Abstract

Diffusion Weighted MR Imaging (DWI) is routinely used for early detection of cerebral ischemic stroke.DWI with higher b-values (b=2000) provide improved sensitivity, higher conspicuity and reduced artifacts and thus improve the detectability of smallest infarcts than conventional DWI (b=1000). We propose a novel framework for accurately detecting stroke regions by combining information from multiple sources:b2000,b1000 data and the apparent diffusion coefficient map.The detected lesions are finally segmented using an active contour approach. The proposed method was tested on 41 datasets acquired with different protocols. A comparison of our method with a leading method [3] validates the effectiveness of our approach. The mediandice coefficient, sensitivity and specificity for stroke segmentation were 0.84, 87.07% and 99.90% respectively.The strength of the proposed method is its ability to capture (and accurately segment) the small (and large) lesions in the data which are often missed by segmentation methods operating on a single b-value data.

Abstract

This paper addresses the data corruption that occurs due to patient motion during a scan which is particularly a problem in perfusion weighted MRI due to long scan times. Motion correction is typically the rate-limiting step in processing as each volume has to be registered to a reference volume. This is compounded by the dynamically varying contrast in the volume series due to passage of an injected contrast agent. We propose an efficient two stage motion correction method, consisting of motion detection and a 2-pass registration method for aligning the motion-corrupted volumes. A 2D block-wise phase correlation in central slices is used for the first stage. Alignment employs a strategy which is sensitive to the status of the bolus in the volume and is based on gamma-variate function fitting for intensity correction to handle dynamic contrast in DSC-MRI. Evaluation of the approach shows that it is fast and accurate.

Abstract

Several computational visual saliency models have been pro-posed in the context of viewing natural scenes. We aim to investigate the relevance of computational saliency models in medical images in the context of abnormality detection.We report on two studies aimed at understanding the role of visual saliency in medical images. Diffuse lesions in ChestX-Ray images, which are characteristic of Pneumoconiosis and high contrast lesions such as ‘Hard Exudates’ in retinal images were chosen for the study. These approximately correspond to conjunctive and disjunctive targets in a visual search task. Saliency maps were computed using three popular models namely Itti-Koch [7], GBVS [3] and SR [4].The obtained maps were evaluated against gaze maps and ground truth from medical experts. Our results show that GBVS is seen to perform the best(Mdn. ROC area= 0.77) for chest X-Ray images while SRperforms the best (ROC area= 0.73) for retinal images,thus asserting that searching for conjunctive targets calls for a more local examination of an image while disjunctive targets call for a global examination. Based on the results of the above study, we propose extensions for the two best performing models. The first extension makes use of top down knowledge such as lung segmentation. This is shown to improve the performance of GBVS to some extent. Inthe second case the extension is by way of including multi-scale information. This is shown to significantly (by 28.76%)improve abnormality detection. The key insight from these studies is that bottom saliency continues to play a predominant role in examining medical images.

Abstract

Glaucoma is an eye disorder that causes irreversible loss of vision and is prevalent in the aging population. Glaucoma is indicated both by structural changes and presence of atrophy in retina. In retinal images, these appear in the form of subtle variation of local intensities. These variations are typically described using local shape based statistics which are prone to error. We propose an automated, global feature based approach to detect glaucoma from images. An image representation is devised to accentuate subtle indicators of the disease such that global image features can discriminate between normal and glaucoma cases effectively.The proposed method is demonstrated on a large image dataset annotated by 3 medical experts. The results show the method to be effective in detecting subtle glaucoma indi-cators. The classification performance on a dataset of 1186color retinal images containing a mixture of normal, suspect and confirmed cases of glaucoma is 97 percent sensitivity at 87 percent specificity. This improves further when the suspect cases are removed from the abnormal cases. Thus,the proposed method offers a good solution for glaucoma screening from retinal images.

Abstract

The blood perfusion measurement is done by injecting a bolus of contrast agent in the brain followed by imaging over a period of time (scan). This process can extend into minutes and hence any patient motion mid-scan results in corrupted data. This is often observed in dynamic magnetic resonance(MR) imaging for both susceptibility (DSC) and contrast enhanced (DCE) scans. Motion correction done after scanning is typically the most time-intensive step in the entire measurement process since it involves registering each volume in the time-series to a reference volume. We argue that detecting the presence of motion prior to correction can mitigate this problem by reducing the number of volumes to be corrected. The challenge in motion detection is that the injected contrast alters the signal intensity as a function of time leading to false alarms. We present a robust multi-stage method: subdivision of the time series data into bolus and non bolus phases; clustering-based identification of bolus-affected pixels followed by correction of their intensity using a Gamma variate function fitting-based method and a 2Dblock-wise phase correlation for detecting motion between adjacent volumes in DSC-MRI data. The proposed method was tested on a DSC MR sequence with simulated motion of varying degrees. The experimental results show that the entropy of the derived motion fields is a good metric for detecting and categorizing the motion. The proposed scheme when applied prior to correction can achieve on average a37% reduction in the time required for motion correction

