2-d spectroscopic imaging of brain tumours

International Nuclear Information System (INIS)

Ferris, N.J.; Brotchie, P.R.

2002-01-01

Full text: This poster illustrates the use of two-dimensional spectroscopic imaging (2-D SI) in the characterisation of brain tumours, and the monitoring of subsequent treatment. After conventional contrast-enhanced MR imaging of patients with known or suspected brain tumours, 2-D SI is performed at a single axial level. The level is chosen to include the maximum volume of abnormal enhancement, or, in non-enhancing lesions. The most extensive T2 signal abnormality. Two different MR systems have been used (Marconi Edge and GE Signa LX); at each site, a PRESS localisation sequence is employed with TE 128-144 ms. Automated software is used to generate spectral arrays, metabolite maps, and metabolite ratio maps from the spectroscopic data. Colour overlays of the maps onto anatomical images are produced using manufacturer software or the Medex imaging data analysis package. High grade gliomas showed choline levels higher than those in apparently normal brain, with decreases in NAA and creatine. Some lesions showed spectral abnormality extending into otherwise normal appearing brain. This was also seen in a case of CNS lymphoma. Lowgrade lesions showed choline levels similar to normal brain, but with decreased NAA. Only a small number of metastases have been studied, but to date no metastasis has shown spectral abnormality beyond the margins suggested by conventional imaging. Follow-up studies generally show spectral heterogeneity. Regions with choline levels higher than those in normal-appearing brain are considered to represent recurrent high-grade tumour. Some regions show choline to be the dominant metabolite, but its level is not greater than that seen in normal brain. These regions are considered suspicious for residual / recurrent tumour when the choline / creatine ratio exceeds 2 (lower ratios may represent treatment effect). 2-D SI improves the initial assessment of brain tumours, and has potential for influencing the radiotherapy treatment strategy. 2-D SI also

Proton MR spectroscopic features of liver cirrhosis : comparing with normal liver

International Nuclear Information System (INIS)

Cho, Soon Gu; Choi, Won; Kim, Young Soo; Kim, Mi Young; Jee, Keum Nahn; Lee, Kyung Hee; Suh, Chang Hae

2000-01-01

The purpose of this study was to determine the proton MR spectroscopic features of liver cirrhosis and the different proton MR spectroscopic features between liver cirrhosis and the normal human liver by comparing the two different conditions. The investigation involved 30 cases of in-vivo proton MR spectra obtained from 15 patients with liver cirrhosis demonstrated on the basis of radiologic and clinical findings, and from 15 normal volunteers without a past or current history of liver disease. MR spectroscopy involved the use of 1.5T GESigna Horizon system (GE Medical Systems, Milwaukee, U. S. A.) with body coil. STEAM (STimulated Echo-Acquisition Mode) with 3000/30 msec of TR/TE was used for signal acquisition; patients were in the prone position and respiration was not interrupted. Cases were assigned to either the cirrhosis or normal group, and using the proton MR spectra of cases of in each group, peak changes occurring in lipids (at 1.3 ppm), glutamate and glutamine (at 2.4-2.5 ppm), phosphomonoesters (at 3.0-3.1 ppm), and glycogen and glucose (at 3.4-3.9 ppm) were evaluated. Mean and standard deviation of the ratio of glutamate + glutamine/lipids, phosphomonoesters/lipids, glycogen + glucose/lipids were calculated from the area of their peaks. The ratio of various metabolites to lipid content was compared between the normal and cirrhosis group. The main characteristic change in proton MR spectra in cases of liver cirrhosis compared with normal liver was decreased relative intensity of lipid peak. Mean and standard deviation of ratio of glutamate + glutamine/lipids, phosphomonoesters /lipids, glycogen + glucose /lipid calculated from the area of their peaks of normal and cirrhotic liver were 0.0204 ±0.0067 and 0.0693 ±0.0371 (p less than 0.05), 0.0146 ± 0.0090 and 0.0881 ±0.0276 (p less than 0.05), 0.0403 ± 0.0267 and 0.2325 ± 0.1071 (p less than 0.05), respectively The other characteristic feature of proton MR spectra of liver cirrhosis was the peak

Textural features for radar image analysis

Science.gov (United States)

Shanmugan, K. S.; Narayanan, V.; Frost, V. S.; Stiles, J. A.; Holtzman, J. C.

1981-01-01

Texture is seen as an important spatial feature useful for identifying objects or regions of interest in an image. While textural features have been widely used in analyzing a variety of photographic images, they have not been used in processing radar images. A procedure for extracting a set of textural features for characterizing small areas in radar images is presented, and it is shown that these features can be used in classifying segments of radar images corresponding to different geological formations.

Saliency image of feature building for image quality assessment

Science.gov (United States)

Ju, Xinuo; Sun, Jiyin; Wang, Peng

2011-11-01

The purpose and method of image quality assessment are quite different for automatic target recognition (ATR) and traditional application. Local invariant feature detectors, mainly including corner detectors, blob detectors and region detectors etc., are widely applied for ATR. A saliency model of feature was proposed to evaluate feasibility of ATR in this paper. The first step consisted of computing the first-order derivatives on horizontal orientation and vertical orientation, and computing DoG maps in different scales respectively. Next, saliency images of feature were built based auto-correlation matrix in different scale. Then, saliency images of feature of different scales amalgamated. Experiment were performed on a large test set, including infrared images and optical images, and the result showed that the salient regions computed by this model were consistent with real feature regions computed by mostly local invariant feature extraction algorithms.

Spectroscopic imaging of X-rays anew look

CERN Document Server

Heijne, Erik H M

2003-01-01

In recent hybrid imaging devices a segmented (50-100mum) semiconductor sensor matrix is matched to a separate readout chip made in some standard silicon CMOS technology. The large number of contacts are made by high-density bump bonding interconnect technology. Extended functionality with hundreds of transistors in each electronics cell can serve a variety of purposes. Fluctuations in the response of the sensor matrix can be compensated in real-time. A single photon processing circuit in each pixel can achieve spectroscopic imaging by energy measurement even at high rates. However, it is necessary to take into account the distribution of the signals over adjacent pixels. Another possibility is the discrimination by energy of photon conversions in stacked layers with increasing absorption.

MOSS spectroscopic camera for imaging time resolved plasma species temperature and flow speed

International Nuclear Information System (INIS)

Michael, Clive; Howard, John

2000-01-01

A MOSS (Modulated Optical Solid-State) spectroscopic camera has been devised to monitor the spatial and temporal variations of temperatures and flow speeds of plasma ion species, the Doppler broadening measurement being made of spectroscopic lines specified. As opposed to a single channel MOSS spectrometer, the camera images light from plasma onto an array of light detectors, being mentioned 2D imaging of plasma ion temperatures and flow speeds. In addition, compared to a conventional grating spectrometer, the MOSS camera shows an excellent light collecting performance which leads to the improvement of signal to noise ratio and of time resolution. The present paper first describes basic items of MOSS spectroscopy, then follows MOSS camera with an emphasis on the optical system of 2D imaging. (author)

MOSS spectroscopic camera for imaging time resolved plasma species temperature and flow speed

Energy Technology Data Exchange (ETDEWEB)

Michael, Clive; Howard, John [Australian National Univ., Plasma Research Laboratory, Canberra (Australia)

2000-03-01

A MOSS (Modulated Optical Solid-State) spectroscopic camera has been devised to monitor the spatial and temporal variations of temperatures and flow speeds of plasma ion species, the Doppler broadening measurement being made of spectroscopic lines specified. As opposed to a single channel MOSS spectrometer, the camera images light from plasma onto an array of light detectors, being mentioned 2D imaging of plasma ion temperatures and flow speeds. In addition, compared to a conventional grating spectrometer, the MOSS camera shows an excellent light collecting performance which leads to the improvement of signal to noise ratio and of time resolution. The present paper first describes basic items of MOSS spectroscopy, then follows MOSS camera with an emphasis on the optical system of 2D imaging. (author)

Remote Sensing Image Registration Using Multiple Image Features

Directory of Open Access Journals (Sweden)

Kun Yang

2017-06-01

Full Text Available Remote sensing image registration plays an important role in military and civilian fields, such as natural disaster damage assessment, military damage assessment and ground targets identification, etc. However, due to the ground relief variations and imaging viewpoint changes, non-rigid geometric distortion occurs between remote sensing images with different viewpoint, which further increases the difficulty of remote sensing image registration. To address the problem, we propose a multi-viewpoint remote sensing image registration method which contains the following contributions. (i A multiple features based finite mixture model is constructed for dealing with different types of image features. (ii Three features are combined and substituted into the mixture model to form a feature complementation, i.e., the Euclidean distance and shape context are used to measure the similarity of geometric structure, and the SIFT (scale-invariant feature transform distance which is endowed with the intensity information is used to measure the scale space extrema. (iii To prevent the ill-posed problem, a geometric constraint term is introduced into the L2E-based energy function for better behaving the non-rigid transformation. We evaluated the performances of the proposed method by three series of remote sensing images obtained from the unmanned aerial vehicle (UAV and Google Earth, and compared with five state-of-the-art methods where our method shows the best alignments in most cases.

Recent advances in the applications of vibrational spectroscopic imaging and mapping to pharmaceutical formulations

Science.gov (United States)

Ewing, Andrew V.; Kazarian, Sergei G.

2018-05-01

Vibrational spectroscopic imaging and mapping approaches have continued in their development and applications for the analysis of pharmaceutical formulations. Obtaining spatially resolved chemical information about the distribution of different components within pharmaceutical formulations is integral for improving the understanding and quality of final drug products. This review aims to summarise some key advances of these technologies over recent years, primarily since 2010. An overview of FTIR, NIR, terahertz spectroscopic imaging and Raman mapping will be presented to give a perspective of the current state-of-the-art of these techniques for studying pharmaceutical samples. This will include their application to reveal spatial information of components that reveals molecular insight of polymorphic or structural changes, behaviour of formulations during dissolution experiments, uniformity of materials and detection of counterfeit products. Furthermore, new advancements will be presented that demonstrate the continuing novel applications of spectroscopic imaging and mapping, namely in FTIR spectroscopy, for studies of microfluidic devices. Whilst much of the recently developed work has been reported by academic groups, examples of the potential impacts of utilising these imaging and mapping technologies to support industrial applications have also been reviewed.

Aberration-free FTIR spectroscopic imaging of live cells in microfluidic devices.

Science.gov (United States)

Chan, K L Andrew; Kazarian, Sergei G

2013-07-21

The label-free, non-destructive chemical analysis offered by FTIR spectroscopic imaging is a very attractive and potentially powerful tool for studies of live biological cells. FTIR imaging of live cells is a challenging task, due to the fact that cells are cultured in an aqueous environment. While the synchrotron facility has proven to be a valuable tool for FTIR microspectroscopic studies of single live cells, we have demonstrated that high quality infrared spectra of single live cells using an ordinary Globar source can also be obtained by adding a pair of lenses to a common transmission liquid cell. The lenses, when placed on the transmission cell window, form pseudo hemispheres which removes the refraction of light and hence improve the imaging and spectral quality of the obtained data. This study demonstrates that infrared spectra of single live cells can be obtained without the focus shifting effect at different wavenumbers, caused by the chromatic aberration. Spectra of the single cells have confirmed that the measured spectral region remains in focus across the whole range, while spectra of the single cells measured without the lenses have shown some erroneous features as a result of the shift of focus. It has also been demonstrated that the addition of lenses can be applied to the imaging of cells in microfabricated devices. We have shown that it was not possible to obtain a focused image of an isolated cell in a droplet of DPBS in oil unless the lenses are applied. The use of the approach described herein allows for well focused images of single cells in DPBS droplets to be obtained.

Hemorrhage detection in MRI brain images using images features

Science.gov (United States)

Moraru, Luminita; Moldovanu, Simona; Bibicu, Dorin; Stratulat (Visan), Mirela

2013-11-01

The abnormalities appear frequently on Magnetic Resonance Images (MRI) of brain in elderly patients presenting either stroke or cognitive impairment. Detection of brain hemorrhage lesions in MRI is an important but very time-consuming task. This research aims to develop a method to extract brain tissue features from T2-weighted MR images of the brain using a selection of the most valuable texture features in order to discriminate between normal and affected areas of the brain. Due to textural similarity between normal and affected areas in brain MR images these operation are very challenging. A trauma may cause microstructural changes, which are not necessarily perceptible by visual inspection, but they could be detected by using a texture analysis. The proposed analysis is developed in five steps: i) in the pre-processing step: the de-noising operation is performed using the Daubechies wavelets; ii) the original images were transformed in image features using the first order descriptors; iii) the regions of interest (ROIs) were cropped from images feature following up the axial symmetry properties with respect to the mid - sagittal plan; iv) the variation in the measurement of features was quantified using the two descriptors of the co-occurrence matrix, namely energy and homogeneity; v) finally, the meaningful of the image features is analyzed by using the t-test method. P-value has been applied to the pair of features in order to measure they efficacy.

Sensitivity-encoded (SENSE) proton echo-planar spectroscopic imaging (PEPSI) in the human brain.

Science.gov (United States)

Lin, Fa-Hsuan; Tsai, Shang-Yueh; Otazo, Ricardo; Caprihan, Arvind; Wald, Lawrence L; Belliveau, John W; Posse, Stefan

2007-02-01

Magnetic resonance spectroscopic imaging (MRSI) provides spatially resolved metabolite information that is invaluable for both neuroscience studies and clinical applications. However, lengthy data acquisition times, which are a result of time-consuming phase encoding, represent a major challenge for MRSI. Fast MRSI pulse sequences that use echo-planar readout gradients, such as proton echo-planar spectroscopic imaging (PEPSI), are capable of fast spectral-spatial encoding and thus enable acceleration of image acquisition times. Combining PEPSI with recent advances in parallel MRI utilizing RF coil arrays can further accelerate MRSI data acquisition. Here we investigate the feasibility of ultrafast spectroscopic imaging at high field (3T and 4T) by combining PEPSI with sensitivity-encoded (SENSE) MRI using eight-channel head coil arrays. We show that the acquisition of single-average SENSE-PEPSI data at a short TE (15 ms) can be accelerated to 32 s or less, depending on the field strength, to obtain metabolic images of choline (Cho), creatine (Cre), N-acetyl-aspartate (NAA), and J-coupled metabolites (e.g., glutamate (Glu) and inositol (Ino)) with acceptable spectral quality and localization. The experimentally measured reductions in signal-to-noise ratio (SNR) and Cramer-Rao lower bounds (CRLBs) of metabolite resonances were well explained by both the g-factor and reduced measurement times. Thus, this technology is a promising means of reducing the scan times of 3D acquisitions and time-resolved 2D measurements. Copyright (c) 2007 Wiley-Liss, Inc.

Infrared Spectroscopic Imaging: The Next Generation

Science.gov (United States)

Bhargava, Rohit

2013-01-01

Infrared (IR) spectroscopic imaging seemingly matured as a technology in the mid-2000s, with commercially successful instrumentation and reports in numerous applications. Recent developments, however, have transformed our understanding of the recorded data, provided capability for new instrumentation, and greatly enhanced the ability to extract more useful information in less time. These developments are summarized here in three broad areas— data recording, interpretation of recorded data, and information extraction—and their critical review is employed to project emerging trends. Overall, the convergence of selected components from hardware, theory, algorithms, and applications is one trend. Instead of similar, general-purpose instrumentation, another trend is likely to be diverse and application-targeted designs of instrumentation driven by emerging component technologies. The recent renaissance in both fundamental science and instrumentation will likely spur investigations at the confluence of conventional spectroscopic analyses and optical physics for improved data interpretation. While chemometrics has dominated data processing, a trend will likely lie in the development of signal processing algorithms to optimally extract spectral and spatial information prior to conventional chemometric analyses. Finally, the sum of these recent advances is likely to provide unprecedented capability in measurement and scientific insight, which will present new opportunities for the applied spectroscopist. PMID:23031693

Feature-Based Retinal Image Registration Using D-Saddle Feature

Directory of Open Access Journals (Sweden)

Roziana Ramli

2017-01-01

Full Text Available Retinal image registration is important to assist diagnosis and monitor retinal diseases, such as diabetic retinopathy and glaucoma. However, registering retinal images for various registration applications requires the detection and distribution of feature points on the low-quality region that consists of vessels of varying contrast and sizes. A recent feature detector known as Saddle detects feature points on vessels that are poorly distributed and densely positioned on strong contrast vessels. Therefore, we propose a multiresolution difference of Gaussian pyramid with Saddle detector (D-Saddle to detect feature points on the low-quality region that consists of vessels with varying contrast and sizes. D-Saddle is tested on Fundus Image Registration (FIRE Dataset that consists of 134 retinal image pairs. Experimental results show that D-Saddle successfully registered 43% of retinal image pairs with average registration accuracy of 2.329 pixels while a lower success rate is observed in other four state-of-the-art retinal image registration methods GDB-ICP (28%, Harris-PIIFD (4%, H-M (16%, and Saddle (16%. Furthermore, the registration accuracy of D-Saddle has the weakest correlation (Spearman with the intensity uniformity metric among all methods. Finally, the paired t-test shows that D-Saddle significantly improved the overall registration accuracy of the original Saddle.

Initial experience of 3 tesla endorectal coil magnetic resonance imaging and 1H-spectroscopic imaging of the prostate.

NARCIS (Netherlands)

Fütterer, J.J.; Scheenen, T.W.J.; Huisman, H.J.; Klomp, D.W.J.; Dorsten, F.A. van; Hulsbergen-van de Kaa, C.A.; Witjes, J.A.; Heerschap, A.; Barentsz, J.O.

2004-01-01

RATIONALE AND OBJECTIVES: We sought to explore the feasibility of magnetic resonance imaging (MRI) of the prostate at 3T, with the knowledge of potential drawbacks of MRI at high field strengths. MATERIAL AND METHOD: MRI, dynamic MRI, and 1H-MR spectroscopic imaging were performed in 10 patients

Quantitative imaging features: extension of the oncology medical image database

Science.gov (United States)

Patel, M. N.; Looney, P. T.; Young, K. C.; Halling-Brown, M. D.

2015-03-01

Radiological imaging is fundamental within the healthcare industry and has become routinely adopted for diagnosis, disease monitoring and treatment planning. With the advent of digital imaging modalities and the rapid growth in both diagnostic and therapeutic imaging, the ability to be able to harness this large influx of data is of paramount importance. The Oncology Medical Image Database (OMI-DB) was created to provide a centralized, fully annotated dataset for research. The database contains both processed and unprocessed images, associated data, and annotations and where applicable expert determined ground truths describing features of interest. Medical imaging provides the ability to detect and localize many changes that are important to determine whether a disease is present or a therapy is effective by depicting alterations in anatomic, physiologic, biochemical or molecular processes. Quantitative imaging features are sensitive, specific, accurate and reproducible imaging measures of these changes. Here, we describe an extension to the OMI-DB whereby a range of imaging features and descriptors are pre-calculated using a high throughput approach. The ability to calculate multiple imaging features and data from the acquired images would be valuable and facilitate further research applications investigating detection, prognosis, and classification. The resultant data store contains more than 10 million quantitative features as well as features derived from CAD predictions. Theses data can be used to build predictive models to aid image classification, treatment response assessment as well as to identify prognostic imaging biomarkers.

Three dimensional nuclear magnetic resonance spectroscopic imaging of sodium ions using stochastic excitation and oscillating gradients

International Nuclear Information System (INIS)

Frederick, B.deB.

1994-12-01

Nuclear magnetic resonance (NMR) spectroscopic imaging of 23 Na holds promise as a non-invasive method of mapping Na + distributions, and for differentiating pools of Na + ions in biological tissues. However, due to NMR relaxation properties of 23 Na in vivo, a large fraction of Na + is not visible with conventional NMR imaging methods. An alternate imaging method, based on stochastic excitation and oscillating gradients, has been developed which is well adapted to measuring nuclei with short T 2 . Contemporary NMR imaging techniques have dead times of up to several hundred microseconds between excitation and sampling, comparable to the shortest in vivo 23 Na T 2 values, causing significant signal loss. An imaging strategy based on stochastic excitation has been developed which greatly reduces experiment dead time by reducing peak radiofrequency (RF) excitation power and using a novel RF circuit to speed probe recovery. Continuously oscillating gradients are used to eliminate transient eddy currents. Stochastic 1 H and 23 Na spectroscopic imaging experiments have been performed on a small animal system with dead times as low as 25μs, permitting spectroscopic imaging with 100% visibility in vivo. As an additional benefit, the encoding time for a 32x32x32 spectroscopic image is under 30 seconds. The development and analysis of stochastic NMR imaging has been hampered by limitations of the existing phase demodulation reconstruction technique. Three dimensional imaging was impractical due to reconstruction time, and design and analysis of proposed experiments was limited by the mathematical intractability of the reconstruction method. A new reconstruction method for stochastic NMR based on Fourier interpolation has been formulated combining the advantage of a several hundredfold reduction in reconstruction time with a straightforward mathematical form

Imaging spectroscopic analysis at the Advanced Light Source

International Nuclear Information System (INIS)

MacDowell, A. A.; Warwick, T.; Anders, S.; Lamble, G.M.; Martin, M.C.; McKinney, W.R.; Padmore, H.A.

1999-01-01

One of the major advances at the high brightness third generation synchrotrons is the dramatic improvement of imaging capability. There is a large multi-disciplinary effort underway at the ALS to develop imaging X-ray, UV and Infra-red spectroscopic analysis on a spatial scale from. a few microns to 10nm. These developments make use of light that varies in energy from 6meV to 15KeV. Imaging and spectroscopy are finding applications in surface science, bulk materials analysis, semiconductor structures, particulate contaminants, magnetic thin films, biology and environmental science. This article is an overview and status report from the developers of some of these techniques at the ALS. The following table lists all the currently available microscopes at the. ALS. This article will describe some of the microscopes and some of the early applications

Image segmentation-based robust feature extraction for color image watermarking

Science.gov (United States)

Li, Mianjie; Deng, Zeyu; Yuan, Xiaochen

2018-04-01

This paper proposes a local digital image watermarking method based on Robust Feature Extraction. The segmentation is achieved by Simple Linear Iterative Clustering (SLIC) based on which an Image Segmentation-based Robust Feature Extraction (ISRFE) method is proposed for feature extraction. Our method can adaptively extract feature regions from the blocks segmented by SLIC. This novel method can extract the most robust feature region in every segmented image. Each feature region is decomposed into low-frequency domain and high-frequency domain by Discrete Cosine Transform (DCT). Watermark images are then embedded into the coefficients in the low-frequency domain. The Distortion-Compensated Dither Modulation (DC-DM) algorithm is chosen as the quantization method for embedding. The experimental results indicate that the method has good performance under various attacks. Furthermore, the proposed method can obtain a trade-off between high robustness and good image quality.

