Ultrasound imaging is widely used in medical diagnostics. The existence of speckle noise tends to impair ultrasound image quality, which has a negative effect on the computer-aided diagnostic pipeline. As a result, a content-preserving noise reduction is an essential part of ultrasound image pre-processing. This paper argues that conventional one-fit-all preprocessing methods on all images irrespective of their quality and/or their content have many limitations. The paper demonstrates that the negative effects of the speckle noise are more significant in regions where solid tissues are present. Consequently, we propose an adaptive approach of using trained classification models to detect such regions within the image and targeting the speckle noise of the detected regions instead of the whole image. The detection is achieved by placing a sliding window over the image and feeding individual windows to a trained classifier. In this study, we first analyse the content of the images to identify the complexity of the speckle noise by training a linear support vector machine classifier on histogram-based measurements such as skewness and kurtosis to determine whether the image partially or fully needs pre-processing. To evaluate the effectiveness of the new adaptive pre-processing methods, a hybrid two-model solution in which the first trainable model decides if an image requires pre-processing or not and applies it respectively on the whole image. The second model takes a step further to check which parts of the images requires pre-processing and adaptively applies it using the block-based trainable system. The results, based on 138 benign and 104 malignant ovarian ultrasound images, show that the two models performed better than other state-of-the-art pre-processing techniques, which confirms the need for the adaptive system that applies pre-processing only when needed.
The main problem associated with using symmetric/ asymmetric keys is how to securely store and exchange the keys between the parties over open networks particularly in the open environment such as cloud computing. Public Key Infrastructure (PKI) have been providing a practical solution for session key exchange for loads of web services. The key limitation of PKI solution is not only the need for a trusted third partly (e.g. certificate authority) but also the absent link between data owner and the encryption keys. The latter is arguably more important where accessing data needs to be linked with identify of the owner. Currently available key exchange protocols depend on using trusted couriers or secure channels, which can be subject to man-in-the-middle attack and various other attacks. This paper proposes a new protocol for Key Exchange using Biometric Identity Based Encryption (KE-BIBE) that enables parties to securely exchange cryptographic keys even an adversary is monitoring the communication channel between the parties. The proposed protocol combines biometrics with IBE in order to provide a secure way to access symmetric keys based on the identity of the users in unsecure environment. In the KE-BIOBE protocol, the message is first encrypted by the data owner using a traditional symmetric key before migrating it to a cloud storage. The symmetric key is then encrypted using public biometrics of the users selected by data owner to decrypt the message based on Fuzzy Identity-Based Encryption. Only the selected users will be able to decrypt the message by providing a fresh sample of their biometric data. The paper argues that the proposed solution eliminates the needs for a key distribution centre in traditional cryptography. It will also give data owner the power of finegrained sharing of encrypted data by control who can access their data.
The brain Hippocampus component is known to be responsible for memory and spatial navigation. Its functionality depends on the status of different blood vessels within the Hippocampus and is severely impaired by Alzheimer's disease as a result blockage of increasing number of blood vessels by accumulation of amyloid-beta (Aβ) protein. Accurate counting of blood vessels within the Hippocampus of mice brain, from microscopic images, is an active research area for the understanding of Alzheimer’s disease. Here, we report our work on automatic detection of the Region of Interest, i.e. the region in which blood vessels are located. This area typically falls between the hippocampus edge and the line of neurons within the Hippocampus. This paper proposes a new method to detect and exclude the neuron line to improve the accuracy of blood vessel counting because some neurons on it might lead to false positive cases as they look like blood vessels. Our proposed solution is based on using trainable segmentation approach with morphological operations, taking into account variation in colour, intensity values, and image texture. Experiments on a sufficient number of microscopy images of mouse brain demonstrate the effectiveness of the developed solution in preparation for blood vessels counting.
Ovarian masses are categorised into different types of malignant and benign. In order to optimize patient treatment, it is necessary to carry out pre-operational characterisation of the suspect ovarian mass to determine its category. Ultrasound imaging has been widely used in differentiating malignant from benign cases due to its safe and non-intrusive nature, and can be used for determining the number of cysts in the ovary. Presently, the gynaecologist is tasked with manually counting the number of cysts shown on the ultrasound image. This paper proposes, a new approach that automatically segments the ovarian masses and cysts from a static B-mode image. Initially, the method uses a trainable segmentation procedure and a trained neural network classifier to accurately identify the position of the masses and cysts. After that, the borders of the masses can be appraised using watershed transform. The effectiveness of the proposed method has been tested by comparing the number of cysts identified by the method against the manual examination by a gynaecologist. A total of 65 ultrasound images were used for the comparison, and the results showed that the proposed solution is a viable alternative to the manual counting method for accurately determining the number of cysts in a US ovarian image.