Abstract

In this paper, we propose a novel identity-based random key pre-distribution scheme called the identity based key pre-distribution using a pseudo random function (IBPRF), which has better trade-off between communication overhead, network connectivity and resilience against node capture compared to the other existing key predistribution schemes. IBPRF always guarantees that no matter how many sensor nodes are captured, the secret communication between non-compromised sensor nodes are still secure. We then propose an improved version of our scheme in a large-scale hierarchical wireless sensor network. This improved approach has better trade off among network connectivity, security, communication, computational and storage overheads, and scalability than the existing random key pre-distribution schemes. The strength of the proposed IBPRF scheme and its improved approach is establishing pairwise secret keys between neighboring nodes with scantling communication and computational overheads. The improved IBPRF approach further supports a large-scale sensor network for the network connectivity. Through the analysis we show that the improved IBPRF scheme provides better security and lower overheads than other existing schemes.

Abstract

Several symmetric-key distribution mechanisms are proposed in the literature, but most of them are not scalable or they are vulnerable to a small number of captured nodes. In this paper, we propose a new dynamic random key establishment mechanism in large-scale distributed sensor networks, which supports deployment of sensor nodes in multiple phases. In the existing random key distribution schemes, nodes do not refresh their own keys, and thus, the keys in their key rings remain static throughout the lifetime of the network. One good property of our dynamic key distribution scheme is that the already deployed nodes in a deployment phase refresh their own keys in key rings before another deployment phase occurs. The strength of the proposed scheme is that it provides high resilience against node capture as compared to that for the other existing random key distribution schemes. Through analysis and simulation results, we show that our scheme achieves better network performances as compared to those for the existing random key distribution schemes. Finally, we propose an extended version of our scheme for practical usefulness to support high network connectivity and resilience against node capture

Abstract

Most queries in wireless sensor network (WSN) applications are issued at the point of the base station or gateway node of the network. However, for critical applications of WSNs there is a great need to access the real-time data inside the WSN from the nodes, because the real-time data may no longer be accessed through the base station only. So, the real-time data can be given access directly to the external users (parties) those who are authorized to access data as and when they demand. The user authentication plays a vital role for this purpose. In this paper, we propose a new password-based user authentication scheme in hierarchical wireless sensor networks. Our proposed scheme achieves better security and efficiency as compared to those for other existing password-based approaches. In addition, our scheme has merit to change dynamically the user's password locally without the help of the base station or gateway node. Furthermore, our scheme supports dynamic nodes addition after the initial deployment of nodes in the existing sensor network.

Abstract

Several key management schemes for dynamic access control in a user hierarchy are proposed in the literature based on elliptic curve cryptosystem (ECC) and polynomial interpolation. Since the elliptic curve scalar multiplication and construction of interpolating polynomials are time-consuming operations, most of the proposed schemes require high storage and computational complexity. Further, most of the proposed schemes are vulnerable to different attacks including the man-in-the-middle attacks. In this paper, we propose a novel key management scheme for hierarchical access control based on linear polynomials only. We show that our scheme is secure against different attacks including the man-in-the-middle attack, which are required for an idle access control scheme. Moreover, the computational cost and the storage space are significantly reduced in our scheme while compared to the recently proposed related schemes.

Abstract

In a key management scheme for hierarchy based access control, each security class having higher clearance can derive the cryptographic secret keys of its other security classes having lower clearances. In 2008, Chung et al. proposed an efficient scheme on access control in user hierarchy based on elliptic curve cryptosystem [Information Sciences 178 (1) (2008) 230–243]. Their scheme provides solution of key management efficiently for dynamic access problems. However, in this paper, we propose an attack on Chung et al.’s scheme to show that Chung et al.’s scheme is insecure against the exterior root finding attack. We show that under this attack, an attacker (adversary) who is not a user in any security class in a user hierarchy attempts to derive the secret key of a security class by using the root finding algorithm. In order to remedy this attack, we further propose a simple improvement on Chung et al.’s scheme. Overall, the main theme of this paper is very simple: a security flaw is presented on Chung et al.’s scheme and then a fix is provided in order to remedy the security flaw found in Chung et al.’s scheme.