Disjunctive Normal Shape and Appearance Priors with Applications to Image Segmentation

Mesadi, Fitsum; Çetin, Müjdat; Taşdizen, Tolga

doi:10.1007/978-3-319-24574-4_84

Cited by 17 publications

(13 citation statements)

References 8 publications

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“…This algorithm exploits DNSM based shape and appearance priors to segment spines with good accuracy [13]. This algorithm takes a region-of-interest (ROI) as input.…”

Section: Methodsmentioning

confidence: 99%

On comparison of manifold learning techniques for dendritic spine classification

Ghani

Argunşah

Israely

et al. 2016

2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)

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Dendritic spines are one of the key functional components of neurons. Their morphological changes are correlated with neuronal activity. Neuroscientists study spine shape variations to understand their relation with neuronal activity. Currently this analysis performed manually, the availability of reliable automated tools would assist neuroscientists and accelerate this research. Previously, morphological features based spine analysis has been performed and reported in the literature. In this paper, we explore the idea of using and comparing manifold learning techniques for classifying spine shapes. We start with automatically segmented data and construct our feature vector by stacking and concatenating the columns of images. Further, we apply unsupervised manifold learning algorithms and compare their performance in the context of dendritic spine classification. We achieved 85.95% accuracy on a dataset of 242 automatically segmented mushroom and stubby spines. We also observed that ISOMAP implicitly computes prominent features suitable for classification purposes.

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“…This algorithm exploits DNSM based shape and appearance priors to segment spines with good accuracy [13]. This algorithm takes a region-of-interest (ROI) as input.…”

Section: Methodsmentioning

confidence: 99%

On comparison of manifold learning techniques for dendritic spine classification

Ghani

Argunşah

Israely

et al. 2016

2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)

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“…Especially, performance of the proposed method on segmentation of 3D objects would be worth exploring. One might also consider building a similar approach on a different shape representation than level sets, e.g Disjunctive Normal Shape Models [15,16]. Our approach can also be modified slightly and be used as a joint segmentation and classification approach.…”

Section: Resultsmentioning

confidence: 99%

Nonparametric joint shape and feature priors for segmentation of dendritic spines

Erdil

Rada

Argunşah

et al. 2016

2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)

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Multimodal shape density estimation is a challenging task in many biomedical image segmentation problems. Existing techniques in the literature estimate the underlying shape distribution by extending Parzen density estimator to the space of shapes. Such density estimates are only expressed in terms of distances between shapes which may not be sufficient for ensuring accurate segmentation when the observed intensities provide very little information about the object boundaries. In such scenarios, employing additional shape-dependent discriminative features as priors and exploiting both shape and feature priors can aid to the segmentation process. In this paper, we propose a segmentation algorithm that uses nonparametric joint shape and feature priors using Parzen density estimator. The joint prior density estimate is expressed in terms of distances between shapes and distances between features. We incorporate the learned joint shape and feature prior distribution into a maximum a posteriori estimation framework for segmentation. The resulting optimization problem is solved using active contours. We present experimental results on dendritic spine segmentation in 2-photon microscopy images which involve a multimodal shape density.Index Terms-Nonparametric joint shape and feature priors, Parzen density estimator, multimodal shape density, dendritic spine segmentation

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“…Our contribution in this paper is a new shape model called Disjunctive Normal Shape Boltzmann Machine (DNSBM) which exploits the property of SBM for learning complex binary distributions and the property of DNSM [1] for representing local parts of shapes. DNSM is an implicit and parametric model that represents a shape by a union of convex polytopes.…”

Section: Training Images Samplesmentioning

confidence: 99%

“…j=1 σij , where x = {x, y} for twodimensional (2D) shapes and x = {x, y, z} for three-dimensional (3D) shapes [1]. The only free parameters of the model are δ ijk and cij , which determine the orientation and location of the sigmoid functions (discriminants) that define the half-spaces.…”

Section: Disjunctive Normal Shape Boltzmann Machinementioning

confidence: 99%

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Disjunctive Normal Shape Boltzmann Machine

Erdil

Mesadi

Taşdizen

et al. 2017

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Shape Boltzmann machine (a type of Deep Boltzmann machine) is a powerful tool for shape modelling; however, has some drawbacks in representation of local shape parts. Disjunctive Normal Shape Model (DNSM) is a strong shape model that can effectively represent local parts of objects. In this paper, we propose a new shape model based on Shape Boltzmann Machine and Disjunctive Normal Shape Model which we call Disjunctive Normal Shape Boltzmann Machine (DNSBM). DNSBM learns binary distributions of shapes by taking both local and global shape constraints into account using a type of Deep Boltzmann Machine. The samples generated using DNSBM look realistic. Moreover, DNSBM is capable of generating novel samples that differ from training examples by exploiting the local shape representation capability of DNSM. We demonstrate the performance of DNSBM for shape completion on two different data sets in which exploitation of local shape parts is important for capturing the statistical variability of the underlying shape distributions. Experimental results show that DNSBM is a strong model for representing shapes that are composed of local parts.

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Disjunctive Normal Shape and Appearance Priors with Applications to Image Segmentation

Cited by 17 publications

References 8 publications

On comparison of manifold learning techniques for dendritic spine classification

On comparison of manifold learning techniques for dendritic spine classification

Nonparametric joint shape and feature priors for segmentation of dendritic spines

Disjunctive Normal Shape Boltzmann Machine

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