INTEGRATION OF IMAGE-DERIVED AND POS-DERIVED FEATURES FOR IMAGE BLUR DETECTION

Directory of Open Access Journals (Sweden)

T.-A. Teo

2016-06-01

Full Text Available The image quality plays an important role for Unmanned Aerial Vehicle (UAV’s applications. The small fixed wings UAV is suffering from the image blur due to the crosswind and the turbulence. Position and Orientation System (POS, which provides the position and orientation information, is installed onto an UAV to enable acquisition of UAV trajectory. It can be used to calculate the positional and angular velocities when the camera shutter is open. This study proposes a POS-assisted method to detect the blur image. The major steps include feature extraction, blur image detection and verification. In feature extraction, this study extracts different features from images and POS. The image-derived features include mean and standard deviation of image gradient. For POS-derived features, we modify the traditional degree-of-linear-blur (blinear method to degree-of-motion-blur (bmotion based on the collinear condition equations and POS parameters. Besides, POS parameters such as positional and angular velocities are also adopted as POS-derived features. In blur detection, this study uses Support Vector Machines (SVM classifier and extracted features (i.e. image information, POS data, blinear and bmotion to separate blur and sharp UAV images. The experiment utilizes SenseFly eBee UAV system. The number of image is 129. In blur image detection, we use the proposed degree-of-motion-blur and other image features to classify the blur image and sharp images. The classification result shows that the overall accuracy using image features is only 56%. The integration of image-derived and POS-derived features have improved the overall accuracy from 56% to 76% in blur detection. Besides, this study indicates that the performance of the proposed degree-of-motion-blur is better than the traditional degree-of-linear-blur.

Fiber optic spectroscopic digital imaging sensor and method for flame properties monitoring

Science.gov (United States)

Zelepouga, Serguei A [Hoffman Estates, IL; Rue, David M [Chicago, IL; Saveliev, Alexei V [Chicago, IL

2011-03-15

A system for real-time monitoring of flame properties in combustors and gasifiers which includes an imaging fiber optic bundle having a light receiving end and a light output end and a spectroscopic imaging system operably connected with the light output end of the imaging fiber optic bundle. Focusing of the light received by the light receiving end of the imaging fiber optic bundle by a wall disposed between the light receiving end of the fiber optic bundle and a light source, which wall forms a pinhole opening aligned with the light receiving end.

Wilson’s disease: Atypical imaging features

Directory of Open Access Journals (Sweden)

Venugopalan Y Vishnu

2016-10-01

Full Text Available Wilson’s disease is a genetic movement disorder with characteristic clinical and imaging features. We report a 17- year-old boy who presented with sialorrhea, hypophonic speech, paraparesis with repeated falls and recurrent seizures along with cognitive decline. He had bilateral Kayser Flescher rings. Other than the typical features of Wilson’s disease in cranial MRI, there were extensive white matter signal abnormalities (T2 and FLAIR hyperintensities and gyriform contrast enhancement which are rare imaging features in Wilson's disease. A high index of suspicion is required to diagnose Wilson’s disease when atypical imaging features are present.

Metabolite ratios in 1H MR spectroscopic imaging of the prostate

NARCIS (Netherlands)

Kobus, T.; Wright, A.J.; Weiland, E.; Heerschap, A.; Scheenen, T.W.J.

2015-01-01

In (1)H MR spectroscopic imaging ((1)H-MRSI) of the prostate the spatial distribution of the signal levels of the metabolites choline, creatine, polyamines, and citrate are assessed. The ratio of choline (plus spermine as the main polyamine) plus creatine over citrate [(Cho+(Spm+)Cr)/Cit] is derived

Unsupervised feature learning for autonomous rock image classification

Science.gov (United States)

Shu, Lei; McIsaac, Kenneth; Osinski, Gordon R.; Francis, Raymond

2017-09-01

Autonomous rock image classification can enhance the capability of robots for geological detection and enlarge the scientific returns, both in investigation on Earth and planetary surface exploration on Mars. Since rock textural images are usually inhomogeneous and manually hand-crafting features is not always reliable, we propose an unsupervised feature learning method to autonomously learn the feature representation for rock images. In our tests, rock image classification using the learned features shows that the learned features can outperform manually selected features. Self-taught learning is also proposed to learn the feature representation from a large database of unlabelled rock images of mixed class. The learned features can then be used repeatedly for classification of any subclass. This takes advantage of the large dataset of unlabelled rock images and learns a general feature representation for many kinds of rocks. We show experimental results supporting the feasibility of self-taught learning on rock images.

Infrared image enhancement with learned features

Science.gov (United States)

Fan, Zunlin; Bi, Duyan; Ding, Wenshan

2017-11-01

Due to the variation of imaging environment and limitations of infrared imaging sensors, infrared images usually have some drawbacks: low contrast, few details and indistinct edges. Hence, to promote the applications of infrared imaging technology, it is essential to improve the qualities of infrared images. To enhance image details and edges adaptively, we propose an infrared image enhancement method under the proposed image enhancement scheme. On the one hand, on the assumption of high-quality image taking more evident structure singularities than low-quality images, we propose an image enhancement scheme that depends on the extractions of structure features. On the other hand, different from the current image enhancement algorithms based on deep learning networks that try to train and build the end-to-end mappings on improving image quality, we analyze the significance of first layer in Stacked Sparse Denoising Auto-encoder and propose a novel feature extraction for the proposed image enhancement scheme. Experiment results prove that the novel feature extraction is free from some artifacts on the edges such as blocking artifacts, ;gradient reversal;, and pseudo contours. Compared with other enhancement methods, the proposed method achieves the best performance in infrared image enhancement.

Spectroscopic magnetic resonance imaging of a tumefactive demyelinating lesion

Energy Technology Data Exchange (ETDEWEB)

Law, M.; Meltzer, D.E.; Cha, S. [MRI Department, Department of Radiology, New York University Medical Center, Schwartz Building, Basement HCC, 530 First Avenue, New York, NY 10016 (United States)

2002-12-01

Tumefactive demyelinating lesions can present with features similar, clinically and radiologically, to those of brain tumours. Proton MR spectroscopy has been increasingly used to characterize intracranial pathology. As the underlying pathophysiology of neoplasms is different from that of demyelinating disease, one may expect the metabolic composition of neoplasms to be significantly different from that of demyelinating lesions. We report a 49-year-old woman in whom the neurologic and radiologic findings were highly suggestive of a high-grade brain tumor, and the spectroscopic features were sufficiently similar to that of a tumor to convince the neurosurgeon to operate. This case emphasizes the need for caution when confronted with a patient who presents with a differential diagnosis of demyelinating lesion versus neoplasm. (orig.)

Imaging features of aggressive angiomyxoma

International Nuclear Information System (INIS)

Jeyadevan, N.N.; Sohaib, S.A.A.; Thomas, J.M.; Jeyarajah, A.; Shepherd, J.H.; Fisher, C.

2003-01-01

AIM: To describe the imaging features of aggressive angiomyxoma in a rare benign mesenchymal tumour most frequently arising from the perineum in young female patients. MATERIALS AND METHODS: We reviewed the computed tomography (CT) and magnetic resonance (MR) imaging features of patients with aggressive angiomyxoma who were referred to our hospital. The imaging features were correlated with clinical information and pathology in all patients. RESULTS: Four CT and five MR studies were available for five patients (all women, mean age 39, range 24-55). Three patients had recurrent tumour at follow-up. CT and MR imaging demonstrated a well-defined mass-displacing adjacent structures. The tumour was of low attenuation relative to muscle on CT. On MR, the tumour was isointense relative to muscle on T1-weighted image, hyperintense on T2-weighted image and enhanced avidly after gadolinium contrast with a characteristic 'swirled' internal pattern. MR imaging demonstrates the extent of the tumour and its relation to the pelvic floor. Recurrent tumour has a similar appearance to the primary lesion. CONCLUSION: The MR appearances of aggressive angiomyxomas are characteristic, and the diagnosis should be considered in any young woman presenting with a well-defined mass arising from the perineum. Jeyadevan, N. N. etal. (2003). Clinical Radiology58, 157--162

Image Processing and Features Extraction of Fingerprint Images ...

African Journals Online (AJOL)

To demonstrate the importance of the image processing of fingerprint images prior to image enrolment or comparison, the set of fingerprint images in databases (a) and (b) of the FVC (Fingerprint Verification Competition) 2000 database were analyzed using a features extraction algorithm. This paper presents the results of ...

Book Review: Reiner Salzer and Heinz W. Siesler (Eds.): Infrared and Raman spectroscopic imaging, 2nd ed

International Nuclear Information System (INIS)

Moore, David Steven

2015-01-01

This second edition of 'Infrared and Raman Spectroscopic Imaging' propels practitioners in that wide-ranging field, as well as other readers, to the current state of the art in a well-produced and full-color, completely revised and updated, volume. This new edition chronicles the expanded application of vibrational spectroscopic imaging from yesterday's time-consuming point-by-point buildup of a hyperspectral image cube, through the improvements afforded by the addition of focal plane arrays and line scan imaging, to methods applicable beyond the diffraction limit, instructs the reader on the improved instrumentation and image and data analysis methods, and expounds on their application to fundamental biomedical knowledge, food and agricultural surveys, materials science, process and quality control, and many others

Feature hashing for fast image retrieval

Science.gov (United States)

Yan, Lingyu; Fu, Jiarun; Zhang, Hongxin; Yuan, Lu; Xu, Hui

2018-03-01

Currently, researches on content based image retrieval mainly focus on robust feature extraction. However, due to the exponential growth of online images, it is necessary to consider searching among large scale images, which is very timeconsuming and unscalable. Hence, we need to pay much attention to the efficiency of image retrieval. In this paper, we propose a feature hashing method for image retrieval which not only generates compact fingerprint for image representation, but also prevents huge semantic loss during the process of hashing. To generate the fingerprint, an objective function of semantic loss is constructed and minimized, which combine the influence of both the neighborhood structure of feature data and mapping error. Since the machine learning based hashing effectively preserves neighborhood structure of data, it yields visual words with strong discriminability. Furthermore, the generated binary codes leads image representation building to be of low-complexity, making it efficient and scalable to large scale databases. Experimental results show good performance of our approach.

Feature-based Alignment of Volumetric Multi-modal Images

Science.gov (United States)

Toews, Matthew; Zöllei, Lilla; Wells, William M.

2014-01-01

This paper proposes a method for aligning image volumes acquired from different imaging modalities (e.g. MR, CT) based on 3D scale-invariant image features. A novel method for encoding invariant feature geometry and appearance is developed, based on the assumption of locally linear intensity relationships, providing a solution to poor repeatability of feature detection in different image modalities. The encoding method is incorporated into a probabilistic feature-based model for multi-modal image alignment. The model parameters are estimated via a group-wise alignment algorithm, that iteratively alternates between estimating a feature-based model from feature data, then realigning feature data to the model, converging to a stable alignment solution with few pre-processing or pre-alignment requirements. The resulting model can be used to align multi-modal image data with the benefits of invariant feature correspondence: globally optimal solutions, high efficiency and low memory usage. The method is tested on the difficult RIRE data set of CT, T1, T2, PD and MP-RAGE brain images of subjects exhibiting significant inter-subject variability due to pathology. PMID:24683955

High grade gilomas and solitary metastases: differentiation using perfusion MR imaging and spectroscopic MR imaging

International Nuclear Information System (INIS)

Law, M.; Cha, S.; Knopp, E.A.; Johnson, G.; Litt, A.W.

2002-01-01

Full text: To determine whether perfusion MRI (pMRI) and spectroscopic MR imaging (sMRI) can be used to differentiate high grade primary gliomas and solitary metastases on the basis of differences in vascularity and metabolite levels in the peritumoral. Fifty-one patients with a solitary brain tumor (33 gliomas, 18 metastases) underwent conventional MRI, contrast enhanced pMRI and sMRI before surgical resection or stereotactic biopsy. The peri-tumoral region is defined as the area within the white matter, immediately adjacent to the enhancing portion of the tumor (hyperintense on T2- weighted imaging but no enhancement on post-contrast T1-weighted imaging). Relative cerebral blood volume (rCBV) measurements were made in these regions from the pMRI data. Spectra from the enhancing tumor, the peritumoral region and normal brain, were obtained from the 2D multi-voxel CSI acquisition (TE = 135ms). The measured rCBV within the abnormal peritumoral region in highgrade gliomas and metastasis were 1.31 ± 0.97 (mean ± standard deviation) and 0.39 ± 0.19, respectively. The difference was statistically significant (p<0.0001). Spectroscopic imaging demonstrated elevated choline (Cho/Cr 2.28 ± 1.24) in the peritumoral region of gliomas but not in metastasis (Cho/Cr = 0.76 ± 0.23). The difference was again statistically significant (p 0.001), with Student's t-test. Although conventional imaging characteristics of solitary metastases and primary high grade gliomas may sometimes be similar, pMRI and sMRI are able to distinguish between the two, based on the rCBV and metabolite ratios within the peri-tumoral region. Copyright (2002) Blackwell Science Pty Ltd

Proton MR spectroscopic imaging of basal ganglia and thalamus in neurofibromatosis type 1: correlation with T2 hyperintensities

International Nuclear Information System (INIS)

Barbier, Charlotte; Barantin, Laurent; Chabernaud, Camille; Bertrand, Philippe; Sembely, Catherine; Sirinelli, Dominique; Castelnau, Pierre; Cottier, Jean-Philippe

2011-01-01

Neurofibromatosis type 1 (NF1) is frequently associated with hyperintense lesions on T2-weighted images called ''unidentified bright objects'' (UBO). To better characterize the functional significance of UBO, we investigate the basal ganglia and thalamus using spectroscopic imaging in children with NF1 and compare the results to anomalies observed on T2-weighted images. Magnetic resonance (MR) data of 25 children with NF1 were analyzed. On the basis of T2-weighted images analysis, two groups were identified: one with normal MR imaging (UBO- group; n = 10) and one with UBO (UBO+ group; n = 15). Within the UBO+ group, a subpopulation of patients (n = 5) only had lesions of the basal ganglia. We analyzed herein seven regions of interest (ROIs) for each side: caudate nucleus, capsulo-lenticular region, lateral and posterior thalamus, thalamus (lateral and posterior voxels combined), putamen, and striatum. For each ROI, a spectrum of the metabolites and their ratio was obtained. Patients with abnormalities on T2-weighted images had significantly lower NAA/Cr, NAA/Cho, and NAA/mI ratios in the lateral right thalamus compared with patients with normal T2. These abnormal spectroscopic findings were not observed in capsulo-lenticular regions that had UBO but in the thalamus region that was devoid of UBO. Multivoxel spectroscopic imaging using short-time echo showed spectroscopic abnormalities in the right thalamus of NF1 patients harboring UBO, which were mainly located in the basal ganglia. This finding could reflect the anatomical and functional interactions of these regions. (orig.)

Spectroscopic imaging of the pilocarpine model of human epilepsy suggests that early NAA reduction predicts epilepsy.

Science.gov (United States)

Gomes, W A; Lado, F A; de Lanerolle, N C; Takahashi, K; Pan, C; Hetherington, H P

2007-08-01

Reduced hippocampal N-acetyl aspartate (NAA) is commonly observed in patients with advanced, chronic temporal lobe epilepsy (TLE). It is unclear, however, whether an NAA deficit is also present during the clinically quiescent latent period that characterizes early TLE. This question has important implications for the use of MR spectroscopic imaging (MRSI) in the early identification of patients at risk for TLE. To determine whether NAA is diminished during the latent period, we obtained high-resolution (1)H spectroscopic imaging during the latent period of the rat pilocarpine model of human TLE. We used actively detuneable surface reception and volume transmission coils to enhance sensitivity and a semiautomated voxel shifting method to accurately position voxels within the hippocampi. During the latent period, 2 and 7 d following pilocarpine treatment, hippocampal NAA was significantly reduced by 27.5 +/- 6.9% (P NAA deficit is not due to neuron loss and therefore likely represents metabolic impairment of hippocampal neurons during the latent phase. Therefore, spectroscopic imaging provides an early marker for metabolic dysfunction in this model of TLE.

Image feature detectors and descriptors foundations and applications

CERN Document Server

Hassaballah, Mahmoud

2016-01-01

This book provides readers with a selection of high-quality chapters that cover both theoretical concepts and practical applications of image feature detectors and descriptors. It serves as reference for researchers and practitioners by featuring survey chapters and research contributions on image feature detectors and descriptors. Additionally, it emphasizes several keywords in both theoretical and practical aspects of image feature extraction. The keywords include acceleration of feature detection and extraction, hardware implantations, image segmentation, evolutionary algorithm, ordinal measures, as well as visual speech recognition. .

Role of endorectal MR imaging and MR spectroscopic imaging in defining treatable intraprostatic tumor foci in prostate cancer: Quantitative analysis of imaging contour compared to whole-mount histopathology

International Nuclear Information System (INIS)

Anwar, Mekhail; Westphalen, Antonio C.; Jung, Adam J.; Noworolski, Susan M.; Simko, Jeffry P.; Kurhanewicz, John; Roach, Mack; Carroll, Peter R.; Coakley, Fergus V.

2014-01-01

Purpose: To investigate the role of endorectal MR imaging and MR spectroscopic imaging in defining the contour of treatable intraprostatic tumor foci in prostate cancer, since targeted therapy requires accurate target volume definition. Materials and methods: We retrospectively identified 20 patients with prostate cancer who underwent endorectal MR imaging and MR spectroscopic imaging prior to radical prostatectomy and subsequent creation of detailed histopathological tumor maps from whole-mount step sections. Two experienced radiologists independently reviewed all MR images and electronically contoured all suspected treatable (⩾0.5 cm 3 ) tumor foci. Deformable co-registration in MATLAB was used to calculate the margin of error between imaging and histopathological contours at both capsular and non-capsular surfaces and the treatment margin required to ensure at least 95% tumor coverage. Results: Histopathology showed 17 treatable tumor foci in 16 patients, of which 8 were correctly identified by both readers and an additional 2 were correctly identified by reader 2. For all correctly identified lesions, both readers accurately identified that tumor contacted the prostatic capsule, with no error in contour identification. On the non-capsular border, the median distance between the imaging and histopathological contour was 1.4 mm (range, 0–12). Expanding the contour by 5 mm at the non-capsular margin included 95% of tumor volume not initially covered within the MR contour. Conclusions: Endorectal MR imaging and MR spectroscopic imaging can be used to accurately contour treatable intraprostatic tumor foci; adequate tumor coverage is achieved by expanding the treatment contour at the non-capsular margin by 5 mm

W-transform method for feature-oriented multiresolution image retrieval

Energy Technology Data Exchange (ETDEWEB)

Kwong, M.K.; Lin, B. [Argonne National Lab., IL (United States). Mathematics and Computer Science Div.

1995-07-01

Image database management is important in the development of multimedia technology. Since an enormous amount of digital images is likely to be generated within the next few decades in order to integrate computers, television, VCR, cables, telephone and various imaging devices. Effective image indexing and retrieval systems are urgently needed so that images can be easily organized, searched, transmitted, and presented. Here, the authors present a local-feature-oriented image indexing and retrieval method based on Kwong, and Tang`s W-transform. Multiresolution histogram comparison is an effective method for content-based image indexing and retrieval. However, most recent approaches perform multiresolution analysis for whole images but do not exploit the local features present in the images. Since W-transform is featured by its ability to handle images of arbitrary size, with no periodicity assumptions, it provides a natural tool for analyzing local image features and building indexing systems based on such features. In this approach, the histograms of the local features of images are used in the indexing, system. The system not only can retrieve images that are similar or identical to the query images but also can retrieve images that contain features specified in the query images, even if the retrieved images as a whole might be very different from the query images. The local-feature-oriented method also provides a speed advantage over the global multiresolution histogram comparison method. The feature-oriented approach is expected to be applicable in managing large-scale image systems such as video databases and medical image databases.

Proton magnetic resonance spectroscopic imaging in neurodegenerative diseases

International Nuclear Information System (INIS)

Schuff, Norbert; Vermathen, Peter; Maudsley, Andrew A.; Weiner, Michael W.

