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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

SU-E-J-262: Variability in Texture Analysis of Gynecological Tumors in the Context of An 18F-FDG PET Adaptive Protocol

Purpose: This study examines the effect on texture analysis due to variable reconstruction of PET images in the context of an adaptive FDG PET protocol for node positive gynecologic cancer patients. By measuring variability in texture features from baseline and intra-treatment PET-CT, we can isolate unreliable texture features due to large variation. Methods: A subset of seven patients with node positive gynecological cancers visible on PET was selected for this study. Prescribed dose varied between 45–50.4Gy, with a 55–70Gy boost to the PET positive nodes. A baseline and intratreatment (between 30–36Gy) PET-CT were obtained on a Siemens Biograph mCT. Each clinical PET image set was reconstructed 6 times using a TrueX+TOF algorithm with varying iterations and Gaussian filter. Baseline and intra-treatment primary GTVs were segmented using PET Edge (MIM Software Inc., Cleveland, OH), a semi-automatic gradient-based algorithm, on the clinical PET and transferred to the other reconstructed sets. Using an in-house MATLAB program, four 3D texture matrices describing relationships between voxel intensities in the GTV were generated: co-occurrence, run length, size zone, and neighborhood difference. From these, 39 textural features characterizing texture were calculated in addition to SUV histogram features. The percent variability among parameters was first calculated. Each reconstructed texture feature from baseline and intra-treatment per patient was normalized to the clinical baseline scan and compared using the Wilcoxon signed-rank test in order to isolate variations due to reconstruction parameters. Results: For the baseline scans, 13 texture features showed a mean range greater than 10%. For the intra scans, 28 texture features showed a mean range greater than 10%. Comparing baseline to intra scans, 25 texture features showed p <0.05. Conclusion: Variability due to different reconstruction parameters increased with treatment, however, the majority of texture features showed significant changes during treatment independent of reconstruction effects.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1992

Nested-hierarchical scene models and image segmentation

An improved model of scenes for image analysis purposes, a nested-hierarchical approach which explicitly acknowledges multiple scales of objects or categories of objects, is presented. A multiple-pass, region-based segmentation algorithm improves the segmentation of images from scenes better modeled as a nested hierarchy. A multiple-pass approach allows slow and careful growth of regions while interregion distances are below a global threshold. Past the global threshold, a minimum region size parameter forces development of regions in areas of high local variance. Maximum and viable region size parameters limit the development of undesirably large regions. Application of the segmentation algorithm for forest stand delineation in TM imagery yields regions corresponding to identifiable features in the landscape. The use of a local variance, adaptive-window texture channel in conjunction with spectral bands improves the ability to define regions corresponding to sparsely stocked forest stands which have high internal variance.

Woodcock, C.↗
DOE OSTI2015

TU-CD-BRB-04: Automated Radiomic Features Complement the Prognostic Value of VASARI in the TCGA-GBM Dataset

Purpose: To compare the complementary prognostic value of automated Radiomic features to that of radiologist-annotated VASARI features in TCGA-GBM MRI dataset. Methods: For 96 GBM patients, pre-operative MRI images were obtained from The Cancer Imaging Archive. The abnormal tumor bulks were manually defined on post-contrast T1w images. The contrast-enhancing and necrotic regions were segmented using FAST. From these sub-volumes and the total abnormal tumor bulk, a set of Radiomic features quantifying phenotypic differences based on the tumor intensity, shape and texture, were extracted from the post-contrast T1w images. Minimum-redundancy-maximum-relevance (MRMR) was used to identify the most informative Radiomic, VASARI and combined Radiomic-VASARI features in 70% of the dataset (training-set). Multivariate Cox-proportional hazards models were evaluated in 30% of the dataset (validation-set) using the C-index for OS. A bootstrap procedure was used to assess significance while comparing the C-Indices of the different models. Results: Overall, the Radiomic features showed a moderate correlation with the radiologist-annotated VASARI features (r = −0.37 – 0.49); however that correlation was stronger for the Tumor Diameter and Proportion of Necrosis VASARI features (r = −0.71 – 0.69). After MRMR feature selection, the best-performing Radiomic, VASARI, and Radiomic-VASARI Cox-PH models showed a validation C-index of 0.56 (p = NS), 0.58 (p = NS) and 0.65 (p = 0.01), respectively. The combined Radiomic-VASARI model C-index was significantly higher than that obtained from either the Radiomic or VASARI model alone (p = <0.001). Conclusion: Quantitative volumetric and textural Radiomic features complement the qualitative and semi-quantitative annotated VASARI feature set. The prognostic value of informative qualitative VASARI features such as Eloquent Brain and Multifocality is increased with the addition of quantitative volumetric and textural features from the contrast-enhancing and necrotic tumor regions. These results should be further evaluated in larger validation cohorts.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-R-38: Impact of Smoothing and Noise On Robustness of CBCT Textural Features for Prediction of Response to Radiotherapy Treatment of Head and Neck Cancers

