J. Dinesh Peter

A wavelet based morphological mass detection and classification in mammograms

This paper presents an efficient mass detection and classification in mammogram images with the use of features extracted from the mass regions obtained by the automatic morphological based segmentation method. In this approach, the mammogram images are preprocessed to extract the breast profile and improve the contrast. The segmentation is done with combination of various morphological operations. In this approach, the wavelet features are extracted from the detected mass regions and is compared with feature extracted using Gray Level Co-occurrence Matrix (GLCM) to differentiate the TP and FP regions. Classifications of the mass regions are carried out through the Support Vector Machine (SVM) to separate the segmented regions into masses and non-masses based on the features. The methodology achieves 95% of accuracy.

A dual stage adaptive thresholding (DuSAT) for automatic mass detection in mammograms

BACKGROUND AND OBJECTIVE Early detection and diagnosis of breast cancer through mammography screening reduces breast cancer mortality by around 20%. However it is often a complex process to differentiate abnormalities due to the ill-defined margins and subtle appearances. METHOD This paper investigates a new computer aided approach to detect the abnormalities in the digital mammograms using a Dual Stage Adaptive Thresholding (DuSAT). The suspicious mass region is identified using global histogram and local window thresholding method. The global thresholding is done based on the Histogram Peak Analysis (HPA) of the entire image and the threshold is obtained by maximizing the proposed threshold selection criteria. The local thresholding is carried out for each pixel in a defined neighborhood window that provides precise segmentation results. RESULTS The algorithm is verified with 300 images in the DDSM database and 170 images in the mini-MIAS database. Experimental results show that the proposed algorithm achieves an average sensitivity of 92.5% with 1.06 FP/image for DDSM database and an average sensitivity of 93.5% with 0.62 FP/image for mini-MIAS database. CONCLUSION The achieved results depict that the proposed approach provides better results compared to other state-of-art methods for mass detection that helps the radiologists in diagnosis of breast cancer at early stage.

Robust Estimation Approach for NL-Means Filter

Edge preserved smoothing techniques have gained importance for the purpose of image denoising. A good edge preserving filter is given by NL-means filter than any other linear model based approaches. Since the weight function in NL-means filter is closely related to the error norm and influence function in robust estimation framework, this paper explores a refined approach of NL-means filter by using robust estimation function rather than the usual exponential function for its weight calculation. Here the filter output at each pixel is the weighted average of pixels in the surrounding neighborhoods using the chosen robust M-estimator function. Validations using various test images have been analyzed and the results were compared with the other known recent methods. There is a reason to believe that this refined algorithm has some interesting and notable points.

Super-resolution of retinal images using multi-kernel SVR for IoT healthcare applications

Abstract Internet of Things (IoT) healthcare is one of the most popular areas of research due to the rapid development in information and communication technologies. IoT system focusing on human vision would be an ideal solution for the people in developing countries to have adequate medical attention. This paper proposes a hybrid architecture for IoT healthcare to process the retinal images captured using smartphone fundoscopy. The proposed super-resolution (SR) algorithm for retinal images use multi-kernel support vector regression (SVR) to improve the quality of the captured images. The experimental results with respect to the peak-signal-to-noise ratio (PSNR) and mean squared error (MSE) show that the proposed super-resolution approach for retinal images performs better when compared to the state-of-art algorithms. Further, the hybrid architecture helps the ophthalmologists in efficient diagnosis by providing high resolution retinal images.

Visual tracking with conditionally adaptive multiple template update scheme for intricate videos

Tracking of moving objects in real-time scenes is a challenging research problem in computer vision. This is due to incessant live changes in the object features, background, occlusions, and illumination deviations occurring at different instances in the scene. With the objective of tracking visual objects in intricate videos, this paper presents a new color-independent tracking approach, the contributions of which are threefold. First, the illumination level of the sequences is maintained constant using fast discrete curvelet transform. Fisher information metric is calculated based on a cumulative score by comparing the template patches with a reference template at different timeframes. This metric is used for quantifying distances between the consecutive frame histogram distributions. Then, a novel iterative algorithm called conditionally adaptive multiple template update is proposed to regulate the object templates for handling dynamic occlusions effectively. The proposed method is evaluated on a set of extensive challenging benchmark datasets. Experimental results in terms of Center Location Error (CLE), Tracking Success Score (TSS), and Occlusion Success Score (OSS) show that the proposed method competes well with other relevant state-of-the-art tracking methods.

