In this paper, a set of scalable discrepancy measures is applied in the context of computer vision. These measures allow tuning of edge detectors and segmentation evaluation when a reference is known. Thanks to a scale parameter in an adjustable area the proposed measures allows to weight the importance of over-detection as well as under-detection. They give the intensity of the discrepancy and its relative position.
In this paper, we investigate the application of fractal concept to the coding of medical images, taking into account the self-similarity at different scales. The approach proposed by Jacquin is very flexible, enabling us to optimize different steps of the associated algorithm. We remark that the choice of the distance used to measure the self-similarity between the range block Ri and the estimated range block Ri is essential to the algorithm. In fact, the choice of the metric determines the optimal parameters of the affine transformation. In our study, we propose two metrics, L2 and L(infinity ) and compare their performances. We also develop a simple fast decoding scheme, necessary for a clinical use. This paper addresses the adaptation of the fractal compression algorithm to medical image modalities. We present the results obtained with two image data bases (numerized mammograms and x-ray angiograms). A comparison with JPEG results shows the improvement with our technique, particularly for low bit rates.
An original 3D subband coding scheme based on a separable 3D wavelet transform is proposed. The 3D images (volumes) are produced with a new true 3D x ray scanner called `Morphometer.' The Morphometer can generate 2563 discrete volumes with isotropic voxels of 356 microns. A separable 3D wavelet decomposes the original volume. A distortion minimization algorithm selects the best number of decompositions and chooses for each subvolume the most appropriate quantization approach.
This work concerns the compression of x-ray images of the breast using an original coding method based on a 2D wavelet transform. A tree-structured analysis/reconstruction system is used with Daubechies wavelet filters in order to decompose the original image into subbands; then we code only the low resolution subimage with the JPEG algorithm. This hybrid method has been compared with the JPEG approach, and results are proposed for different compression ratio.
Conference Committee Involvement (6)
Three-Dimensional Image Processing, Measurement (3DIPM), and Applications 2015
10 February 2015 | San Francisco, California, United States
3D Image Processing, Measurement (3DIPM), and Applications 2014
5 February 2014 | San Francisco, California, United States
3D Image Processing (3DIP) and Applications 2013
6 February 2013 | Burlingame, California, United States
3D Image Processing (3DIP) and Applications 2012
24 January 2012 | Burlingame, California, United States
3D Image Processing (3DIP) and Applications II
26 January 2011 | San Francisco Airport, California, United States
3D Image Processing (3DIP) and Applications
18 January 2010 | San Jose, California, United States
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.