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AdaSLIC: adaptive supervoxel generation for volumetric medical images

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Abstract

In the last decade, supervoxels have become a useful mid-level representation of volumetric medical images such as MRIs and CT scans. Several methods were suggested to produce uniform supervoxels, yet little has been done to generate content-sensitive over-segmentations. This is particularly beneficial to 3D medical image analysis, where sizes of anatomical structures vary largely. In this paper, we propose AdaSLIC as an adaptive supervoxel generation technique that applies to volumetric medical images. In small structures, it generates tiny supervoxels to capture the details of the image. Meanwhile, it partitions large structures into bigger supervoxels, hence leading to a sparse description. The proposed technique is an extension of the Simple Linear Iterative Clustering (SLIC) algorithm. Rather than using a regular sampling to initiate supervoxel centers, a content-sensitive initialization is performed using a Poisson-disk sampling algorithm (PDS). It relies on a map of distances to the main image contours. The size of each supervoxel depends on the distance of its center to the closest image contour. We compare our algorithm to the SLIC algorithm as well as to an extension of the DBSCAN algorithm (Density-Based Spatial Clustering of Applications with Noise). Two datasets are used for this purpose: knee MRIs and cardiovascular magnetic resonance (CMR) images. We use different metrics to assess the quality of the generated over-segmentations. Experimental results show that our algorithm achieves comparable or better boundary adherence than the state of the art algorithms while producing compact and adaptive supervoxels.

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This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Ecole Nationale d’Ingénieurs de Tunis, Laboratoire de Signaux et Systèmes, Université de Tunis El Manar, Tunis, Tunisie

    Amal Amami, Zouhour Ben Azouz & Monia Turki-Hadj Alouane

  2. Intitut Supérieur d’Informatique, Université de Tunis El Manar, Tunis, Tunisie

    Zouhour Ben Azouz

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  1. Amal Amami
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  2. Zouhour Ben Azouz
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Correspondence to Amal Amami.

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Amami, A., Azouz, Z.B. & Alouane, M.TH. AdaSLIC: adaptive supervoxel generation for volumetric medical images. Multimed Tools Appl 78, 3723–3745 (2019). https://doi.org/10.1007/s11042-017-5563-3

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