1999-01-01

Proton magnetic resonance spectroscopic imaging ( 1 H MRSI) was used to investigate changes in brain metabolites in Alzheimer's disease, epilepsy, and amyotrophic lateral sclerosis. Examples of results from several ongoing clinical studies are provided. Multislice 1 H MRSI of the human brain, without volume pre selection offers considerable advantage over previously available techniques. Furthermore, MRI tissue segmentation and completely automated spectral curve fitting greatly facilitate quantitative data analysis. Future efforts will be devoted to obtain full volumetric brain coverage and data acquisition at short spin-echo times (TE<30 ms) for the detection of metabolites. (author)

Proton MR spectroscopic features of chronic hepatitis and liver cirrhosis

International Nuclear Information System (INIS)

Cho, Soon Gu; Chung, Won Kyun; Kim, Young Soo; Choi, Won; Shin, Seok Hwan; Kim, Hyung Jin; Suh, Chang Hae

2000-01-01

The purpose of this study was to evaluate change in the proton MR spectroscopic ( 1 H-MRS) features of the liver according to changes in the severity of the chronic hepatitis spectrum (normal-chronic hepatitis-liver cirrhosis), and to determine the possibility of replacing liver biopsy by 1 H-MRS. Sixty profiles of 1 H-MRS features from 15 normal volunteers, 30 cases of chronic hepatitis, and 15 of liver cirrhosis were evaluated. All cases of chronic hepatitis and liver cirrhosis were confirmed by biopsy, and histopathologic disease severity was categorized according to Ludwig's classification. Using the STEAM (STimulated Echo-Aquisition Mode) sequence, 1 H-MRS was performed. The ratios of peak areas of (glutamate + glutamine)/lipid, phosphomonoesters/lipid, (glycogen + glucose)/lipid, and (3.9-4.1 ppm unknown peak)/lipid and their mean and standard deviation were calculated in normal, chronic hepatitis stages I and II, and early and late liver cirrhosis groups and the results were compared between these groups. One-way variable analysis was applied to the statistics. Mean and standard deviation of phosphomonoesters/lipid in the normal, chronic hepatitis grades I and II, and early and late liver cirrhosis groups were 0.0146±0.0090, 0.0222±0.0170, 0.0341±0.0276, 0.0698±0.0360, and 0.0881±0.0276, respectively, and (glycogen + glucose)/lipid were 0.0403±0.0267, 0.0922±0.0377, 0.1230±0.0364, 0.1853±0.0667, 0.2325±0.1071, respectively. These results implied that the ratio of the above metabolites to lipid content increased according to increasing disease severity (p less than 0.05). For (glutamate + glutamine)/lipid however, the ratios for each group were 0.0204±0.0067, 0.0117±0.0078, 0.0409±0.0167, 0.0212±0.0103, and 0.0693±0.0371, respectively, and there was no correlation with disease severity. In the chronic hepatitis grades I and II, and early and late liver cirrhosis groups, the ratios for (3.9-4.1 ppm unknown peak)/lipid were 0.0302±0.0087, 0

Field application of feature-enhanced imaging

International Nuclear Information System (INIS)

Mucciardi, A.N.

1988-01-01

One of the more challenging ultrasonic inspection problems is bimetallic weld inspection or, in general, dissimilar metal welds. These types of welds involve complicated geometries and various mixtures of materials. Attempts to address this problem with imaging alone have fallen short of desired goals. The probable reason for this is the lack of information supplied by imaging systems, which are limited to amplitude and time displays. Having RF information available for analysis greatly enhances the information obtainable from dissimilar metal welds and, coupled with the spatial map generated by an imaging system, can significantly improve the reliability of dissimilar metal weld inspections. Ultra Image and TestPro are, respectively, an imaging system and a feature-based signal analysis system. The purpose of this project is to integrate these two systems to produce a feature-enhanced imaging system. This means that a software link is established between Ultra Image and the PC-based TestPro system so that the user of the combined system can perform all the usual imaging functions and also have available a wide variety of RF signal analysis functions. The analysis functions include waveform feature-based pattern recognition as well as artificial intelligence/expert system techniques

A comparison of image features for registering LWIR and visual images

CSIR Research Space (South Africa)

Cronje, J

2012-11-01

Full Text Available This paper presents a comparison of several established and recent image feature-descriptors to register long wave infra-red images in the 8–14 m band to visual band images. The feature descriptors were chosen to include robust algorithms, SURF...

Superpixel-Based Feature for Aerial Image Scene Recognition

Directory of Open Access Journals (Sweden)

Hongguang Li

2018-01-01

Full Text Available Image scene recognition is a core technology for many aerial remote sensing applications. Different landforms are inputted as different scenes in aerial imaging, and all landform information is regarded as valuable for aerial image scene recognition. However, the conventional features of the Bag-of-Words model are designed using local points or other related information and thus are unable to fully describe landform areas. This limitation cannot be ignored when the aim is to ensure accurate aerial scene recognition. A novel superpixel-based feature is proposed in this study to characterize aerial image scenes. Then, based on the proposed feature, a scene recognition method of the Bag-of-Words model for aerial imaging is designed. The proposed superpixel-based feature that utilizes landform information establishes top-task superpixel extraction of landforms to bottom-task expression of feature vectors. This characterization technique comprises the following steps: simple linear iterative clustering based superpixel segmentation, adaptive filter bank construction, Lie group-based feature quantification, and visual saliency model-based feature weighting. Experiments of image scene recognition are carried out using real image data captured by an unmanned aerial vehicle (UAV. The recognition accuracy of the proposed superpixel-based feature is 95.1%, which is higher than those of scene recognition algorithms based on other local features.

Prostate cancer in magnetic resonance imaging: diagnostic utilites of spectroscopic sequences

International Nuclear Information System (INIS)

Caivano, Rocchina; Cirillo, Patrizia; Lotumolo, Antonella; Fortunato, Giovanna; Zandolino, Alexis; Cammarota, Aldo; Balestra, Antonio; Macarini, Luca; Vita, Giulia

2012-01-01

The aim of our work is to determine the efficacy of a combined study 3 Tesla Magnetic Resonance Imaging (3T MRI), with phased-array coil, for the detection of prostate cancer using magnetic resonance spectroscopy (MRS) and diffusion-weighted images (DWI) in identifying doubt nodules. In this study, we prospectively studied 46 patients who consecutively underwent digital-rectal exploration for high doses of prostate specific antigen (PSA), as well as a MRI examination and a subsequent rectal biopsy. The study of magnetic resonance imaging was performed with a Philips Achieva 3T scanner and phased-array coil. The images were obtained with turbo spin-echo sequences T2-weighted images, T1-weighted before and after the administration of contrast medium, DWI sequences and 3D spectroscopic sequences. The ultrasound-guided prostate biopsy was performed approximately 15 days after the MRI. The data obtained from MR images and spectroscopy were correlated with histological data. MRI revealed sensitivity and specificity of 88% and 61% respectively and positive predictive value (PPV) of 73%, negative predicted value (NPV) of 81% and accuracy of 76%. In identifying the location of prostate cancer, the sensitivity of 3T MRS was 92%, with a specificity of 89%, PPV of 87%, NPV of 88% and accuracy of 87%; DWI showed a sensitivity of 88%, specificity of 61%, PPV of 73%, NPV of 81% and accuracy of 76%. The 3T MR study with phased-array coil and the use of DWI and spectroscopic sequences, in addition to T2-weighted sequences, revealed to be accurate in the diagnosis of prostate cancer and in the identification of nodules to be biopsied. It may be indicated as a resolute way before biopsy in patients with elevated PSA value and can be proposed in the staging and follow-up.

Solving jigsaw puzzles using image features

DEFF Research Database (Denmark)

Nielsen, Ture R.; Drewsen, Peter; Hansen, Klaus

2008-01-01

In this article, we describe a method for automatic solving of the jigsaw puzzle problem based on using image features instead of the shape of the pieces. The image features are used for obtaining an accurate measure for edge similarity to be used in a new edge matching algorithm. The algorithm i...

Feature Evaluation for Building Facade Images - AN Empirical Study

Science.gov (United States)

Yang, M. Y.; Förstner, W.; Chai, D.

2012-08-01

The classification of building facade images is a challenging problem that receives a great deal of attention in the photogrammetry community. Image classification is critically dependent on the features. In this paper, we perform an empirical feature evaluation task for building facade images. Feature sets we choose are basic features, color features, histogram features, Peucker features, texture features, and SIFT features. We present an approach for region-wise labeling using an efficient randomized decision forest classifier and local features. We conduct our experiments with building facade image classification on the eTRIMS dataset, where our focus is the object classes building, car, door, pavement, road, sky, vegetation, and window.

Imaging features of kaposiform lymphangiomatosis

International Nuclear Information System (INIS)

Goyal, Pradeep; Alomari, Ahmad I.; Shaikh, Raja; Chaudry, Gulraiz; Kozakewich, Harry P.; Perez-Atayde, Antonio R.; Trenor, Cameron C.; Fishman, Steven J.; Greene, Arin K.

2016-01-01

Kaposiform lymphangiomatosis is a rare, aggressive lymphatic disorder. The imaging and presenting features of kaposiform lymphangiomatosis can overlap with those of central conducting lymphatic anomaly and generalized lymphatic anomaly. To analyze the imaging findings of kaposiform lymphangiomatosis disorder and highlight features most suggestive of this diagnosis. We retrospectively identified and characterized 20 children and young adults with histopathological diagnosis of kaposiform lymphangiomatosis and radiologic imaging referred to the vascular anomalies center between 1995 and 2015. The median age at onset was 6.5 years (range 3 months to 27 years). The most common presenting features were respiratory compromise (dyspnea, cough, chest pain; 55.5%), swelling/mass (25%), bleeding (15%) and fracture (5%). The thoracic cavity was involved in all patients; all patients had mediastinal involvement followed by lung parenchymal disease (90%) and pleural (85%) and pericardial (50%) effusions. The most common extra-thoracic sites of disease were the retroperitoneum (80%), bone (60%), abdominal viscera (55%) and muscles (45%). There was characteristic enhancing and infiltrative soft-tissue thickening in the mediastinum and retroperitoneum extending along the lymphatic distribution. Kaposiform lymphangiomatosis has overlapping imaging features with central conducting lymphatic anomaly and generalized lymphatic anomaly. Presence of mediastinal or retroperitoneal enhancing and infiltrative soft-tissue disease along the lymphatic distribution, hemorrhagic effusions and moderate thrombocytopenia (50-100,000/μl) should favor diagnosis of kaposiform lymphangiomatosis. (orig.)

Image fusion using sparse overcomplete feature dictionaries

Science.gov (United States)

Brumby, Steven P.; Bettencourt, Luis; Kenyon, Garrett T.; Chartrand, Rick; Wohlberg, Brendt

2015-10-06

Approaches for deciding what individuals in a population of visual system "neurons" are looking for using sparse overcomplete feature dictionaries are provided. A sparse overcomplete feature dictionary may be learned for an image dataset and a local sparse representation of the image dataset may be built using the learned feature dictionary. A local maximum pooling operation may be applied on the local sparse representation to produce a translation-tolerant representation of the image dataset. An object may then be classified and/or clustered within the translation-tolerant representation of the image dataset using a supervised classification algorithm and/or an unsupervised clustering algorithm.

An Effective Combined Feature For Web Based Image Retrieval

Directory of Open Access Journals (Sweden)

H.M.R.B Herath

2015-08-01

Full Text Available Abstract Technology advances as well as the emergence of large scale multimedia applications and the revolution of the World Wide Web has changed the world into a digital age. Anybody can use their mobile phone to take a photo at any time anywhere and upload that image to ever growing image databases. Development of effective techniques for visual and multimedia retrieval systems is one of the most challenging and important directions of the future research. This paper proposes an effective combined feature for web based image retrieval. Frequently used colour and texture features are explored in order to develop a combined feature for this purpose. Widely used three colour features Colour moments Colour coherence vector and Colour Correlogram and three texture features Grey Level Co-occurrence matrix Tamura features and Gabor filter were analyzed for their performance. Precision and Recall were used to evaluate the performance of each of these techniques. By comparing precision and recall values the methods that performed best were taken and combined to form a hybrid feature. The developed combined feature was evaluated by developing a web based CBIR system. A web crawler was used to first crawl through Web sites and images found in those sites are downloaded and the combined feature representation technique was used to extract image features. The test results indicated that this web system can be used to index web images with the combined feature representation schema and to find similar images. Random image retrievals using the web system shows that the combined feature can be used to retrieve images belonging to the general image domain. Accuracy of the retrieval can be noted high for natural images like outdoor scenes images of flowers etc. Also images which have a similar colour and texture distribution were retrieved as similar even though the images were belonging to deferent semantic categories. This can be ideal for an artist who wants

A compact imaging spectroscopic system for biomolecular detections on plasmonic chips.

Science.gov (United States)

Lo, Shu-Cheng; Lin, En-Hung; Wei, Pei-Kuen; Tsai, Wan-Shao

2016-10-17

In this study, we demonstrate a compact imaging spectroscopic system for high-throughput detection of biomolecular interactions on plasmonic chips, based on a curved grating as the key element of light diffraction and light focusing. Both the curved grating and the plasmonic chips are fabricated on flexible plastic substrates using a gas-assisted thermal-embossing method. A fiber-coupled broadband light source and a camera are included in the system. Spectral resolution within 1 nm is achieved in sensing environmental index solutions and protein bindings. The detected sensitivities of the plasmonic chip are comparable with a commercial spectrometer. An extra one-dimensional scanning stage enables high-throughput detection of protein binding on a designed plasmonic chip consisting of several nanoslit arrays with different periods. The detected resonance wavelengths match well with the grating equation under an air environment. Wavelength shifts between 1 and 9 nm are detected for antigens of various concentrations binding with antibodies. A simple, mass-productive and cost-effective method has been demonstrated on the imaging spectroscopic system for real-time, label-free, highly sensitive and high-throughput screening of biomolecular interactions.

In Vivo H MR spectroscopic imaging of human brain

International Nuclear Information System (INIS)

Choe, Bo Young; Suh, Tae Suk; Choi, Kyo Ho; Bahk, Yong Whee; Shinn, Kyung Sub

1994-01-01

To evaluate the spatial distribution of various proton metabolites in the human brain with use of water-suppressed in vivo H MR spectroscopic imaging (MRSI) technique. All of water-suppressed in vivo H MRSI were performed on 1.5 T whole-body MRI/MRS system using Stimulated Echo Acquisition Method (STEAM) Chemical Shift Imaging (CSI) pulse sequence. T1-weighted MR images were used for CSI field of view (FOV; 24 cm). Voxel size of 1.5 cm 3 was designated from the periphery of the brain which was divided by 1024 X 16 X 16 data points. Metabolite images of N-acetylaspartate (NAA), creatine/ phosphocreatine (Cr) + choline/phosphocholine (Cho), and complex of γ-aminobutyric acid (GABA) + glutamate (Glu) were obtained on the human brain. Our preliminary study suggests that in vivo H MRSI could provide the metabolite imaging to compensate for hypermetabolism on Positron Emission Tomography (PET) scans on the basis of the metabolic informations on brain tissues. The unique ability of in vivo H MRSI to offer noninvasive information about tissue biochemistry in disease states will stimulate on clinical research and disease diagnosis

Correlated topographic and spectroscopic imaging by combined atomic force microscopy and optical microscopy

International Nuclear Information System (INIS)

Hu Dehong; Micic, Miodrag; Klymyshyn, Nicholas; Suh, Y.D.; Lu, H.P.

2004-01-01

Near-field scanning microscopy is a powerful approach to obtain topographic and spectroscopic characterization simultaneously for imaging biological and nanoscale systems. To achieve optical imaging at high spatial resolution beyond the diffraction limit, aperture-less metallic scanning tips have been utilized to enhance the laser illumination local electromagnetic field at the apex of the scanning tips. In this paper, we discuss and review our work on combined fluorescence imaging with AFM-metallic tip enhancement, finite element method simulation of the tip enhancement, and their applications on AFM-tip enhanced fluorescence lifetime imaging (AFM-FLIM) and correlated AFM and FLIM imaging of the living cells

Spectroscopic Profiles of Comets Garradd and McNaught

Science.gov (United States)

Harris, Ien; Pierce, Donna M.; Cochran, Anita L.

2017-10-01

We have used the integral-field unit spectrograph (the George and Cynthia Mitchell Spectrograph) on the 2.7m Harlan J. Smith telescope at McDonald Observatory to obtain spectroscopic images of the comae of several comets. The images were obtained for various radical species (C2, C3, CN, NH2). Radial and azimuthal average profiles of the radical species were created to enhance any observed cometary coma morphological features. We compare the observed coma features across the observed species and over the different observation periods in order to constrain possible rotational states of the observed comets, as well as determine possible source differences in the coma between the observed radical species. We will present results for several comets, including C/2009 P1 (Garradd) and 260P (McNaught).

A kernel-based multi-feature image representation for histopathology image classification

International Nuclear Information System (INIS)

Moreno J; Caicedo J Gonzalez F

2010-01-01

This paper presents a novel strategy for building a high-dimensional feature space to represent histopathology image contents. Histogram features, related to colors, textures and edges, are combined together in a unique image representation space using kernel functions. This feature space is further enhanced by the application of latent semantic analysis, to model hidden relationships among visual patterns. All that information is included in the new image representation space. Then, support vector machine classifiers are used to assign semantic labels to images. Processing and classification algorithms operate on top of kernel functions, so that; the structure of the feature space is completely controlled using similarity measures and a dual representation. The proposed approach has shown a successful performance in a classification task using a dataset with 1,502 real histopathology images in 18 different classes. The results show that our approach for histological image classification obtains an improved average performance of 20.6% when compared to a conventional classification approach based on SVM directly applied to the original kernel.

A KERNEL-BASED MULTI-FEATURE IMAGE REPRESENTATION FOR HISTOPATHOLOGY IMAGE CLASSIFICATION

Directory of Open Access Journals (Sweden)

J Carlos Moreno

2010-09-01

Full Text Available This paper presents a novel strategy for building a high-dimensional feature space to represent histopathology image contents. Histogram features, related to colors, textures and edges, are combined together in a unique image representation space using kernel functions. This feature space is further enhanced by the application of Latent Semantic Analysis, to model hidden relationships among visual patterns. All that information is included in the new image representation space. Then, Support Vector Machine classifiers are used to assign semantic labels to images. Processing and classification algorithms operate on top of kernel functions, so that, the structure of the feature space is completely controlled using similarity measures and a dual representation. The proposed approach has shown a successful performance in a classification task using a dataset with 1,502 real histopathology images in 18 different classes. The results show that our approach for histological image classification obtains an improved average performance of 20.6% when compared to a conventional classification approach based on SVM directly applied to the original kernel.

Breast image feature learning with adaptive deconvolutional networks

Science.gov (United States)

Jamieson, Andrew R.; Drukker, Karen; Giger, Maryellen L.

2012-03-01

Feature extraction is a critical component of medical image analysis. Many computer-aided diagnosis approaches employ hand-designed, heuristic lesion extracted features. An alternative approach is to learn features directly from images. In this preliminary study, we explored the use of Adaptive Deconvolutional Networks (ADN) for learning high-level features in diagnostic breast mass lesion images with potential application to computer-aided diagnosis (CADx) and content-based image retrieval (CBIR). ADNs (Zeiler, et. al., 2011), are recently-proposed unsupervised, generative hierarchical models that decompose images via convolution sparse coding and max pooling. We trained the ADNs to learn multiple layers of representation for two breast image data sets on two different modalities (739 full field digital mammography (FFDM) and 2393 ultrasound images). Feature map calculations were accelerated by use of GPUs. Following Zeiler et. al., we applied the Spatial Pyramid Matching (SPM) kernel (Lazebnik, et. al., 2006) on the inferred feature maps and combined this with a linear support vector machine (SVM) classifier for the task of binary classification between cancer and non-cancer breast mass lesions. Non-linear, local structure preserving dimension reduction, Elastic Embedding (Carreira-Perpiñán, 2010), was then used to visualize the SPM kernel output in 2D and qualitatively inspect image relationships learned. Performance was found to be competitive with current CADx schemes that use human-designed features, e.g., achieving a 0.632+ bootstrap AUC (by case) of 0.83 [0.78, 0.89] for an ultrasound image set (1125 cases).

Smart Images Search based on Visual Features Fusion

International Nuclear Information System (INIS)

Saad, M.H.

2013-01-01

Image search engines attempt to give fast and accurate access to the wide range of the huge amount images available on the Internet. There have been a number of efforts to build search engines based on the image content to enhance search results. Content-Based Image Retrieval (CBIR) systems have achieved a great interest since multimedia files, such as images and videos, have dramatically entered our lives throughout the last decade. CBIR allows automatically extracting target images according to objective visual contents of the image itself, for example its shapes, colors and textures to provide more accurate ranking of the results. The recent approaches of CBIR differ in terms of which image features are extracted to be used as image descriptors for matching process. This thesis proposes improvements of the efficiency and accuracy of CBIR systems by integrating different types of image features. This framework addresses efficient retrieval of images in large image collections. A comparative study between recent CBIR techniques is provided. According to this study; image features need to be integrated to provide more accurate description of image content and better image retrieval accuracy. In this context, this thesis presents new image retrieval approaches that provide more accurate retrieval accuracy than previous approaches. The first proposed image retrieval system uses color, texture and shape descriptors to form the global features vector. This approach integrates the yc b c r color histogram as a color descriptor, the modified Fourier descriptor as a shape descriptor and modified Edge Histogram as a texture descriptor in order to enhance the retrieval results. The second proposed approach integrates the global features vector, which is used in the first approach, with the SURF salient point technique as local feature. The nearest neighbor matching algorithm with a proposed similarity measure is applied to determine the final image rank. The second approach

Multispectral Image Feature Points

Directory of Open Access Journals (Sweden)

Cristhian Aguilera

2012-09-01

Full Text Available This paper presents a novel feature point descriptor for the multispectral image case: Far-Infrared and Visible Spectrum images. It allows matching interest points on images of the same scene but acquired in different spectral bands. Initially, points of interest are detected on both images through a SIFT-like based scale space representation. Then, these points are characterized using an Edge Oriented Histogram (EOH descriptor. Finally, points of interest from multispectral images are matched by finding nearest couples using the information from the descriptor. The provided experimental results and comparisons with similar methods show both the validity of the proposed approach as well as the improvements it offers with respect to the current state-of-the-art.

A COMPARISON OF SPECTROSCOPIC VERSUS IMAGING TECHNIQUES FOR DETECTING CLOSE COMPANIONS TO KEPLER OBJECTS OF INTEREST

International Nuclear Information System (INIS)

Teske, Johanna K.; Everett, Mark E.; Hirsch, Lea; Furlan, Elise; Ciardi, David R.; Horch, Elliott P.; Howell, Steve B.; Gonzales, Erica; Crepp, Justin R.

