Automatic pelvis segmentation from x-ray images of a mouse model

The automatic detection and quantification of skeletal structures has a variety of different applications for biological research. Accurate segmentation of the pelvis from X-ray images of mice in a high-throughput project such as the Mouse Genomes Project not only saves time and cost but also helps achieving an unbiased quantitative analysis within the phenotyping pipeline. This paper proposes an automatic solution for pelvis segmentation based on structural and orientation properties of the pelvis in X-ray images. The solution consists of three stages including pre-processing image to extract pelvis area, initial pelvis mask preparation and final pelvis segmentation. Experimental results on a set of 100 X-ray images showed consistent performance of the algorithm. The automated solution overcomes the weaknesses of a manual annotation procedure where intra- and inter-observer variations cannot be avoided

On the Discrimination Power of Dynamic Features for Online Signature

The mobile market has taken huge leap in the last two decades, re-defining the rules of communication, networking, socializing and transactions among individuals and organizations. Authentication based on verification of signature on mobile devices, is slowly gaining popularity. Most online signature verification algorithms focus on computing the global Equal Error Rate across all users for a dataset. In this work, contrary to such a representation, it is shown that there are user-specific differences on the combined features and user-specific differences on each feature of the Equal Error Rate(EER) values. The experiments to test the hypothesis is carried out on the two publicly available dataset using the dynamic time warping algorithm. From the experiments, it is observed that for the MCYT-100 dataset, which yields an overall EER of 0.08, the range of user-specific EER is between 0 and 0.27

Automatic Spine Curvature Estimation from X-ray Images of a Mouse Model

Automatic segmentation and quantification of skeletal structures has a variety of applications for biological research. Although solutions for good quality X-ray images of human skeletal structures are in existence in recent years, automatic solutions working on poor quality X-ray images of mice are rare. This paper proposes a fully automatic solution for spine segmentation and curvature quantification from X-ray images of mice. The proposed solution consists of three stages, namely preparation of the region of interest, spine segmentation, and spine curvature quantification, aiming to overcome technical difficulties in processing the X-ray images. We examined six different automatic measurements for quantifying the spine curvature through tests on a sample data set of 100 images. The experimental results show that some of the automatic measures are very close to and consistent with the best manual measurement results by annotators. The test results also demonstrate the effectiveness of the curvature quantification produced by the proposed solution in distinguishing abnormally shaped spines from the normal ones with accuracy up to 98.6%

oBiometrics: A Software protection scheme using biometric-based obfuscation

This paper proposes to integrate biometric-based key generation into an obfuscated interpretation algorithm to protect authentication application software from illegitimate use or reverse-engineering. This is especially necessary for mCommerce because application programmes on mobile devices, such as Smartphones and Tablet-PCs are typically open for misuse by hackers. Therefore, the scheme proposed in this paper ensures that a correct interpretation / execution of the obfuscated program code of the authentication application requires a valid biometric generated key of the actual person to be authenticated, in real-time. Without this key, the real semantics of the program can not be understood by an attacker even if he/she gains access to this application code. Furthermore, the security provided by this scheme can be a vital aspect in protecting any application running on mobile devices that are increasingly used to perform business/financial or other security related applications, but are easily lost or stolen. The scheme starts by creating a personalised copy of any application based on the biometric key generated during an enrolment process with the authenticator as well as a nuance created at the time of communication between the client and the authenticator. The obfuscated code is then shipped to the client’s mobile devise and integrated with real-time biometric extracted data of the client to form the unlocking key during execution. The novelty of this scheme is achieved by the close binding of this application program to the biometric key of the client, thus making this application unusable for others. Trials and experimental results on biometric key generation, based on client's faces, and an implemented scheme prototype, based on the Android emulator, prove the concept and novelty of this proposed scheme

Automated Biometric Authentication with Cloud Computing

The convenience provided by cloud computing has led to an increasing trend of many business organizations, government agencies and individual customers to migrate their services and data into cloud environments. However, once clients’ data is migrated, the overall security control will be immedicably shifted form data owners to the hand of cloud service providers. In fact, most cloud clients do not even know where their data is physically stored, and therefore the question of how to limit data access to authorized users has been one of the biggest challenges in cloud environments. Although security tokens and passwords are widely used form of remote user authentication, they can be lost or stolen as they are not linked with the identity of data owner. Therefore, biometric based authentication can potentially offer a practical and reliable option for remote access control. This chapter starts with a brief introduction that covers the fundamental concepts of cloud computing and biometric based authentication. It then provides and in-depth discussions on authentication challenges for the cloud computing environment and the limitation of traditional solutions. This leads to the key sections related to biometric solutions for cloud computing in which we present state-of-art approaches that offer convenient and privacy-preserving authentication needed for cloud environment. The chapter argues that addressing privacy concerns surrounding the use of biometrics in cloud computing is one of the key challenges that has to be an integral part of any viable solution for any biometric-based authentication. It also argues that assuring cloud clients that their biometric templates will not be used without their permission to, for example, track them is not enough. Such solutions should make it technically infeasible to do so even if a cloud service provider wants to. This chapter explains a number of interesting solutions that have been recently proposed to improve security and, at the same time, maintain user privacy. Finally, we identify some challenges that still need to be addressed and highlight relevant Research Directions

Privacy preserving, real-time and location secured biometrics for mCommerce authentication

