Abstract

This paper describes a novel tortuosity measure, based on the premise that tortuosity is a measure of deviation from an ideal non-tortuous vessel. Hence, we propose to model the overall shape of an ideal vessel as a quadratic polynomial at a larger scale while the deviations are modeled as quadratic polynomials at smaller scales. Thus, a given vessel center-line is decomposed as a sum of quadratic polynomials of decreasing scale. This Quadratic Polynomial Decomposition is used as a framework for defining a quantitative measure of tortuosity. As opposed to the existing proposed measures, our method can distinguish between the relative size, shapes and orientations of the vessel bends. The measure is position and scale invariant and satisfies two key desired properties: it varies directly with frequency of twists at fixed amplitude and it varies directly with amplitude of twists when their frequency is fixed. The proposed method has been tested on a standard data set containing 30 artery and 30 vein vessel segments, and shown to be among the best measures as compared to the results of existing methods.

Abstract

Diffusion Weighted Magnetic Resonance Imaging (DWI) is routinely used for early detection of cerebral ischemic changes in acute stroke. Fast acquisition with a standard echoplanar imaging technique generally compromises the image signal-to-noise ratio and in-plane resolution resulting in a reduction of the conspicuity and definition of lesions in the acquired data when viewed on a standard 8-bit display. We present a novel method for automatically and adaptively determining the window settings that enhance the contrast of the image relative to the ischemic lesions. The method performs a coarse segmentation of the lesions followed by contrast-to-noise ratio based computation of the optimal window parameters. The proposed method was tested on 24 datasets acquired with different protocols. The contrast improvement of the lesions is validated through a mirror region of interest analysis and by using the contrast improvement ratio metric. The average obtained improvement in contrast ranges from 25% to 60%. Preliminary results of segmentation showed a good reduction in the false positives and improvement in the lesion boundaries. A perception study of the windowed results against 8 radiologists was conducted. Reduction of 14.17% in the mean response time of detection was observed. Statistical analysis performed using t-test validates the reduction in mean response time to be significant. Results presented in the study show promise in the method.

Abstract

Population screening for sight threatening diseases based on fundus imaging is in place or being considered worldwide. Most existing programs are focussed on a specific disease and are based on manual reading of images, though automated image analysis based solutions are being developed. Exudates and drusen are bright lesions which indicate very different diseases, but can appear to be similar. Discriminating between them is of interest to increase screening performance. In this paper, we present a Bag of Words approach which can be used to design a system that can play the dual role of content based retrieval (of images with exudates or drusen) system and a decision support system to address the problem of bright lesion discrimination. The approach consists of a novel partitioning of an image into patches from which color, texture, edge and granulometry based features are extracted to build a dictionary. A bag of Words approach is then employed to help retrieve images matching a query image as well as derive a decision on the type of bright lesion in the given (query) image. This approach has been implemented and tested on a combination of public and local dataset of 415 images. The area under the curve for image classification is 0.90 and retrieved precision is 0.76.

Abstract

Abnormality detection is the first step performed by doctors during evaluation of medical images in image based diagnosis, followed by disease-specific evaluation of abnormalities. Perception studies have shown that experts primarily focus on abnormal structures during visual examination for diagnosis. One way to model this behavior in automated image analysis is through visual saliency computation. In this paper, we investigate the potential role of visual saliency for computer aided diagnosis algorithm design. We propose a framework for detecting abnormalities that uses visual saliency computation for sparse representation of the image data that preserves the essential features of a normal image. The proposed method is evaluated for the task of bright lesion detection and classification in color retinal images which is of significance in disease screening. An evaluation of the proposed approach on 5 publicly available datasets yielded area under ROC curve of 0.88 to 0.98 for the detection task and accuracies ranging from 0.93 to 0.96 for lesion discrimination. These results establish visual saliency as an alternate avenue for automated abnormality detection.

Abstract

In this paper, we present a semi-automated method to magnify small motions in videos. This method amplifies invisible or hidden motions in videos. To achieve motion magnification, we process the spatial and temporal information obtained from the video itself. Advantage of this work is that it is application independent. Proposed technique estimates required parameters to get desirable results. We demonstrate performance on 9 different videos. Motion magnification performance is equivalent to existing manual methods.

