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

Multiresolution analysis using wavelet, ridgelet, and curvelet transforms for medical image segmentation

Copyright @ 2011 Shadi AlZubi et al. This article has been made available through the Brunel Open Access Publishing Fund.The experimental study presented in this paper is aimed at the development of an automatic image segmentation system for classifying region of interest (ROI) in medical images which are obtained from different medical scanners such as PET, CT, or MRI. Multiresolution analysis (MRA) using wavelet, ridgelet, and curvelet transforms has been used in the proposed segmentation system. It is particularly a challenging task to classify cancers in human organs in scanners output using shape or gray-level information; organs shape changes throw different slices in medical stack and the gray-level intensity overlap in soft tissues. Curvelet transform is a new extension of wavelet and ridgelet transforms which aims to deal with interesting phenomena occurring along curves. Curvelet transforms has been tested on medical data sets, and results are compared with those obtained from the other transforms. Tests indicate that using curvelet significantly improves the classification of abnormal tissues in the scans and reduce the surrounding noise

3D multiresolution statistical approaches for accelerated medical image and volume segmentation

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Medical volume segmentation got the attraction of many researchers; therefore, many techniques have been implemented in terms of medical imaging including segmentations and other imaging processes. This research focuses on an implementation of segmentation system which uses several techniques together or on their own to segment medical volumes, the system takes a stack of 2D slices or a full 3D volumes acquired from medical scanners as a data input. Two main approaches have been implemented in this research for segmenting medical volume which are multi-resolution analysis and statistical modeling. Multi-resolution analysis has been mainly employed in this research for extracting the features. Higher dimensions of discontinuity (line or curve singularity) have been extracted in medical images using a modified multi-resolution analysis transforms such as ridgelet and curvelet transforms. The second implemented approach in this thesis is the use of statistical modeling in medical image segmentation; Hidden Markov models have been enhanced here to segment medical slices automatically, accurately, reliably and with lossless results. But the problem with using Markov models here is the computational time which is too long. This has been addressed by using feature reduction techniques which has also been implemented in this thesis. Some feature reduction and dimensionality reduction techniques have been used to accelerate the slowest block in the proposed system. This includes Principle Components Analysis, Gaussian Pyramids and other methods. The feature reduction techniques have been employed efficiently with the 3D volume segmentation techniques such as 3D wavelet and 3D Hidden Markov models. The system has been tested and validated using several procedures starting at a comparison with the predefined results, crossing the specialists’ validations, and ending by validating the system using a survey filled by the end users explaining the techniques and the results. This concludes that Markovian models segmentation results has overcome all other techniques in most patients’ cases. Curvelet transform has been also proved promising segmentation results; the end users rate it better than Markovian models due to the long time required with Hidden Markov models

A secure and lightweight drones-access protocol for smart city surveillance

The rising popularity of ICT and the Internet has enabled Unmanned Aerial Vehicle (UAV) to offer advantageous assistance to Vehicular Ad-hoc Network (VANET), realizing a relay node's role among the disconnected segments in the road. In this scenario, the communication is done between Vehicles to UAVs (V2U), subsequently transforming into a UAV-assisted VANET. UAV-assisted VANET allows users to access real-time data, especially the monitoring data in smart cities using current mobile networks. Nevertheless, due to the open nature of communication infrastructure, the high mobility of vehicles along with the security and privacy constraints are the significant concerns of UAV-assisted VANET. In these scenarios, Deep Learning Algorithms (DLA) could play an effective role in the security, privacy, and routing issues of UAV-assisted VANET. Keeping this in mind, we have devised a DLA-based key-exchange protocol for UAV-assisted VANET. The proposed protocol extends the scalability and uses secure bitwise XOR operations, one-way hash functions, including user's biometric verification when users and drones are mutually authenticated. The proposed protocol can resist many well-known security attacks and provides formal and informal security under the Random Oracle Model (ROM). The security comparison shows that the proposed protocol outperforms the security performance in terms of running time cost and communication cost and has effective security features compared to other related protocols

Efficient 3D Medical Image Segmentation Algorithm over a Secured Multimedia Network

Image segmentation has proved its importance and plays an important role in various domains such as health systems and satellite-oriented military applications. In this context, accuracy, image quality, and execution time deem to be the major issues to always consider. Although many techniques have been applied, and their experimental results have shown appealing achievements for 2D images in real-time environments, however, there is a lack of works about 3D image segmentation despite its importance in improving segmentation accuracy. Specifically, HMM was used in this domain. However, it suffers from the time complexity, which was updated using different accelerators. As it is important to have efficient 3D image segmentation, we propose in this paper a novel system for partitioning the 3D segmentation process across several distributed machines. The concepts behind distributed multimedia network segmentation were employed to accelerate the segmentation computational time of training Hidden Markov Model (HMMs). Furthermore, a secure transmission has been considered in this distributed environment and various bidirectional multimedia security algorithms have been applied. The contribution of this work lies in providing an efficient and secure algorithm for 3D image segmentation. Through a number of extensive experiments, it was proved that our proposed system is of comparable efficiency to the state of art methods in terms of segmentation accuracy, security and execution time

Malicious and Spam Posts in Online Social Networks

A large-scale study of more than half a million Facebook posts suggests that members of online social networks can expect a significant chance of encountering spam posts and a much lower but not negligible chance of coming across malicious links. © 2006 IEEE

Carbon Footprint Modeling of a Clinical Lab

Modeling of a clinical lab carbon footprint is performed in this study from the aspects of electricity, water, gas consumption and waste production from lab instruments. These environmental impact indicators can be expressed in the form of the CO2 equivalent. For each type of clinical test, the corresponding consumption of energy resources and the production of plastics and papers are taken into consideration. In addition, the basic lab infrastructures such as heating, ventilation, air-conditioning (HVAC) systems, lights, and computers also contribute to the environmental impact. Human comfort is to be taken into account when optimizing the operation of lab instruments, and is related to the operation of HVAC and lighting systems. The detailed modeling takes into consideration the types of clinical tests, operating times, and instrument specifications. Two ways of disposing waste are classified. Moreover, the indoor environment is modeled. A case study of the Biochrom 30+ amino acid analyzer physiological system in Alder Hey Children’s Hospital is carried out, and the methods of mitigating the overall environmental impacts are discussed. Furthermore, the influence of climate on the results is investigated by using the climate data in Liverpool and Athens in October