Purpose: To examine the impact of image smoothing and noise on the robustness of textural information extracted from CBCT images for prediction of radiotherapy response for patients with head/neck (H/N) cancers. Methods: CBCT image datasets for 14 patients with H/N cancer treated with radiation (70 Gy in 35 fractions) were investigated. A deformable registration algorithm was used to fuse planning CT’s to CBCT’s. Tumor volume was automatically segmented on each CBCT image dataset. Local control at 1-year was used to classify 8 patients as responders (R), and 6 as non-responders (NR). A smoothing filter [2D Adaptive Weiner (2DAW) with 3 different windows (ψ=3, 5, and 7)], and two noise models (Poisson and Gaussian, SNR=25) were implemented, and independently applied to CBCT images. Twenty-two textural features, describing the spatial arrangement of voxel intensities calculated from gray-level co-occurrence matrices, were extracted for all tumor volumes. Results: Relative to CBCT images without smoothing, none of 22 textural features extracted showed any significant differences when smoothing was applied (using the 2DAW with filtering parameters of ψ=3 and 5), in the responder and non-responder groups. When smoothing, 2DAW with ψ=7 was applied, one textural feature, Information Measure of Correlation, was significantly different relative to no smoothing. Only 4 features (Energy, Entropy, Homogeneity, and Maximum-Probability) were found to be statistically different between the R and NR groups (Table 1). These features remained statistically significant discriminators for R and NR groups in presence of noise and smoothing. Conclusion: This preliminary work suggests that textural classifiers for response prediction, extracted from H&N CBCT images, are robust to low-power noise and low-pass filtering. While other types of filters will alter the spatial frequencies differently, these results are promising. The current study is subject to Type II errors. A much larger cohort of patients is needed to confirm these results. This work was supported in part by a grant from Varian Medical Systems (Palo Alto, CA)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Remote sensing detection enhancement

Big Data in the area of Remote Sensing has been growing rapidly. Remote sensors are used in surveillance, security, traffic, environmental monitoring, and autonomous sensing. Real-time detection of small moving targets using a remote sensor is an ongoing, challenging problem. Since the object is located far away from the sensor, the object often appears too small. The object’s signal-to-noise-ratio (SNR) is often very low. Occurrences such as camera motion, moving backgrounds (e.g., rustling leaves), low contrast and resolution of foreground objects makes it difficult to segment out the targeted moving objects of interest. Due to the limited appearance of the target, it is tough to obtain the target’s characteristics such as its shape and texture. Without these characteristics, filtering out false detections can be a difficult task. Detecting these targets, would often require the detector to operate under a low detection threshold. However, lowering the detection threshold could lead to an increase of false alarms. In this paper, the author will introduce a new method that improves the probability to detect low SNR objects, while decreasing the number of false alarms as compared to using the traditional baseline detection technique.

47 OTHER INSTRUMENTATION↗
NASA NTRS1979

An evaluation of several different classification schemes - Their parameters and performance

The overall objective of this study was to apply and evaluate several of the currently available classification schemes for crop identification. The approaches examined were: (1) a per point Gaussian maximum likelihood classifier, (2) a per point sum of normal densities classifier, (3) a per point linear classifier, (4) a per point Gaussian maximum likelihood decision tree classifier, and (5) a texture sensitive per field Gaussian maximum likelihood classifier. Three agricultural data sets were used in the study: areas from Fayette County, Illinois, and Pottawattamie and Shelby Counties in Iowa. The segments were located in two distinct regions of the Corn Belt to sample variability in soils, climate, and agricultural practices.

