Introduction. Determining the tumor (nodule) characteristics in terms of the shape, location, and type is an essential step after nodule detection in medical images for selecting the appropriate clinical intervention by radiologists. Computer-aided detection (CAD) systems efficiently succeeded in the nodule detection by 2D processing of computed tomography (CT)-scan lung images; however, the nodule (tumor) description in more detail is still a big challenge that faces these systems.Materials and Methods. In this paper, the 3D clustering is carried out on volumetric CT-scan images containing the nodule and its structures to describe the nodule progress through the consecutive slices of the lung in CT images.Results. This paper combines algorithms to cluster and define nodule’s features in 3D visualization. Applying some 3D functions to the objects, clustered using the K-means technique of CT lung images, provides a 3D visual exploration of the nodule shape and location. This study mainly focuses on clustering in 3D to discover complex information for a case missed in the radiologist’s report. In addition, the 3D-Density-based spatial clustering of applications with noise (DBSCAN) method and another 3D application (plotly) have been applied to evaluate the proposed system in this work. The proposed method has discovered a complicated case in data and automatically provides information about the nodule types (spherical, juxta-pleural, and pleural-tail). The algorithm is validated on the standard data consisting of the lung computed tomography scans with nodules greater and less than 3mm in size.Discussion and Conclusions. Based on the proposed model, it is possible to cluster lung nodules in volumetric CT scan and determine a set of characteristics such as the shape, location and type.

Glioma grading is vital for therapeutic planning where the higher level of glioma is associated with high mortality. It is a challenging task as different glioma grades have mixed morphological characteristics of brain tumour. A computeraided diagnosis (CAD) system based on three-dimensional textural grey level co-occurrence matrix (GLCM) and machine learning is proposed for glioma grading. The purpose of this paper is to assess the usefulness of the 3D textural analysis in establishing a malignancy prediction model for glioma grades. Furthermore, this paper aims to find the best classification model based on textural analysis for glioma grading. The classification system was evaluated using leave-one-out cross-validation technique. The experimental design includes feature extraction, feature selection, and finally the classification that includes single and ensemble classification models in a comparative study. Experimental results illustrate that single and ensemble classification models, can achieve efficient prediction performance based on 3D textural analysis and the classification accuracy result has significantly improved after using feature selection methods. In this paper, we compare the proficiency of applying different angles of 3D textural analysis and different classification models to determine the malignant level of glioma. The obtained sensitivity, accuracy and specificity are 100%, 96.6%, 90% respectively. The prediction system presents an effective approach to assess the malignancy level of glioma with a non-invasive, reproducible and accurate CAD system for glioma grading.

This paper presents the process of integrating digital watermarking technique into medical imaging workflow to evaluate, validate and verify its applicability and appropriateness to medical domains. This is significant to ensure the ability of the proposed approach to tackle security threats that may face medical images during routine medical practices. This work considers two key objectives within the aim of defining a secure and practical digital medical imaging system: current digital medical workflows are deeply analyzed to define security limitations in Picture Archiving and Communication Systems (PACS) of medical imaging; the proposed watermarking approach is then theoretically tested and validated in its ability to operate in a real-world scenario (e.g. PACS). These have been undertaken through identified case studies related to manipulations of medical images within PACS workflow during acquisition, viewing, exchanging and archiving. This work assures the achievement of the identified particular requirements of digital watermarking when applied to digital medical images and also provides robust controls within medical imaging pipelines to detect modifications that may be applied to medical images during viewing, storing and transmitting.

The preoperative diagnosis of brain Glioma grades is crucial for therapeutic planning as it impacts on the tumour's prognosis. The development of machine learning methods that can accurately evaluate Glioma grades is of great interest since it is a repeatable and reliable diagnosis procedure. Moreover, the classification accuracy of a single classifier can be further improved by using the ensemble of different classifiers. In this paper, a new strategy has been developed, which uses a deep neural network incorporating an extensive iteration matrix based on the combination of eleven different machine learning algorithms. The classification system is evaluated using a cross-validation technique, to add more generalization to the results of the classification system's reliability in unseen cases. Experimental results indicate that, when compared to both the single classification model, and the majority vote scheme, the grading accuracy has significantly improved using our proposed approach. The obtained sensitivity, specificity and accuracy are 100%, 90% and 93.3% respectively. The proposed approach has improved upon the highest accuracy of the single classification model by 13.3%. The proposed classification system presents an efficient method to evaluate the malignancy level of Glioma with more reliable and accurate clinical outcomes.