2015-01-01

Kepler planet candidates require both spectroscopic and imaging follow-up observations to rule out false positives and detect blended stars. Traditionally, spectroscopy and high-resolution imaging have probed different host star companion parameter spaces, the former detecting tight binaries and the latter detecting wider bound companions as well as chance background stars. In this paper, we examine a sample of 11 Kepler host stars with companions detected by two techniques—near-infrared adaptive optics and/or optical speckle interferometry imaging, and a new spectroscopic deblending method. We compare the companion effective temperatures (T eff ) and flux ratios (F B /F A , where A is the primary and B is the companion) derived from each technique and find no cases where both companion parameters agree within 1σ errors. In 3/11 cases the companion T eff values agree within 1σ errors, and in 2/11 cases the companion F B /F A values agree within 1σ errors. Examining each Kepler system individually considering multiple avenues (isochrone mapping, contrast curves, probability of being bound), we suggest two cases for which the techniques most likely agree in their companion detections (detect the same companion star). Overall, our results support the advantage that the spectroscopic deblending technique has for finding very close-in companions (θ ≲ 0.″02–0.″05) that are not easily detectable with imaging. However, we also specifically show how high-contrast AO and speckle imaging observations detect companions at larger separations (θ ≥ 0.″02–0.″05) that are missed by the spectroscopic technique, provide additional information for characterizing the companion and its potential contamination (e.g., position angle, separation, magnitude differences), and cover a wider range of primary star effective temperatures. The investigation presented here illustrates the utility of combining the two techniques to reveal higher-order multiples in known

A COMPARISON OF SPECTROSCOPIC VERSUS IMAGING TECHNIQUES FOR DETECTING CLOSE COMPANIONS TO KEPLER OBJECTS OF INTEREST

Energy Technology Data Exchange (ETDEWEB)

Teske, Johanna K. [Carnegie DTM, 5241 Broad Branch Road, NW, Washington, DC 20015 (United States); Everett, Mark E. [National Optical Astronomy Observatory, 950 N. Cherry Ave., Tucson, AZ 85719 (United States); Hirsch, Lea [Astronomy Department, University of California at Berkeley, Berkeley, CA 94720 (United States); Furlan, Elise; Ciardi, David R. [NASA Exoplanet Science Institute, California Institute of Technology, 770 South Wilson Ave., Pasadena, CA 91125 (United States); Horch, Elliott P. [Department of Physics, Southern Connecticut State University, 501 Crescent Street, New Haven, CT 06515 (United States); Howell, Steve B. [NASA Ames Research Center, Moffett Field, CA 94035 (United States); Gonzales, Erica; Crepp, Justin R., E-mail: jteske@carnegiescience.edu [Department of Physics, University of Notre Dame, 225 Nieuwland Science Hall, Notre Dame, IN 46556 (United States)

2015-11-15

Kepler planet candidates require both spectroscopic and imaging follow-up observations to rule out false positives and detect blended stars. Traditionally, spectroscopy and high-resolution imaging have probed different host star companion parameter spaces, the former detecting tight binaries and the latter detecting wider bound companions as well as chance background stars. In this paper, we examine a sample of 11 Kepler host stars with companions detected by two techniques—near-infrared adaptive optics and/or optical speckle interferometry imaging, and a new spectroscopic deblending method. We compare the companion effective temperatures (T{sub eff}) and flux ratios (F{sub B}/F{sub A}, where A is the primary and B is the companion) derived from each technique and find no cases where both companion parameters agree within 1σ errors. In 3/11 cases the companion T{sub eff} values agree within 1σ errors, and in 2/11 cases the companion F{sub B}/F{sub A} values agree within 1σ errors. Examining each Kepler system individually considering multiple avenues (isochrone mapping, contrast curves, probability of being bound), we suggest two cases for which the techniques most likely agree in their companion detections (detect the same companion star). Overall, our results support the advantage that the spectroscopic deblending technique has for finding very close-in companions (θ ≲ 0.″02–0.″05) that are not easily detectable with imaging. However, we also specifically show how high-contrast AO and speckle imaging observations detect companions at larger separations (θ ≥ 0.″02–0.″05) that are missed by the spectroscopic technique, provide additional information for characterizing the companion and its potential contamination (e.g., position angle, separation, magnitude differences), and cover a wider range of primary star effective temperatures. The investigation presented here illustrates the utility of combining the two techniques to reveal higher

Image Recommendation Algorithm Using Feature-Based Collaborative Filtering

Science.gov (United States)

Kim, Deok-Hwan

As the multimedia contents market continues its rapid expansion, the amount of image contents used in mobile phone services, digital libraries, and catalog service is increasing remarkably. In spite of this rapid growth, users experience high levels of frustration when searching for the desired image. Even though new images are profitable to the service providers, traditional collaborative filtering methods cannot recommend them. To solve this problem, in this paper, we propose feature-based collaborative filtering (FBCF) method to reflect the user's most recent preference by representing his purchase sequence in the visual feature space. The proposed approach represents the images that have been purchased in the past as the feature clusters in the multi-dimensional feature space and then selects neighbors by using an inter-cluster distance function between their feature clusters. Various experiments using real image data demonstrate that the proposed approach provides a higher quality recommendation and better performance than do typical collaborative filtering and content-based filtering techniques.

Accelerated echo-planar J-resolved spectroscopic imaging in the human brain using compressed sensing: a pilot validation in obstructive sleep apnea.

Science.gov (United States)

Sarma, M K; Nagarajan, R; Macey, P M; Kumar, R; Villablanca, J P; Furuyama, J; Thomas, M A

2014-06-01

Echo-planar J-resolved spectroscopic imaging is a fast spectroscopic technique to record the biochemical information in multiple regions of the brain, but for clinical applications, time is still a constraint. Investigations of neural injury in obstructive sleep apnea have revealed structural changes in the brain, but determining the neurochemical changes requires more detailed measurements across multiple brain regions, demonstrating a need for faster echo-planar J-resolved spectroscopic imaging. Hence, we have extended the compressed sensing reconstruction of prospectively undersampled 4D echo-planar J-resolved spectroscopic imaging to investigate metabolic changes in multiple brain locations of patients with obstructive sleep apnea and healthy controls. Nonuniform undersampling was imposed along 1 spatial and 1 spectral dimension of 4D echo-planar J-resolved spectroscopic imaging, and test-retest reliability of the compressed sensing reconstruction of the nonuniform undersampling data was tested by using a brain phantom. In addition, 9 patients with obstructive sleep apnea and 11 healthy controls were investigated by using a 3T MR imaging/MR spectroscopy scanner. Significantly reduced metabolite differences were observed between patients with obstructive sleep apnea and healthy controls in multiple brain regions: NAA/Cr in the left hippocampus; total Cho/Cr and Glx/Cr in the right hippocampus; total NAA/Cr, taurine/Cr, scyllo-Inositol/Cr, phosphocholine/Cr, and total Cho/Cr in the occipital gray matter; total NAA/Cr and NAA/Cr in the medial frontal white matter; and taurine/Cr and total Cho/Cr in the left frontal white matter regions. The 4D echo-planar J-resolved spectroscopic imaging technique using the nonuniform undersampling-based acquisition and compressed sensing reconstruction in patients with obstructive sleep apnea and healthy brain is feasible in a clinically suitable time. In addition to brain metabolite changes previously reported by 1D MR

Feature Detector and Descriptor for Medical Images

Science.gov (United States)

Sargent, Dusty; Chen, Chao-I.; Tsai, Chang-Ming; Wang, Yuan-Fang; Koppel, Daniel

2009-02-01

The ability to detect and match features across multiple views of a scene is a crucial first step in many computer vision algorithms for dynamic scene analysis. State-of-the-art methods such as SIFT and SURF perform successfully when applied to typical images taken by a digital camera or camcorder. However, these methods often fail to generate an acceptable number of features when applied to medical images, because such images usually contain large homogeneous regions with little color and intensity variation. As a result, tasks like image registration and 3D structure recovery become difficult or impossible in the medical domain. This paper presents a scale, rotation and color/illumination invariant feature detector and descriptor for medical applications. The method incorporates elements of SIFT and SURF while optimizing their performance on medical data. Based on experiments with various types of medical images, we combined, adjusted, and built on methods and parameter settings employed in both algorithms. An approximate Hessian based detector is used to locate scale invariant keypoints and a dominant orientation is assigned to each keypoint using a gradient orientation histogram, providing rotation invariance. Finally, keypoints are described with an orientation-normalized distribution of gradient responses at the assigned scale, and the feature vector is normalized for contrast invariance. Experiments show that the algorithm detects and matches far more features than SIFT and SURF on medical images, with similar error levels.

Proton MR spectroscopic features of the human liver: in-vivo application to the normal condition

International Nuclear Information System (INIS)

Cho, Soon Gu; Kim, Mi Young; Kim, Young Soo; Choi, Won; Shin, Seok Hwan; Ok, Chul Soo; Suh, Chang Hae

1999-01-01

To determine the feasibility of MR spectroscopy in the living human liver, and to evaluate the corresponding proton MR spectroscopic features. In fifteen normal volunteers with neither previous nor present liver disease, the proton MR spectroscopic findings were reviewed. Twelve subjects were male and three were female ; they were aged between 28 and 32 (mean, 30) years. MR spectroscopy involved the use of a 1.5T GE Signa Horizon system with body coil(GE Medical System, Milwaukee, U.S.A). We used STEAM (Stimulated Echo-Acquisition Mode) with 3000/30 msec of TR/TE for signal acquisition, and the prone position without respiratory interruption. Mean and standard deviation of the ratios of glutamate+glutamine/lipids, phosphomonoesters/lipids, and glycogen+glucose/lipids were calculated from the area of their peaks. The proton MR spectroscopic findings of normal human livers showed four distinctive peaks, i.e. lipids, glutamate and glutamine complex, phosphomonoesters, and glycogen and glucose complex. The mean and standard deviation of the ratios of glutamate+glutamine/lipids, phosphomonoesters/lipids, and glycogen+glucose/lipids were 0.02±0.01, 0.01±0.01, and 0.04±0.03, respectively. In living normal human livers, MR spectroscopy can be successfully applied. When applied to a liver whose condition is pathologic, the findings can be used as a standard

Adapting Local Features for Face Detection in Thermal Image

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Chao Ma

2017-11-01

Full Text Available A thermal camera captures the temperature distribution of a scene as a thermal image. In thermal images, facial appearances of different people under different lighting conditions are similar. This is because facial temperature distribution is generally constant and not affected by lighting condition. This similarity in face appearances is advantageous for face detection. To detect faces in thermal images, cascade classifiers with Haar-like features are generally used. However, there are few studies exploring the local features for face detection in thermal images. In this paper, we introduce two approaches relying on local features for face detection in thermal images. First, we create new feature types by extending Multi-Block LBP. We consider a margin around the reference and the generally constant distribution of facial temperature. In this way, we make the features more robust to image noise and more effective for face detection in thermal images. Second, we propose an AdaBoost-based training method to get cascade classifiers with multiple types of local features. These feature types have different advantages. In this way we enhance the description power of local features. We did a hold-out validation experiment and a field experiment. In the hold-out validation experiment, we captured a dataset from 20 participants, comprising 14 males and 6 females. For each participant, we captured 420 images with 10 variations in camera distance, 21 poses, and 2 appearances (participant with/without glasses. We compared the performance of cascade classifiers trained by different sets of the features. The experiment results showed that the proposed approaches effectively improve the performance of face detection in thermal images. In the field experiment, we compared the face detection performance in realistic scenes using thermal and RGB images, and gave discussion based on the results.

Imaging and spectroscopic observations of the 9 March 2016 Total Solar Eclipse in Palangkaraya

International Nuclear Information System (INIS)

Kholish, Abdul Majid Al; Jihad, Imanul; Andika, Irham Taufik; Puspitaningrum, Evaria; Ainy, Fathin Q.; Ramadhan, Sahlan; Arifyanto, M. Ikbal; Malasan, Hakim L.

2016-01-01

The March 9 th 2016 total solar eclipse observation was carried out at Universitas Palangkaraya, Central Kalimantan. Time-resolved imaging of the Sun has been conducted before, after, and during totality of eclipse while optical spectroscopic observation has been carried out only at the totality. The imaging observation in white light was done to take high resolution images of solar corona. The images were taken with a DSLR camera that is attached to a refractor telescope (d=66 mm, f/5.9). Despite cloudy weather during the eclipse moments, we managed to obtain the images with lower signal-to-noise ratio, including identifiable diamond ring, prominence and coronal structure. The images were processed using standard reduction procedure to increase the signal-to-noise ratio and to enhance the corona. Then, the coronal structure is determined and compared with ultraviolet data from SOHO to analyze the correlation between visual and ultraviolet corona. The spectroscopic observation was conducted using a slit-less spectrograph and a DSLR camera to obtain solar flash spectra. The flash spectra taken during the eclipse show emissions of H 4861 Å, He I 5876 Å, and H 6563 Å. The Fe XIV 5303 Å and Fe X 6374 Å lines are hardly detected due to low signal-to-noise ratio. Spectral reduction and analysis are conducted to derive the emission lines intensity relative to continuum intensity. We use the measured parameters to determine the temperature of solar chromosphere. (paper)

Features Selection for Skin Micro-Image Symptomatic Recognition

Institute of Scientific and Technical Information of China (English)

HUYue-li; CAOJia-lin; ZHAOQian; FENGXu

2004-01-01

Automatic recognition of skin micro-image symptom is important in skin diagnosis and treatment. Feature selection is to improve the classification performance of skin micro-image symptom.This paper proposes a hybrid approach based on the support vector machine (SVM) technique and genetic algorithm (GA) to select an optimum feature subset from the feature group extracted from the skin micro-images. An adaptive GA is introduced for maintaining the convergence rate. With the proposed method, the average cross validation accuracy is increased from 88.25% using all features to 96.92% using only selected features provided by a classifier for classification of 5 classes of skin symptoms. The experimental results are satisfactory.

Features Selection for Skin Micro-Image Symptomatic Recognition

Institute of Scientific and Technical Information of China (English)

HU Yue-li; CAO Jia-lin; ZHAO Qian; FENG Xu

2004-01-01

Automatic recognition of skin micro-image symptom is important in skin diagnosis and treatment. Feature selection is to improve the classification performance of skin micro-image symptom.This paper proposes a hybrid approach based on the support vector machine (SVM) technique and genetic algorithm (GA) to select an optimum feature subset from the feature group extracted from the skin micro-images. An adaptive GA is introduced for maintaining the convergence rate. With the proposed method, the average cross validation accuracy is increased from 88.25% using all features to 96.92 % using only selected features provided by a classifier for classification of 5 classes of skin symptoms. The experimental results are satisfactory.

FEATURE EVALUATION FOR BUILDING FACADE IMAGES – AN EMPIRICAL STUDY

Directory of Open Access Journals (Sweden)

M. Y. Yang

2012-08-01

Full Text Available The classification of building facade images is a challenging problem that receives a great deal of attention in the photogrammetry community. Image classification is critically dependent on the features. In this paper, we perform an empirical feature evaluation task for building facade images. Feature sets we choose are basic features, color features, histogram features, Peucker features, texture features, and SIFT features. We present an approach for region-wise labeling using an efficient randomized decision forest classifier and local features. We conduct our experiments with building facade image classification on the eTRIMS dataset, where our focus is the object classes building, car, door, pavement, road, sky, vegetation, and window.

An important step forward in continuous spectroscopic imaging of ionising radiations using ASICs

Energy Technology Data Exchange (ETDEWEB)

Fessler, P. [11 rue Rabelais, 92170 Vanves (France); Coffin, J. [Institut de Recherches Subatomiques, B.P. 28, 67037 Strasbourg (France); Eberle, H. [Institut de Recherches Subatomiques, B.P. 28, 67037 Strasbourg (France); Raad Iseli, C. de [Smart Silicon Systems SA, Ch. de la Graviere 6, CH-1007 Lausanne (Switzerland); Hilt, B. [Universite de Haute-Alsace, GRPHE, 61, rue Albert Camus, 68093 Mulhouse (France); Huss, D. [Universite de Haute-Alsace, GRPHE, 61, rue Albert Camus, 68093 Mulhouse (France); Krummenacher, F. [Smart Silicon Systems SA, Ch. de la Graviere 6, CH-1007 Lausanne (Switzerland); Lutz, J.R. [Institut de Recherches Subatomiques, B.P. 28, 67037 Strasbourg (France); Prevot, G. [Institut de Recherches Subatomiques, B.P. 28, 67037 Strasbourg (France); Renouprez, A. [Institut de Recherche sur la Catalyse, 2 Avenue Albert Einstein, 69626 Villeurbanne (France); Sigward, M.H. [Institut de Recherches Subatomiques, B.P. 28, 67037 Strasbourg (France); Schwaller, B. [Universite de Haute-Alsace, GRPHE, 61, rue Albert Camus, 68093 Mulhouse (France); Voltolini, C. [Institut de Recherches Subatomiques, B.P. 28, 67037 Strasbourg (France)

1999-01-21

Characterization results are given for an original ASIC allowing continuous acquisition of ionising radiation images in spectroscopic mode. Ionising radiation imaging in general and spectroscopic imaging in particular must primarily be guided by the attempt to decrease statistical noise, which requires detection systems designed to allow very high counting rates. Any source of dead time must therefore be avoided. Thus, the use of on-line corrections of the inevitable dispersion of characteristics between the large number of electronic channels of the detection system, shall be precluded. Without claiming to achieve ultimate noise levels, the work described is focused on how to prevent good individual acquisition channel noise performance from being totally destroyed by the dispersion between channels without introducing dead times. With this goal, we developed an automatic charge amplifier output voltage offset compensation system which operates regardless of the cause of the offset (detector or electronic). The main performances of the system are the following: the input equivalent noise charge is 190 e rms (input non connected, peaking time 500 ns), the highest gain is 255 mV/fC, the peaking time is adjustable between 200 ns and 2 {mu}s and the power consumption is 10 mW per channel. The agreement between experimental data and theoretical simulation results is excellent.

An important step forward in continuous spectroscopic imaging of ionising radiations using ASICs

International Nuclear Information System (INIS)

Fessler, P.; Coffin, J.; Eberle, H.; Raad Iseli, C. de; Hilt, B.; Huss, D.; Krummenacher, F.; Lutz, J.R.; Prevot, G.; Renouprez, A.; Sigward, M.H.; Schwaller, B.; Voltolini, C.

1999-01-01

Characterization results are given for an original ASIC allowing continuous acquisition of ionising radiation images in spectroscopic mode. Ionising radiation imaging in general and spectroscopic imaging in particular must primarily be guided by the attempt to decrease statistical noise, which requires detection systems designed to allow very high counting rates. Any source of dead time must therefore be avoided. Thus, the use of on-line corrections of the inevitable dispersion of characteristics between the large number of electronic channels of the detection system, shall be precluded. Without claiming to achieve ultimate noise levels, the work described is focused on how to prevent good individual acquisition channel noise performance from being totally destroyed by the dispersion between channels without introducing dead times. With this goal, we developed an automatic charge amplifier output voltage offset compensation system which operates regardless of the cause of the offset (detector or electronic). The main performances of the system are the following: the input equivalent noise charge is 190 e rms (input non connected, peaking time 500 ns), the highest gain is 255 mV/fC, the peaking time is adjustable between 200 ns and 2 μs and the power consumption is 10 mW per channel. The agreement between experimental data and theoretical simulation results is excellent

Practical protocols for fast histopathology by Fourier transform infrared spectroscopic imaging

Science.gov (United States)

Keith, Frances N.; Reddy, Rohith K.; Bhargava, Rohit

2008-02-01

Fourier transform infrared (FT-IR) spectroscopic imaging is an emerging technique that combines the molecular selectivity of spectroscopy with the spatial specificity of optical microscopy. We demonstrate a new concept in obtaining high fidelity data using commercial array detectors coupled to a microscope and Michelson interferometer. Next, we apply the developed technique to rapidly provide automated histopathologic information for breast cancer. Traditionally, disease diagnoses are based on optical examinations of stained tissue and involve a skilled recognition of morphological patterns of specific cell types (histopathology). Consequently, histopathologic determinations are a time consuming, subjective process with innate intra- and inter-operator variability. Utilizing endogenous molecular contrast inherent in vibrational spectra, specially designed tissue microarrays and pattern recognition of specific biochemical features, we report an integrated algorithm for automated classifications. The developed protocol is objective, statistically significant and, being compatible with current tissue processing procedures, holds potential for routine clinical diagnoses. We first demonstrate that the classification of tissue type (histology) can be accomplished in a manner that is robust and rigorous. Since data quality and classifier performance are linked, we quantify the relationship through our analysis model. Last, we demonstrate the application of the minimum noise fraction (MNF) transform to improve tissue segmentation.

The analysis of image feature robustness using cometcloud

Directory of Open Access Journals (Sweden)

Xin Qi

2012-01-01

Full Text Available The robustness of image features is a very important consideration in quantitative image analysis. The objective of this paper is to investigate the robustness of a range of image texture features using hematoxylin stained breast tissue microarray slides which are assessed while simulating different imaging challenges including out of focus, changes in magnification and variations in illumination, noise, compression, distortion, and rotation. We employed five texture analysis methods and tested them while introducing all of the challenges listed above. The texture features that were evaluated include co-occurrence matrix, center-symmetric auto-correlation, texture feature coding method, local binary pattern, and texton. Due to the independence of each transformation and texture descriptor, a network structured combination was proposed and deployed on the Rutgers private cloud. The experiments utilized 20 randomly selected tissue microarray cores. All the combinations of the image transformations and deformations are calculated, and the whole feature extraction procedure was completed in 70 minutes using a cloud equipped with 20 nodes. Center-symmetric auto-correlation outperforms all the other four texture descriptors but also requires the longest computational time. It is roughly 10 times slower than local binary pattern and texton. From a speed perspective, both the local binary pattern and texton features provided excellent performance for classification and content-based image retrieval.

Color and neighbor edge directional difference feature for image retrieval

Institute of Scientific and Technical Information of China (English)

Chaobing Huang; Shengsheng Yu; Jingli Zhou; Hongwei Lu

2005-01-01

@@ A novel image feature termed neighbor edge directional difference unit histogram is proposed, in which the neighbor edge directional difference unit is defined and computed for every pixel in the image, and is used to generate the neighbor edge directional difference unit histogram. This histogram and color histogram are used as feature indexes to retrieve color image. The feature is invariant to image scaling and translation and has more powerful descriptive for the natural color images. Experimental results show that the feature can achieve better retrieval performance than other color-spatial features.

