Bounded Labeling Function for Global Segmentation of Multi-part Objects with Geometric Constraints

Nosrati, Masoud S.; Andrews, Shawn; Hamarneh, Ghassan

doi:10.1109/iccv.2013.254

Cited by 9 publications

(7 citation statements)

References 31 publications

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“…When manually segmenting an image, a clinician often must rely on their prior knowledge of anatomy in order to distinguish different structures; automated segmentation methods must somehow encode similar anatomical information to achieve adequate accuracy. For example, Yazdanpanah et al and Garvin et al [1], [2], encoded spatial relationships between retinal layers S. Andrews [3], [4] encoded containment and exclusion constraints in level sets and graph cut segmentation frameworks, respectively, and applied their methods to cardiac, bone, microscopy, and other segmentation tasks.…”

Section: Introductionmentioning

confidence: 99%

The Generalized Log-Ratio Transformation: Learning Shape and Adjacency Priors for Simultaneous Thigh Muscle Segmentation

Andrews

Hamarneh

2015

IEEE Trans. Med. Imaging

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Abstract-We present a novel probabilistic shape representation that implicitly includes prior anatomical volume and adjacency information, termed the generalized log-ratio (GLR) representation. We demonstrate the usefulness of this representation in the task of thigh muscle segmentation. Analysis of the shapes and sizes of thigh muscles can lead to a better understanding of the effects of chronic obstructive pulmonary disease (COPD), which often results in skeletal muscle weakness in lower limbs. However, segmenting these muscles from one another is difficult due to a lack of distinctive features and intermuscular boundaries that are difficult to detect. We overcome these difficulties by building a shape model in the space of GLR representations. We remove pose variability from the model by employing a presegmentation-based alignment scheme. We also design a rotationally invariant random forest boundary detector that learns common appearances of the interface between muscles from training data. We combine the shape model and the boundary detector into a fully automatic globally optimal segmentation technique. Our segmentation technique produces a probabilistic segmentation that can be used to generate uncertainty information, which can be used to aid subsequent analysis. Our experiments on challenging 3D magnetic resonance imaging data sets show that the use of the GLR representation improves the segmentation accuracy, and yields an average Dice similarity coefficient of 0.808 ± 0.074, comparable to other state-of-the-art thigh segmentation techniques.

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Section: Introductionmentioning

confidence: 99%

The Generalized Log-Ratio Transformation: Learning Shape and Adjacency Priors for Simultaneous Thigh Muscle Segmentation

Andrews

Hamarneh

2015

IEEE Trans. Med. Imaging

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“…The particular novelty of this article in contrast to previous discrete or continuous optimization approaches to semantic segmentation (Bergbauer et al 2013;Delong and Boykov 2009;Ladicky et al 2010;Nosrati et al 2013;Souiai et al 2013a, b;Strekalovskiy et al 2012) is the introduction of midrange geometric constraints between regions concerning relative location, distances and directions. Ladicky et al (2010) and Souiai et al (2013b) introduced co-occurrence priors into semantic segmentation which penalize the simultaneous occurrence of specific label combinations within the same image.…”

Section: Related Workmentioning

confidence: 94%

Midrange Geometric Interactions for Semantic Segmentation

2015

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In this article we introduce the concept of midrange geometric constraints into semantic segmentation. We call these constraints 'midrange' since they are neither global constraints, which take into account all pixels without any spatial limitation, nor are they local constraints, which only regard single pixels or pairwise relations. Instead, the proposed constraints allow to discourage the occurrence of labels in the vicinity of each other, e.g., 'wolf' and 'sheep'. 'Vicinity' encompasses spatial distance as well as specific spatial directions simultaneously, e.g., 'plates' are found directly above 'tables', but do not fly over them. It is up to the user to specifically define the spatial extent of the constraint between each two labels. Such constraints are not only interesting for scene segmentation, but also for partbased articulated or rigid objects. The reason is that object parts such as for example arms, torso and legs usually obey specific spatial rules, which are among the few things that remain valid for articulated objects over many images and which can be expressed in terms of the proposed midrange constraints, i.e. closeness and/or direction. We show, how midrange geometric constraints are formulated within a continuous multi-label optimization framework, and we give a convex relaxation, which allows us to find globally optimal Communicated by solutions of the relaxed problem independent of the initialization.

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“…Schmidt et al [56] modified [18] by adding the Hausdorff distance prior to the MRF-based segmentation framework to impose maximum distance constraint. Inspired by [18], [61], Nosrati et al [45] proposed a method to encode containment and detachment, between different regions with a specified minimum distance between their boundaries in the continuous domain. Their approach guarantees the global optimal solution using functional lifting technique.…”

Section: A Related Workmentioning

confidence: 99%

Local Optimization Based Segmentation of Spatially-Recurring, Multi-Region Objects With Part Configuration Constraints

Nosrati

Hamarneh

2014

IEEE Trans. Med. Imaging

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The level set framework has been a popular medical image segmentation technique for many years due to its several advantages, such as parametrization independence, ease of implementation , extendibility from a curve in 2D to higher dimensions , and automatic handling of topological changes. However, existence of noise, low contrast and objects complexity in medical images cause many segmentation algorithms (including level set-based methods) to fail. Incorporating prior knowledge into image segmentation algorithms has proven useful for obtaining more accurate and plausible results. Two important constraints, containment and exclusion of regions, have gained attention in recent years mainly due to their descriptive power and intuitive definitions. In this paper, we augment the level set framework with the ability to handle these two intuitive geometric relationships, containment and exclusion, along with a distance constraint between boundaries of multi-region objects. Level set's important property of automatically handling topological changes of evolving contours/surfaces enables us to segment spatially-recurring objects (e.g. multiple instances of multi-region cells in a large microscopy image) while satisfying the two aforementioned constraints. In addition, the level set approach gives us a very simple and natural way to compute the distance between contours/surfaces and impose constraints on it. The downside, however, is a local optimization framework in which the final segmentation solution depends on the initialization. In fact, here, we sacrifice the optimizibility (local instead of global solution) in exchange for lower space complexity (less memory usage) and faster runtime (especially for large microscopic images) as well as no grid artifacts. Nevertheless, the result from validating our method on synthetic and several biomedical applications, mainly on multi-region cell segmentation in microscopy images and cardiac segmentation in MR images, showed the utility and advantages of this augmented level set framework (even with fully automatic or rough initialization that is distant from the desired boundaries). We also compared our framework with its counterpart methods in the discrete domain and reported the pros and cons of each of these methods in terms of metrication error and efficiency in memory usage and runtime.

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Bounded Labeling Function for Global Segmentation of Multi-part Objects with Geometric Constraints

Cited by 9 publications

References 31 publications

The Generalized Log-Ratio Transformation: Learning Shape and Adjacency Priors for Simultaneous Thigh Muscle Segmentation

The Generalized Log-Ratio Transformation: Learning Shape and Adjacency Priors for Simultaneous Thigh Muscle Segmentation

Midrange Geometric Interactions for Semantic Segmentation

Local Optimization Based Segmentation of Spatially-Recurring, Multi-Region Objects With Part Configuration Constraints

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