Poster: ECR 2018 / C-1048 / A comparison of single and combined observer data for the detection of masses and microcalcifications in images containing different magnitudes of simulated blurring by: A. K. Abdullah1, J. D. Thompson1, C. E. Mercer1, J. F. Kelly2, R. Aspin1, P. Hogg 1; 1Manchester/UK, 2Chester/UK

This paper proposes a novel reversible and imperceptible watermarking method to detect intentional and accidental alterations within brain Magnetic Resonance (MR) images. Authentication data is encoded into the informative part of the medical images using reversible watermarking based on the Difference Expansion (DE) method. Experimental results demonstrate that the proposed technique, whilst completely reversible, can also produce a watermarked image with low distortion. This is realized by encoding the watermark data into ‘smooth’ blocks inside the informative region of the image and through eliminating the need for location map required for extracting the concealed data and reconstructing the original unmodified image. Our proposed scheme delivers highly imperceptible watermarked images, at 92.9 - 97.98 dB Peak Signal to Noise Ratio (PSNR), and exceeds the others approaches presented in previous studies. Reliability of medical images is also guaranteed through detecting subsequent modifications applied to the watermarked images. This enhanced security measure, therefore, enables the detection of image alterations, by a reversible and imperceptible approach, that may offer increased confidence in the digital medical practices.

Reversible and imperceptible watermarking is recognized as a robust approach to confirm the integrity and authenticity of medical images and to verify that alterations can be detected and tracked back. In this paper, a novel blind reversible watermarking approach is presented to detect intentional and unintentional changes within brain Magnetic Resonance (MR) images. The scheme segments images into two parts; the Region of Interest (ROI) and the Region of Non Interest (RONI). Watermark data is encoded into the ROI using reversible watermarking based on the Difference Expansion (DE) technique. Experimental results show that the proposed method, whilst fully reversible, can also realize a watermarked image with low degradation for reasonable and controllable embedding capacity. This is fulfilled by concealing the data into ‘smooth’ regions inside the ROI and through the elimination of the large location map required for extracting the watermark and retrieving the original image. Our scheme delivers highly imperceptible watermarked images, at 92.18–99.94 dB Peak Signal to Noise Ratio (PSNR) evaluated through implementing a clinical trial based on relative Visual Grading Analysis (relative VGA). This trial defines the level of modification that can be applied to medical images without perceptual distortion. This compares favorably to outcomes reported under current state-of-art techniques. Integrity and authenticity of medical images are also ensured through detecting subsequent changes enacted on the watermarked images. This enhanced security measure, therefore, enables the detection of image manipulations, by an imperceptible approach, that may establish increased trust in the digital medical workflow.

The digital medical workflow faces many circumstances in which the images can be manipulated during viewing, extracting and exchanging. Reversible and imperceptible watermarking approaches have the potential to enhance trust within the medical imaging pipeline through ensuring the authenticity and integrity of the images to confirm that the changes can be detected and tracked. This study concentrates on the imperceptibility issue. Unlike reversibility, for which an objective assessment can be easily made, imperceptibility is a factor of human cognition that needs to be evaluated within the human context. By defining a perceptual boundary of detecting the modification, this study enables the formation of objective guidelines for the method of data encoding and level of image/pixel modification that translates to a specific watermark magnitude. This study implements a relative Visual Grading Analysis (VGA) evaluation of 117 brain MR images (8 original and 109 watermarked), modified by varying techniques and magnitude of image/pixel modification to determine where this perceptual boundary exists and relate the point at which change becomes noticeable to the objective measures of the image fidelity evaluation. The outcomes of the visual assessment were linked to the images Peak Signal to Noise Ratio (PSNR) values, thereby identifying the visual degradation threshold. The results suggest that, for watermarking applications, if a watermark is applied to the 512 x 512 pixel (16 bpp grayscale) images used in the study, a subsequent assessment of PSNR=82dB or greater would mean that there would be no reason to suspect that the watermark would be visually detectable.