Combined spectroscopic imaging and chemometric approach for automatically partitioning tissue types in human prostate tissue biopsies

Science.gov (United States)

Haka, Abigail S.; Kidder, Linda H.; Lewis, E. Neil

2001-07-01

We have applied Fourier transform infrared (FTIR) spectroscopic imaging, coupling a mercury cadmium telluride (MCT) focal plane array detector (FPA) and a Michelson step scan interferometer, to the investigation of various states of malignant human prostate tissue. The MCT FPA used consists of 64x64 pixels, each 61 micrometers 2, and has a spectral range of 2-10.5 microns. Each imaging data set was collected at 16-1 resolution, resulting in 512 image planes and a total of 4096 interferograms. In this article we describe a method for separating different tissue types contained within FTIR spectroscopic imaging data sets of human prostate tissue biopsies. We present images, generated by the Fuzzy C-Means clustering algorithm, which demonstrate the successful partitioning of distinct tissue type domains. Additionally, analysis of differences in the centroid spectra corresponding to different tissue types provides an insight into their biochemical composition. Lastly, we demonstrate the ability to partition tissue type regions in a different data set using centroid spectra calculated from the original data set. This has implications for the use of the Fuzzy C-Means algorithm as an automated technique for the separation and examination of tissue domains in biopsy samples.

Fatty infiltration of the liver: evaluation by proton spectroscopic imaging

International Nuclear Information System (INIS)

Heiken, J.P.; Lee, J.K.; Dixon, W.T.

1985-01-01

The reliability of proton spectroscopic imaging in evaluating fatty infiltration of the liver was investigated in 35 subjects (12 healthy volunteers and 23 patients with fatty livers). With this modified spin-echo technique, fatty liver could be separated from normal liver both visually and quantitatively. On the opposed image, normal liver had an intermediate signal intensity, greater than that of muscle, whereas fatty liver had a lower signal intensity, equal to or less than that of muscle. In normal livers, the lipid signal fraction was less than 10%, while in fatty livers it was greater than 10% and usually exceeded 20%. With this technique, nonuniform fatty infiltration of the liver can be differentiated from hepatic metastases, and the technique may prove useful in the differentiation of some hepatic disorders

Tumor recognition in wireless capsule endoscopy images using textural features and SVM-based feature selection.

Science.gov (United States)

Li, Baopu; Meng, Max Q-H

2012-05-01

Tumor in digestive tract is a common disease and wireless capsule endoscopy (WCE) is a relatively new technology to examine diseases for digestive tract especially for small intestine. This paper addresses the problem of automatic recognition of tumor for WCE images. Candidate color texture feature that integrates uniform local binary pattern and wavelet is proposed to characterize WCE images. The proposed features are invariant to illumination change and describe multiresolution characteristics of WCE images. Two feature selection approaches based on support vector machine, sequential forward floating selection and recursive feature elimination, are further employed to refine the proposed features for improving the detection accuracy. Extensive experiments validate that the proposed computer-aided diagnosis system achieves a promising tumor recognition accuracy of 92.4% in WCE images on our collected data.

Histological image classification using biologically interpretable shape-based features

International Nuclear Information System (INIS)

Kothari, Sonal; Phan, John H; Young, Andrew N; Wang, May D

2013-01-01

Automatic cancer diagnostic systems based on histological image classification are important for improving therapeutic decisions. Previous studies propose textural and morphological features for such systems. These features capture patterns in histological images that are useful for both cancer grading and subtyping. However, because many of these features lack a clear biological interpretation, pathologists may be reluctant to adopt these features for clinical diagnosis. We examine the utility of biologically interpretable shape-based features for classification of histological renal tumor images. Using Fourier shape descriptors, we extract shape-based features that capture the distribution of stain-enhanced cellular and tissue structures in each image and evaluate these features using a multi-class prediction model. We compare the predictive performance of the shape-based diagnostic model to that of traditional models, i.e., using textural, morphological and topological features. The shape-based model, with an average accuracy of 77%, outperforms or complements traditional models. We identify the most informative shapes for each renal tumor subtype from the top-selected features. Results suggest that these shapes are not only accurate diagnostic features, but also correlate with known biological characteristics of renal tumors. Shape-based analysis of histological renal tumor images accurately classifies disease subtypes and reveals biologically insightful discriminatory features. This method for shape-based analysis can be extended to other histological datasets to aid pathologists in diagnostic and therapeutic decisions

Determination of the Image Complexity Feature in Pattern Recognition

Directory of Open Access Journals (Sweden)

Veacheslav L. Perju

2003-11-01

Full Text Available The new image complexity informative feature is proposed. The experimental estimation of the image complexity is carried out. There are elaborated two optical-electronic processors for image complexity calculation. The determination of the necessary number of the image's digitization elements depending on the image complexity was carried out. The accuracy of the image complexity feature calculation was made.

Thumb-size ultrasonic-assisted spectroscopic imager for in-situ glucose monitoring as optional sensor of conventional dialyzers

Science.gov (United States)

Nogo, Kosuke; Mori, Keita; Qi, Wei; Hosono, Satsuki; Kawashima, Natsumi; Nishiyama, Akira; Wada, Kenji; Ishimaru, Ichiro

2016-03-01

We proposed the ultrasonic-assisted spectroscopic imaging for the realization of blood-glucose-level monitoring during dialytic therapy. Optical scattering and absorption caused by blood cells deteriorate the detection accuracy of glucose dissolved in plasma. Ultrasonic standing waves can agglomerate blood cells at nodes. In contrast, around anti-node regions, the amount of transmitted light increases because relatively clear plasma appears due to decline the number of blood cells. Proposed method can disperse the transmitted light of plasma without time-consuming pretreatment such as centrifugation. To realize the thumb-size glucose sensor which can be easily attached to dialysis tubes, an ultrasonic standing wave generator and a spectroscopic imager are required to be small. Ultrasonic oscillators are ∅30[mm]. A drive circuit of oscillators, which now size is 41×55×45[mm], is expected to become small. The trial apparatus of proposed one-shot Fourier spectroscopic imager, whose size is 30×30×48[mm], also can be little-finger size in principal. In the experiment, we separated the suspension mixed water and micro spheres (Θ10[mm) into particles and liquid regions with the ultrasonic standing wave (frequency: 2[MHz]). Furthermore, the spectrum of transmitted light through the suspension could be obtained in visible light regions with a white LED.

SDSS-IV MaNGA: the spectroscopic discovery of strongly lensed galaxies

Science.gov (United States)

Talbot, Michael S.; Brownstein, Joel R.; Bolton, Adam S.; Bundy, Kevin; Andrews, Brett H.; Cherinka, Brian; Collett, Thomas E.; More, Anupreeta; More, Surhud; Sonnenfeld, Alessandro; Vegetti, Simona; Wake, David A.; Weijmans, Anne-Marie; Westfall, Kyle B.

2018-06-01

We present a catalogue of 38 spectroscopically detected strong galaxy-galaxy gravitational lens candidates identified in the Sloan Digital Sky Survey IV (SDSS-IV). We were able to simulate narrow-band images for eight of them demonstrating evidence of multiple images. Two of our systems are compound lens candidates, each with two background source-planes. One of these compound systems shows clear lensing features in the narrow-band image. Our sample is based on 2812 galaxies observed by the Mapping Nearby Galaxies at APO (MaNGA) integral field unit (IFU). This Spectroscopic Identification of Lensing Objects (SILO) survey extends the methodology of the Sloan Lens ACS Survey (SLACS) and BOSS Emission-Line Survey (BELLS) to lower redshift and multiple IFU spectra. We searched ˜1.5 million spectra, of which 3065 contained multiple high signal-to-noise ratio background emission-lines or a resolved [O II] doublet, that are included in this catalogue. Upon manual inspection, we discovered regions with multiple spectra containing background emission-lines at the same redshift, providing evidence of a common source-plane geometry which was not possible in previous SLACS and BELLS discovery programs. We estimate more than half of our candidates have an Einstein radius ≳ 1.7 arcsec, which is significantly greater than seen in SLACS and BELLS. These larger Einstein radii produce more extended images of the background galaxy increasing the probability that a background emission-line will enter one of the IFU spectroscopic fibres, making detection more likely.

Application of eigen value expansion to feature extraction from MRI images

International Nuclear Information System (INIS)

Kinosada, Yasutomi; Takeda, Kan; Nakagawa, Tsuyoshi

1991-01-01

The eigen value expansion technique was utilized for feature extraction of magnetic resonance (MR) images. The eigen value expansion is an orthonormal transformation method which decomposes a set of images into some statistically uncorrelated images. The technique was applied to MR images obtained with various imaging parameters at the same anatomical site. It generated one mean image and another set of images called bases for the images. Each basis corresponds to a feature in the images. A basis is, therefore, utilized for the feature extraction from MR images and a weighted sum of bases is also used for the feature enhancement. Furthermore, any MR image with specific feature can be obtained from a linear combination of the mean image and all of the bases. Images of hemorrhaged brain with a spin echo sequence and a series of cinematic cerebro spinal fluid flow images with ECG gated gradient refocused echo sequence were employed to estimate the ability of the feature extraction and the contrast enhancement. Results showed us that proposed application of an eigen value expansion technique to the feature extraction of MR images is good enough to clinical use and superior to other feature extraction methods such as producing a calculated MR image with a given TR and TE or the matched-filter method in processing speed and reproducibility of results. (author)

Invariant Feature Matching for Image Registration Application Based on New Dissimilarity of Spatial Features

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Mousavi Kahaki, Seyed Mostafa; Nordin, Md Jan; Ashtari, Amir H.; J. Zahra, Sophia

2016-01-01

An invariant feature matching method is proposed as a spatially invariant feature matching approach. Deformation effects, such as affine and homography, change the local information within the image and can result in ambiguous local information pertaining to image points. New method based on dissimilarity values, which measures the dissimilarity of the features through the path based on Eigenvector properties, is proposed. Evidence shows that existing matching techniques using similarity metrics—such as normalized cross-correlation, squared sum of intensity differences and correlation coefficient—are insufficient for achieving adequate results under different image deformations. Thus, new descriptor’s similarity metrics based on normalized Eigenvector correlation and signal directional differences, which are robust under local variation of the image information, are proposed to establish an efficient feature matching technique. The method proposed in this study measures the dissimilarity in the signal frequency along the path between two features. Moreover, these dissimilarity values are accumulated in a 2D dissimilarity space, allowing accurate corresponding features to be extracted based on the cumulative space using a voting strategy. This method can be used in image registration applications, as it overcomes the limitations of the existing approaches. The output results demonstrate that the proposed technique outperforms the other methods when evaluated using a standard dataset, in terms of precision-recall and corner correspondence. PMID:26985996

Invariant Feature Matching for Image Registration Application Based on New Dissimilarity of Spatial Features.

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Seyed Mostafa Mousavi Kahaki

Full Text Available An invariant feature matching method is proposed as a spatially invariant feature matching approach. Deformation effects, such as affine and homography, change the local information within the image and can result in ambiguous local information pertaining to image points. New method based on dissimilarity values, which measures the dissimilarity of the features through the path based on Eigenvector properties, is proposed. Evidence shows that existing matching techniques using similarity metrics--such as normalized cross-correlation, squared sum of intensity differences and correlation coefficient--are insufficient for achieving adequate results under different image deformations. Thus, new descriptor's similarity metrics based on normalized Eigenvector correlation and signal directional differences, which are robust under local variation of the image information, are proposed to establish an efficient feature matching technique. The method proposed in this study measures the dissimilarity in the signal frequency along the path between two features. Moreover, these dissimilarity values are accumulated in a 2D dissimilarity space, allowing accurate corresponding features to be extracted based on the cumulative space using a voting strategy. This method can be used in image registration applications, as it overcomes the limitations of the existing approaches. The output results demonstrate that the proposed technique outperforms the other methods when evaluated using a standard dataset, in terms of precision-recall and corner correspondence.

THE EFFECT OF IMAGE ENHANCEMENT METHODS DURING FEATURE DETECTION AND MATCHING OF THERMAL IMAGES

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O. Akcay

2017-05-01

Full Text Available A successful image matching is essential to provide an automatic photogrammetric process accurately. Feature detection, extraction and matching algorithms have performed on the high resolution images perfectly. However, images of cameras, which are equipped with low-resolution thermal sensors are problematic with the current algorithms. In this paper, some digital image processing techniques were applied to the low-resolution images taken with Optris PI 450 382 x 288 pixel optical resolution lightweight thermal camera to increase extraction and matching performance. Image enhancement methods that adjust low quality digital thermal images, were used to produce more suitable images for detection and extraction. Three main digital image process techniques: histogram equalization, high pass and low pass filters were considered to increase the signal-to-noise ratio, sharpen image, remove noise, respectively. Later on, the pre-processed images were evaluated using current image detection and feature extraction methods Maximally Stable Extremal Regions (MSER and Speeded Up Robust Features (SURF algorithms. Obtained results showed that some enhancement methods increased number of extracted features and decreased blunder errors during image matching. Consequently, the effects of different pre-process techniques were compared in the paper.

Biomedical imaging modality classification using combined visual features and textual terms.

Science.gov (United States)

Han, Xian-Hua; Chen, Yen-Wei

2011-01-01

We describe an approach for the automatic modality classification in medical image retrieval task of the 2010 CLEF cross-language image retrieval campaign (ImageCLEF). This paper is focused on the process of feature extraction from medical images and fuses the different extracted visual features and textual feature for modality classification. To extract visual features from the images, we used histogram descriptor of edge, gray, or color intensity and block-based variation as global features and SIFT histogram as local feature. For textual feature of image representation, the binary histogram of some predefined vocabulary words from image captions is used. Then, we combine the different features using normalized kernel functions for SVM classification. Furthermore, for some easy misclassified modality pairs such as CT and MR or PET and NM modalities, a local classifier is used for distinguishing samples in the pair modality to improve performance. The proposed strategy is evaluated with the provided modality dataset by ImageCLEF 2010.

PRISM: Processing routines in IDL for spectroscopic measurements (installation manual and user's guide, version 1.0)

Science.gov (United States)

Kokaly, Raymond F.

2011-01-01

This report describes procedures for installing and using the U.S. Geological Survey Processing Routines in IDL for Spectroscopic Measurements (PRISM) software. PRISM provides a framework to conduct spectroscopic analysis of measurements made using laboratory, field, airborne, and space-based spectrometers. Using PRISM functions, the user can compare the spectra of materials of unknown composition with reference spectra of known materials. This spectroscopic analysis allows the composition of the material to be identified and characterized. Among its other functions, PRISM contains routines for the storage of spectra in database files, import/export of ENVI spectral libraries, importation of field spectra, correction of spectra to absolute reflectance, arithmetic operations on spectra, interactive continuum removal and comparison of spectral features, correction of imaging spectrometer data to ground-calibrated reflectance, and identification and mapping of materials using spectral feature-based analysis of reflectance data. This report provides step-by-step instructions for installing the PRISM software and running its functions.

The biocompatibility of carbon hydroxyapatite/β-glucan composite for bone tissue engineering studied with Raman and FTIR spectroscopic imaging.

Science.gov (United States)

Sroka-Bartnicka, Anna; Kimber, James A; Borkowski, Leszek; Pawlowska, Marta; Polkowska, Izabela; Kalisz, Grzegorz; Belcarz, Anna; Jozwiak, Krzysztof; Ginalska, Grazyna; Kazarian, Sergei G

2015-10-01

The spectroscopic approaches of FTIR imaging and Raman mapping were applied to the characterisation of a new carbon hydroxyapatite/β-glucan composite developed for bone tissue engineering. The composite is an artificial bone material with an apatite-forming ability for the bone repair process. Rabbit bone samples were tested with an implanted bioactive material for a period of several months. Using spectroscopic and chemometric methods, we were able to determine the presence of amides and phosphates and the distribution of lipid-rich domains in the bone tissue, providing an assessment of the composite's bioactivity. Samples were also imaged in transmission using an infrared microscope combined with a focal plane array detector. CaF2 lenses were also used on the infrared microscope to improve spectral quality by reducing scattering artefacts, improving chemometric analysis. The presence of collagen and lipids at the bone/composite interface confirmed biocompatibility and demonstrate the suitability of FTIR microscopic imaging with lenses in studying these samples. It confirmed that the composite is a very good background for collagen growth and increases collagen maturity with the time of the bone growth process. The results indicate the bioactive and biocompatible properties of this composite and demonstrate how Raman and FTIR spectroscopic imaging have been used as an effective tool for tissue characterisation.

A visual perceptual descriptor with depth feature for image retrieval

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Wang, Tianyang; Qin, Zhengrui

2017-07-01

This paper proposes a visual perceptual descriptor (VPD) and a new approach to extract perceptual depth feature for 2D image retrieval. VPD mimics human visual system, which can easily distinguish regions that have different textures, whereas for regions which have similar textures, color features are needed for further differentiation. We apply VPD on the gradient direction map of an image, capture texture-similar regions to generate a VPD map. We then impose the VPD map on a quantized color map and extract color features only from the overlapped regions. To reflect the nature of perceptual distance in single 2D image, we propose and extract the perceptual depth feature by computing the nuclear norm of the sparse depth map of an image. Extracted color features and the perceptual depth feature are both incorporated to a feature vector, we utilize this vector to represent an image and measure similarity. We observe that the proposed VPD + depth method achieves a promising result, and extensive experiments prove that it outperforms other typical methods on 2D image retrieval.

Image mosaicking based on feature points using color-invariant values

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Lee, Dong-Chang; Kwon, Oh-Seol; Ko, Kyung-Woo; Lee, Ho-Young; Ha, Yeong-Ho

2008-02-01

In the field of computer vision, image mosaicking is achieved using image features, such as textures, colors, and shapes between corresponding images, or local descriptors representing neighborhoods of feature points extracted from corresponding images. However, image mosaicking based on feature points has attracted more recent attention due to the simplicity of the geometric transformation, regardless of distortion and differences in intensity generated by camera motion in consecutive images. Yet, since most feature-point matching algorithms extract feature points using gray values, identifying corresponding points becomes difficult in the case of changing illumination and images with a similar intensity. Accordingly, to solve these problems, this paper proposes a method of image mosaicking based on feature points using color information of images. Essentially, the digital values acquired from a real digital color camera are converted to values of a virtual camera with distinct narrow bands. Values based on the surface reflectance and invariant to the chromaticity of various illuminations are then derived from the virtual camera values and defined as color-invariant values invariant to changing illuminations. The validity of these color-invariant values is verified in a test using a Macbeth Color-Checker under simulated illuminations. The test also compares the proposed method using the color-invariant values with the conventional SIFT algorithm. The accuracy of the matching between the feature points extracted using the proposed method is increased, while image mosaicking using color information is also achieved.

Detection of prostate cancer with MR spectroscopic imaging: an expanded paradigm incorporating polyamines

Energy Technology Data Exchange (ETDEWEB)

Shukla-Dave, A.; Hricak, H.; Moskowitz, C.; Ishill, N.; Akin, O.; Kuroiwa, K.; Spector, J.; Kumar, M.; Reuter, V.E.; Koutcher, J.A.; Zakian, K.L. [Memorial Sloan-Kettering Cancer Center, New York, NY (United States). Dept. of Medical Physics

2007-11-15

Purpose: To characterize benign and malignant prostate peripheral zone (PZ) tissue retrospectively by using a commercial magnetic resonance (MR) spectroscopic imaging package and incorporating the choline plus creatine-to-citrate ratio ([Cho + Cr]/Cit) and polyamine (PA) information into a statistically based voxel classification procedure. Materials and methods: The institutional review board approved this HIPAA-compliant study and waived the requirement for informed consent. Fifty men (median age, 60 years; range, 44-69 years) with untreated biopsy-proved prostate cancer underwent combined endorectal MR imaging and MR spectroscopic imaging. Commercial software was used to acquire and process MR spectroscopic imaging data. The (Cho + Cr)/Cit and the PA level were tabulated for each voxel. The PA level was scored on a scale of 0 (PA undetectable) to 2 (PA peak as high as or higher than Cho peak). Whole-mount step-section histopathologic analysis constituted the reference standard. Classification and regression tree analysis in a training set generated a decision-making tree (rule) for classifying voxels as malignant or benign, which was validated in a test set. Receiver operating characteristic and generalized estimating equation regression analyses were used to assess accuracy and sensitivity, respectively. Results: The median (Cho + Cr)/Cit was 0.55 (mean {+-} standard deviation, 0.59 {+-} 0.03) in benign and 0.77 (mean, 1.08 {+-} 0.20) in malignant PZ voxels (P = .027). A significantly higher percentage of benign (compared with malignant) voxels had higher PA than choline peaks (P < .001). In the 24-patient training set (584 voxels), the rule yielded 54% sensitivity and 91% specificity for cancer detection; in the 26-patient test set (667 voxels), it yielded 42% sensitivity and 85% specificity. The percentage of cancer in the voxel at histopathologic analysis correlated positively (P < .001) with the sensitivity of the classification and regression tree rule

Model-Based Learning of Local Image Features for Unsupervised Texture Segmentation

Science.gov (United States)

Kiechle, Martin; Storath, Martin; Weinmann, Andreas; Kleinsteuber, Martin

2018-04-01

Features that capture well the textural patterns of a certain class of images are crucial for the performance of texture segmentation methods. The manual selection of features or designing new ones can be a tedious task. Therefore, it is desirable to automatically adapt the features to a certain image or class of images. Typically, this requires a large set of training images with similar textures and ground truth segmentation. In this work, we propose a framework to learn features for texture segmentation when no such training data is available. The cost function for our learning process is constructed to match a commonly used segmentation model, the piecewise constant Mumford-Shah model. This means that the features are learned such that they provide an approximately piecewise constant feature image with a small jump set. Based on this idea, we develop a two-stage algorithm which first learns suitable convolutional features and then performs a segmentation. We note that the features can be learned from a small set of images, from a single image, or even from image patches. The proposed method achieves a competitive rank in the Prague texture segmentation benchmark, and it is effective for segmenting histological images.