Scholz, D.↗
NASA NTRS1982

Verification of LANDSAT imagery for morphametric and topological studies of drainage basins in a section of the western plateau of Sao Paulo State: Tiete-Aguapei watershed

The potential of using LANDSAT MSS imagery for morphometric and topological studies of drainage basins was verified. Using Tiete and Aguapei watershed (Western Plateau) as the test site because of its homogeneous landscape. Morphometric variables collected for ten drainage basins include: circularity index; river density; drainage density; topographic texture; areal and index length; basin parameter; and main river length 1st order and 2nd order channel length. The topographical variables determined were: order; magnitude; bifuraction ratio; weighted bifuraction ratio; number of segments; number of linking; trajectory length; and topological diameter. Data were collected on topographical maps at the scale of 1:250,000 and 1:59,000 and on LANDSAT imagery at the scale of 1:250,000. The results which were summarized on tables for further analysis, show that LANDSAT imagery can supply the lack of topographic charts for drainage studies.

Parada, N. D. J.↗
DOE OSTI2015

SU-E-J-275: Review - Computerized PET/CT Image Analysis in the Evaluation of Tumor Response to Therapy

Purpose: To review the literature in using computerized PET/CT image analysis for the evaluation of tumor response to therapy. Methods: We reviewed and summarized more than 100 papers that used computerized image analysis techniques for the evaluation of tumor response with PET/CT. This review mainly covered four aspects: image registration, tumor segmentation, image feature extraction, and response evaluation. Results: Although rigid image registration is straightforward, it has been shown to achieve good alignment between baseline and evaluation scans. Deformable image registration has been shown to improve the alignment when complex deformable distortions occur due to tumor shrinkage, weight loss or gain, and motion. Many semi-automatic tumor segmentation methods have been developed on PET. A comparative study revealed benefits of high levels of user interaction with simultaneous visualization of CT images and PET gradients. On CT, semi-automatic methods have been developed for only tumors that show marked difference in CT attenuation between the tumor and the surrounding normal tissues. Quite a few multi-modality segmentation methods have been shown to improve accuracy compared to single-modality algorithms. Advanced PET image features considering spatial information, such as tumor volume, tumor shape, total glycolytic volume, histogram distance, and texture features have been found more informative than the traditional SUVmax for the prediction of tumor response. Advanced CT features, including volumetric, attenuation, morphologic, structure, and texture descriptors, have also been found advantage over the traditional RECIST and WHO criteria in certain tumor types. Predictive models based on machine learning technique have been constructed for correlating selected image features to response. These models showed improved performance compared to current methods using cutoff value of a single measurement for tumor response. Conclusion: This review showed that computerized PET/CT image analysis holds great potential to improve the accuracy in evaluation of tumor response. This work was supported in part by the National Cancer Institute Grant R01CA172638.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Multiscale characterization and representation of variability in ceramic matrix composites

Low density, high strength, and high creep and oxidation resistance properties of ceramic matrix composites (CMCs) make them an ideal choice for use in extreme environments in space and military applications. This paper presents a detailed characterization study of structural and manufacturing flaws in Carbon fiber Silicon-Carbide-Nitride matrix (C/SiNC) CMCs at different length-scales. Energy-dispersive spectroscopy (EDS) is used for the chemical characterization of the material’s elemental constituents. High-resolution multiscale graphs obtained from scanning electron microscope (SEM) and confocal laser scanning microscope (LSM) are used to characterize the distribution and morphology of defects at different length scales. This is followed by the classification and quantification of the common manufacturing defects. An image processing algorithm based on the image segmentation process is developed to quantify the variability of various scale-dependent architectural parameters. Finally, a three-dimensional stochastic representative volume element (SRVE) generation algorithm is developed to provide precise representations of material textures at multiple length scales. The developed algorithm accurately accounts for material features and flaws based on a range of multiscale structural and defects characterization results.

36 MATERIALS SCIENCE↗
NASA NTRS2007

Fusing Image Data for Calculating Position of an Object

A computer program has been written for use in maintaining the calibration, with respect to the positions of imaged objects, of a stereoscopic pair of cameras on each of the Mars Explorer Rovers Spirit and Opportunity. The program identifies and locates a known object in the images. The object in question is part of a Moessbauer spectrometer located at the tip of a robot arm, the kinematics of which are known. In the program, the images are processed through a module that extracts edges, combines the edges into line segments, and then derives ellipse centroids from the line segments. The images are also processed by a feature-extraction algorithm that performs a wavelet analysis, then performs a pattern-recognition operation in the wavelet-coefficient space to determine matches to a texture feature measure derived from the horizontal, vertical, and diagonal coefficients. The centroids from the ellipse finder and the wavelet feature matcher are then fused to determine co-location. In the event that a match is found, the centroid (or centroids if multiple matches are present) is reported. If no match is found, the process reports the results of the analyses for further examination by human experts.

