The Laplacian Pyramid as a Compact Image Code

Burt, Peter J.; Adelson, Edward H.

doi:10.1016/b978-0-08-051581-6.50065-9

Cited by 1,025 publications

(1,215 citation statements)

References 6 publications

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“…2. At the end of each iteration of the SART, each reconstructed DBT slice is separated into high (HF) and low frequency (LF) bands using Laplacian pyramid decomposition (Burt and Adelson, 1983) in three scales. The resulting high frequency bands consist of noise and small structures including microcalcifications.…”

Section: Methodsmentioning

confidence: 99%

Digital breast tomosynthesis: computer-aided detection of clustered microcalcifications on planar projection images

Samala

Chan

et al. 2014

Phys. Med. Biol.

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This paper describes a new approach to detection of microcalcification clusters (MCs) in digital breast tomosynthesis (DBT) via its planar projection (PPJ) image. With IRB approval, two-view (cranio-caudal and mediolateral oblique views) DBTs of human subject breasts were obtained with a GE GEN2 prototype DBT system that acquires 21 projection angles spanning 600 in 30 increments. A data set of 307 volumes (154 human subjects) was divided by case into independent training (127 with MCs) and test sets (104 with MCs and 76 free of MCs). Simultaneous algebraic reconstruction technique with multiscale bilateral filtering (MSBF) regularization was used to enhance microcalcifications and suppress noise. During the MSBF regularized reconstruction, the DBT volume was separated into high frequency (HF) and low frequency components representing microcalcifications and larger structures. At the final iteration, maximum intensity projection was applied to the regularized HF volume to generate a PPJ image that contained MCs with increased contrast-to-noise ratio (CNR) and reduced search space. High CNR objects in the PPJ image were extracted and labeled as microcalcification candidates. Convolution neural network (CNN) trained to recognize the image pattern of microcalcifications was used to classify the candidates into true calcifications and tissue structures and artifacts. The remaining microcalcification candidates were grouped into MCs by dynamic conditional clustering based on adaptive CNR threshold and radial distance criteria. False positive (FP) clusters were further reduced using the number of candidates in a cluster, CNR and size of microcalcification candidates. At 85% sensitivity an FP rate of 0.71 and 0.54 was achieved for view- and case-based sensitivity, respectively, compared to 2.16 and 0.85 achieved in DBT. The improvement was significant (p-value = 0.003) by JAFROC analysis.

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Section: Methodsmentioning

confidence: 99%

Digital breast tomosynthesis: computer-aided detection of clustered microcalcifications on planar projection images

Samala

Chan

et al. 2014

Phys. Med. Biol.

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“…Inspired by Grenander’s [22] analysis by synthesis theory of inference, and by Burt and Adelson’s [23] Laplacian pyramid for efficient coding, Mumford [24] proposed that the visual hierarchy might form an efficient image pyramid, and that vision is accomplished by an analysis process and a synthesis process in a close loop. Here, the feedforward computation extracts features and proposals, and the feedback computation synthesizes expectations based on the high level of interpretation of the proposals using the internal models.…”

Section: Varieties Of Internal Modelsmentioning

confidence: 99%

The Visual System's Internal Model of the World

Lee

2015

Proc. IEEE

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The Bayesian paradigm has provided a useful conceptual theory for understanding perceptual computation in the brain. While the detailed neural mechanisms of Bayesian inference are not fully understood, recent computational and neurophysiological works have illuminated the underlying computational principles and representational architecture. The fundamental insights are that the visual system is organized as a modular hierarchy to encode an internal model of the world, and that perception is realized by statistical inference based on such internal model. In this paper, I will discuss and analyze the varieties of representational schemes of these internal models and how they might be used to perform learning and inference. I will argue for a unified theoretical framework for relating the internal models to the observed neural phenomena and mechanisms in the visual cortex.

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“…It is mainly required at the disocclusion stage. The coarse-tofine refinement step consists to propagate the displacement field and the self-occlusion map H using pyramid expansion operations [4]. The latter has to be binarized.…”

Section: The Cost Functionmentioning

confidence: 99%

Direct Estimation of Non-Rigid Registrations with Image-Based Self-Occlusion Reasoning

Gripon

Bartoli²,

Sayd

2007

2007 IEEE 11th International Conference on Computer Vision

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The registration problem for images of a deforming surface has been well studied. External occlusions are usually well-handled. In 2D image-based registration, selfocclusions are more challenging. Consequently, the surface is usually assumed to be only slightly self-occluding.This paper is about image-based non-rigid registration with self-occlusion reasoning. A specific framework explicitly modeling self-occlusions is proposed. It is combined with an intensity-based, i.e. direct, data term for registration. Self-occlusions are detected as shrinking areas in the 2D warp.Experimental results on several challenging datasets show that our approach successfully registers images with self-occlusions while effectively detecting the occluded regions.

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The Laplacian Pyramid as a Compact Image Code

Cited by 1,025 publications

References 6 publications

Digital breast tomosynthesis: computer-aided detection of clustered microcalcifications on planar projection images

Digital breast tomosynthesis: computer-aided detection of clustered microcalcifications on planar projection images

The Visual System's Internal Model of the World

Direct Estimation of Non-Rigid Registrations with Image-Based Self-Occlusion Reasoning

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