Ovarian cysts are a common pathology in women of all age groups. It is estimated that 5-10% of women have a surgical intervention to remove an ovarian cyst in their lifetime. Given this frequency rate, characterization of ovarian masses is essential for optimal management of patients. Patients with benign ovarian masses can be managed conservatively if they are asymptomatic. Mature teratomas are common benign ovarian cysts that occur, in most cases, in premenopausal women. These ovarian cysts can contain different types of human tissue including bone, cartilage, fat, hair, or other tissue. If they are causing no symptoms, they can be harmless and may not require surgery. Subjective assessment by ultrasound examiners has a high diagnostic accuracy when characterising mature teratomas from other types of tumours. The aim of this study is to develop a computerised technique with the potential to characterise mature teratomas and distinguish them from other types of benign ovarian tumours. Local Binary Pattern (LBP) was applied to extract texture features that are specific in distinguishing teratomas. Neural Networks (NN) was then used as a classifier for recognising mature teratomas. A pilot sample set of 130 B-mode static ovarian ultrasound images (41 mature teratomas tumours and 89 other types of benign tumours) was used to test the effectiveness of the proposed technique. Test results show an average accuracy rate of 99.4% with a sensitivity of 100%, specificity of 98.8% and positive predictive value of 98.9%. This study demonstrates that the NN and LBP techniques can accurately classify static 2D B-mode ultrasound images of benign ovarian masses into mature teratomas and other types of benign tumours.
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.
The hippocampus is the region of the brain that is primarily associated with memory and spatial navigation. It is one of the first brain regions to be damaged when a person suffers from Alzheimer's disease. Recent research in this field has focussed on the assessment of damage to different blood vessels within the hippocampal region from a high throughput brain microscopic images. The ultimate aim of our research is the creation of an automatic system to count and classify different blood vessels such as capillaries, veins, and arteries in the hippocampus region. This work should provide biologists with efficient and accurate tools in their investigation of the causes of Alzheimer’s disease. Locating the boundary of the Region of Interest in the hippocampus from microscopic images of mice brain is the first essential stage towards developing such a system. This task benefits from the variation in colour channels and texture between the two sides of the hippocampus and the boundary region. Accordingly, the developed initial step of our research to locating the hippocampus edge uses a colour-based segmentation of the brain image followed by Hough transforms on the colour channel that isolate the hippocampus region. The output is then used to split the brain image into two sides of the detected section of the boundary: the inside region and the outside region. Experimental results on a sufficiently number of microscopic images demonstrate the effectiveness of the developed solution.
Ultrasound imagery has been widely used for medical diagnoses. Ultrasound scanning is safe and non-invasive, and hence used throughout pregnancy for monitoring growth. In the first trimester, an important measurement is that of the Gestation Sac (GS). The task of measuring the GS size from an ultrasound image is done manually by a Gynecologist. This paper presents a new approach to automatically segment a GS from a static B-mode image by exploiting its geometric features for early identification of miscarriage cases. To accurately locate the GS in the image, the proposed solution uses wavelet transform to suppress the speckle noise by eliminating the high-frequency sub-bands and prepare an enhanced image. This is followed by a segmentation step that isolates the GS through the several stages. First, the mean value is used as a threshold to binarise the image, followed by filtering unwanted objects based on their circularity, size and mean of greyscale. The mean value of each object is then used to further select candidate objects. A Region Growing technique is applied as a post-processing to finally identify the GS. We evaluated the effectiveness of the proposed solution by firstly comparing the automatic size measurements of the segmented GS against the manual measurements, and then integrating the proposed segmentation solution into a classification framework for identifying miscarriage cases and pregnancy of unknown viability (PUV). Both test results demonstrate that the proposed method is effective in segmentation the GS and classifying the outcomes with high level accuracy (sensitivity (miscarriage) of 100% and specificity (PUV) of 99.87%).
Mammalian skin is a complex organ composed of a variety of cells and tissue types. The automatic detection and quantification of changes in skin structures has a wide range of applications for biological research. To accurately segment and quantify nuclei, sebaceous gland, hair follicles, and other skin structures, there is a need for a reliable segmentation of different skin layers. This paper presents an efficient segmentation algorithm to segment the three main layers of mice skin, namely epidermis, dermis, and subcutaneous layers. It also segments the epidermis layer into two sub layers, basal and cornified layers. The proposed algorithm uses adaptive colour deconvolution technique on H&E stain images to separate different tissue structures, inter-modes and Otsu thresholding techniques were effectively combined to segment the layers. It then uses a set of morphological and logical operations on each layer to removing unwanted objects. A dataset of 7000 H&E microscopic images of mutant and wild type mice were used to evaluate the effectiveness of the algorithm. Experimental results examined by domain experts have confirmed the viability of the proposed algorithms.