Hyperspectral image classifier based on beach spectral feature

International Nuclear Information System (INIS)

Liang, Zhang; Lianru, Gao; Bing, Zhang

2014-01-01

The seashore, especially coral bank, is sensitive to human activities and environmental changes. A multispectral image, with coarse spectral resolution, is inadaptable for identify subtle spectral distinctions between various beaches. To the contrary, hyperspectral image with narrow and consecutive channels increases our capability to retrieve minor spectral features which is suit for identification and classification of surface materials on the shore. Herein, this paper used airborne hyperspectral data, in addition to ground spectral data to study the beaches in Qingdao. The image data first went through image pretreatment to deal with the disturbance of noise, radiation inconsistence and distortion. In succession, the reflection spectrum, the derivative spectrum and the spectral absorption features of the beach surface were inspected in search of diagnostic features. Hence, spectra indices specific for the unique environment of seashore were developed. According to expert decisions based on image spectrums, the beaches are ultimately classified into sand beach, rock beach, vegetation beach, mud beach, bare land and water. In situ surveying reflection spectrum from GER1500 field spectrometer validated the classification production. In conclusion, the classification approach under expert decision based on feature spectrum is proved to be feasible for beaches

Biomedical Imaging Modality Classification Using Combined Visual Features and Textual Terms

Directory of Open Access Journals (Sweden)

Xian-Hua Han

2011-01-01

extraction from medical images and fuses the different extracted visual features and textual feature for modality classification. To extract visual features from the images, we used histogram descriptor of edge, gray, or color intensity and block-based variation as global features and SIFT histogram as local feature. For textual feature of image representation, the binary histogram of some predefined vocabulary words from image captions is used. Then, we combine the different features using normalized kernel functions for SVM classification. Furthermore, for some easy misclassified modality pairs such as CT and MR or PET and NM modalities, a local classifier is used for distinguishing samples in the pair modality to improve performance. The proposed strategy is evaluated with the provided modality dataset by ImageCLEF 2010.

Diffusion tensor image registration using hybrid connectivity and tensor features.

Science.gov (United States)

Wang, Qian; Yap, Pew-Thian; Wu, Guorong; Shen, Dinggang

2014-07-01

Most existing diffusion tensor imaging (DTI) registration methods estimate structural correspondences based on voxelwise matching of tensors. The rich connectivity information that is given by DTI, however, is often neglected. In this article, we propose to integrate complementary information given by connectivity features and tensor features for improved registration accuracy. To utilize connectivity information, we place multiple anchors representing different brain anatomies in the image space, and define the connectivity features for each voxel as the geodesic distances from all anchors to the voxel under consideration. The geodesic distance, which is computed in relation to the tensor field, encapsulates information of brain connectivity. We also extract tensor features for every voxel to reflect the local statistics of tensors in its neighborhood. We then combine both connectivity features and tensor features for registration of tensor images. From the images, landmarks are selected automatically and their correspondences are determined based on their connectivity and tensor feature vectors. The deformation field that deforms one tensor image to the other is iteratively estimated and optimized according to the landmarks and their associated correspondences. Experimental results show that, by using connectivity features and tensor features simultaneously, registration accuracy is increased substantially compared with the cases using either type of features alone. Copyright © 2013 Wiley Periodicals, Inc.

MR imaging features of craniodiaphyseal dysplasia

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Marden, Franklin A. [Mallinckrodt Institute of Radiology, Washington University Medical Center, 510 South Kingshighway Blvd., MO 63110, St. Louis (United States); Department of Radiology, St. Louis Children' s Hospital, Children' s Place, MO 63110, St. Louis (United States); Wippold, Franz J. [Mallinckrodt Institute of Radiology, Washington University Medical Center, 510 South Kingshighway Blvd., MO 63110, St. Louis (United States); Department of Radiology, St. Louis Children' s Hospital, Children' s Place, MO 63110, St. Louis (United States); Department of Radiology/Nuclear Medicine, F. Edward Hebert School of Medicine, Uniformed Services University of the Health Sciences, MD 20814, Bethesda (United States)

2004-02-01

We report the magnetic resonance (MR) imaging findings in a 4-year-old girl with characteristic radiographic and computed tomography (CT) features of craniodiaphyseal dysplasia. MR imaging exquisitely depicted cranial nerve compression, small foramen magnum, hydrocephalus, and other intracranial complications of this syndrome. A syrinx of the cervical spinal cord was demonstrated. We suggest that MR imaging become a routine component of the evaluation of these patients. (orig.)

MR Imaging Features of Fibrocystic Change of the Breast

Science.gov (United States)

Chen, Jeon-Hor; Liu, Hui; Baek, Hyeon-Man; Nalcioglu, Orhan; Su, Min-Ying

2008-01-01

Purpose Studies specifically reporting MR imaging of fibrocystic change (FCC) of the breast are very few and its MR imaging features are not clearly known. The purpose of this study was to analyze the MR imaging features of FCC of the breast. Materials and Methods Thirty one patients of pathologically proved FCC of the breast were retrospectively reviewed. The MRI study was performed using a 1.5 T MR scanner with standard bilateral breast coil. The imaging protocol consisted of pre-contrast T1W imaging and dynamic contrast-enhanced axial T1W imaging. The MRI features were interpreted based on the morphologic and enhancement kinetic descriptors defined on ACR BIRADS-MRI lexicon. Results FCC of the breast had a wide spectrum of morphologic and kinetic features on MRI. Two types of FCC were found, including a more diffuse type of non-mass lesion (12/31, 39%) showing benign enhancement kinetic pattern with medium wash-in in early phase (9/10, 90%) and a focal mass type lesion (11/31, 35%) with enhancement kinetic usually showing rapid up-slope mimicking a breast cancer (8/11, 73%). Conclusion MRI is able to elaborate the diverse imaging features of fibrocystic change of the breast. Our result showed that FCC presenting as focal mass type lesion were usually over-diagnosed as malignancy. Understanding MR imaging of FCC is important to determine which cohort of patients should be followed up alone or receive aggressive management. PMID:18436406

Uniform competency-based local feature extraction for remote sensing images

Science.gov (United States)

Sedaghat, Amin; Mohammadi, Nazila

2018-01-01

Local feature detectors are widely used in many photogrammetry and remote sensing applications. The quantity and distribution of the local features play a critical role in the quality of the image matching process, particularly for multi-sensor high resolution remote sensing image registration. However, conventional local feature detectors cannot extract desirable matched features either in terms of the number of correct matches or the spatial and scale distribution in multi-sensor remote sensing images. To address this problem, this paper proposes a novel method for uniform and robust local feature extraction for remote sensing images, which is based on a novel competency criterion and scale and location distribution constraints. The proposed method, called uniform competency (UC) local feature extraction, can be easily applied to any local feature detector for various kinds of applications. The proposed competency criterion is based on a weighted ranking process using three quality measures, including robustness, spatial saliency and scale parameters, which is performed in a multi-layer gridding schema. For evaluation, five state-of-the-art local feature detector approaches, namely, scale-invariant feature transform (SIFT), speeded up robust features (SURF), scale-invariant feature operator (SFOP), maximally stable extremal region (MSER) and hessian-affine, are used. The proposed UC-based feature extraction algorithms were successfully applied to match various synthetic and real satellite image pairs, and the results demonstrate its capability to increase matching performance and to improve the spatial distribution. The code to carry out the UC feature extraction is available from href="https://www.researchgate.net/publication/317956777_UC-Feature_Extraction.

Robust Image Hashing Using Radon Transform and Invariant Features

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Y.L. Liu

2016-09-01

Full Text Available A robust image hashing method based on radon transform and invariant features is proposed for image authentication, image retrieval, and image detection. Specifically, an input image is firstly converted into a counterpart with a normalized size. Then the invariant centroid algorithm is applied to obtain the invariant feature point and the surrounding circular area, and the radon transform is employed to acquire the mapping coefficient matrix of the area. Finally, the hashing sequence is generated by combining the feature vectors and the invariant moments calculated from the coefficient matrix. Experimental results show that this method not only can resist against the normal image processing operations, but also some geometric distortions. Comparisons of receiver operating characteristic (ROC curve indicate that the proposed method outperforms some existing methods in classification between perceptual robustness and discrimination.

Feature representation of RGB-D images using joint spatial-depth feature pooling

DEFF Research Database (Denmark)

Pan, Hong; Olsen, Søren Ingvor; Zhu, Yaping

2016-01-01

Recent development in depth imaging technology makes acquisition of depth information easier. With the additional depth cue, RGB-D cameras can provide effective support for many RGB-D perception tasks beyond traditional RGB information. However, current feature representation based on RGB-D image...

FEATURE MATCHING OF HISTORICAL IMAGES BASED ON GEOMETRY OF QUADRILATERALS

Directory of Open Access Journals (Sweden)

F. Maiwald

2018-05-01

Full Text Available This contribution shows an approach to match historical images from the photo library of the Saxon State and University Library Dresden (SLUB in the context of a historical three-dimensional city model of Dresden. In comparison to recent images, historical photography provides diverse factors which make an automatical image analysis (feature detection, feature matching and relative orientation of images difficult. Due to e.g. film grain, dust particles or the digitalization process, historical images are often covered by noise interfering with the image signal needed for a robust feature matching. The presented approach uses quadrilaterals in image space as these are commonly available in man-made structures and façade images (windows, stones, claddings. It is explained how to generally detect quadrilaterals in images. Consequently, the properties of the quadrilaterals as well as the relationship to neighbouring quadrilaterals are used for the description and matching of feature points. The results show that most of the matches are robust and correct but still small in numbers.

Reproducibility of P-31 spectroscopic imaging of normal human myocardium

International Nuclear Information System (INIS)

Tavares, N.J.; Chew, W.; Auffermann, W.; Higgins, C.B.

1988-01-01

To assess reproducibility of P-31 MR spectroscopy of human myocardium, ten normal male volunteers were studied on two separate occasions. Spectra were acquired on a clinical 1.5-T MR imaging unit (Signa, General Electric) using a one-dimensional gated spectroscopic imaging sequence (matrix size, 32 X 256) over 20 minutes. Peaks in the adenosine triphosphate (ATP) region, phosphocreatine (PCR), phosphodiesters (PD), and peaks attributable to 2,3 diphosphoglycerate from blood were observed. Interindividual and intraindividual variability expressed as standard errors of the mean (mean +- SEM) were 1.54 +- 0.04 (variability among subjects) and 0.04 (variability between first and second studies) for PCR/β ATP; 0.97 +- 0.18 and 0.06 for PD/β ATP; and 0.62 +- 0.10 and 0.05 for PD/PCR, respectively. In conclusion, P-31 MR spectroscopy yields consistent and reproducible myocardial spectra that might be useful in the future for the evaluation and monitoring of cardiac disease

Hand and wrist arthritis of Behcet disease: Imaging features

International Nuclear Information System (INIS)

Sugawara, Shunsuke; Ehara, Shigeru; Hitachi, Shin; Sugimoto, Hideharu

2010-01-01

Background: Reports on arthritis in Behcet disease are relatively scarce, and imaging features vary. Purpose: To document the various imaging features of articular disorders of the hand and wrist in Behcet disease. Material and Methods: Four patients, four women aged 26 to 65 years, fulfilling the diagnostic criteria of Behcet disease, with imaging findings of hand and wrist arthritis, were seen in two institutions. Radiography and magnetic resonance (MR) imaging were studied to elucidate the pattern and distribution. Results: Both non-erosive arthritis and erosive arthritis of different features were noted: one with non-erosive synovitis of the wrist, one with wrist synovitis with minimal erosion, and two with erosive arthritis of the distal interphalangeal joint. Conclusion: Imaging manifestations of arthritis of Behcet disease vary, and may be similar to other seronegative arthritides

Image Retrieval based on Integration between Color and Geometric Moment Features

International Nuclear Information System (INIS)

Saad, M.H.; Saleh, H.I.; Konbor, H.; Ashour, M.

2012-01-01

Content based image retrieval is the retrieval of images based on visual features such as colour, texture and shape. .the Current approaches to CBIR differ in terms of which image features are extracted; recent work deals with combination of distances or scores from different and usually independent representations in an attempt to induce high level semantics from the low level descriptors of the images. content-based image retrieval has many application areas such as, education, commerce, military, searching, commerce, and biomedicine and Web image classification. This paper proposes a new image retrieval system, which uses color and geometric moment feature to form the feature vectors. Bhattacharyya distance and histogram intersection are used to perform feature matching. This framework integrates the color histogram which represents the global feature and geometric moment as local descriptor to enhance the retrieval results. The proposed technique is proper for precisely retrieving images even in deformation cases such as geometric deformations and noise. It is tested on a standard the results shows that a combination of our approach as a local image descriptor with other global descriptors outperforms other approaches.

Feature Importance for Human Epithelial (HEp-2 Cell Image Classification

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Vibha Gupta

2018-02-01

Full Text Available Indirect Immuno-Fluorescence (IIF microscopy imaging of human epithelial (HEp-2 cells is a popular method for diagnosing autoimmune diseases. Considering large data volumes, computer-aided diagnosis (CAD systems, based on image-based classification, can help in terms of time, effort, and reliability of diagnosis. Such approaches are based on extracting some representative features from the images. This work explores the selection of the most distinctive features for HEp-2 cell images using various feature selection (FS methods. Considering that there is no single universally optimal feature selection technique, we also propose hybridization of one class of FS methods (filter methods. Furthermore, the notion of variable importance for ranking features, provided by another type of approaches (embedded methods such as Random forest, Random uniform forest is exploited to select a good subset of features from a large set, such that addition of new features does not increase classification accuracy. In this work, we have also, with great consideration, designed class-specific features to capture morphological visual traits of the cell patterns. We perform various experiments and discussions to demonstrate the effectiveness of FS methods along with proposed and a standard feature set. We achieve state-of-the-art performance even with small number of features, obtained after the feature selection.

Renal angiomyoadenomatous tumour: Imaging features

Science.gov (United States)

Sahni, V. Anik; Hirsch, Michelle S.; Silverman, Stuart G.

2012-01-01

Renal angiomyoadenomatous tumour is a rare, recently described neoplasm with a distinctive histological appearance. Although reported in the pathology literature, to our knowledge, no prior reports have described its imaging appearance. We describe the computed tomography and magnetic resonance imaging features of an incidentally detected renal angiomyoadenomatous tumour that appeared as a well-marginated, solid T2-hypointense enhancing mass, in a 50-year-old woman. It is indistinguishable from a variety of benign and malignant renal neoplasms. PMID:23093565

Generating description with multi-feature fusion and saliency maps of image

Science.gov (United States)

Liu, Lisha; Ding, Yuxuan; Tian, Chunna; Yuan, Bo

2018-04-01

Generating description for an image can be regard as visual understanding. It is across artificial intelligence, machine learning, natural language processing and many other areas. In this paper, we present a model that generates description for images based on RNN (recurrent neural network) with object attention and multi-feature of images. The deep recurrent neural networks have excellent performance in machine translation, so we use it to generate natural sentence description for images. The proposed method uses single CNN (convolution neural network) that is trained on ImageNet to extract image features. But we think it can not adequately contain the content in images, it may only focus on the object area of image. So we add scene information to image feature using CNN which is trained on Places205. Experiments show that model with multi-feature extracted by two CNNs perform better than which with a single feature. In addition, we make saliency weights on images to emphasize the salient objects in images. We evaluate our model on MSCOCO based on public metrics, and the results show that our model performs better than several state-of-the-art methods.

Special feature on imaging systems and techniques

Science.gov (United States)

Yang, Wuqiang; Giakos, George

2013-07-01

The IEEE International Conference on Imaging Systems and Techniques (IST'2012) was held in Manchester, UK, on 16-17 July 2012. The participants came from 26 countries or regions: Austria, Brazil, Canada, China, Denmark, France, Germany, Greece, India, Iran, Iraq, Italy, Japan, Korea, Latvia, Malaysia, Norway, Poland, Portugal, Sweden, Switzerland, Taiwan, Tunisia, UAE, UK and USA. The technical program of the conference consisted of a series of scientific and technical sessions, exploring physical principles, engineering and applications of new imaging systems and techniques, as reflected by the diversity of the submitted papers. Following a rigorous review process, a total of 123 papers were accepted, and they were organized into 30 oral presentation sessions and a poster session. In addition, six invited keynotes were arranged. The conference not only provided the participants with a unique opportunity to exchange ideas and disseminate research outcomes but also paved a way to establish global collaboration. Following the IST'2012, a total of 55 papers, which were technically extended substantially from their versions in the conference proceeding, were submitted as regular papers to this special feature of Measurement Science and Technology . Following a rigorous reviewing process, 25 papers have been finally accepted for publication in this special feature and they are organized into three categories: (1) industrial tomography, (2) imaging systems and techniques and (3) image processing. These papers not only present the latest developments in the field of imaging systems and techniques but also offer potential solutions to existing problems. We hope that this special feature provides a good reference for researchers who are active in the field and will serve as a catalyst to trigger further research. It has been our great pleasure to be the guest editors of this special feature. We would like to thank the authors for their contributions, without which it would

Prototype Theory Based Feature Representation for PolSAR Images

OpenAIRE

Huang Xiaojing; Yang Xiangli; Huang Pingping; Yang Wen

2016-01-01

This study presents a new feature representation approach for Polarimetric Synthetic Aperture Radar (PolSAR) image based on prototype theory. First, multiple prototype sets are generated using prototype theory. Then, regularized logistic regression is used to predict similarities between a test sample and each prototype set. Finally, the PolSAR image feature representation is obtained by ensemble projection. Experimental results of an unsupervised classification of PolSAR images show that our...

HDR IMAGING FOR FEATURE DETECTION ON DETAILED ARCHITECTURAL SCENES

Directory of Open Access Journals (Sweden)

G. Kontogianni

2015-02-01

Full Text Available 3D reconstruction relies on accurate detection, extraction, description and matching of image features. This is even truer for complex architectural scenes that pose needs for 3D models of high quality, without any loss of detail in geometry or color. Illumination conditions influence the radiometric quality of images, as standard sensors cannot depict properly a wide range of intensities in the same scene. Indeed, overexposed or underexposed pixels cause irreplaceable information loss and degrade digital representation. Images taken under extreme lighting environments may be thus prohibitive for feature detection/extraction and consequently for matching and 3D reconstruction. High Dynamic Range (HDR images could be helpful for these operators because they broaden the limits of illumination range that Standard or Low Dynamic Range (SDR/LDR images can capture and increase in this way the amount of details contained in the image. Experimental results of this study prove this assumption as they examine state of the art feature detectors applied both on standard dynamic range and HDR images.

Development of a spectroscopic Mueller matrix imaging ellipsometer for nanostructure metrology

International Nuclear Information System (INIS)

Chen, Xiuguo; Du, Weichao; Yuan, Kui; Chen, Jun; Jiang, Hao; Zhang, Chuanwei; Liu, Shiyuan

2016-01-01

In this paper, we describe the development of a spectroscopic Mueller matrix imaging ellipsometer (MMIE), which combines the great power of Mueller matrix ellipsometry with the high spatial resolution of optical microscopy. A dual rotating-compensator configuration is adopted to collect the full 4 × 4 imaging Mueller matrix in a single measurement. The light wavelengths are scanned in the range of 400–700 nm by a monochromator. The instrument has measurement accuracy and precision better than 0.01 for all the Mueller matrix elements in both the whole image and the whole spectral range. The instrument was then applied for the measurement of nanostructures combined with an inverse diffraction problem solving technique. The experiment performed on a photoresist grating sample has demonstrated the great potential of MMIE for accurate grating reconstruction from spectral data collected by a single pixel of the camera and for efficient quantification of geometrical profile of the grating structure over a large area with pixel resolution. It is expected that MMIE will be a powerful tool for nanostructure metrology in future high-volume nanomanufacturing.

Development of a spectroscopic Mueller matrix imaging ellipsometer for nanostructure metrology

Energy Technology Data Exchange (ETDEWEB)

Chen, Xiuguo; Du, Weichao; Yuan, Kui; Chen, Jun; Jiang, Hao, E-mail: hjiang@hust.edu.cn [State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074 (China); Zhang, Chuanwei; Liu, Shiyuan, E-mail: hjiang@hust.edu.cn [State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074 (China); Wuhan Eoptics Technology Co. Ltd., Wuhan 430075 (China)

2016-05-15

In this paper, we describe the development of a spectroscopic Mueller matrix imaging ellipsometer (MMIE), which combines the great power of Mueller matrix ellipsometry with the high spatial resolution of optical microscopy. A dual rotating-compensator configuration is adopted to collect the full 4 × 4 imaging Mueller matrix in a single measurement. The light wavelengths are scanned in the range of 400–700 nm by a monochromator. The instrument has measurement accuracy and precision better than 0.01 for all the Mueller matrix elements in both the whole image and the whole spectral range. The instrument was then applied for the measurement of nanostructures combined with an inverse diffraction problem solving technique. The experiment performed on a photoresist grating sample has demonstrated the great potential of MMIE for accurate grating reconstruction from spectral data collected by a single pixel of the camera and for efficient quantification of geometrical profile of the grating structure over a large area with pixel resolution. It is expected that MMIE will be a powerful tool for nanostructure metrology in future high-volume nanomanufacturing.

Textural features for image classification

Science.gov (United States)

Haralick, R. M.; Dinstein, I.; Shanmugam, K.

1973-01-01

Description of some easily computable textural features based on gray-tone spatial dependances, and illustration of their application in category-identification tasks of three different kinds of image data - namely, photomicrographs of five kinds of sandstones, 1:20,000 panchromatic aerial photographs of eight land-use categories, and ERTS multispectral imagery containing several land-use categories. Two kinds of decision rules are used - one for which the decision regions are convex polyhedra (a piecewise-linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89% for the photomicrographs, 82% for the aerial photographic imagery, and 83% for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications.

Radiomic features analysis in computed tomography images of lung nodule classification.

