Abstract
Superpixel algorithms have proven to be a useful initial step for segmentation and subsequent processing of images, reducing computational complexity by replacing the use of expensive per-pixel primitives with a higher-level abstraction, superpixels. They have been successfully applied both in the context of traditional image analysis and deep learning based approaches. In this work, we present a general- ized implementation of the simple linear iterative clustering (SLIC) superpixel algorithm that has been generalized for n-dimensional scalar and multi-channel images. Additionally, the standard iterative im- plementation is replaced by a parallel, multi-threaded one. We describe the implementation details and analyze its scalability using a strong scaling formulation. Quantitative evaluation is performed using a 3D image, the Visible Human cryosection dataset, and a 2D image from the same dataset. Results show good scalability with runtime gains even when using a large number of threads that exceeds the physical number of available cores (hyperthreading).
Highlights
Pixels, or voxels in three dimensions, are the basic primitive of an image, usually defining a rectilinear grid
Superpixel labeled images are included for qualitative evaluation from a selection of datasets to represent some of the diverse image types the Scalable SLIC (SSLIC) algorithm is capable of operating upon
In this work we presented SSLIC, an Insight Segmentation and Registration Toolkit (ITK) based extension of the Simple Linear Iterative Clustering (SLIC) algorithm that accommodates ndimensional scalar and multi-channel images and parallelizes the original sequential implementation
Summary
Voxels in three dimensions, are the basic primitive of an image, usually defining a rectilinear grid. Superpixels reduce the number of primitives representing an image by grouping pixels based on low level features, properties such as color, texture and physical proximity. Introduced in [13] as a method for reducing the complexity of higher-level image analysis tasks, they have been successfully used in many computer vision tasks such as object detection, depth estimation, and segmentation. The SLIC algorithm has been used both in the context of classical image analysis algorithms and in the context of deep learning. Examples of using the SLIC algorithm in combination with deep learning include segmentation of the pancreas in CT [6], general salient object detection in color pictures [8], hyperspectral image classification [14], detection of cell nuclei in digital histology slides [15], and classification of epithelial and stromal regions in histopathology images [20]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
