Ronald Joe Stanley

Feature and decision level sensor fusion of electromagnetic induction and ground penetrating radar sensors for landmine detection with hand-held units

Abstract Strategies for fusion of electromagnetic induction (metal detector (MD)) and ground penetrating radar (GPR) sensors for landmine detection are investigated. Feature and decision level algorithms are devised and compared. Features are extracted from the MD signals by correlating with weighted density distribution functions. A multi-frequency band linear prediction method generates features for the GPR. Feature level fusion combines MD and GPR features in a single neural network. Decision level fusion is performed by using the MD features as inputs to one neural network and the GPR features as inputs to the geometric mean and combining the output values. Experimental results are reported on a very large real data set containing 2315 mine encounters of different size, shape, content and metal composition that are measured under different soil conditions at three distinct geographical locations.

Fuzzy logic techniques for blotch feature evaluation in dermoscopy images

Blotches, also called structureless areas, are critical in differentiating malignant melanoma from benign lesions in dermoscopy skin lesion images. In this paper, fuzzy logic techniques are investigated for the automatic detection of blotch features for malignant melanoma discrimination. Four fuzzy sets representative of blotch size and relative and absolute blotch colors are used to extract blotchy areas from a set of dermoscopy skin lesion images. Five previously reported blotch features are computed from the extracted blotches as well as four new features. Using a neural network classifier, malignant melanoma discrimination results are optimized over the range of possible alpha-cuts and compared with results using crisp blotch features. Features computed from blotches using the fuzzy logic techniques based on three plane relative color and blotch size yield the highest diagnostic accuracy of 81.2%.

Automatic telangiectasia analysis in dermoscopy images using adaptive critic design.

Telangiectasia, tiny skin vessels, are important dermoscopy structures used to discriminate basal cell carcinoma (BCC) from benign skin lesions. This research builds off of previously developed image analysis techniques to identify vessels automatically to discriminate benign lesions from BCCs.

Automatic detection of basal cell carcinoma using telangiectasia analysis in dermoscopy skin lesion images

Background: Telangiectasia, dilated blood vessels near the surface of the skin of small, varying diameter, are critical dermoscopy structures used in the detection of basal cell carcinoma (BCC). Distinguishing these vessels from other telangiectasia, that are commonly found in sun‐damaged skin, is challenging.

Region Processing of Ground Penetrating Radar and Electromagnetic Induction for Handheld Landmine Detection

An analysis of the utility of region-based processing of Ground Penetrating Radar (GPR) and Electromagnetic Induction (EMI) is presented. Algorithms for re-sampling GPR data acquired over non-rectangular and non-regular grids are presented. Depth-dependent whitening is used to form GPR images as functions of depth bins. Shape, size, and contrast-based features are used to distinguish mines from non-mines. The processing is compared to point-wise processing of the same data. Comparisons are made to GPR data collected by machine and by humans. Evaluations are performed on calibration data, for which the ground truth is known to the algorithm developers, and blind data, for which the ground truth is not known to the algorithm developers.

Detecting landmines using weighted density distribution function features

Land mine detection using metal detector (MD) and ground penetrating radar (GPR) sensors in hand-held units is a difficult problem. Detection difficulties arise due to: 1) the varying composition and type of metal in land mines, 2) the time-varying nature of background and 3) the variation in height and velocity of the hand-held unit in data measurement. This research introduces new spatially distributed MD features for differentiating land mine signatures from background. The spatially distributed features involve correlating sequences of MD energy values with six weighted density distribution functions. These features are evaluated using a standard back propagation neural network on real data sets containing more than 2,300 mine encounters of different size, shape, content and metal composition that are measured under different soil conditions.

Homologue matching applications: Recognition of overlapped chromosomes

Automated Giemsa-banded chromosome image research has been largely restricted to classification schemes associated with isolated chromosomes within metaphase spreads. Overlapping chromosomes cause difficulties in the automated chromosome karyotyping process. First, overlapping chromosomes must be recognised and decomposed into the proper chromosome parts. Secondly, the decomposed chromosomes must be classified. The first difficulty is associated with image segmentation. The second area is a pattern recognition problem. Even if chromosomes within overlapping clusters are decomposed properly, classification capability is impaired due to feature distortion in the overlapped regions. In normal human metaphase spreads, chromosomes occur in homologous pairs for the autosomal classes, 1–22, and X chromosome for females. This research presents a homologue matching approach for overlapped chromosome recognition. The undistorted grey level information in isolated chromosomes is used for identifying overlapped chromosomes. An isolated chromosome prototype is obtained using neural networks. Dynamic programming and neural networks are compared for matching the prototype to its overlapped homoloque. The homologue matching method is applied to identifying chromosome 2 in 50 metaphase spreads. Experimental results showed that homologue matching using dynamic programming matching based on the density profile achieved a higher correct recognition rate than homologue matching using three different neural network approaches.

Fuzzy Color Clustering for Melanoma Diagnosis in Dermoscopy Images

A fuzzy logic-based color histogram analysis technique is presented for discriminating benign skin lesions from malignant melanomas in dermoscopy images. The approach extends previous research for utilizing a fuzzy set for skin lesion color for a specified class of skin lesions, using alpha-cut and support set cardinality for quantifying a fuzzy ratio skin lesion color feature. Skin lesion discrimination results are reported for the fuzzy clustering ratio over different regions of the lesion over a data set of 517 dermoscopy images consisting of 175 invasive melanomas and 342 benign lesions. Experimental results show that the fuzzy clustering ratio applied over an eight-connected neighborhood on the outer 25% of the skin lesion with an alpha-cut of 0.08 can recognize 92.6% of melanomas with approximately 13.5% false positive lesions. These results show the critical importance of colors in the lesion periphery. Our fuzzy logic-based description of lesion colors offers relevance to clinical descriptions of malignant melanoma.

Impact of weighted density distribution function features on land mine detection using hand-held units

Landmine detection using metal detector (MD) and ground penetrating radar (GPR) sensors in hand-held units is a difficult problem. Detection difficulties arise due to: 1) the varying composition and type of metal in landmines, 2) the time-varying nature of background and 3) the variation in height and velocity of the hand-held unit in data measurement. In prior research, spatially distributed MD features were explored for differentiating landmine signatures from background and non-landmine objects. These features were computed based on correlating sequences of MD energy values with six weighted density distribution functions. In this research the effectiveness of these features to detect landmines of varying metal composition and type is investigated. Experimental results are presented from statistical analysis for feature assessment. Preliminary experimental results are also presented for evaluating the impact on MD feature calculations from varying height and sweep rate of the hand-held unit for data acquisition.

Advances in EMI and GPR algorithms in discrimination mode processing for handheld landmine detectors

This paper presents some advancement in the detection algorithms using EMI sensor, GPR sensor and their fusion. In the EMI algorithm, we propose the application of the weighted distributed density (WDD) functions on the wavelet domain and the time domain of the EMI data for feature based detection. A multilayer perceptron technique is then applied to discriminate between mine and clutter objects based on the wavelet domain and time domain features separately. When the results from the two domains are fused together, the probability of false alarms is reduced by a factor of two. The enhancement in the GPR algorithm includes the depth processing which selects a certain data segment below the ground surface for detection, as well as utilizing the phase variation of the signal return across a mine to achieve better detection. Finally, we present fusion results from EMI and GPR sensors to demonstrate that the two sensors provide complementary information and when they are properly fused together the probability of false alarm can be reduced significantly.