Graph-based compensated wavelet lifting for 3-D+t medical CT data

Lanz, Daniela; Kaup, André

doi:10.1109/pcs.2016.7906385

Cited by 4 publications

(5 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since the matrices F and L are not used in (4), a perfect splitting of nodes should be intended [9]. Considering images as graph signals as introduced in [10] and applying the graph-based wavelet transform, every single pixel of a frame has to be interpreted as a node. Accordingly the intensity values of the pixels are stored in vector X.…”

Section: Graph-based Motion Compensationmentioning

confidence: 99%

Improving mesh-based motion compensation by using edge adaptive graph-based compensated wavelet lifting for medical data sets

Lanz¹,

Schober²

2023

Preprint

View full text Add to dashboard Cite

Medical applications like Computed Tomography (CT) or MagneticResonance Tomography (MRT) often require an efficient scalable representation of their huge output volumes in the further processing chain of medical routine. A downscaled version of such a signal can be obtained by using image and video coders based on wavelet transforms. The visual quality of the resulting lowpass band, which shall be used as a representative, can be improved by applying motion compensation methods during the transform. This paper presents a new approach of using the distorted edge lengths of a mesh-based compensated grid instead of the approximated intensity values of the underlying frame to perform a motion compensation. We will show that an edge adaptive graph-based compensation and its usage for compensated wavelet lifting improves the visual quality of the lowpass band by approximately 2.5 dB compared to the traditional mesh-based compensation, while the additional filesize required for coding the motion information doesn't change.

show abstract

Section: Graph-based Motion Compensationmentioning

confidence: 99%

Improving mesh-based motion compensation by using edge adaptive graph-based compensated wavelet lifting for medical data sets

Lanz¹,

Schober²

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Hence, every node of the even frame f 2t is predicted by a weighted average of its assigned neighboring nodes in frame f 2t−1 . According to [17], an increasing radius for the considered neighborhood of a single node results in an increasing visual quality of the LP subband and decreasing mean energy in the HP subband. However, to assure perfect reconstruction, the chosen neighborhood as well as the prediction and update weights have to be known at the decoder side.…”

Section: B Graph-based Motion Compensationmentioning

confidence: 99%

“…So far, the update step is performed by predicting from f 2t−1 to f 2t [17]. The resulting edge weights are stored in K U and have to be known at decoder side, too.…”

Section: Inversion Of the Graph-based Motion Compensationmentioning

confidence: 99%

“…1) Smoothing of Motion Maps: As described in [17], an increasing radius r results in an increasing visual quality of the LP subband and a decreasing mean energy in the HP subband. However, the variance of the assigned end nodes rises with increasing radius r, which is adversely for encoding the resulting symbol stream.…”

Section: B Construction Of Motion Mapsmentioning

confidence: 99%

“…However, a further improvement of the data fidelity without a major increase of the required bit rate is desirable. By applying graph-based MC as introduced in [17] instead of mesh-based MC in the lifting structure of the MCTF coder, the geometric structure of the data can directly be incorporated into the WT [18] and allows for a superior visual quality of the LP subband. The motion information used for prediction in graph-based MC is stored in adjacency matrices.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Graph-Based Compensated Wavelet Lifting for Scalable Lossless Coding of Dynamic Medical Data

Lanz¹,

Schober²

2023

Preprint

View full text Add to dashboard Cite

Lossless compression of dynamic 2-D+t and 3-D+t medical data is challenging regarding the huge amount of data, the characteristics of the inherent noise, and the high bit depth. Beyond that, a scalable representation is often required in telemedicine applications. Motion Compensated Temporal Filtering works well for lossless compression of medical volume data and additionally provides temporal, spatial, and quality scalability features. To achieve a high quality lowpass subband, which shall be used as a downscaled representative of the original data, graph-based motion compensation was recently introduced to this framework. However, encoding the motion information, which is stored in adjacency matrices, is not well investigated so far. This work focuses on coding these adjacency matrices to make the graph-based motion compensation feasible for data compression. We propose a novel coding scheme based on constructing socalled motion maps. This allows for the first time to compare the performance of graph-based motion compensation to traditional block-and mesh-based approaches. For high quality lowpass subbands our method is able to outperform the block-and meshbased approaches by increasing the visual quality in terms of PSNR by 0.53 dB and 0.28 dB for CT data, as well as 1.04 dB and 1.90 dB for MR data, respectively, while the bit rate is reduced at the same time.

show abstract

Investigation of PCA as a compression pre-processing tool for X-ray image classification

Doorsamy

Rameshar

2021

Neural Comput & Applic

View full text Add to dashboard Cite

Graph-based compensated wavelet lifting for 3-D+t medical CT data

Cited by 4 publications

References 12 publications

Improving mesh-based motion compensation by using edge adaptive graph-based compensated wavelet lifting for medical data sets

Improving mesh-based motion compensation by using edge adaptive graph-based compensated wavelet lifting for medical data sets

Graph-Based Compensated Wavelet Lifting for Scalable Lossless Coding of Dynamic Medical Data

Investigation of PCA as a compression pre-processing tool for X-ray image classification

Contact Info

Product

Resources

About