3D medical volume segmentation using hybrid multiresolution statistical approaches

This article is available through the Brunel Open Access Publishing Fund. Copyright © 2010 S AlZu’bi and A Amira.3D volume segmentation is the process of partitioning voxels into 3D regions (subvolumes) that represent meaningful physical entities which are more meaningful and easier to analyze and usable in future applications. Multiresolution Analysis (MRA) enables the preservation of an image according to certain levels of resolution or blurring. Because of multiresolution quality, wavelets have been deployed in image compression, denoising, and classification. This paper focuses on the implementation of efficient medical volume segmentation techniques. Multiresolution analysis including 3D wavelet and ridgelet has been used for feature extraction which can be modeled using Hidden Markov Models (HMMs) to segment the volume slices. A comparison study has been carried out to evaluate 2D and 3D techniques which reveals that 3D methodologies can accurately detect the Region Of Interest (ROI). Automatic segmentation has been achieved using HMMs where the ROI is detected accurately but suffers a long computation time for its calculations

Low-Complexity 3D-DWT video encoder applicable to IPTV

3D-DWT encoders are good candidates for applications like professional video editing, IPTV video surveillance, live event IPTV broadcast, multispectral satellite imaging, HQ video delivery, etc., where a frame must be reconstructed as fast as possible. However, the main drawback of the algorithms that compute the 3D-DWT is the huge memory requirement in practical implementations. In this paper, and in order to considerably reduce the memory requirements of this kind of video encoders, we present a new 3D-DWT video encoder based on (a) the use of a novel frame-based 3D-DWT transform that avoids video sequence partitioning in Groups Of Pictures (GOP) and (b) a very fast run-length encoder. Furthermore, an exhaustive evaluation of the proposed encoder (3D-RLW) has been performed, analyzing the sensibility of the ¿lters employed in the 3D-DWT transform and comparing the evaluation results with other video encoders in terms of R/D, coding/decoding delay and memory consumptionThanks to Spanish Ministry of Education and Science under grants DPI2007-66796-C03-03 for funding.López ., O.; Piñol ., P.; Martinez Rach, MO.; Perez Malumbres, MJ.; Oliver Gil, JS. (2011). Low-Complexity 3D-DWT video encoder applicable to IPTV. Signal Processing: Image Communication. 26(7):358-369. https://doi.org/10.1016/j.image.2011.01.008S35836926

An Efficient Coding Method for Teleconferencing Video and Confocal Microscopic Image Sequences

In this paper we propose a three-dimensional vector quantization based video coding scheme. The algorithm uses a 3D vector quantization pyramidal code book based model with adaptive code book pyramidal codebook for compression. The pyramidal code book based model helps in getting high compression in case of modest motion. The adaptive vector quantization algorithm is used to train the code book for optimal performance with time. Some of the distinguished features of our algorithm are its excellent performance due to its adaptive behavior to the video composition and excellent compression due to codebook approach. We also propose an efficient codebook based post processing technique which enables the vector quantizer to possess higher correlation preservation property. Based on the special pattern of the codebook imposed by post-processing technique, a window based fast search (WBFS) algorithm is proposed. The WBFS algorithm not only accelerates the vector quantization processing, but also results in better rate-distortion performance. The proposed approach can be used for both teleconferencing videos and to compress images obtained from confocal laser scanning microscopy (CLSM). The results show that the proposed method gave higher subjective and objective image quality of reconstructed images at a better compression ratio and presented more acceptable results when applying image processing filters such as edge detection on reconstructed images. The experimental results demonstrate that the proposed method outperforms the teleconferencing compression standards H.261 and LBG based vector quantization technique

Inter-Modal Selective 3D Coding of PET-CT Datasets

In this work we introduce a new selective coding approach suitable for co-registered multi-modal medical images and we apply it to large PET-CT volumes. Salience information guiding a space variant reconstruction quality of the anatomical volume (CT) is generated through an automatic analysis of the functional volume (PET). This allows a versatile multiple volume-of-interest coding with arbitrary 3D-shape and scaling-factors and without the need of side information to be transmitted. The proposed solutions are suitable for critical applications where high and optimized compression ratio, minimization of human intervention and full diagnostic quality preservation are all required

Efficient storage of microCT data preserving bone morphometry assessment

Available online 15 July 2015Preclinical micro-computed tomography (microCT) images are of utility for 3D morphological bone evaluation, which is of great interest in cancer detection and treatment development. This work introduces a compression strategy for microCTs that allocates specific substances in different Volumes of Interest (VoIs). The allocation procedure is conducted by the Hounsfield scale. The VoIs are coded independently and then grouped in a single DICOM-compliant file. The proposed method permits the use of different codecs, identifies and transmit data corresponding to a particular substance in the compressed domain without decoding the volume(s), and allows the computation of the 3D morphometry without needing to store or transmit the whole image. The proposed approach reduces the transmitted data in more than 90% when the 3D morphometry evaluation is performed in high density and low density bone. This work can be easily extended to other imaging modalities and applications that work with the Hounsfield scale

Correlation modeling for compression of computed tomography images

Abstract-Computed Tomography (CT) is a noninvasive medical test obtained via a series of X-ray exposures resulting in 3D images that aid medical diagnosis. Previous approaches for coding such 3D images propose to employ multi-component transforms to exploit correlation among CT slices, but these approaches do not always improve coding performance with respect to a simpler slice-by-slice coding approach. In this work, we propose a novel analysis which accurately predicts when the use of a multi-component transform is profitable. This analysis models the correlation coefficient r based on image acquisition parameters readily available at acquisition time. Extensive experimental results from multiple image sensors suggest that multi-component transforms are appropriate for images with correlation coefficient r in excess of 0.87