OBJECTIVE:Motion blur is a known phenomenon in full-field digital mammography, but the impact on lesion detection is unknown. This is the first study to investigate detection performance with varying magnitudes of simulated motion blur.METHODS:7 observers (15 ± 5 years' reporting experience) evaluated 248 cases (62 containing malignant masses, 62 containing malignant microcalcifications and 124 normal cases) for 3 conditions: no blurring (0 mm) and 2 magnitudes of simulated blurring (0.7 and 1.5 mm). Abnormal cases were biopsy proven. Mathematical simulation was used to provide a pixel shift in order to simulate motion blur. A free-response observer study was conducted to compare lesion detection performance for the three conditions. The equally weighted jackknife alternative free-response receiver operating characteristic was used as the figure of merit. Test alpha was set at 0.05 to control probability of Type I error.RESULTS:The equally weighted jackknife alternative free-response receiver operating characteristic analysis found a statistically significant difference in lesion detection performance for both masses [F(2,22) = 6.01, p = 0.0084] and microcalcifications [F(2,49) = 23.14, p < 0.0001]. The figures of merit reduced as the magnitude of simulated blurring increased. Statistical differences were found between some of the pairs investigated for the detection of masses (0.0 vs 0.7 and 0.0 vs 1.5 mm) and all pairs for microcalcifications (0.0 vs 0.7, 0.0 vs 1.5 and 0.7 vs 1.5 mm). No difference was detected between 0.7 and 1.5 mm for masses.CONCLUSION:The mathematical simulation of motion blur caused a statistically significant reduction in lesion detection performance. These false-negative decisions could have implications for clinical practice. Advances in knowledge: This research demonstrates for the first time that motion blur has a negative and statistically significant impact on lesion detection performance in digital mammography.

OBJECTIVE:Blurred images in full-field digital mammography are a problem in the UK Breast Screening Programme. Technical recalls may be due to blurring not being seen on lower resolution monitors used for review. This study assesses the visual detection of blurring on a 2.3-MP monitor and a 5-MP report grade monitor and proposes an observer standard for the visual detection of blurring on a 5-MP reporting grade monitor.METHODS:28 observers assessed 120 images for blurring; 20 images had no blurring present, whereas 100 images had blurring imposed through mathematical simulation at 0.2, 0.4, 0.6, 0.8 and 1.0 mm levels of motion. Technical recall rate for both monitors and angular size at each level of motion were calculated. χ2 tests were used to test whether significant differences in blurring detection existed between 2.3- and 5-MP monitors.RESULTS:The technical recall rate for 2.3- and 5-MP monitors are 20.3% and 9.1%, respectively. The angular size for 0.2- to 1-mm motion varied from 55 to 275 arc s. The minimum amount of motion for visual detection of blurring in this study is 0.4 mm. For 0.2-mm simulated motion, there was no significant difference [χ2 (1, N = 1095) = 1.61, p = 0.20] in blurring detection between the 2.3- and 5-MP monitors.CONCLUSION:According to this study, monitors ≤2.3 MP are not suitable for technical review of full-field digital mammography images for the detection of blur. Advances in knowledge: This research proposes the first observer standard for the visual detection of blurring.

The paper presents an automated algorithm for detecting and classifying MRI brain slices into normal and abnormal based on a novel three-dimensional modified grey level co-occurrence matrix. This approach is used to analyze and measure asymmetry between the two brain hemispheres. The experimental results demonstrate the efficacy of proposed algorithm in detecting brain abnormalities with high accuracy and low computational time. The dataset used in the experiment comprises 165 patients with 88 having different brain abnormalities whilst the remaining do not exhibit any detectable pathology. The algorithm was tested using a ten-fold cross-validation technique with 10 repetitions to avoid the result depending on the sample order. The maximum accuracy achieved for the brain tumors detection was 93.3% using a Multi-Layer Perceptron Neural Network.