Secure wireless connectivity between mobile devices and financial/commercial establishments is mature, and so is the security of remote authentication for mCommerce. However, the current techniques are open for hacking, false misrepresentation, replay and other attacks. This is because of the lack of real-time and current-precise-location in the authentication process. This paper proposes a new technique that includes freshly-generated real-time personal biometric data of the client and present-position of the mobile device used by the client to perform the mCommerce so to form a real-time biometric representation to authenticate any remote transaction. A fresh GPS fix generates the "time and location" to stamp the biometric data freshly captured to produce a single, real-time biometric representation on the mobile device. A trusted Certification Authority (CA) acts as an independent authenticator of such client's claimed real time location and his/her provided fresh biometric data. Thus eliminates the necessity of user enrolment with many mCommerce services and application providers. This CA can also "independently from the client" and "at that instant of time" collect the client's mobile device "time and location" from the cellular network operator so to compare with the received information, together with the client's stored biometric information. Finally, to preserve the client's location privacy and to eliminate the possibility of cross-application client tracking, this paper proposes shielding the real location of the mobile device used prior to submission to the CA or authenticators

Privacy in Biometric Systems

Biometrics are physiological and/or behavioral characteristics of a person that have been used to provide an automatic proof of identity in a growing list of applications including crime/terrorism fighting, forensics, access and border control, securing e-/m-commerce transactions and service entitlements. In recent years, a great deal of research into a variety of new and traditional biometrics has widened the scope of investigations beyond improving accuracy into mechanisms that deal with serious concerns raised about the potential misuse of collected biometric data. Despite the long list of biometrics’ benefits, privacy concerns have become widely shared due to the fact that every time the biometric of a person is checked, a trace is left that could reveal personal and confidential information. In fact, biometric-based recognition has an inherent privacy problem as it relies on capturing, analyzing, and storing personal data about us as individuals. For example, biometric systems deal with data related to the way we look (face, iris), the way we walk (gait), the way we talk (speaker recognition), the way we write (handwriting), the way we type on a keyboard (keystroke), the way we read (eye movement), and many more. Privacy has become a serious concern for the public as biometric systems are increasingly deployed in many applications ranging from accessing our account on a Smartphone or computer to border control and national biometric cards on a very large scale. For example, the Unique Identification Authority of India (UIDAI) has issued 56 million biometric cards as of January 2014 [1], where each biometric card holds templates of the 10 fingers, the two irises and the face. An essential factor behind the growing popularity of biometrics in recent years is the fact that biometric sensors have become a lot cheaper as well as easier to install and handle. CCTV cameras are installed nearly everywhere and almost all Smartphones are equipped with a camera, microphone, fingerprint scanner, and probably very soon, an iris scanner

LocBiometrics: Mobile phone based multifactor biometric authentication with time and location assurance

The continuing growth of Smartphones and Superphones has significantly increased mCommerce. The security of personal information on the phone and lack of the face-to-face identification has made the authentication process prone to identity theft and false impersonation. Biometric authentication offers personal identification but is missing the real-time and precise-position associated with the person. This paper proposes the use of freshly generated real-time personal-data and present-position to form a “one-time multi-factor biometric” representation. i.e. using GPS "time and location" to stamp the user’s fresh biometric data on the phone side, and then, the authenticator will compare this information with the position of the phone obtained independently from the cellular network at that instant in time

Automatic Identification of Miscarriage Cases Supported by Decision Strength Using Ultrasound Images of the Gestational Sac

Ultrasound imaging is one of the most widely used multipurpose imaging modalities for monitoring and diagnosing early pregnancy events. The first sign and measurable element of an early pregnancy is the appearance of the Gestational Sac (GS). Currently, the size of the GS is manually estimated from ultrasound images. The manual measurements tend to result in inter- and intraobserver variations, which may lead to difficulties in diagnosis. This paper proposes a new method for automatic identification of miscarriage cases in the first trimester of pregnancy. The proposed method automatically segments the GS and calculates the Mean Sac Diameter (MSD) and other geometric features of the segmented sac. After classifying the image based on the extracted features into either a pregnancy of unknown viability (PUV) or a possible miscarriage case, we assign the decision with a strength level to reflect its reliability. The paper argues that the level of decision strength gives more insight into decision making than other classical alternatives and makes the automated decision process closer to the diagnosis practice by exper

Automated characterisation of ultrasound images of ovarian tumours: the diagnostic accuracy of a support vector machine and image processing with a local binary pattern operator

Introduction: Preoperative characterisation of ovarian masses into benign or malignant is of paramount importance to optimise patient management. Objectives: In this study, we developed and validated a computerised model to characterise ovarian masses as benign or malignant. Materials and methods: Transvaginal 2D B mode static ultrasound images of 187 ovarian masses with known histological diagnosis were included. Images were first pre-processed and enhanced, and Local Binary Pattern Histograms were then extracted from 2 × 2 blocks of each image. A Support Vector Machine (SVM) was trained using stratified cross validation with randomised sampling. The process was repeated 15 times and in each round 100 images were randomly selected. Results: The SVM classified the original non-treated static images as benign or malignant masses with an average accuracy of 0.62 (95% CI: 0.59-0.65). This performance significantly improved to an average accuracy of 0.77 (95% CI: 0.75-0.79) when images were pre-processed, enhanced and treated with a Local Binary Pattern operator (mean difference 0.15: 95% 0.11-0.19, p < 0.0001, two-tailed t test). Conclusion: We have shown that an SVM can classify static 2D B mode ultrasound images of ovarian masses into benign and malignant categories. The accuracy improves if texture related LBP features extracted from the images are considered