On Combining Morphological Component Analysis and Concentric Morphology Model for Mammographic Mass Detection

Mammographic mass detection is an important task for the early diagnosis of breast cancer. However, it is difficult to distinguish masses from normal regions because of their abundant morphological characteristics and ambiguous margins. To improve the mass detection performance, it is essential to effectively preprocess mammogram to preserve both the intensity distribution and morphological characteristics of regions. In this paper, morphological component analysis is first introduced to decompose a mammogram into a piecewise-smooth component and a texture component. The former is utilized in our detection scheme as it effectively suppresses both structural noises and effects of blood vessels. Then, we propose two novel concentric layer criteria to detect different types of suspicious regions in a mammogram. The combination is evaluated based on the Digital Database for Screening Mammography, where 100 malignant cases and 50 benign cases are utilized. The sensitivity of the proposed scheme is 99% in malignant, 88% in benign, and 95.3% in all types of cases. The results show that the proposed detection scheme achieves satisfactory detection performance and preferable compromises between sensitivity and false positive rates.

Analysis of amphetamines in urine with liquid-liquid extraction by capillary electrophoresis with simultaneous electrochemical and electrochemiluminescence detection

Amphetamines including methamphetamine, 3,4-methylenedioxyamphetamine and 3,4-methylenedioxymethamphetamine were separated and detected by CE using simultaneous electrochemical (EC) and electrochemiluminescence (ECL) detection (CE-EC/ ECL). Factors that influenced the separation and detection performance, such as the detection potential, the pH value and concentration of the running buffer, the separation voltage and the pH of the detection buffer, were investigated.

Capillary electrophoresis with electrochemiluminescence detection for measurement of aspartate aminotransferase and alanine aminotransferase activities in biofluids

A new sensitive assay for aspartate aminotransterase (AST) and alanine aminotransferase (ALT) activities in biofluids was developed, based on the separation and detection of alanine, glutamate, and aspartate using capillary electrophoresis (CE) with electrochemiluminescence (ECL) detection. The three amino acids were separated in 5 mM phosphate of pH 2.1 as background electrolyte, and detected on a 500 mu m platinum disk electrode at 1.2 V (versus Ag/AgCl) in the presence of 10 mM tris(2,2'-bipyridyl)ruthenium(II) dissolved in 80 mM phosphate of pH 10.5. A mass detection limit of 37.3 fmol (or 81.5 fmol) for glutamate, corresponding to the product in the enzyme reaction catalyzed by 1.24 x 10(-9) U AST (or 2.72 x 10(-9) U ALT) in a 30 min reaction period, was achieved. This assay was applied to investigate the cytotoxicity effect of ethanol on HepG2 cells and differentiating nonalcoholic steatohepatitis (NASH) from alcoholic liver disease, indicating that the technique is promising for the application in the cell biological and clinical fields.

Simultaneous determination of tramadol and lidocaine in urine by end-column capillary electrophoresis with electrochemiluminescence detection

Tramadol and lidocaine, used as analgesic and local anesthetic in surgery, are partly excreted by kidney. For the first time, we developed a simple and sensitive method, based on capillary electrophoresis with electrochemiluminescence (ECL) detection by end column mode without joint to monitor tramadol and lidocaine in urine. To eliminate the influence of ionic strength of urine sample, analytes were extracted by ether. Tripropylamine (TPA) was used as internal standard. ne recoveries of tramadol and lidocaine were between 94% and 97% at different levels. The method exhibited the linear range for the tramadol and lidocaine from 1.0 X 10(-7) to 1.0 X 10(-4) mol/L with correlation efficient of 0.998. The relative standard deviation (RSD) was 2.9% and 2.7% (n = 8) for tramadol and lidocaine, respectively. The limit of detection (LOD) was 6.0 x 10(-8) mol/L and 4.5 x 10(-8), mol/L (S/N = 3) for tramadol and lidocaine, respectively. The application for detecting tramadol and lidocaine in urine of patients showed that the method was valuable in clinical and biochemical laboratories for detecting tramadol, lidocaine and other tertiary amine pharmaceuticals for various purpose, such as metabolism investigation.

Miniaturized tris(2,2 '-bipyridyl)ruthenium(II) electrochemiluminescence detection cell for capillary electrophoresis and flow injection analysis

The design and performance of a miniaturized chip-type tris(2,2'-bipyridyl)ruthenium(II) [Ru(bpy)(3)(2+)] electrochemiluminescence (ECL) detection cell suitable for both capillary electrophoresis (CE) and flow injection (FI) analysis are described. The cell was fabricated from two pieces of glass (20 x 15 x 1.7 mm), and the 0.5-mm-diameter platinum disk was used as working electrode held at +1.15 V (vs silver wire quasi-reference), the stainless steel guide tubing as counter electrode, and the silver wire as quasi-reference electrode. The performance traits of the cell in both CE and FI modes were evaluated using tripropylamine, proline, and oxalate and compared favorably to those reported for CE and FI detection cells. The advantages of versatility, sensitivity, and accuracy make the device attractive for the routine analysis of amine-containing species or oxalate by CE and FI with Ru(bPY)(3)(2divided by) ECL detection.

