Wavelet Compression of Active Appearance Models

Wolstenholme, Chris; Ce, Taylor

doi:10.1007/10704282_59

Cited by 21 publications

(18 citation statements)

References 9 publications

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“…Medina et al [16] used AAM to segment prostate in Two Dimensional (2D) TRUS images with an overlap ratio of 96%. However, it is argued by Wolstenholme and Taylor [19] that the time complexity involved with AAM is high and is unsuitable for real time procedures. Instead they proposed to use wavelet coefficients of training images for building the AAM.…”

Section: Introductionmentioning

confidence: 99%

Texture Guided Active Appearance Model Propagation for Prostate Segmentation

Ghose

Oliver

Martí

et al. 2010

Prostate Cancer Imaging. Computer-Aided Diagnosis, Prognosis, and Intervention

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Abstract. Fusion of Magnetic Resonance Imaging (MRI) and TransRectal Ultra Sound (TRUS) images during TRUS guided prostate biopsy improves localization of the malignant tissues. Segmented prostate in TRUS and MRI improve registration accuracy and reduce computational cost of the procedure. However, accurate segmentation of the prostate in TRUS images can be a challenging task due to low signal to noise ratio, heterogeneous intensity distribution inside the prostate, and imaging artifacts like speckle noise and shadow. We propose to use texture features from approximation coefficients of Haar wavelet transform for propagation of a shape and appearance based statistical model to segment the prostate in a multi-resolution framework. A parametric model of the propagating contour is derived from Principal Component Analysis of prior shape and texture informations of the prostate from the training data. The parameters are then modified with prior knowledge of the optimization space to achieve optimal prostate segmentation. The proposed method achieves a mean Dice Similarity Coefficient value of 0.95 ± 0.01, and mean segmentation time of 0.72 ± 0.05 seconds when validated on 25 TRUS images, grabbed from video sequences, in a leaveone-out validation framework. Our proposed model performs computationally efficient accurate prostate segmentation in presence of intensity heterogeneity and imaging artifacts.

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Section: Introductionmentioning

confidence: 99%

Texture Guided Active Appearance Model Propagation for Prostate Segmentation

Ghose

Oliver

Martí

et al. 2010

Prostate Cancer Imaging. Computer-Aided Diagnosis, Prognosis, and Intervention

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“…The model generates localized shape and texture variations. In [6], Wolstenholme and Taylor proposed using Haar-based wavelet compression in Active Appearance Models. A compression ratio of 20:1 was accomplished while maintaining acceptable accuracy of the model.…”

Section: Related Workmentioning

confidence: 99%

“…A compression ratio of 20:1 was accomplished while maintaining acceptable accuracy of the model. In [7], Stegman and Cootes extended the work in 1597 [6] by presenting a wavelet enhanced appearance model using biorthogonal CDF 9-7 wavelet basis. In [8], Larsen et al presented a texture enhanced appearance model using Wedgelets.…”

Section: Related Workmentioning

confidence: 99%

Multiscale Sparse Appearance Modeling and Simulation of Pathological Deformations

Zewail¹,

Hag-ElSafi²

2017

IJIVP

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Machine learning and statistical modeling techniques has drawn much interest within the medical imaging research community. However, clinically-relevant modeling of anatomical structures continues to be a challenging task. This paper presents a novel method for multiscale sparse appearance modeling in medical images with application to simulation of pathological deformations in X-ray images of human spine. The proposed appearance model benefits from the non-linear approximation power of Contourlets and its ability to capture higher order singularities to achieve a sparse representation while preserving the accuracy of the statistical model. Independent Component Analysis is used to extract statistical independent modes of variations from the sparse Contourlet-based domain. The new model is then used to simulate clinically-relevant pathological deformations in radiographic images.

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“…image compression and local textures. In the first approach, wavelet based approaches were proposed to compress the texture to eliminate re dundant pixels with small variances [29]. However, the compression ratio is still limited at the fitting stage.…”

Section: Prior Work On Aamsmentioning

confidence: 99%

Contourlet appearance model for facial age estimation

Luu

Seshadri

Savvides

et al. 2011

2011 International Joint Conference on Biometrics (IJCB)

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In this paper we propose a novel Contourlet Appearance Model (CAM) that is more accurate and faster at localizing facial landmarks than Active Appearance Models (AAMs). Our CAM also has the ability to not only extract holis tic texture information, as AAMs do, but can also extract local texture information using the Nonsubsampled Con tourlet Transform (NSCT). We demonstrate the efficiency of our method by applying it to the problem of facial age es timation. Compared to previously published age estimation techniques, our approach yields more accurate results when tested on various face aging databases.

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Wavelet Compression of Active Appearance Models

Cited by 21 publications

References 9 publications

Texture Guided Active Appearance Model Propagation for Prostate Segmentation

Texture Guided Active Appearance Model Propagation for Prostate Segmentation

Multiscale Sparse Appearance Modeling and Simulation of Pathological Deformations

Contourlet appearance model for facial age estimation

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