Ultrasound is an effective multipurpose imaging modality that has been widely used for monitoring and diagnosing early
pregnancy events. Technology developments coupled with wide public acceptance has made ultrasound an ideal tool for
better understanding and diagnosing of early pregnancy. The first measurable signs of an early pregnancy are the
geometric characteristics of the Gestational Sac (GS). Currently, the size of the GS is manually estimated from
ultrasound images. The manual measurement involves multiple subjective decisions, in which dimensions are taken in
three planes to establish what is known as Mean Sac Diameter (MSD). The manual measurement results in inter- and
intra-observer variations, which may lead to difficulties in diagnosis. This paper proposes a fully automated diagnosis
solution to accurately identify miscarriage cases in the first trimester of pregnancy based on automatic quantification of
the MSD. Our study shows a strong positive correlation between the manual and the automatic MSD estimations. Our
experimental results based on a dataset of 68 ultrasound images illustrate the effectiveness of the proposed scheme in
identifying early miscarriage cases with classification accuracies comparable with those of domain experts using K
nearest neighbor classifier on automatically estimated MSDs.
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 cannot 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.
With the added security provided by LTE, geographical location has become an important factor for authentication to
enhance the security of remote client authentication during mCommerce applications using Smartphones. Tight
combination of geographical location with classic authentication factors like PINs/Biometrics in a real-time, remote
verification scheme over the LTE layer connection assures the authenticator about the client itself (via PIN/biometric) as
well as the client’s current location, thus defines the important aspects of “who”, “when”, and “where” of the
authentication attempt without eaves dropping or man on the middle attacks. To securely integrate location as an
authentication factor into the remote authentication scheme, client’s location must be verified independently, i.e. the
authenticator should not solely rely on the location determined on and reported by the client’s Smartphone. The latest
wireless data communication technology for mobile phones (4G LTE, Long-Term Evolution), recently being rolled out
in various networks, can be employed to enhance this location-factor requirement of independent location verification.
LTE’s Control Plane LBS provisions, when integrated with user-based authentication and independent source of
localisation factors ensures secure efficient, continuous location tracking of the Smartphone. This feature can be
performed during normal operation of the LTE-based communication between client and network operator resulting in
the authenticator being able to verify the client’s claimed location more securely and accurately. Trials and experiments
show that such algorithm implementation is viable for nowadays Smartphone-based banking via LTE communication.
Although biometric authentication is perceived to be more reliable than traditional authentication schemes, it becomes
vulnerable to many attacks when it comes to remote authentication over open networks and raises serious privacy
concerns. This paper proposes a biometric-based challenge-response approach to be used for remote authentication
between two parties A and B over open networks. In the proposed approach, a remote authenticator system B (e.g. a
bank) challenges its client A who wants to authenticate his/her self to the system by sending a one-time public random
challenge. The client A responds by employing the random challenge along with secret information obtained from a
password and a token to produce a one-time cancellable representation of his freshly captured biometric sample. The
one-time biometric representation, which is based on multi-factor, is then sent back to B for matching. Here, we argue
that eavesdropping of the one-time random challenge and/or the resulting one-time biometric representation does not
compromise the security of the system, and no information about the original biometric data is leaked. In addition to
securing biometric templates, the proposed protocol offers a practical solution for the replay attack on biometric systems.
Moreover, we propose a new scheme for generating a password-based pseudo random numbers/permutation to be used
as a building block in the proposed approach. The proposed scheme is also designed to provide protection against
repudiation. We illustrate the viability and effectiveness of the proposed approach by experimental results based on two
biometric modalities: fingerprint and face biometrics.
Biometric systems such as face recognition must address four key challenges: efficiency, robustness, accuracy and
security. Isometric projection has been proposed as a robust dimension reduction technique for a number of applications,
but it is computationally demanding when applied to high dimensional spaces such as the space of face images. On the
other hand, wavelet transforms have shown to provide an efficient tool for facial feature representation and face
recognition with significant reduction in dimension. In this paper, we propose a hybrid approach that combines the
efficiency and robustness of wavelet transforms with isometric projections for face features extraction in the transformed
domain to be used for recognition. We shall compare the recognition accuracy of our approach with the accuracy of
other commonly used projection techniques in the wavelet domain such as PCA and LDA. The security of biometric
templates is addressed by adopting a lightweight random projection technique as an add-on subsystem. The results are
based on experiments conducted on a publicly available benchmark face database.
KEYWORDS: Mobile devices, Biometrics, Global Positioning System, Computer security, Binary data, Network security, Receivers, Information security, Mobile communications, Cell phones
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 realtime
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 and security are vital concerns for practical biometric systems. The concept of cancelable or revocable
biometrics has been proposed as a solution for biometric template security. Revocable biometric means that biometric
templates are no longer fixed over time and could be revoked in the same way as lost or stolen credit cards are. In this
paper, we describe a novel and an efficient approach to biometric template protection that meets the revocability
property. This scheme can be incorporated into any biometric verification scheme while maintaining, if not improving,
the accuracy of the original biometric system. However, we shall demonstrate the result of applying such transforms on
face biometric templates and compare the efficiency of our approach with that of the well-known random projection
techniques. We shall also present the results of experimental work on recognition accuracy before and after applying the
proposed transform on feature vectors that are generated by wavelet transforms. These results are based on experiments
conducted on a number of well-known face image databases, e.g. Yale and ORL databases.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.