Abstract

In this paper, we present a novel automated method to detect motion in perfusion weighted images (PWI), which is a type of magnetic resonance imaging (MRI). In PWI, blood perfusion is measured by injecting an exogenous tracer called bolus into the blood flow of a patient and then tracking it in the brain. PWI requires a long data acquisition time to forma time series of volumes. Hence, motion occurs due to patient’s unavoidable movements during a scan, which in turn results into motion corrupted data. There is a necessity of detection of these motion artifacts on captured data for correct disease diagnosis.In PWI, intensity profile gets disturbed due to occurrence of motion and/or bolus passage through the blood vessels. There is no way to distinguish between motion occurrence and bolus passage. In this paper, we propose an efficient time-frequency analysis based motion detection method. We show that proposed method is computationally inexpensive and fast. This method is evaluated on a DSC-MRI sequence with simulated motion of different degrees. We show that our approach detects motion in a few seconds.

Abstract

In this paper, we propose a new security protocol for proxy signature by a hierarchy of proxy signers. In this protocol, the original signer delegates his/her signing capability to a predefined hierarchy of proxy signers. Given the documents of a security class to be signed by the original signer, our scheme suggests a protocol for the hierarchy of proxy signers to sign the document on behalf of the original signer. The concept of hierarchical access control limits the number of people who could sign the document to the people who have the required security clearances. User in a security class requires two secret keys: one which identifies his/her security clearance, and that can also be derived by a user of upper level security clearance and second is his/her private key which identifies him/her as a proxy signer for the signature generation. We show that our scheme is efficient in terms of computational complexity as compared to the existing related proxy signature schemes based on the hierarchical access control. Our scheme also supports addition and deletion of security classes in the hierarchy. We show through security analysis that our scheme is secure against possible attacks. Furthermore, through the formal security analysis using the AVISPA (Automated Validation of Internet Security Protocols and Applications) tool we show that our scheme is also secure against passive and active attacks.

Abstract

Recently Lee and Liu proposed an efficient password based authentication and key agreement scheme using smart card for the telecare medicine information system [J. Med. Syst. (2013) 37:9933]. In this paper, we show that though their scheme is efficient, their scheme still has two security weaknesses such as (1) it has design flaws in authentication phase and (2) it has design flaws in password change phase. In order to withstand these flaws found in Lee-Liu’s scheme, we propose an improvement of their scheme. Our improved scheme keeps also the original merits of Lee-Liu’s scheme. We show that our scheme is efficient as compared to Lee-Liu’s scheme. Further, through the security analysis, we show that our scheme is secure against possible known attacks. In addition, we simulate our scheme for the formal security verification using the widely-accepted AVISPA (Automated Validation of Internet Security Protocols and Applications) tool to show that our scheme is secure against passive and active attacks.

Abstract

Several key management schemes for access control in a user hierarchy have been proposed in the recent years. However, most of them have dealt with key management issues for tree structured hierarchies, which result overheads when they are applied to the linear hierarchies. Linear hierarchy comprises a particularly interesting type of hierarchies, and it appears in a wide range of applications such as secure communications within security corporations and multi-layered data streaming. In this paper, we propose an effective key management scheme for linear hierarchies. Compared to the recently proposed Hassen et al.'s scheme, our scheme reduces the storage overhead of each class significantly. Further, use of symmetric-key cryptosystem (Advanced Encryption Standard algorithm) makes the key sizes for security classes to reduce in our scheme. In addition, our scheme is secure against possible attacks required for a linear hierarchy access control scheme. Security along with low storage and computational overheads make our scheme to be much suitable for practical applications for linear hierarchies.

Abstract

In an access control scheme, a deployed sensor node proves its identity to its neighbor nodes through authentication and also proves that it has the proper right to access the sensor network. After successful authentication, the shared secret keys should be established between a deployed sensor node and its neighbor nodes to protect communications. In a wireless sensor network, we often require deployment of new nodes to extend the lifetime of the network because sensor network may be lost due to power exhaustion problem or malicious nodes. In order to protect malicious nodes from joining the sensor network, access control mechanism becomes a major challenge in the design of sensor network protocols due to resource limitations of sensor nodes. Until now, there have been ample of access control schemes published in the literature, and each published scheme has its own merits and demerits. In this paper, we have identified all the functionality features and security requirements which must be satisfied for an ideal access control scheme. We have presented and discussed the recently proposed access control schemes available so far in the literature and their cryptanalysis. We have critically analyzed the storage, communication, computational overheads requirement, functionality and security analysis of the existing schemes. Further, we have performed formal security analysis of existing schemes using the widely-accepted AVISPA (Automated Validation of Internet Security Protocols and Applications) tool. All the schemes are vulnerable to different attacks except the Zhou et al.’s scheme and the Chatterjee et al.’s scheme. However he Zhou et al.’s scheme requires high storage, communication and computational costs. Hence, we feel that there is a strong need to design an ideal efficient access control scheme in future, which should meet all the security requirements and achieve all the functionality features.