Medical images can be intentionally or unintentionally manipulated both within the secure medical system environment and outside, as images are viewed, extracted and transmitted. Many organisations have invested heavily in Picture Archiving and Communication Systems (PACS), which are intended to facilitate data security. However, it is common for images, and records, to be extracted from these for a wide range of accepted practices, such as external second opinion, transmission to another care provider, patient data request, etc. Therefore, confirming trust within medical imaging workflows has become essential. Digital watermarking has been recognised as a promising approach for ensuring the authenticity and integrity of medical images. Authenticity refers to the ability to identify the information origin and prove that the data relates to the right patient. Integrity means the capacity to ensure that the information has not been altered without authorisation.This paper presents a survey of medical images watermarking and offers an evident scene for concerned researchers by analysing the robustness and limitations of various existing approaches. This includes studying the security levels of medical images within PACS system, clarifying the requirements of medical images watermarking and defining the purposes of watermarking approaches when applied to medical images.

Brain tumor segmentation in magnetic resonance imaging (MRI) is considered a complex procedure because of the variability of tumor shapes and the complexity of determining the tumor location, size, and texture. Manual tumor segmentation is a time-consuming task highly prone to human error. Hence, this study proposes an automated method that can identify tumor slices and segment the tumor across all image slices in volumetric MRI brain scans. First, a set of algorithms in the pre-processing stage is used to clean and standardize the collected data. A modified gray-level co-occurrence matrix and Analysis of Variance (ANOVA) are employed for feature extraction and feature selection, respectively. A multi-layer perceptron neural network is adopted as a classifier, and a bounding 3D-box-based genetic algorithm is used to identify the location of pathological tissues in the MRI slices. Finally, the 3D active contour without edge is applied to segment the brain tumors in volumetric MRI scans. The experimental dataset consists of 165 patient images collected from the MRI Unit of Al-Kadhimiya Teaching Hospital in Iraq. Results of the tumor segmentation achieved an accuracy of 89% ± 4.7% compared with manual processes.

Crime surveys are conducted to record crimes by the Office for National Statistics (ONS) in the United Kingdom every year. They contain rich information about crime. They record the crimes that are not reported to the police. However, their exploitation for gaining a better understanding of crime activities is limited. When used, traditional statistical models and descriptive statistics are adopted. The data they contain is very complex and changes from one year to the other. In this paper, we report the preprocessing activities that were performed on survey data to allow their use with data mining models. We reported the results of early analysis of the survey data using decision trees and the users' interpretation of these results.

In today's society where we live in a world of constant connectivity, many people are now looking to cloud services in order to store their files so they can have access to them wherever they are. By using cloud services, users can access files anywhere with an internet connection. However, while cloud storage is convenient, it also presents security risks. From a forensics perspective, the increasing popularity of cloud storage platforms, makes investigation into such exploits much more difficult, especially since many platforms such as mobile devices as well as computers are able to use these services. This paper presents investigation of hubiC as one of popular cloud platforms running on Microsoft Windows 8.1. Remaining artefacts pertaining different usage of hubiC namely upload, download, installation and uninstallation on Microsoft Windows 8.1are presented.