New technique for capillary electrophoresis directly coupled with end-column electrochemiluminescence detection

capillary electrophoresis (CE) is characterized. A 300 mum diameter Pt working electrode was used to directly couple with a 75 mum inner diameter separation capillary without an electric field decoupler. The hydrodynamic cyclic voltammogram (CV) of Ru(bpy)(3)(2+) showed that electrophoretic current did not affect the ECL reaction. The presence of high-voltage (HV) field only resulted in the shift of the ECL detection potential. The distance of capillary to electrode was an important parameter for optimizing detection performance as it determined the characteristics of mass transport toward the electrode and the actual concentration of Ru(bpy)(3)(2+) in the detection region. The optimum distance of capillary to electrode was decided by the inner diameter of the capillary, too. For a 75 mum capillary, the working electrode should be placed away from the capillary outlet at a distance within the range of 20-260 mum. The effects of pH value of ECL solution and molecular structure of analytes on peak height and theoretical plate numbers were discussed. Using the 75 mum capillary, under the optimum conditions, the method provided a linear range for tripropylamine (TPA) between 1 x 10(-10) and 1 X 10(-5) mol/L with correlation coefficient of 0.998. The detection limit (signal-to-noise ratio S/N = 3) was 5.0 x 10(-11) mol/L. The relative standard deviation in peak height for eight consecutive injections was 5.6%. By this new technique lidocaine spiked in a urine sample was determined. The method exhibited the linear range for lidocaine from 5.0 x 10(-8) to 1.0 X 10(-5) mol/L with correlation efficient of 0.998. The limit of detection (S/N = 3) was 2.0 x 10(-1) mol/L.

Improving Ship Detection with Polarimetric SAR based on Convolution between Co-polarization Channels

The convolution between co-polarization amplitude only data is studied to improve ship detection performance. The different statistical behaviors of ships and surrounding ocean are characterized a by two-dimensional convolution function (2D-CF) between different polarization channels. The convolution value of the ocean decreases relative to initial data, while that of ships increases. Therefore the contrast of ships to ocean is increased. The opposite variation trend of ocean and ships can distinguish the high intensity ocean clutter from ships' signatures. The new criterion can generally avoid mistaken detection by a constant false alarm rate detector. Our new ship detector is compared with other polarimetric approaches, and the results confirm the robustness of the proposed method.

Contextual models for object detection using boosted random fields

We seek to both detect and segment objects in images. To exploit both local image data as well as contextual information, we introduce Boosted Random Fields (BRFs), which uses Boosting to learn the graph structure and local evidence of a conditional random field (CRF). The graph structure is learned by assembling graph fragments in an additive model. The connections between individual pixels are not very informative, but by using dense graphs, we can pool information from large regions of the image; dense models also support efficient inference. We show how contextual information from other objects can improve detection performance, both in terms of accuracy and speed, by using a computational cascade. We apply our system to detect stuff and things in office and street scenes.

On the Performance of Eigenvalue-Based Cooperative Spectrum Sensing for Cognitive Radio

In this paper, the distribution of the ratio of extreme eigenvalues of a complex Wishart matrix is studied in order to calculate the exact decision threshold as a function of the desired probability of false alarm for the maximum-minimum eigenvalue (MME) detector. In contrast to the asymptotic analysis reported in the literature, we consider a finite number of cooperative receivers and a finite number of samples and derive the exact decision threshold for the probability of false alarm. The proposed exact formulation is further reduced to the case of two receiver-based cooperative spectrum sensing. In addition, an approximate closed-form formula of the exact threshold is derived in terms of a desired probability of false alarm for a special case having equal number of receive antennas and signal samples. Finally, the derived analytical exact decision thresholds are verified with Monte-Carlo simulations. We show that the probability of detection performance using the proposed exact decision thresholds achieves significant performance gains compared to the performance of the asymptotic decision threshold.

Tree-Based Adaptive Spatial Detection for Adaptive Modulated MIMO Systems

Adaptive Multiple-Input Multiple-Output (MIMO) systems achieve a much higher information rate than conventional fixed schemes due to their ability to adapt their configurations according to the wireless communications environment. However, current adaptive MIMO detection schemes exhibit either low performance (and hence low spectral efficiency) or huge computational
complexity. In particular, whilst deterministic Sphere Decoder (SD) detection schemes are well established for static MIMO systems, exhibiting deterministic parallel structure, low computational complexity and quasi-ML detection performance, there are no corresponding adaptive schemes. This paper solves
this problem, describing a hybrid tree based adaptive modulation detection scheme. Fixed Complexity Sphere Decoding (FSD) and Real-Values FSD (RFSD) are modified and combined into a hybrid scheme exploited at low and medium SNR to provide the highest possible information rate with quasi-ML Bit Error
Rate (BER) performance, while Reduced Complexity RFSD, BChase and Decision Feedback (DFE) schemes are exploited in the high SNR regions. This algorithm provides the facility to balance the detection complexity with BER performance with compatible information rate in dynamic, adaptive MIMO communications
environments.