Directory of Open Access Journals (Sweden)

Chia-Hung Chen

Full Text Available Radiomics, which extract large amount of quantification image features from diagnostic medical images had been widely used for prognostication, treatment response prediction and cancer detection. The treatment options for lung nodules depend on their diagnosis, benign or malignant. Conventionally, lung nodule diagnosis is based on invasive biopsy. Recently, radiomics features, a non-invasive method based on clinical images, have shown high potential in lesion classification, treatment outcome prediction.Lung nodule classification using radiomics based on Computed Tomography (CT image data was investigated and a 4-feature signature was introduced for lung nodule classification. Retrospectively, 72 patients with 75 pulmonary nodules were collected. Radiomics feature extraction was performed on non-enhanced CT images with contours which were delineated by an experienced radiation oncologist.Among the 750 image features in each case, 76 features were found to have significant differences between benign and malignant lesions. A radiomics signature was composed of the best 4 features which included Laws_LSL_min, Laws_SLL_energy, Laws_SSL_skewness and Laws_EEL_uniformity. The accuracy using the signature in benign or malignant classification was 84% with the sensitivity of 92.85% and the specificity of 72.73%.The classification signature based on radiomics features demonstrated very good accuracy and high potential in clinical application.

Prostate cancer multi-feature analysis using trans-rectal ultrasound images

International Nuclear Information System (INIS)

Mohamed, S S; Salama, M M A; Kamel, M; El-Saadany, E F; Rizkalla, K; Chin, J

2005-01-01

This note focuses on extracting and analysing prostate texture features from trans-rectal ultrasound (TRUS) images for tissue characterization. One of the principal contributions of this investigation is the use of the information of the images' frequency domain features and spatial domain features to attain a more accurate diagnosis. Each image is divided into regions of interest (ROIs) by the Gabor multi-resolution analysis, a crucial stage, in which segmentation is achieved according to the frequency response of the image pixels. The pixels with a similar response to the same filter are grouped to form one ROI. Next, from each ROI two different statistical feature sets are constructed; the first set includes four grey level dependence matrix (GLDM) features and the second set consists of five grey level difference vector (GLDV) features. These constructed feature sets are then ranked by the mutual information feature selection (MIFS) algorithm. Here, the features that provide the maximum mutual information of each feature and class (cancerous and non-cancerous) and the minimum mutual information of the selected features are chosen, yeilding a reduced feature subset. The two constructed feature sets, GLDM and GLDV, as well as the reduced feature subset, are examined in terms of three different classifiers: the condensed k-nearest neighbour (CNN), the decision tree (DT) and the support vector machine (SVM). The accuracy classification results range from 87.5% to 93.75%, where the performance of the SVM and that of the DT are significantly better than the performance of the CNN. (note)

Image Mosaic Method Based on SIFT Features of Line Segment

Directory of Open Access Journals (Sweden)

Jun Zhu

2014-01-01

Full Text Available This paper proposes a novel image mosaic method based on SIFT (Scale Invariant Feature Transform feature of line segment, aiming to resolve incident scaling, rotation, changes in lighting condition, and so on between two images in the panoramic image mosaic process. This method firstly uses Harris corner detection operator to detect key points. Secondly, it constructs directed line segments, describes them with SIFT feature, and matches those directed segments to acquire rough point matching. Finally, Ransac method is used to eliminate wrong pairs in order to accomplish image mosaic. The results from experiment based on four pairs of images show that our method has strong robustness for resolution, lighting, rotation, and scaling.

SU-E-J-237: Image Feature Based DRR and Portal Image Registration

Energy Technology Data Exchange (ETDEWEB)

Wang, X; Chang, J [NY Weill Cornell Medical Ctr, NY (United States)

2014-06-01

Purpose: Two-dimensional (2D) matching of the kV X-ray and digitally reconstructed radiography (DRR) images is an important setup technique for image-guided radiotherapy (IGRT). In our clinics, mutual information based methods are used for this purpose on commercial linear accelerators, but with often needs for manual corrections. This work proved the feasibility that feature based image transform can be used to register kV and DRR images. Methods: The scale invariant feature transform (SIFT) method was implemented to detect the matching image details (or key points) between the kV and DRR images. These key points represent high image intensity gradients, and thus the scale invariant features. Due to the poor image contrast from our kV image, direct application of the SIFT method yielded many detection errors. To assist the finding of key points, the center coordinates of the kV and DRR images were read from the DICOM header, and the two groups of key points with similar relative positions to their corresponding centers were paired up. Using these points, a rigid transform (with scaling, horizontal and vertical shifts) was estimated. We also artificially introduced vertical and horizontal shifts to test the accuracy of our registration method on anterior-posterior (AP) and lateral pelvic images. Results: The results provided a satisfactory overlay of the transformed kV onto the DRR image. The introduced vs. detected shifts were fit into a linear regression. In the AP image experiments, linear regression analysis showed a slope of 1.15 and 0.98 with an R2 of 0.89 and 0.99 for the horizontal and vertical shifts, respectively. The results are 1.2 and 1.3 with R2 of 0.72 and 0.82 for the lateral image shifts. Conclusion: This work provided an alternative technique for kV to DRR alignment. Further improvements in the estimation accuracy and image contrast tolerance are underway.

Magnetic resonance spectroscopic imaging at superresolution: Overview and perspectives.

Science.gov (United States)

Kasten, Jeffrey; Klauser, Antoine; Lazeyras, François; Van De Ville, Dimitri

2016-02-01

The notion of non-invasive, high-resolution spatial mapping of metabolite concentrations has long enticed the medical community. While magnetic resonance spectroscopic imaging (MRSI) is capable of achieving the requisite spatio-spectral localization, it has traditionally been encumbered by significant resolution constraints that have thus far undermined its clinical utility. To surpass these obstacles, research efforts have primarily focused on hardware enhancements or the development of accelerated acquisition strategies to improve the experimental sensitivity per unit time. Concomitantly, a number of innovative reconstruction techniques have emerged as alternatives to the standard inverse discrete Fourier transform (DFT). While perhaps lesser known, these latter methods strive to effect commensurate resolution gains by exploiting known properties of the underlying MRSI signal in concert with advanced image and signal processing techniques. This review article aims to aggregate and provide an overview of the past few decades of so-called "superresolution" MRSI reconstruction methodologies, and to introduce readers to current state-of-the-art approaches. A number of perspectives are then offered as to the future of high-resolution MRSI, with a particular focus on translation into clinical settings. Copyright © 2015 Elsevier Inc. All rights reserved.

Scattering features for lung cancer detection in fibered confocal fluorescence microscopy images.

Science.gov (United States)

Rakotomamonjy, Alain; Petitjean, Caroline; Salaün, Mathieu; Thiberville, Luc

2014-06-01

To assess the feasibility of lung cancer diagnosis using fibered confocal fluorescence microscopy (FCFM) imaging technique and scattering features for pattern recognition. FCFM imaging technique is a new medical imaging technique for which interest has yet to be established for diagnosis. This paper addresses the problem of lung cancer detection using FCFM images and, as a first contribution, assesses the feasibility of computer-aided diagnosis through these images. Towards this aim, we have built a pattern recognition scheme which involves a feature extraction stage and a classification stage. The second contribution relies on the features used for discrimination. Indeed, we have employed the so-called scattering transform for extracting discriminative features, which are robust to small deformations in the images. We have also compared and combined these features with classical yet powerful features like local binary patterns (LBP) and their variants denoted as local quinary patterns (LQP). We show that scattering features yielded to better recognition performances than classical features like LBP and their LQP variants for the FCFM image classification problems. Another finding is that LBP-based and scattering-based features provide complementary discriminative information and, in some situations, we empirically establish that performance can be improved when jointly using LBP, LQP and scattering features. In this work we analyze the joint capability of FCFM images and scattering features for lung cancer diagnosis. The proposed method achieves a good recognition rate for such a diagnosis problem. It also performs well when used in conjunction with other features for other classical medical imaging classification problems. Copyright © 2014 Elsevier B.V. All rights reserved.

Feature extraction for magnetic domain images of magneto-optical recording films using gradient feature segmentation

International Nuclear Information System (INIS)

Quanqing, Zhu.; Xinsai, Wang; Xuecheng, Zou; Haihua, Li; Xiaofei, Yang

2002-01-01

In this paper, we present a method to realize feature extraction on low contrast magnetic domain images of magneto-optical recording films. The method is based on the following three steps: first, Lee-filtering method is adopted to realize pre-filtering and noise reduction; this is followed by gradient feature segmentation, which separates the object area from the background area; finally the common linking method is adopted and the characteristic parameters of magnetic domain are calculated. We describe these steps with particular emphasis on the gradient feature segmentation. The results show that this method has advantages over other traditional ones for feature extraction of low contrast images

Blind image quality assessment based on aesthetic and statistical quality-aware features

Science.gov (United States)

Jenadeleh, Mohsen; Masaeli, Mohammad Masood; Moghaddam, Mohsen Ebrahimi

2017-07-01

The main goal of image quality assessment (IQA) methods is the emulation of human perceptual image quality judgments. Therefore, the correlation between objective scores of these methods with human perceptual scores is considered as their performance metric. Human judgment of the image quality implicitly includes many factors when assessing perceptual image qualities such as aesthetics, semantics, context, and various types of visual distortions. The main idea of this paper is to use a host of features that are commonly employed in image aesthetics assessment in order to improve blind image quality assessment (BIQA) methods accuracy. We propose an approach that enriches the features of BIQA methods by integrating a host of aesthetics image features with the features of natural image statistics derived from multiple domains. The proposed features have been used for augmenting five different state-of-the-art BIQA methods, which use statistical natural scene statistics features. Experiments were performed on seven benchmark image quality databases. The experimental results showed significant improvement of the accuracy of the methods.

Convolutional neural network features based change detection in satellite images

Science.gov (United States)

Mohammed El Amin, Arabi; Liu, Qingjie; Wang, Yunhong

2016-07-01

With the popular use of high resolution remote sensing (HRRS) satellite images, a huge research efforts have been placed on change detection (CD) problem. An effective feature selection method can significantly boost the final result. While hand-designed features have proven difficulties to design features that effectively capture high and mid-level representations, the recent developments in machine learning (Deep Learning) omit this problem by learning hierarchical representation in an unsupervised manner directly from data without human intervention. In this letter, we propose approaching the change detection problem from a feature learning perspective. A novel deep Convolutional Neural Networks (CNN) features based HR satellite images change detection method is proposed. The main guideline is to produce a change detection map directly from two images using a pretrained CNN. This method can omit the limited performance of hand-crafted features. Firstly, CNN features are extracted through different convolutional layers. Then, a concatenation step is evaluated after an normalization step, resulting in a unique higher dimensional feature map. Finally, a change map was computed using pixel-wise Euclidean distance. Our method has been validated on real bitemporal HRRS satellite images according to qualitative and quantitative analyses. The results obtained confirm the interest of the proposed method.

Spectroscopic imaging studies of nanoscale polarity and mass transport phenomena in self-assembled organic nanotubes.

Science.gov (United States)

Xu, Hao; Nagasaka, Shinobu; Kameta, Naohiro; Masuda, Mitsutoshi; Ito, Takashi; Higgins, Daniel A

2017-08-02

Synthetic organic nanotubes self-assembled from bolaamphiphile surfactants are now being explored for use as drug delivery vehicles. In this work, several factors important to their implementation in drug delivery are explored. All experiments are performed with the nanotubes immersed in ethanol. First, Nile Red (NR) and a hydroxylated Nile Red derivative (NR-OH) are loaded into the nanotubes and spectroscopic fluorescence imaging methods are used to determine the apparent dielectric constant of their local environment. Both are found in relatively nonpolar environments, with the NR-OH molecules preferring regions of relatively higher dielectric constant compared to NR. Unique two-color imaging fluorescence correlation spectroscopy (imaging FCS) measurements are then used along with the spectroscopic imaging results to deduce the dielectric properties of the environments sensed by mobile and immobile populations of probe molecules. The results reveal that mobile NR molecules pass through less polar regions, likely within the nanotube walls, while immobile NR molecules are found in more polar regions, possibly near the nanotube surfaces. In contrast, mobile and immobile NR-OH molecules are found to locate in environments of similar polarity. The imaging FCS results also provide quantitative data on the apparent diffusion coefficient for each dye. The mean diffusion coefficient for the NR dye was approximately two-fold larger than that of NR-OH. Slower diffusion by the latter could result from its additional hydrogen bonding interactions with polar triglycine, amine, and glucose moieties near the nanotube surfaces. The knowledge gained in these studies will allow for the development of nanotubes that are better engineered for applications in the controlled transport and release of uncharged, dipolar drug molecules.

Texture Feature Analysis for Different Resolution Level of Kidney Ultrasound Images

Science.gov (United States)

Kairuddin, Wan Nur Hafsha Wan; Mahmud, Wan Mahani Hafizah Wan

2017-08-01

Image feature extraction is a technique to identify the characteristic of the image. The objective of this work is to discover the texture features that best describe a tissue characteristic of a healthy kidney from ultrasound (US) image. Three ultrasound machines that have different specifications are used in order to get a different quality (different resolution) of the image. Initially, the acquired images are pre-processed to de-noise the speckle to ensure the image preserve the pixels in a region of interest (ROI) for further extraction. Gaussian Low- pass Filter is chosen as the filtering method in this work. 150 of enhanced images then are segmented by creating a foreground and background of image where the mask is created to eliminate some unwanted intensity values. Statistical based texture features method is used namely Intensity Histogram (IH), Gray-Level Co-Occurance Matrix (GLCM) and Gray-level run-length matrix (GLRLM).This method is depends on the spatial distribution of intensity values or gray levels in the kidney region. By using One-Way ANOVA in SPSS, the result indicated that three features (Contrast, Difference Variance and Inverse Difference Moment Normalized) from GLCM are not statistically significant; this concludes that these three features describe a healthy kidney characteristics regardless of the ultrasound image quality.

Robust Tomato Recognition for Robotic Harvesting Using Feature Images Fusion

Directory of Open Access Journals (Sweden)

Yuanshen Zhao

2016-01-01

Full Text Available Automatic recognition of mature fruits in a complex agricultural environment is still a challenge for an autonomous harvesting robot due to various disturbances existing in the background of the image. The bottleneck to robust fruit recognition is reducing influence from two main disturbances: illumination and overlapping. In order to recognize the tomato in the tree canopy using a low-cost camera, a robust tomato recognition algorithm based on multiple feature images and image fusion was studied in this paper. Firstly, two novel feature images, the  a*-component image and the I-component image, were extracted from the L*a*b* color space and luminance, in-phase, quadrature-phase (YIQ color space, respectively. Secondly, wavelet transformation was adopted to fuse the two feature images at the pixel level, which combined the feature information of the two source images. Thirdly, in order to segment the target tomato from the background, an adaptive threshold algorithm was used to get the optimal threshold. The final segmentation result was processed by morphology operation to reduce a small amount of noise. In the detection tests, 93% target tomatoes were recognized out of 200 overall samples. It indicates that the proposed tomato recognition method is available for robotic tomato harvesting in the uncontrolled environment with low cost.

Robust Tomato Recognition for Robotic Harvesting Using Feature Images Fusion.

Science.gov (United States)

Zhao, Yuanshen; Gong, Liang; Huang, Yixiang; Liu, Chengliang

2016-01-29

Automatic recognition of mature fruits in a complex agricultural environment is still a challenge for an autonomous harvesting robot due to various disturbances existing in the background of the image. The bottleneck to robust fruit recognition is reducing influence from two main disturbances: illumination and overlapping. In order to recognize the tomato in the tree canopy using a low-cost camera, a robust tomato recognition algorithm based on multiple feature images and image fusion was studied in this paper. Firstly, two novel feature images, the  a*-component image and the I-component image, were extracted from the L*a*b* color space and luminance, in-phase, quadrature-phase (YIQ) color space, respectively. Secondly, wavelet transformation was adopted to fuse the two feature images at the pixel level, which combined the feature information of the two source images. Thirdly, in order to segment the target tomato from the background, an adaptive threshold algorithm was used to get the optimal threshold. The final segmentation result was processed by morphology operation to reduce a small amount of noise. In the detection tests, 93% target tomatoes were recognized out of 200 overall samples. It indicates that the proposed tomato recognition method is available for robotic tomato harvesting in the uncontrolled environment with low cost.

Three-dimensional proton magnetic resonance spectroscopic imaging with and without an endorectal coil: a prostate phantom study

NARCIS (Netherlands)

Ma, C.; Chen, L.; Scheenen, T.W.J.; Lu, J.; Wang, J

2015-01-01

Proton magnetic resonance spectroscopic imaging (MRSI) of the prostate has been used with only a combination of external surface coils. The quality of spectral fitting of the (choline + creatine)/citrate ([Cho + Cr]/Cit) ratio at different field strengths and different coils is important for

Classification of radiolarian images with hand-crafted and deep features

Science.gov (United States)

Keçeli, Ali Seydi; Kaya, Aydın; Keçeli, Seda Uzunçimen

2017-12-01

Radiolarians are planktonic protozoa and are important biostratigraphic and paleoenvironmental indicators for paleogeographic reconstructions. Radiolarian paleontology still remains as a low cost and the one of the most convenient way to obtain dating of deep ocean sediments. Traditional methods for identifying radiolarians are time-consuming and cannot scale to the granularity or scope necessary for large-scale studies. Automated image classification will allow making these analyses promptly. In this study, a method for automatic radiolarian image classification is proposed on Scanning Electron Microscope (SEM) images of radiolarians to ease species identification of fossilized radiolarians. The proposed method uses both hand-crafted features like invariant moments, wavelet moments, Gabor features, basic morphological features and deep features obtained from a pre-trained Convolutional Neural Network (CNN). Feature selection is applied over deep features to reduce high dimensionality. Classification outcomes are analyzed to compare hand-crafted features, deep features, and their combinations. Results show that the deep features obtained from a pre-trained CNN are more discriminative comparing to hand-crafted ones. Additionally, feature selection utilizes to the computational cost of classification algorithms and have no negative effect on classification accuracy.

Characterization of intact subcellular bodies in whole bacteria by cryo-electron tomography and spectroscopic imaging.

Science.gov (United States)

Comolli, L R; Kundmann, M; Downing, K H

2006-07-01

We illustrate the combined use of cryo-electron tomography and spectroscopic difference imaging in the study of subcellular structure and subcellular bodies in whole bacteria. We limited our goal and focus to bodies with a distinct elemental composition that was in a sufficiently high concentration to provide the necessary signal-to-noise level at the relatively large sample thicknesses of the intact cell. This combination proved very powerful, as demonstrated by the identification of a phosphorus-rich body in Caulobacter crescentus. We also confirmed the presence of a body rich in carbon, demonstrated that these two types of bodies are readily recognized and distinguished from each other, and provided, for the first time to our knowledge, structural information about them in their intact state. In addition, we also showed the presence of a similar type of phosphorus-rich body in Deinococcus grandis, a member of a completely unrelated bacteria genus. Cryo-electron microscopy and tomography allowed the study of the biogenesis and morphology of these bodies at resolutions better than 10 nm, whereas spectroscopic difference imaging provided a direct identification of their chemical composition.

Dual-wavelength differential spectroscopic imaging for diagnostics of laser-induced plasma

Energy Technology Data Exchange (ETDEWEB)

Motto-Ros, V., E-mail: vincent.motto-ros@univ-lyon1.fr [Universite de Lyon, F-69622, Lyon, Universite Lyon 1, Villeurbanne, CNRS, UMR5579, LASIM (France); Ma, Q.L. [Universite de Lyon, F-69622, Lyon, Universite Lyon 1, Villeurbanne, CNRS, UMR5579, LASIM (France); Gregoire, S. [CRITT Matriaux Alsace, 19 rue de St Junien, 67300 Schiltigheim (France); Lei, W.Q.; Wang, X.C. [Universite de Lyon, F-69622, Lyon, Universite Lyon 1, Villeurbanne, CNRS, UMR5579, LASIM (France); Pelascini, F.; Surma, F. [CRITT Matriaux Alsace, 19 rue de St Junien, 67300 Schiltigheim (France); Detalle, V. [Laboratoire de Recherche des Monuments Historiques, 29 rue de Paris, 77420 Champs-sur-Marne (France); Yu, J. [Universite de Lyon, F-69622, Lyon, Universite Lyon 1, Villeurbanne, CNRS, UMR5579, LASIM (France)

2012-08-15

A specific configuration for plasma fast spectroscopic imaging was developed, where a pair of narrowband filters, one fitting an emission line of a species to be studied and the other out of its emission line, allowed double images to be taken for a laser-induced plasma. A dedicated software was developed for the subtraction between the double images. The result represents therefore the monochromatic emission image of the species in the plasma. We have shown in this work that such configuration is especially efficient for the monitoring of a plasma generated under the atmospheric pressure at very short delays after the impact of the laser pulse on the target, when a strong continuum emission is observed. The efficiency of the technique has been particularly demonstrated in the study of laser-induced plasma on a polymer target. Molecular species, such as C{sub 2} and CN, as well as atomic species, such as C and N, were imaged starting from 50 ns after the laser impact. Moreover space segregation of different species, atomic or molecular, inside of the plasma was clearly observed. - Highlights: Black-Right-Pointing-Pointer Imaging to study species with time and space resolution in laser induced plasma. Black-Right-Pointing-Pointer Image display of multiple species is proposed based on RGB color model. Black-Right-Pointing-Pointer Molecular emission (CN and C{sub 2}) is observed at very short delays (50 ns). Black-Right-Pointing-Pointer Segregation of different species inside the plasma is clearly established.