Supporting a wide set of linked non-verbal resources remains an evergreen challenge for communication technology, limiting effectiveness in many applications. Interpersonal distance, gaze, posture and facial expression, are interpreted together to manage and add meaning to most conversations. Yet today's technologies favor some above others. This induces confusion in conversations, and is believed to limit both feelings of togetherness and trust, and growth of empathy and rapport. Solving this problem will allow technologies to support most rather than a few interactional scenarios. It is likely to benefit teamwork and team cohesion, distributed decision-making and health and wellbeing applications such as tele-therapy, tele-consultation, and isolation. We introduce withyou, our telepresence research platform. This paper describes the end-to-end system including the psychology of human interaction and how this drives requirements throughout the design and implementation. Our technology approach is to combine the winning characteristics of video conferencing and immersive collaborative virtual environments. This is to allow, for example, people walking past each other to exchange a glance and smile. A systematic explanation of the theory brings together the linked nature of non-verbal communication and how it is influenced by technology. This leads to functional requirements for telepresence, in terms of the balance of visual, spatial and temporal qualities. The first end-to-end description of withyou describes all major processes and the display and capture environment. An unprecedented characterization of our approach is given in terms of the above qualities and what influences them. This leads to non-functional requirements in terms of number and place of cameras and the avoidance of resultant bottlenecks. Proposals are given for improved distribution of processes across networks, computers, and multi-core CPU and GPU. Simple conservative estimation shows that both approaches should meet our requirements. One is implemented and shown to meet minimum and come close to desirable requirements.

Objective: Image blurring in mammography can cause significant image degradation and interpretational problems. A potential source is due to paddle movement during image formation. Paddle movement has been shown to be as much as 1.5mm. No study has yet been performed to determine how much motion would be noticeable visually. The aim of this study is to determine the minimum amount of simulated breast movement at which blurring can be detected visually.Methods: 25 artefact-free mammogram images were selected. Mathematical simulation software was created to mimic the effect of blurring produced by breast movement during exposure. Motion simulation was imposed to 15 levels, from 0.1 to 1.5mm stepping through 0.1mm increments. 15 degraded images and 1 without blurring were de-identified, randomized and assessed on a blinded basis by two clinical experts to determine the presence or absence of blurring. Statistical testing was carried out to determine the consistency between the two observers.Results: The probability of simulated blurred image detection is the highest for the gaussian method and the lowest for soft-edged mask estimation.Conclusion: The amount of simulated breast movement at which blurring can be detected visually for gaussian blur, hard-edge mask estimation and soft-edge mask estimation is 0.4, 0.8 and 0.7mm, respectively. Cohen's kappa for all the levels of simulated blurring is 0.689 (p < 0.05).Advances in knowledge: This research establishes the concept of using probability to represent visual detection of blurring rather than defining a hard cut-off level.

Capturing, recreating and representing a high fidelity virtual representation of the dynamic human form has long been a target for a diverse range of applications including tele-presence, games, film and TV special effects. The complexity of the challenge, to achieve a lifelike, faithful and believable representation, is such that a wide range of techniques and approaches have been developed. These are both due to research lead curiosity and requirements to address specific objective for particular problems. This work starts from a novel standpoint: that the processes of surfacing, tessellation and texturing, commonly used in 3D reconstruction, are computationally expensive and un-necessary. This work argues that by integrating the reconstruction and rendering processes into a single process that is aligned with the architecture of modern graphics hardware, a lightweight component solution can be achieved that is suitable for application on the end user systems within the many application domains. In order to achieve this aim the research undertaken seeks to both define an appropriate technique and develop detailed understanding of the reconstruction process pipeline and impacting factors. This is achieved through a complementary investigation of the tools and frameworks that are necessary to support iterative development of the approach with reliable, repeatable objective assessment. This reasons that by understanding the nature of the capture, reconstruction and presentation pipeline and by objective evaluation of the emerging reconstruction techniques this research will define an approach for 3D video based reconstruction that effectively utilises the processing potential of a single system to deliver acceptable levels of performance (speed) and fidelity (visual quality) for a componentised, multi-purpose 3D reconstruction and rendering solution. This thesis describes the research that has driven the evolution of technique and documents the iterations made. It presents a novel framework for experimentation and evaluation of the techniques and demonstrates how the use of these tools has enabled both rapid prototyping of approach and objective evaluation of improvement. The work concludes with a review of the approach taken and identifies approaches for evaluation of performance (speed) and fidelity (visual quality) that enable both repeatable experimentation within the research pipeline and reliable comparison of the end-to-end process against other techniques.