Impact of Metric Selection on Wireless DeAuthentication DoS Attack Performance

DeAuthentication Denial of Service attacks in Public Access WiFi operate by exploiting the lack of authentication of management frames in the 802.11 protocol. Detection of these attacks rely almost exclusively on the selection of appropriate thresholds. In this work the authors demonstrate that there are additional, previously unconsidered, metrics which also influence DoS detection performance. A method of systematically tuning these metrics to optimal values is proposed which ensures that parameter choices are repeatable and verifiable.

State detection of bond wires in IGBT modules using eddy current pulsed thermography

Insulated gate bipolar transistor (IGBT) modules are important safety critical components in electrical power systems. Bond wire lift-off, a plastic deformation between wire bond and adjacent layers of a device caused by repeated power/thermal cycles, is the most common failure mechanism in IGBT modules. For the early detection and characterization of such failures, it is important to constantly detect or monitor the health state of IGBT modules, and the state of bond wires in particular. This paper introduces eddy current pulsed thermography (ECPT), a nondestructive evaluation technique, for the state detection and characterization of bond wire lift-off in IGBT modules. After the introduction of the experimental ECPT system, numerical simulation work is reported. The presented simulations are based on the 3-D electromagnetic-thermal coupling finite-element method and analyze transient temperature distribution within the bond wires. This paper illustrates the thermal patterns of bond wires using inductive heating with different wire statuses (lifted-off or well bonded) under two excitation conditions: nonuniform and uniform magnetic field excitations. Experimental results show that uniform excitation of healthy bonding wires, using a Helmholtz coil, provides the same eddy currents on each, while different eddy currents are seen on faulty wires. Both experimental and numerical results show that ECPT can be used for the detection and characterization of bond wires in power semiconductors through the analysis of the transient heating patterns of the wires. The main impact of this paper is that it is the first time electromagnetic induction thermography, so-called ECPT, has been employed on power/electronic devices. Because of its capability of contactless inspection of multiple wires in a single pass, and as such it opens a wide field of investigation in power/electronic devices for failure detection, performance characterization, and health monitoring. 

Energy Detection based Spectrum Sensing over Two-wave with Diffuse Power Fading Channels

One of the most important factors that affects the performance of energy detection (ED) is the fading channel between the wireless nodes. This article investigates the performance of ED-based spectrum sensing, for cognitive radio (CR), over two-wave with diffuse power (TWDP) fading channels. The TWDP fading model characterizes a variety of fading channels, including well-known canonical fading distributions, such as Rayleigh and Rician, as well as worse than Rayleigh fading conditions modeled by the two-ray fading model. Novel analytic expressions for the average probability of detection over TWDP fading that account for single-user and cooperative spectrum sensing as well as square law selection diversity reception are derived. These expressions are used to analyze the behavior of ED-based spectrum sensing over moderate, severe and extreme fading conditions, and to investigate the use of cooperation and diversity as a means of mitigating the fading effects. Our results indicate that TWDP fading conditions can significantly degrade the sensing performance; however, it is shown that detection performance can be improved when cooperation and diversity are employed. The presented outcomes enable us to identify the limits of ED-based spectrum sensing and quantify the trade-offs between detection performance and energy efficiency for cognitive radio systems deployed within confined environments such as in-vehicular wireless networks.

Classification of Mammograms and DW T based Detection of Microcalcification

Cancer treatment is most effective when it is detected early and the progress in treatment will be closely related to the ability to reduce the proportion of misses in the cancer detection task. The effectiveness of algorithms for detecting cancers can be greatly increased if these algorithms work synergistically with those for characterizing normal mammograms. This research work combines computerized image analysis techniques and neural networks to separate out some fraction of the normal mammograms with extremely high reliability, based on normal tissue identification and removal. The presence of clustered microcalcifications is one of the most important and sometimes the only sign of cancer on a mammogram. 60% to 70% of non-palpable breast carcinoma demonstrates microcalcifications on mammograms [44], [45], [46].WT based techniques are applied on the remaining mammograms, those are obviously abnormal, to detect possible microcalcifications. The goal of this work is to improve the detection performance and throughput of screening-mammography, thus providing a ‘second opinion ‘ to the radiologists. The state-of- the- art DWT computation algorithms are not suitable for practical applications with memory and delay constraints, as it is not a block transfonn. Hence in this work, the development of a Block DWT (BDWT) computational structure having low processing memory requirement has also been taken up.

Automatic Detection and Classification of Glioma Tumors using Statistical Features

The characterization and grading of glioma tumors, via image derived features, for diagnosis, prognosis, and treatment response has been an active research area in medical image computing. This paper presents a novel method for automatic detection and classification of glioma from conventional T2 weighted MR images. Automatic detection of the tumor was established using newly developed method called Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA).Statistical Features were extracted from the detected tumor texture using first order statistics and gray level co-occurrence matrix (GLCM) based second order statistical methods. Statistical significance of the features was determined by t-test and its corresponding p-value. A decision system was developed for the grade detection of glioma using these selected features and its p-value. The detection performance of the decision system was validated using the receiver operating characteristic (ROC) curve. The diagnosis and grading of glioma using this non-invasive method can contribute promising results in medical image computing