In this paper, we present a new non-rigid target tracking method within 2D ultrasound (US) image sequence. Due to the poor quality of US images, the motion tracking of a tumor or cyst during needle insertion is considered as an open research issue. Our approach is based on well-known compression algorithm in order to make our method work in real-time which is a necessary condition for many clinical applications. Toward that end, we employed a dedicated hierarchical grid interpolation algorithm (HGI) which can represent a large variety of deformations compared to other motion estimation algorithms such as Overlapped Block Motion Compensation (OBMC), or Block Motion Algorithm (BMA). The sum of squared difference of image intensity is selected as similarity criterion because it provides a good trade-off between computation time and motion estimation quality. Contrary to the others methods proposed in the literature, our approach has the ability to distinguish both rigid and non-rigid motions which are observed in ultrasound image modality. Furthermore, this technique does not take into account any prior knowledge about the target, and limits the user interaction which usually complicates the medical validation process. Finally, a technique aiming at identifying the main phases of a periodic motion (e.g. breathing motion) is introduced. The new approach has been validated from 2D ultrasound images of real human tissues which undergo rigid and non-rigid deformations.
In this paper, we present a new scalable segmentation algorithm called JHMS (Joint Hierarchical and Multiresolution
Segmentation) that is characterized by region-based hierarchy and resolution scalability. Most of the
proposed algorithms either apply a multiresolution segmentation or a hierarchical segmentation. The proposed
approach combines both multiresolution and hierarchical segmentation processes. Indeed, the image is considered
as a set of images at different levels of resolution, where at each level a hierarchical segmentation is performed.
Multiresolution implies that a segmentation of a given level is reused in further segmentation processes operated
at next levels so that to insure contour consistency between different resolutions. Each level of resolution provides
a Region Adjacency Graph (RAG) that describes the neighborhood relationships between regions within
a given level of the multiresolution representation. Region label consistency is preserved thanks to a dedicated
projection algorithm based on inter-level relationships. Moreover, a preprocess based on a quadtree partitioning
reduces the amount of input data thus leading to a lower overall complexity of the segmentation framework.
Experiments show that we obtain effective results when compared to the state of the art together with a lower
H.264/AVC standard offers an efficient way of reducing the noticeable artefacts of former video coding schemes,
but it can be perfectible for the coding of detailed texture areas. This paper presents a conceptual coding
framework, utilizing visual perception redundancy, which aims at improving both bit-rate and quality on textured
areas. The approach is generic and can be integrated into usual coding scheme. The proposed scheme is divided
into three steps: a first algorithm analyses texture regions, with an eye to build a dictionary of the most
representative texture sub-regions (RTS). The encoder preserves then them at a higher quality than the rest of
the picture, in order to enable a refinement algorithm to finally spread the preserved information over textured
areas. In this paper, we present a first solution to validate the framework, detailing then the encoder side in
order to define a simple method for dictionary building. The proposed H.264/AVC compliant scheme creates a
dictionary of macroblocks
Rate control is a capital issue in video coding. It allows a regulation of the bitrate out from the encoder, to
cope with some network transmission or quality constraints. Scalable Video Coding emerged several years ago
as an answer to the growing need of application-adaptable video streams. Although the interest of scalable
video coding has been confirrmed by recent studies, it can not be used in practical contexts without proper rate
control techniques. In this paper we present a new rate control scheme for scalable video, based on a simple yet
attractive bitrate modelling framework called ρ-domain. Our scheme performs accurate rate control on spatial,
temporal and quality scalabilities, while maintaining a constant PSNR. Inter layer prediction is also handled
Within the framework of telemedicine, the amount of images leads first to use efficient lossless compression methods for the aim of storing information. Furthermore, multiresolution scheme including Region of Interest processing is an important feature for a remote access to medical images. Moreover, the securization of sensitive data (e.g. metadata from DICOM images) constitutes one more expected functionality: indeed the lost of IP packets could have tragic effects on a given diagnosis. For this purpose, we present in this paper an original scalable image compression technique (LAR method) used in association with a channel coding method based on the Mojette Transform, so that a hierarchical priority encoding system is elaborated.
The LAR (Locally Adaptive Resolution) coder, based on an non-uniform subsampling of the image, is a multi-layered scheme that provides just as well lossless representation of data as very low-bit rates encoded images. The Mojette transform technique realizes multiple description of information elements in a very low order of complexity. These descriptions are transmitted without adding any specific mechanism for regulating flows purpose. This global system provides a solution for secured transmission of medical images through low-bandwidth networks such as the Internet.