Huntsberger, Terrance↗
NASA NTRS1990

Detecting perceptual groupings in textures by continuity considerations

A generalization is presented for the second derivative of a Gaussian D(sup 2)G operator to apply to problems of perceptual organization involving textures. Extensions to other problems of perceptual organization are evident and a new research direction can be established. The technique presented is theoretically pleasing since it has the potential of unifying the entire area of image segmentation under the mathematical notion of continuity and presents a single algorithm to form perceptual groupings where many algorithms existed previously. The eventual impact on both the approach and technique of image processing segmentation operations could be significant.

Greene, Richard J.↗
NASA NTRS1975

Faults on Skylab imagery of the Salton Trough area, Southern California

The author has identified the following significant results. Large segments of the major high angle faults in the Salton Trough area are readily identifiable in Skylab images. Along active faults, distinctive topographic features such as scarps and offset drainage, and vegetation differences due to ground water blockage in alluvium are visible. Other fault-controlled features along inactive as well as active faults visible in Skylab photography include straight mountain fronts, linear valleys, and lithologic differences producing contrasting tone, color or texture. A northwestern extension of a fault in the San Andreas set, is postulated by the regional alignment of possible fault-controlled features. The suspected fault is covered by Holocene deposits, principally windblown sand. A northwest trending tonal change in cultivated fields across Mexicali Valley is visible on Skylab photos. Surface evidence for faulting was not observed; however, the linear may be caused by differences in soil conditions along an extension of a segment of the San Jacinto fault zone. No evidence of faulting could be found along linears which appear as possible extensions of the Substation and Victory Pass faults, demonstrating that the interpretation of linears as faults in small scale photography must be corroborated by field investigations.

Merifield, P. M.↗
DOE OSTI2016

SU-F-R-46: Predicting Distant Failure in Lung SBRT Using Multi-Objective Radiomics Model

Purpose: To predict distant failure in lung stereotactic body radiation therapy (SBRT) in early stage non-small cell lung cancer (NSCLC) by using a new multi-objective radiomics model. Methods: Currently, most available radiomics models use the overall accuracy as the objective function. However, due to data imbalance, a single object may not reflect the performance of a predictive model. Therefore, we developed a multi-objective radiomics model which considers both sensitivity and specificity as the objective functions simultaneously. The new model is used to predict distant failure in lung SBRT using 52 patients treated at our institute. Quantitative imaging features of PET and CT as well as clinical parameters are utilized to build the predictive model. Image features include intensity features (9), textural features (12) and geometric features (8). Clinical parameters for each patient include demographic parameters (4), tumor characteristics (8), treatment faction schemes (4) and pretreatment medicines (6). The modelling procedure consists of two steps: extracting features from segmented tumors in PET and CT; and selecting features and training model parameters based on multi-objective. Support Vector Machine (SVM) is used as the predictive model, while a nondominated sorting-based multi-objective evolutionary computation algorithm II (NSGA-II) is used for solving the multi-objective optimization. Results: The accuracy for PET, clinical, CT, PET+clinical, PET+CT, CT+clinical, PET+CT+clinical are 71.15%, 84.62%, 84.62%, 85.54%, 82.69%, 84.62%, 86.54%, respectively. The sensitivities for the above seven combinations are 41.76%, 58.33%, 50.00%, 50.00%, 41.67%, 41.67%, 58.33%, while the specificities are 80.00%, 92.50%, 90.00%, 97.50%, 92.50%, 97.50%, 97.50%. Conclusion: A new multi-objective radiomics model for predicting distant failure in NSCLC treated with SBRT was developed. The experimental results show that the best performance can be obtained by combining all features.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1991

Aqueous alteration of the Nakhla meteorite

Interior samples of three different Nakhla specimens contain an iron-rich silicate 'rust' (which includes a tentatively identified smectite), Ca-carbonate (probably calcite), Ca-sulfate (possibly gypsum or bassanite), Mg-sulfate (possibly epsomite or kieserite), and NaCl (halite); the total abundance of these phases is estimated as less than 0.01 weight percent of the bulk meteorite. Rust veins are truncated and decrepitated by fusion crust and are preserved as faulted segments in partially healed olivine crystals, indicating that the rust is preterrestrial in origin. Because Ca-carbonate and Ca-sulfate are intergrown with the rust, they are also indicated to be of preterrestrial origin. Similar textural evidence regarding origins of the NaCl and Mg-sulfate is lacking. Impure and poorly crystallized sulfates and halides on the fusion crust of the meteorite suggest leaching of interior (preterrestrial) salts from the interior after Makhla arrived on earth, but coincidental addition of these same salts by terrestrial contamination cannot be exluded. At least the clay-like silicate 'rust', Ca-carbonate, and Ca-sulfate were formed by precipitation from water-based solutions on the Nakhla parent planet, although temperature and pressure conditions of aqueous precipitation are unconstrained by currently available data. It is possible that aqueous alteration on the parent body was responsible for the previously observed disturbance of the Rb-Sr geochronometer in Nakhla at or near 1.3 Ga.

