate ellipse fitting to expected object boundaries. Last included method is region growing with learned shape prior also running on superpixel grid.
The package contains several low-level Cython implementation to speed up some
feature extraction methods.
Overall the project/repository contains example codes with visualisation
in ipython notebooks and experiments required for replicating all published results.

## Superpixel segmentation with GraphCut regularisation Image segmentation is widely used as an initial phase of many image processing

tasks in computer vision and image analysis. Many recent segmentation methods use superpixels because they reduce the size of the segmentation problem by order of magnitude. Also, features on superpixels are much more robust than features on pixels only. We use spatial regularisation on superpixels to make segmented regions more compact. The segmentation pipeline comprises (i) computation of superpixels; (ii) extraction of descriptors such as colour and texture; (iii) soft classification, using a standard classifier for supervised learning, or the Gaussian Mixture Model for unsupervised learning; (iv) final segmentation using Graph Cut. We use this segmentation pipeline on real-world applications in medical imaging. We also show that unsupervised segmentation is sufficient for some situations, and provides similar results to those obtained using trained segmentation.

## Object centre detection and Ellipse approximation An image processing pipeline to detect and localize Drosophila egg chambers that

consists of the following steps: (i) superpixel-based image segmentation into relevant tissue classes (see above); (ii) detection of egg center candidates using label histograms and ray features; (iii) clustering of center candidates and; (iv) area-based maximum likelihood ellipse model fitting.

## Superpixel Region Growing with Shape prior Region growing is a classical image segmentation method based on hierarchical

region aggregation using local similarity rules. Our proposed approach differs from standard region growing in three essential aspects. First, it works on the level of superpixels instead of pixels, which leads to a substantial speedup. Second, our method uses learned statistical shape properties which encourage growing leading to plausible shapes. In particular, we use ray features to describe the object boundary. Third, our method can segment multiple objects and ensure that the segmentations do not overlap. The problem is represented as energy minimisation and is solved either greedily, or iteratively using GraphCuts.

## References * Borovec J., Svihlik J., Kybic J., Habart D. (2017). Supervised and unsupervised

segmentation using superpixels, model estimation, and Graph Cut. SPIE Journal of Electronic Imaging 26(6), 061610. DOI: 10.1117/1.JEI.26.6.061610.
  • Borovec J., Kybic J., Nava R. (2017) Detection and Localization of Drosophila
Egg Chambers in Microscopy Images. In: Wang Q., Shi Y., Suk HI., Suzuki K. (eds) Machine Learning in Medical Imaging. MLMI 2017. LNCS, vol 10541. Springer, Cham. DOI: 10.1007/978-3-319-67389-9_3.
  • Borovec J., Kybic J., Sugimoto, A. (2017). Region growing using superpixels
with learned shape prior. SPIE Journal of Electronic Imaging 26(6), 061611. DOI: 10.1117/1.JEI.26.6.061611.