Low-Complexity Image Processing for Real-Time Detection of Neonatal Clonic Seizures

ABSTRACT:-

In this paper, we consider a novel low-complexity real-time image-processing-based approach to the detection of neonatal clonic seizures. Our approach is based on the extraction, from a video of a newborn, of an average luminance signal representative of the body movements. Since clonic seizures are characterized by periodic movements of parts of the body (e.g., the limbs), by evaluating the periodicity of the extracted average luminance signal it is possible to detect the presence of a clonic seizure. The periodicity is investigated, through a hybrid autocorrelation-Yin estimation technique, on a per-window basis, where a time window is defined as a sequence of consecutive video frames. While processing is first carried out on a single window basis, we extend our approach to interlaced windows. The performance of the proposed detection algorithm is investigated, in terms of sensitivity and specificity, through receiver operating characteristic curves, considering video recordings of newborns affected by neonatal seizures.

Keywords:- IEEE Project 2012 Titles, Networking titles 2012, Image Processing Titles 2012, Matlab Titles 2012

ISP An Optimal Out-Of-Core Image-Set Processing Streaming Architecture for Parallel Heterogeneous Systems

ABSTRACT:-

Image population analysis is the class of statistical methods that plays a central role in understanding the development, evolution, and disease of a population. However, these techniques often require excessive computational power and memory that are compounded with a large number of volumetric inputs. Restricted access to supercomputing power limits its influence in general research and practical applications. In this paper we introduce ISP, an Image-Set Processing streaming framework that harnesses the processing power of commodity heterogeneous CPU/GPU systems and attempts to solve this computational problem. In ISP, we introduce specially designed streaming algorithms and data structures that provide an optimal solution for out-of-core multiimage processing problems both in terms of memory usage and computational efficiency. ISP makes use of the asynchronous execution mechanism supported by parallel heterogeneous systems to efficiently hide the inherent latency of the processing pipeline of out-of-core approaches. Consequently, with computationally intensive problems, the ISP out-of-core solution can achieve the same performance as the in-core solution. We demonstrate the efficiency of the ISP framework on synthetic and real datasets.

keywords: IEEE Project Title 2012, Image Processing Title 2012, Networking Title 2012

Hyperconnections and Hierarchical Representations for Grayscale and Multiband Image Processing

ABSTRACT:-

Connections in image processing are an important notion that describes how pixels can be grouped together according to their spatial relationships and/or their gray-level values. In recent years, several works were devoted to the development of new theories of connections among which hyperconnection (h-connection) is a very promising notion. This paper addresses two major issues of this theory. First, we propose a new axiomatic that ensures that every h-connection generates decompositions that are consistent for image processing and, more precisely, for the design of h-connected filters. Second, we develop a general framework to represent the decomposition of an image into h-connections as a tree that corresponds to the generalization of the connected component tree. Such trees are indeed an efficient and intuitive way to design attribute filters or to perform detection tasks based on qualitative or quantitative attributes. These theoretical developments are applied to a particular fuzzy h-connection, and we test this new framework on several classical applications in image processing, i.e., segmentation, connected filtering, and document image binarization. The experiments confirm the suitability of the proposed approach: It is robust to noise, and it provides an efficient framework to design selective filters.

Keywords: IEEE Project Title 2012, Image Processing Title 2012, Networking Title 2012.

Classification of Dielectric Barrier Discharges Using Digital Image Processing Technology

ABSTRACT:-

A digital image processing technology-gray level histogram obtained by image processing of the discharge-is proposed to classify the two kinds of dielectric barrier discharge (DBD) modes. With an increase of the applied voltage, frequency, or exposure time, the kurtosis and the skewness of the gray levels decrease, the standard deviation of the gray levels increases significantly in the case of filamentary mode, in contradistinction to the case of homogeneous mode, where its kurtosis, skewness, and standard deviation remain almost constant. The majority of the pixels correspond to near-zero gray levels in the case of the filamentary mode and have a larger gray level for the homogeneous mode. With a decrease of the pressure, the mean gray level in the homogeneous mode increases significantly when the voltage is kept fixed. This suggests that the onset voltage in higher pressure is larger than that in lower pressure. The mean gray level in Ar is larger than that in He when the voltage is kept fixed. It may mean that the onset voltage in He is larger than that in Ar. These results indicate that the method is not only effective but also simple to classify the DBD modes.

