Lossy to lossless object-based coding of 3-D MRI data

Abstract

Abstract-We propose a fully three-dimensional (3-D) object-based coding system exploiting the diagnostic relevance of the different regions of the volumetric data for rate allocation. The data are first decorrelated via a 3-D discrete wavelet transform. The implementation via the lifting steps scheme allows to map integer-to-integer values, enabling lossless coding, and facilitates the definition of the object-based inverse transform. The coding process assigns disjoint segments of the bitstream to the different objects, which can be independently accessed and reconstructed at any up-to-lossless quality. Two fully 3-D coding strategies are considered: embedded zerotree coding (EZW-3D) and multidimensional layered zero coding (MLZC), both generalized for region of interest (ROI)-based processing. In order to avoid artifacts along region boundaries, some extra coefficients must be encoded for each object. This gives rise to an overheading of the bitstream with respect to the case where the volume is encoded as a whole. The amount of such extra information depends on both the filter length and the decomposition depth. The system is characterized on a set of head magnetic resonance images. Results show that MLZC and EZW-3D have competitive performances. In particular, the best MLZC mode outperforms the others state-of-the-art techniques on one of the datasets for which results are available in the literature.

Contextual encoding in uniform and adaptive mesh-based lossless compression of MR images

“…Let , in the th frame be the corresponding nodes of in the th frame, . The affine transformation can then be written as (3) This system of linear equations is solved for the parameters for each element . The motion vectors are sent as side information to the decoder.…”

“…Some of the existing 3-D compression schemes for medical images can be found in [2]- [12]. 3-D progressive transmission schemes with lossless compression capability are reported in [3]- [8] and [10]. The schemes of [2] and [11] are based on motion compensation.…”

“…We also propose an algorithm to avoid the classical aperture problem [19] that exists in regions of the image that have strongly oriented intensity gradients (edges) in both uniform and adaptive mesh-based schemes. Recent lossless still image compression schemes such as [3]- [7], [12]- [17] successfully employ context-based entropy coding methods. By choosing proper contexts, one can model the sources efficiently and reduce the entropy of the symbols to be coded.…”

SVD para la transmisión progresiva de imágenes y la codificación de vídeo digital.

“…Por otra parte, las opciones 7(a), 7(b) y 7(c) han sido consideradas extensamente desde diferentes puntos de vista, por ejemplo: usando una cierta interpolación de las imágenes [28], [61], [8] y [29], mediante la utilización de wavelets [31], [45], [71] y [40], usando otro tipo de transformadas [72], con téc-nicas de decorrelación [39] y [28], o introduciendo estrategias adaptativas en la transmisión progresiva [6].…”

“…Por ejemplo, cuestiones semejantes se comentan en [31], [45] y [71] donde usando JPEG200, la codificación basada en la ROI es realizada por el método MAXSHIFT descrito en [14] y ya comentado en el capítulo 2, y donde se observó cómo este método requiere la elección, a priori, de la ROI y a continuación, la codificación de la imagen junto con la ROI seleccionada anteriormente. Mientras que como ya hemos comentado, con nuestro método las ROIs podrán ser seleccionadas durante el proceso de transmisión y sin la necesidad de codificar la imagen de nuevo para transmitir los datos correspondientes de la ROI seleccionada.…”