Resource-aware virtual machine migration in IoT cloud

Abstract Internet of Things (IoT) is a promising paradigm enabling many applications to network together through the internet. A huge volume of data is generated by such IoT applications for computation, storage, and analytics through the infrastructure and platform as service offered by cloud computing. Placement and execution of IoT applications in the cloud is a challenging task. In cloud-based IOT application, sudden changes in the sensing environment cause spikes of data flowing into the cloud. This causes resource starvation in the virtual machine and initiates migration of virtual machine from one physical server to other. However, unplanned migration causes a severe performance degradation to the application running on the cloud. Selection of suitable destination server for the virtual machine during migration is an important concern. This paper proposes a resource-aware virtual machine migration technique. Any sudden change in the sensing environment is observed by clustering the servers. The suitable target server is selected based on the resource utilization and job arrival rate of the destination server. The proposed technique is implemented in cloud platform running analytics on smart agriculture application. The evaluation results show that the proposed method outperforms the state of art techniques in terms of the number of migrations, energy utilization and migration time.

SK-SVR

Learning based single image super-resolution approach is proposed.Learning without requiring any prior knowledge or large dictionary.Effective modeling of relationship among patches using sigmoid SVR.Bayesian decision theory to minimize prediction error.Produces better peak signal to noise ratio over existing algorithms. Learning based image super-resolution (SR) has been a striking area of research for generating high-resolution (HR) images from low-resolution (LR) images. A new in-scale single image super-resolution approach is proposed in this paper. The proposed approach effectively applies support vector regression (SVR) for learning and generates high resolution image. Contrasting to many learning based SR algorithms; the proposed approach does not require any training dataset in advance. In addition, sigmoid kernel SVR is used for generating error models and Bayesian decision theory is applied to select the model with the least errors. The performance of the proposed approach is evaluated in terms of peak signal-to-noise ratio (PSNR) and compared with state of the art learning based single image SR algorithms. The experimental results show that the proposed approach outperforms the other SR algorithms.

A combined forecast-based virtual machine migration in cloud data centers

Abstract Live virtual machine (VM) migration improves the performance of cloud data center in terms of energy efficiency, fault tolerance, and availability. The workload handled by cloud data center is dynamic in nature. This increases the resource requirement of either the migrated virtual machine or collocated virtual machine at any time leading to further migration. Inappropriately handled live VM migration imposes severe application performance degradation. In this paper, a combined forecasting technique to predict the resource requirement of any virtual machine is proposed. Based on the current and predicted resource utilization, live migration is performed by Combined Forecast Load-Aware technique. Experiments were carried out to evaluate the performance of the proposed technique on live VM migration. The outcomes indicate that the proposed approach has minimized the number of migrations, energy usage, and the message overhead when compared with the existing state-of-art technique.

Modified Graph-Cut Algorithm with Adaptive Shape Prior

In this paper, a new method to adaptively apply shape prior in “modified graph-cut segmentation technique” has been proposed. The modified graph-cut technique has a better performance in terms of speed and accuracy when compared to the conventional graph-cut approach, introducing adaptive shape prior to this novel (modified) graph-cut approach yields a more efficient and effective result. Adaptive shape prior takes care of noise or object occlusion in a graph-cut segmentation process, it can be realized via a shape probability map, whose presence helps to showcase regions where the presence of a shape is required in an image. If employing adaptive shape prior to a conventional graph-cut technique yielded a better result than the classical approach, it is evident that applying adaptive shape prior to a modified graph-cut would yield a far better result.

A Two-Stage Queue Model for Context-Aware Task Scheduling in Mobile Multimedia Cloud Environments

Multimedia cloud is an emerging computing paradigm that can effectively process media services and provide adequate quality of service (QoS) for multimedia applications from anywhere and on any device at lower cost. However, the mobile clients are still not getting their services in full due to its intrinsic nature such as limited battery life, disconnection, and mobility. In this paper, we propose a context-aware task scheduling algorithm that efficiently allocates the suitable resources to the clients. A queuing-based system model is presented with heuristic resource allocation. The simulation results showed that the proposed solutions provide better performance as compared to the state-of-the-art approaches.