Using Subspace Multiple Linear Regression for 3D Face Shape Prediction from a Single Image

Castelán, Mario; Puerto-Souza, Gustavo A.; Horebeek, Johan Van

doi:10.1007/978-3-642-10520-3_63

Cited by 6 publications

(6 citation statements)

References 18 publications

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“…The linear prediction functions undertake to model data, while the unknown parameters are estimated from the data [88]. Every single observation y i is defined as…”

Section: Multiple Linear Regression (Mlr)mentioning

confidence: 99%

A survey of prostate modeling for image analysis

Chilali¹,

Ouzzane²,

Diaf³

et al. 2014

Computers in Biology and Medicine

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Computer technology is widely used for multimodal image analysis of the prostate gland. Several techniques have been developed, most of which incorporate a priori knowledge extracted from organ features. Knowledge extraction and modeling are multi-step tasks. Here, we review these steps and classify the modeling according to the data analysis methods employed and the features used. We conclude with a survey of some clinical applications where these techniques are employed.

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“…The linear prediction functions undertake to model data, while the unknown parameters are estimated from the data [88]. Every single observation y i is defined as…”

Section: Multiple Linear Regression (Mlr)mentioning

confidence: 99%

A survey of prostate modeling for image analysis

Chilali¹,

Ouzzane²,

Diaf³

et al. 2014

Computers in Biology and Medicine

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“…The outcome of PLS has also been explored in threedimensional (3D) face prediction in the form of depth values from greyscale values [18,19]. Despite obtaining encouraging results, the main disadvantage of the method is the loss of fine surface detail in the final 3D face shape prediction because of the high two-dimensional spatial variability caused by misalignment errors, specially around the nose, eye and mouth areas.…”

Section: Introductionmentioning

confidence: 98%

Face colour synthesis using partial least squares and the luminance-α-β colour transform

2012

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For many tasks, it is necessary to synthesise realistic colour in faces from greyscale values. This is the problem the authors address in this study. Rather than propagating colour information in some regions of the image or transferring colour from an image source to a greyscale using some corresponding criterion, as many colouring systems attempt to do, they seek to synthesise facial colour information using a database of examples. This methodology is divided into two main stages. In the first stage the facial skin tone is predicted through the multiple linear regression method known as partial least squares. This regression allows to define a linear transformation between facial greyscale and colour subspaces. The second stage involves the luminance-a-b (Lab) colour transform which is responsible for the recovery of the fine facial detail. The core of the proposed methodology is the combination of statistical subspace analysis with the appropriate colour transform so as to produce realistic facial colourisation results in a direct manner.

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“…CCA is used to model the canonical correlation between the prostate boundary correspondences and the nonboundary regional correspondences. [31][32][33][34][35][36] Before performing CCA, PCA should be first performed on boundary correspondences and regional correspondences in order to get a compact representation for fC i j g and {R i j }, respectively, as described by Eqs. (1) and (2) below.…”

Section: Cca-based Correlation Modelmentioning

confidence: 99%

“…For example, Reiter et al 35 proposed to estimate face-depth maps from color face images based on CCA, which exploits the correlation between face color texture and surface depth. Castelan et al 36 compared four different subspace multiple linear regression (MLR) methods for 3D face shape prediction from a single 2D intensity image, and then principal component regression (PCR), partial least squares (PLS), CCA, and ridge regression (RR) were used to estimate a regression operator while maximizing specific criteria between 2D and 3D face subspaces. In our study, three types of MLR methods, namely CCA, PCR and RR, are used and compared to learn the statistical deformation correlation between prostate boundary and nonboundary regions.…”

Section: Introductionmentioning

confidence: 99%

“…[31][32][33][34][35][36] For example, Davatzikos et al 32 presented a purely shape-based framework for capturing the principal modes of covariation between anatomy and deformation in principal component analysis (PCA) formulation, in order to statistically represent deformability induced by tumor growth. Thus, when a patient's anatomy is considered, it can be used in conjunction with the statistical model to predict the way in which the anatomy will deform.…”

Section: Introductionmentioning

confidence: 99%

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Learning statistical correlation for fast prostate registration in image‐guided radiotherapy

Shi¹,

Liao²,

Shen³

2011

Medical Physics

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Purpose: In adaptive radiation therapy of prostate cancer, fast and accurate registration between the planning image and treatment images of the patient is of essential importance. With the authors' recently developed deformable surface model, prostate boundaries in each treatment image can be rapidly segmented and their correspondences (or relative deformations) to the prostate boundaries in the planning image are also established automatically. However, the dense correspondences on the nonboundary regions, which are important especially for transforming the treatment plan designed in the planning image space to each treatment image space, are remained unresolved. This paper presents a novel approach to learn the statistical correlation between deformations of prostate boundary and nonboundary regions, for rapidly estimating deformations of the nonboundary regions when given the deformations of the prostate boundary at a new treatment image. Methods: The main contributions of the proposed method lie in the following aspects. First, the statistical deformation correlation will be learned from both current patient and other training patients, and further updated adaptively during the radiotherapy. Specifically, in the initial treatment stage when the number of treatment images collected from the current patient is small, the statistical deformation correlation is mainly learned from other training patients. As more treatment images are collected from the current patient, the patient-specific information will play a more important role in learning patient-specific statistical deformation correlation to effectively reflect prostate deformation of the current patient during the treatment. Eventually, only the patient-specific statistical deformation correlation is used to estimate dense correspondences when a sufficient number of treatment images have been acquired from the current patient. Second, the statistical deformation correlation will be learned by using a multiple linear regression (MLR) model, i.e., ridge regression (RR) model, which has the best prediction accuracy than other MLR models such as canonical correlation analysis (CCA) and principal component regression (PCR). Results: To demonstrate the performance of the proposed method, we first evaluate its registration accuracy by comparing the deformation field predicted by our method with the deformation field estimated by the thin plate spline (TPS) based correspondence interpolation method on 306 serial prostate CT images of 24 patients. The average predictive error on the voxels around 5 mm of prostate boundary is 0.38 mm for our method of RR-based correlation model. Also, the corresponding maximum error is 2.89 mm. We then compare the speed for deformation interpolation by different methods. When considering the larger region of interest (ROI) with the size of 512 Â 512 Â 61, our method takes 24.41 seconds to interpolate the dense deformation field while TPS method needs 6.7 minutes; when considering a small ROI (surrounding prostate) with size of 1...

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Using Subspace Multiple Linear Regression for 3D Face Shape Prediction from a Single Image

Cited by 6 publications

References 18 publications

A survey of prostate modeling for image analysis

A survey of prostate modeling for image analysis

Face colour synthesis using partial least squares and the luminance-α-β colour transform

Learning statistical correlation for fast prostate registration in image‐guided radiotherapy

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