Keywords:- IEEE Project Title 2012, Image Processing Title 2012, Networking Title 2012.

Automated Multiscale Morphometry of Muscle Disease From Second Harmonic Generation Microscopy Using Tensor-Based Image Processing

ABSTRACT:-

Practically, all chronic diseases are characterized by tissue remodeling that alters organ and cellular function through changes to normal organ architecture. Some morphometric alterations become irreversible and account for disease progression even on cellular levels. Early diagnostics to categorize tissue alterations, as well as monitoring progression or remission of disturbed cytoarchitecture upon treatment in the same individual, are a new emerging field. They strongly challenge spatial resolution and require advanced imaging techniques and strategies for detecting morphological changes. We use a combined second harmonic generation (SHG) microscopy and automated image processing approach to quantify morphology in an animal model of inherited Duchenne muscular dystrophy ( mdx mouse) with age. Multiphoton XYZ image stacks from tissue slices reveal vast morphological deviation in muscles from old mdx mice at different scales of cytoskeleton architecture: cell calibers are irregular, myofibrils within cells are twisted, and sarcomere lattice disruptions (detected as “verniers”) are larger in number compared to samples from healthy mice. In young mdx mice, such alterations are only minor. The boundary-tensor approach, adapted and optimized for SHG data, is a suitable approach to allow quick quantitative morphometry in whole tissue slices. The overall detection performance of the automated algorithm compares very well with manual “ by eye” detection, the latter being time consuming and prone to subjective errors. Our algorithm outperfoms manual detection by time with similar reliability. This approach will be an important prerequisite for the implementation of a clinical image databases to diagnose and monitor specific morphological alterations in chronic (muscle) diseases.

Keywords: IEEE Project Title 2012, Image  processing Title 2012, Cloud Computing 2012.

A Generalized Logarithmic Image Processing Model Based on the Gigavision Sensor Model

ABSTRACT

The logarithmic image processing (LIP) model is a mathematical theory providing generalized linear operations for image processing. The gigavision sensor (GVS) is a new imaging device that can be described by a statistical model. In this paper, by studying these two seemingly unrelated models, we develop a generalized LIP (GLIP) model. With the LIP model being its special case, the GLIP model not only provides new insights into the LIP model but also defines new image representations and operations for solving general image processing problems that are not necessarily related to the GVS. A new parametric LIP model is also developed. To illustrate the application of the new scalar multiplication operation, we propose an energy-preserving algorithm for tone mapping, which is a necessary step in image dehazing. By comparing with results using two state-of-the-art algorithms, we show that the new scalar multiplication operation is an effective tool for tone mapping.

Keywords: IEEE Project title 2012, Image Processing 2012, Networking Tilte 2012.

A Complete Processing Chain for Shadow Detection and Reconstruction in VHR Images

ABSTRACT

The presence of shadows in very high resolution (VHR) images can represent a serious obstacle for their full exploitation. This paper proposes to face this problem as a whole through the proposal of a complete processing chain, which relies on various advanced image processing and pattern recognition tools. The first key point of the chain is that shadow areas are not only detected but also classified to allow their customized compensation. The detection and classification tasks are implemented by means of the state-of-the-art support vector machine approach. A quality check mechanism is integrated in order to reduce subsequent misreconstruction problems. The reconstruction is based on a linear regression method to compensate shadow regions by adjusting the intensities of the shaded pixels according to the statistical characteristics of the corresponding nonshadow regions. Moreover, borders are explicitly handled by making use of adaptive morphological filters and linear interpolation for the prevention of possible border artifacts in the reconstructed image. Experimental results obtained on three VHR images representing different shadow conditions are reported, discussed, and compared with two other reconstruction techniques.

Keywords:- IEEE Project Title 2012, Matlab Title, Image Processing Title, Data Mining Title.