Gooding, James L.↗
NASA NTRS1994

The Large Impact Process Inferred from the Geology of Lunar Multiring Basins

The study of the geology of multiring impact basins on the Moon over the past ten years has given us a rudimentary understanding of how these large structures have formed and evolved on the Moon and other bodies. Two-ring basins on the Moon begin to form at diameters of about 300 km; the transition diameter at which more than two rings appear is uncertain, but it appears to be between 400 and 500 km in diameter. Inner rings tend to be made up of clusters or aligned segments of massifs and are arranged into a crudely concentric pattern; scarp-like elements may or may not be present. Outer rings are much more scarp-like and massifs are rare to absent. Basins display textured deposits, interpreted as ejecta, extending roughly an apparent basin radius exterior to the main topographic rim. Ejecta may have various morphologies, ranging from wormy and hummocky deposits to knobby surfaces; the causes of these variations are not known, but may be related to the energy regime in which the ejecta are deposited. Outside the limits of the textured ejecta are found both fields of satellitic craters (secondaries) and light plains deposits. Impact melt sheets are observed on the floors of relatively unflooded basins. Samples of impact melts from lunar basins have basaltic major-element chemistry, characterized by K, rare-earth elements (REE), P, and other trace elements of varying concentration (KREEP); ages are between 3.8 and 3.9 Ga. These lithologies cannot be produced through the fusion of known pristine (plutonic) rock types, suggesting the occurrence of unknown lithologies within the Moon. These melts were probably generated at middle to lower crustal levels. Ejecta compositions, preservation of pre-basin topography, and deposit morphologies all indicate that the excavation cavity of multiring basins is between about 0.4 and 0.6 times the diameter of the apparent crater diameter. Basin depths of excavation can be inferred from the composition of basin ejecta. A variety of mechanisms has been proposed to account for the formation of basin rings but none of them are entirely plausible. Mechanisms can be divided into two broad groups: (1) forcible uplift due to fluidization of the target; (2) concentric, brittle, fracturing and failure of the target, on regional (megaterraces) to global scales (lithospheric fracturing). Most basin rings are spaced at a constant factor on all planets. Evidence supports divergent ringforming models, so it may be that the ring-locating mechanism differs from the ring-forming mechanism. Thus, large-scale crustal foundering (megaterracing) could occur along concentric zones of weakness created by some type of resonant wave mechanism (fluidization and uplift); such immediate crustal adjustment could then be followed by long-term adjustment of the fractured lithosphere.

Spudis, Paul D.↗
DOE OSTI2016

SU-D-202-02: Quantitative Imaging: Correlation Between Image Feature Analysis and the Accuracy of Manually Drawn Contours On PET Images

Purpose: To evaluate the correlation between image features and the accuracy of manually drawn target contours on synthetic PET images Methods: A digital PET phantom was used in combination with Monte Carlo simulation to create a set of 26 simulated PET images featuring a variety of tumor shapes and activity heterogeneity. These tumor volumes were used as a gold standard in comparisons with manual contours delineated by 10 radiation oncologist on the simulated PET images. Metrics used to evaluate segmentation accuracy included the dice coefficient, false positive dice, false negative dice, symmetric mean absolute surface distance, and absolute volumetric difference. Image features extracted from the simulated tumors consisted of volume, shape complexity, mean curvature, and intensity contrast along with five texture features derived from the gray-level neighborhood difference matrices including contrast, coarseness, busyness, strength, and complexity. Correlation between these features and contouring accuracy were examined. Results: Contour accuracy was reasonably well correlated with a variety of image features. Dice coefficient ranged from 0.7 to 0.90 and was correlated closely with contrast (r=0.43, p=0.02) and complexity (r=0.5, p<0.001). False negative dice ranged from 0.10 to 0.50 and was correlated closely with contrast (r=0.68, p<0.001) and complexity (r=0.66, p<0.001). Absolute volumetric difference ranged from 0.0002 to 0.67 and was correlated closely with coarseness (r=0.46, p=0.02) and complexity (r=0.49, p=0.008). Symmetric mean absolute difference ranged from 0.02 to 1 and was correlated closely with mean curvature (r=0.57, p=0.02) and contrast (r=0.6, p=0.001). Conclusion: The long term goal of this study is to assess whether contouring variability can be reduced by providing feedback to the practitioner based on image feature analysis. The results are encouraging and will be used to develop a statistical model which will enable a prediction of contour accuracy based purely on image feature analysis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

