Disjunctive normal shape models

Ramesh, Nisha; Mesadi, Fitsum; Çetin, Müjdat; Taşdizen, Tolga

doi:10.1109/isbi.2015.7164170

Cited by 17 publications

(11 citation statements)

References 23 publications

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“…Before applying manifold learning algorithms, we applied the disjunctive normal shape models (DNSM) [12] based algorithm to segment spines. This algorithm exploits DNSM based shape and appearance priors to segment spines with good accuracy [13].…”

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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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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“…The level set f ( x ) = 0.5 is taken to represent the interface between the foreground ( f ( x ) > 0.5) and background ( f ( x ) < 0.5) regions. DNSMs can be used for segmentation by minimizing edge-based and region-based energy terms when no training data are available [5]. The contributions of this paper are the construction of shape and appearance priors for the DNSM from training data and their use in segmentation.…”

Section: Disjunctive Normal Shape Modelmentioning

confidence: 99%

“…We use an implicit and parametric shape model called Disjunctive Normal Shape Models (DNSM) [5], which were previously used for interactive segmentation, to construct novel shape and appearance priors. DNSM’s parametric nature allows the use of a powerful local prior statistics, while its implicit nature removes the need to use landmark points.…”

Section: Introductionmentioning

confidence: 99%

Disjunctive Normal Shape and Appearance Priors with Applications to Image Segmentation

Mesadi

Çetin

Taşdizen

2015

Lecture Notes in Computer Science

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The use of appearance and shape priors in image segmentation is known to improve accuracy; however, existing techniques have several drawbacks. Active shape and appearance models require landmark points and assume unimodal shape and appearance distributions. Level set based shape priors are limited to global shape similarity. In this paper, we present a novel shape and appearance priors for image segmentation based on an implicit parametric shape representation called disjunctive normal shape model (DNSM). DNSM is formed by disjunction of conjunctions of half-spaces defined by discriminants. We learn shape and appearance statistics at varying spatial scales using nonparametric density estimation. Our method can generate a rich set of shape variations by locally combining training shapes. Additionally, by studying the intensity and texture statistics around each discriminant of our shape model, we construct a local appearance probability map. Experiments carried out on both medical and natural image datasets show the potential of the proposed method.

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Disjunctive normal shape models

Cited by 17 publications

References 23 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 and Appearance Priors with Applications to Image Segmentation

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