A region-based segmentation method for ultrasound images in HIFU therapy

Purpose: Precisely and efficiently locating a tumor with less manual intervention in ultrasound-guided high-intensity focused ultrasound (HIFU) therapy is one of the keys to guaranteeing the therapeutic result and improving the efficiency of the treatment. The segmentation of ultrasound images has always been difficult due to the influences of speckle, acoustic shadows, and signal attenuation as well as the variety of tumor appearance. The quality of HIFU guidance images is even poorer than that of conventional diagnostic ultrasound images because the ultrasonic probe used for HIFU guidance usually obtains images without making contact with the patient’s body. Therefore, the segmentation becomes more difficult. To solve the segmentation problem of ultrasound guidance image in the treatment planning procedure for HIFU therapy, a novel region-based segmentation method for uterine fibroids in HIFU guidance images is proposed. Methods: Tumor partitioning in HIFU guidance image without manual intervention is achieved by a region-based split-and-merge framework. A new iterative multiple region growing algorithm is proposed to first split the image into homogenous regions (superpixels). The features extracted within these homogenous regions will be more stable than those extracted within the conventional neighborhood of a pixel. The split regions are then merged by a superpixel-based adaptive spectral clustering algorithm. To ensure the superpixels that belong to the same tumor can be clustered together in the merging process, a particular construction strategy for the similarity matrix is adopted for the spectral clustering, and the similarity matrix is constructed by taking advantage of a combination of specifically selected first-order and second-order texture features computed from the gray levels and the gray level co-occurrence matrixes, respectively. The tumor region is picked out automatically from the background regions by an algorithm according to a priori information about the tumor position, shape, and size. Additionally, an appropriate cluster number for spectral clustering can be determined by the same algorithm, thus the automatic segmentation of the tumor region is achieved. Results: To evaluate the performance of the proposed method, 50 uterine fibroid ultrasound images from different patients receiving HIFU therapy were segmented, and the obtained tumor contours were compared with those delineated by an experienced radiologist. For area-based evaluation results, the mean values of the true positive ratio, the false positive ratio, and the similarity were 94.42%, 4.71%, and 90.21%, respectively, and the corresponding standard deviations were 2.54%, 3.12%, and 3.50%, respectively. For distance-based evaluation results, the mean values of the normalized Hausdorff distance and the normalized mean absolute distance were 4.93% and 0.90%, respectively, and the corresponding standard deviations were 2.22% and 0.34%, respectively. The running time of the segmentation process was 12.9 s for a 318 × 333 (pixels) image. Conclusions: Experiments show that the proposed method can segment the tumor region accurately and efficiently with less manual intervention, which provides for the possibility of automatic segmentation and real-time guidance in HIFU therapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

The correlation study of parallel feature extractor and noise reduction approaches

This paper presents literature reviews that show variety of techniques to develop parallel feature extractor and finding its correlation with noise reduction approaches for low light intensity images. Low light intensity images are normally displayed as darker images and low contrast. Without proper handling techniques, those images regularly become evidences of misperception of objects and textures, the incapability to section them. The visual illusions regularly clues to disorientation, user fatigue, poor detection and classification performance of humans and computer algorithms. Noise reduction approaches (NR) therefore is an essential step for other image processing steps such as edge detection, image segmentation, image compression, etc. Parallel Feature Extractor (PFE) meant to capture visual contents of images involves partitioning images into segments, detecting image overlaps if any, and controlling distributed and redistributed segments to extract the features. Working on low light intensity images make the PFE face challenges and closely depend on the quality of its pre-processing steps. Some papers have suggested many well established NR as well as PFE strategies however only few resources have suggested or mentioned the correlation between them. This paper reviews best approaches of the NR and the PFE with detailed explanation on the suggested correlation. This finding may suggest relevant strategies of the PFE development. With the help of knowledge based reasoning, computational approaches and algorithms, we present the correlation study between the NR and the PFE that can be useful for the development and enhancement of other existing PFE.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