Three-dimensional magnetic resonance spectroscopic imaging in the substantia nigra of healthy controls and patients with Parkinson's disease

International Nuclear Information System (INIS)

Groeger, Adriane; Godau, Jana; Berg, Daniela; Chadzynski, Grzegorz; Klose, Uwe

2011-01-01

To investigate the substantia nigra in patients with Parkinson's disease three-dimensional magnetic resonance spectroscopic imaging with high spatial resolution at 3 Tesla was performed. Regional variations of spectroscopic data between the rostral and caudal regions of the substantia nigra as well as the midbrain tegmentum areas were evaluated in healthy controls and patients with Parkinson's disease. Nine patients with Parkinson's disease and eight age- and gender-matched healthy controls were included in this study. Data were acquired by using three-dimensional magnetic resonance spectroscopic imaging measurements. The ratios between rostral and caudal voxels of the substantia nigra as well as the midbrain tegmentum areas were calculated for the main-metabolites N-acetyl aspartate, creatine, choline, and myo-inositol. Additionally, the metabolite/creatine ratios were calculated. In all subjects spectra of acceptable quality could be obtained with a nominal voxel size of 0.252 ml. The calculated rostral-to-caudal ratios of the metabolites as well as of the metabolite/creatine ratios showed with exception of choline/creatine ratio significant differences between healthy controls and patients with Parkinson's disease. The findings from this study indicate that regional variations in N-acetyl aspartate/creatine ratios in the regions of the substantia nigra may differentiate patients with Parkinson's disease and healthy controls. (orig.)

SU-D-BRA-04: Computerized Framework for Marker-Less Localization of Anatomical Feature Points in Range Images Based On Differential Geometry Features for Image-Guided Radiation Therapy

International Nuclear Information System (INIS)

Soufi, M; Arimura, H; Toyofuku, F; Nakamura, K; Hirose, T; Umezu, Y; Shioyama, Y

2016-01-01

Purpose: To propose a computerized framework for localization of anatomical feature points on the patient surface in infrared-ray based range images by using differential geometry (curvature) features. Methods: The general concept was to reconstruct the patient surface by using a mathematical modeling technique for the computation of differential geometry features that characterize the local shapes of the patient surfaces. A region of interest (ROI) was firstly extracted based on a template matching technique applied on amplitude (grayscale) images. The extracted ROI was preprocessed for reducing temporal and spatial noises by using Kalman and bilateral filters, respectively. Next, a smooth patient surface was reconstructed by using a non-uniform rational basis spline (NURBS) model. Finally, differential geometry features, i.e. the shape index and curvedness features were computed for localizing the anatomical feature points. The proposed framework was trained for optimizing shape index and curvedness thresholds and tested on range images of an anthropomorphic head phantom. The range images were acquired by an infrared ray-based time-of-flight (TOF) camera. The localization accuracy was evaluated by measuring the mean of minimum Euclidean distances (MMED) between reference (ground truth) points and the feature points localized by the proposed framework. The evaluation was performed for points localized on convex regions (e.g. apex of nose) and concave regions (e.g. nasofacial sulcus). Results: The proposed framework has localized anatomical feature points on convex and concave anatomical landmarks with MMEDs of 1.91±0.50 mm and 3.70±0.92 mm, respectively. A statistically significant difference was obtained between the feature points on the convex and concave regions (P<0.001). Conclusion: Our study has shown the feasibility of differential geometry features for localization of anatomical feature points on the patient surface in range images. The proposed

SU-D-BRA-04: Computerized Framework for Marker-Less Localization of Anatomical Feature Points in Range Images Based On Differential Geometry Features for Image-Guided Radiation Therapy

Energy Technology Data Exchange (ETDEWEB)

Soufi, M; Arimura, H; Toyofuku, F [Kyushu University, Fukuoka, Fukuoka (Japan); Nakamura, K [Hamamatsu University School of Medicine, Hamamatsu, Shizuoka (Japan); Hirose, T; Umezu, Y [Kyushu University Hospital, Fukuoka, Fukuoka (Japan); Shioyama, Y [Saga Heavy Ion Medical Accelerator in Tosu, Tosu, Saga (Japan)

2016-06-15

Purpose: To propose a computerized framework for localization of anatomical feature points on the patient surface in infrared-ray based range images by using differential geometry (curvature) features. Methods: The general concept was to reconstruct the patient surface by using a mathematical modeling technique for the computation of differential geometry features that characterize the local shapes of the patient surfaces. A region of interest (ROI) was firstly extracted based on a template matching technique applied on amplitude (grayscale) images. The extracted ROI was preprocessed for reducing temporal and spatial noises by using Kalman and bilateral filters, respectively. Next, a smooth patient surface was reconstructed by using a non-uniform rational basis spline (NURBS) model. Finally, differential geometry features, i.e. the shape index and curvedness features were computed for localizing the anatomical feature points. The proposed framework was trained for optimizing shape index and curvedness thresholds and tested on range images of an anthropomorphic head phantom. The range images were acquired by an infrared ray-based time-of-flight (TOF) camera. The localization accuracy was evaluated by measuring the mean of minimum Euclidean distances (MMED) between reference (ground truth) points and the feature points localized by the proposed framework. The evaluation was performed for points localized on convex regions (e.g. apex of nose) and concave regions (e.g. nasofacial sulcus). Results: The proposed framework has localized anatomical feature points on convex and concave anatomical landmarks with MMEDs of 1.91±0.50 mm and 3.70±0.92 mm, respectively. A statistically significant difference was obtained between the feature points on the convex and concave regions (P<0.001). Conclusion: Our study has shown the feasibility of differential geometry features for localization of anatomical feature points on the patient surface in range images. The proposed

Imaging features of musculoskeletal tuberculosis

International Nuclear Information System (INIS)

Vuyst, Dimitri De; Vanhoenacker, Filip; Bernaerts, Anja; Gielen, Jan; Schepper, Arthur M. de

2003-01-01

The purpose of this article is to review the imaging characteristics of musculoskeletal tuberculosis. Skeletal tuberculosis represents one-third of all cases of tuberculosis occurring in extrapulmonary sites. Hematogenous spread from a distant focus elsewhere in the body is the cornerstone in the understanding of imaging features of musculoskeletal tuberculosis. The most common presentations are tuberculous spondylitis, arthritis, osteomyelitis, and soft tissue involvement. The diagnostic value of the different imaging techniques, which include conventional radiography, CT, and MR imaging, are emphasized. Whereas conventional radiography is the mainstay in the diagnosis of tuberculous arthritis and osteomyelitis, MR imaging may detect associated bone marrow and soft tissue abnormalities. MR imaging is generally accepted as the imaging modality of choice for diagnosis, demonstration of the extent of the disease of tuberculous spondylitis, and soft tissue tuberculosis. Moreover, it may be very helpful in the differential diagnosis with pyogenic spondylodiscitis, as it may easily demonstrate anterior corner destruction, the relative preservation of the intervertebral disk, multilevel involvement with or without skip lesions, and a large soft tissue abscess, as these are all arguments in favor of a tuberculous spondylitis. On the other hand, CT is still superior in the demonstration of calcifications, which are found in chronic tuberculous abscesses. (orig.)

Iris recognition based on key image feature extraction.

Science.gov (United States)

Ren, X; Tian, Q; Zhang, J; Wu, S; Zeng, Y

2008-01-01

In iris recognition, feature extraction can be influenced by factors such as illumination and contrast, and thus the features extracted may be unreliable, which can cause a high rate of false results in iris pattern recognition. In order to obtain stable features, an algorithm was proposed in this paper to extract key features of a pattern from multiple images. The proposed algorithm built an iris feature template by extracting key features and performed iris identity enrolment. Simulation results showed that the selected key features have high recognition accuracy on the CASIA Iris Set, where both contrast and illumination variance exist.

Focal hepatic lesions with peripheral eosinophilia: imaging features of various disease

Energy Technology Data Exchange (ETDEWEB)

Seo, Joon Beom; Han, Joon Koo; Kim, Tae Kyoung; Choi, Byung Ihn; Han, Man Chung [Seoul National Univ. College of Medicine, Seoul (Korea, Republic of); Song, Chi Sung [Seoul City Boramae Hospital, Seoul (Korea, Republic of)

1999-01-01

Due to the recent advent of various imaging modalities such as ultrasonography, computed tomography and magnetic resonance imaging, as well as knowledge of the characteristic imaging features of hepatic lesions, radiologic examination plays a major role in the differential diagnosis of focal hepatic lesions. However, various 'nonspecific' or 'unusual' imaging features of focal hepatic lesions are occasionally encountered, and this makes correct diagnosis difficult. In such a situation, the presence of peripheral eosinophilia helps narrow the differential diagnoses. The aim of this pictorial essay is to describe the imaging features of various disease entities which cause focal hepatic lesions and peripheral eosinophilia.

Online Feature Selection for Classifying Emphysema in HRCT Images

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M. Prasad

2008-06-01

Full Text Available Feature subset selection, applied as a pre- processing step to machine learning, is valuable in dimensionality reduction, eliminating irrelevant data and improving classifier performance. In the classic formulation of the feature selection problem, it is assumed that all the features are available at the beginning. However, in many real world problems, there are scenarios where not all features are present initially and must be integrated as they become available. In such scenarios, online feature selection provides an efficient way to sort through a large space of features. It is in this context that we introduce online feature selection for the classification of emphysema, a smoking related disease that appears as low attenuation regions in High Resolution Computer Tomography (HRCT images. The technique was successfully evaluated on 61 HRCT scans and compared with different online feature selection approaches, including hill climbing, best first search, grafting, and correlation-based feature selection. The results were also compared against ldensity maskr, a standard approach used for emphysema detection in medical image analysis.

A spectroscopic study of absorption and emission features of interstellar dust components

International Nuclear Information System (INIS)

Zwet, G.P. van der.

1986-01-01

The spectroscopic properties of silicate interstellar dust grains are the subject of this thesis. The process of accretion and photolysis is simulated in the laboratory by condensing mixtures of gases onto a cold substrate (T ∼ 12 K) in a vacuum chamber and photolyzing these mixtures with a vacuum ultraviolet source. Alternatively, the gas mixtures may be passed through a microwave discharge first, before deposition. The spectroscopic properties of the ices are investigated using ultraviolet, visible and infrared spectroscopy. (Auth.)

Two-Level Evaluation on Sensor Interoperability of Features in Fingerprint Image Segmentation

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Ya-Shuo Li

2012-03-01

Full Text Available Features used in fingerprint segmentation significantly affect the segmentation performance. Various features exhibit different discriminating abilities on fingerprint images derived from different sensors. One feature which has better discriminating ability on images derived from a certain sensor may not adapt to segment images derived from other sensors. This degrades the segmentation performance. This paper empirically analyzes the sensor interoperability problem of segmentation feature, which refers to the feature’s ability to adapt to the raw fingerprints captured by different sensors. To address this issue, this paper presents a two-level feature evaluation method, including the first level feature evaluation based on segmentation error rate and the second level feature evaluation based on decision tree. The proposed method is performed on a number of fingerprint databases which are obtained from various sensors. Experimental results show that the proposed method can effectively evaluate the sensor interoperability of features, and the features with good evaluation results acquire better segmentation accuracies of images originating from different sensors.

Comparisons of feature extraction algorithm based on unmanned aerial vehicle image

Directory of Open Access Journals (Sweden)

Xi Wenfei

2017-07-01

Full Text Available Feature point extraction technology has become a research hotspot in the photogrammetry and computer vision. The commonly used point feature extraction operators are SIFT operator, Forstner operator, Harris operator and Moravec operator, etc. With the high spatial resolution characteristics, UAV image is different from the traditional aviation image. Based on these characteristics of the unmanned aerial vehicle (UAV, this paper uses several operators referred above to extract feature points from the building images, grassland images, shrubbery images, and vegetable greenhouses images. Through the practical case analysis, the performance, advantages, disadvantages and adaptability of each algorithm are compared and analyzed by considering their speed and accuracy. Finally, the suggestions of how to adapt different algorithms in diverse environment are proposed.

A Novel Technique for Shape Feature Extraction Using Content Based Image Retrieval

Directory of Open Access Journals (Sweden)

Dhanoa Jaspreet Singh

2016-01-01

Full Text Available With the advent of technology and multimedia information, digital images are increasing very quickly. Various techniques are being developed to retrieve/search digital information or data contained in the image. Traditional Text Based Image Retrieval System is not plentiful. Since it is time consuming as it require manual image annotation. Also, the image annotation differs with different peoples. An alternate to this is Content Based Image Retrieval (CBIR system. It retrieves/search for image using its contents rather the text, keywords etc. A lot of exploration has been compassed in the range of Content Based Image Retrieval (CBIR with various feature extraction techniques. Shape is a significant image feature as it reflects the human perception. Moreover, Shape is quite simple to use by the user to define object in an image as compared to other features such as Color, texture etc. Over and above, if applied alone, no descriptor will give fruitful results. Further, by combining it with an improved classifier, one can use the positive features of both the descriptor and classifier. So, a tryout will be made to establish an algorithm for accurate feature (Shape extraction in Content Based Image Retrieval (CBIR. The main objectives of this project are: (a To propose an algorithm for shape feature extraction using CBIR, (b To evaluate the performance of proposed algorithm and (c To compare the proposed algorithm with state of art techniques.

Time-resolved spectroscopic imaging reveals the fundamentals of cellular NADH fluorescence.

Science.gov (United States)

Li, Dong; Zheng, Wei; Qu, Jianan Y

2008-10-15

A time-resolved spectroscopic imaging system is built to study the fluorescence characteristics of nicotinamide adenine dinucleotide (NADH), an important metabolic coenzyme and endogenous fluorophore in cells. The system provides a unique approach to measure fluorescence signals in different cellular organelles and cytoplasm. The ratios of free over protein-bound NADH signals in cytosol and nucleus are slightly higher than those in mitochondria. The mitochondrial fluorescence contributes about 70% of overall cellular fluorescence and is not a completely dominant signal. Furthermore, NADH signals in mitochondria, cytosol, and the nucleus respond to the changes of cellular activity differently, suggesting that cytosolic and nuclear fluorescence may complicate the well-known relationship between mitochondrial fluorescence and cellular metabolism.

Extraction of Lesion-Partitioned Features and Retrieval of Contrast-Enhanced Liver Images

Directory of Open Access Journals (Sweden)

Mei Yu

2012-01-01

Full Text Available The most critical step in grayscale medical image retrieval systems is feature extraction. Understanding the interrelatedness between the characteristics of lesion images and corresponding imaging features is crucial for image training, as well as for features extraction. A feature-extraction algorithm is developed based on different imaging properties of lesions and on the discrepancy in density between the lesions and their surrounding normal liver tissues in triple-phase contrast-enhanced computed tomographic (CT scans. The algorithm includes mainly two processes: (1 distance transformation, which is used to divide the lesion into distinct regions and represents the spatial structure distribution and (2 representation using bag of visual words (BoW based on regions. The evaluation of this system based on the proposed feature extraction algorithm shows excellent retrieval results for three types of liver lesions visible on triple-phase scans CT images. The results of the proposed feature extraction algorithm show that although single-phase scans achieve the average precision of 81.9%, 80.8%, and 70.2%, dual- and triple-phase scans achieve 86.3% and 88.0%.

Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters.

Science.gov (United States)

Brynolfsson, Patrik; Nilsson, David; Torheim, Turid; Asklund, Thomas; Karlsson, Camilla Thellenberg; Trygg, Johan; Nyholm, Tufve; Garpebring, Anders

2017-06-22

In recent years, texture analysis of medical images has become increasingly popular in studies investigating diagnosis, classification and treatment response assessment of cancerous disease. Despite numerous applications in oncology and medical imaging in general, there is no consensus regarding texture analysis workflow, or reporting of parameter settings crucial for replication of results. The aim of this study was to assess how sensitive Haralick texture features of apparent diffusion coefficient (ADC) MR images are to changes in five parameters related to image acquisition and pre-processing: noise, resolution, how the ADC map is constructed, the choice of quantization method, and the number of gray levels in the quantized image. We found that noise, resolution, choice of quantization method and the number of gray levels in the quantized images had a significant influence on most texture features, and that the effect size varied between different features. Different methods for constructing the ADC maps did not have an impact on any texture feature. Based on our results, we recommend using images with similar resolutions and noise levels, using one quantization method, and the same number of gray levels in all quantized images, to make meaningful comparisons of texture feature results between different subjects.

Ground-based multi-station spectroscopic imaging with ALIS. - Scientific highlights, project status and future prospects

Science.gov (United States)

Brändström; Gustavsson, Björn; Pellinen-Wannberg, Asta; Sandahl, Ingrid; Sergienko, Tima; Steen, Ake

2005-08-01

The Auroral Large Imaging System (ALIS) was first proposed at the ESA-PAC meeting in Lahnstein 1989. The first spectroscopic imaging station was operational in 1994, and since then up to six stations have been in simultaneous operation. Each station has a scientific-grade CCD-detector and a filter-wheel for narrow-band interference-filters with six positions. The field-of-view is around 70°. Each imager is mounted in a positioning system, enabling imaging of a common volume from several sites. This enables triangulation and tomography. Raw data from ALIS is freely available at ("http://alis.irf.se") and ALIS is open for scientific colaboration. ALIS made the first unambiguous observations of Radio-induced optical emissions at high latitudes, and the detection of water in a Leonid meteor-trail. Both rockets and satellite coordination are considered for future observations with ALIS.

High Energy Solar Spectroscopic Imager (HESSI) Team Investigations

Science.gov (United States)

Emslie, A. Gordon

1998-01-01

This report covers activities on the above grant for the period through the end of September 1997. The work originally proposed to be performed under a three-year award was converted at that time to a two-year award for the remainder of the period, and is now funded under award NAGS-4027 through Goddard Space Flight Center. The P.I. is a co-investigator on the High Energy Solar Spectroscopic Imager (HESSI) team, selected as a Small-Class Explorer (SNMX) mission in 1997. He has also been a participant in the Space Physics Roadmap Planning Group. Our research has been strongly influenced by the NASA mission opportunities related to these activities. The report is subdivided into four sections, each dealing with a different aspect of our research within this guiding theme. Personnel involved in this research at UAH include the P.I. and graduate students Michele Montgomery and Amy Winebarger. Much of the work has been carried out in collaboration with investigators at other institutions, as detailed below. Attachment: Laser wakefield acceleration and astrophysical applications.

SAR Image Classification Based on Its Texture Features

Institute of Scientific and Technical Information of China (English)

LI Pingxiang; FANG Shenghui

2003-01-01

SAR images not only have the characteristics of all-ay, all-eather, but also provide object information which is different from visible and infrared sensors. However, SAR images have some faults, such as more speckles and fewer bands. The authors conducted the experiments of texture statistics analysis on SAR image features in order to improve the accuracy of SAR image interpretation.It is found that the texture analysis is an effective method for improving the accuracy of the SAR image interpretation.

Derivative-based scale invariant image feature detector with error resilience.

Science.gov (United States)

Mainali, Pradip; Lafruit, Gauthier; Tack, Klaas; Van Gool, Luc; Lauwereins, Rudy

2014-05-01

We present a novel scale-invariant image feature detection algorithm (D-SIFER) using a newly proposed scale-space optimal 10th-order Gaussian derivative (GDO-10) filter, which reaches the jointly optimal Heisenberg's uncertainty of its impulse response in scale and space simultaneously (i.e., we minimize the maximum of the two moments). The D-SIFER algorithm using this filter leads to an outstanding quality of image feature detection, with a factor of three quality improvement over state-of-the-art scale-invariant feature transform (SIFT) and speeded up robust features (SURF) methods that use the second-order Gaussian derivative filters. To reach low computational complexity, we also present a technique approximating the GDO-10 filters with a fixed-length implementation, which is independent of the scale. The final approximation error remains far below the noise margin, providing constant time, low cost, but nevertheless high-quality feature detection and registration capabilities. D-SIFER is validated on a real-life hyperspectral image registration application, precisely aligning up to hundreds of successive narrowband color images, despite their strong artifacts (blurring, low-light noise) typically occurring in such delicate optical system setups.

In vivo measurement of regional brain metabolic response to hyperventilation using magnetic resonance: proton echo planar spectroscopic imaging (PEPSI).

Science.gov (United States)

Posse, S; Dager, S R; Richards, T L; Yuan, C; Ogg, R; Artru, A A; Müller-Gärtner, H W; Hayes, C

1997-06-01

A new rapid spectroscopic imaging technique with improved sensitivity and lipid suppression, referred to as Proton Echo Planar Spectroscopic Imaging (PEPSI), has been developed to measure the 2-dimensional distribution of brain lactate increases during hyperventilation on a conventional clinical scanner equipped with a head surface coil phased array. PEPSI images (nominal voxel size: 1.125 cm3) in five healthy subjects from an axial section approximately 20 mm inferior to the intercommissural line were obtained during an 8.5-min baseline period of normocapnia and during the final 8.5 min of a 10-min period of capnometry-controlled hyperventilation (end-tidal PCO2 of 20 mmHg). The lactate/N-acetyl aspartate signal increased significantly from baseline during hyperventilation for the insular cortex, temporal cortex, and occipital regions of both the right and left hemisphere, but not in the basal ganglia. Regional or hemispheric right-to-left differences were not found. The study extends previous work using single-voxel MR spectroscopy to dynamically study hyperventilation effects on brain metabolism.

Quality Evaluation in Wireless Imaging Using Feature-Based Objective Metrics

OpenAIRE

Engelke, Ulrich; Zepernick, Hans-Jürgen

2007-01-01

This paper addresses the evaluation of image quality in the context of wireless systems using feature-based objective metrics. The considered metrics comprise of a weighted combination of feature values that are used to quantify the extend by which the related artifacts are present in a processed image. In view of imaging applications in mobile radio and wireless communication systems, reduced-reference objective quality metrics are investigated for quantifying user-perceived quality. The exa...

Quantitative Image Feature Engine (QIFE): an Open-Source, Modular Engine for 3D Quantitative Feature Extraction from Volumetric Medical Images.

Science.gov (United States)

Echegaray, Sebastian; Bakr, Shaimaa; Rubin, Daniel L; Napel, Sandy

2017-10-06

The aim of this study was to develop an open-source, modular, locally run or server-based system for 3D radiomics feature computation that can be used on any computer system and included in existing workflows for understanding associations and building predictive models between image features and clinical data, such as survival. The QIFE exploits various levels of parallelization for use on multiprocessor systems. It consists of a managing framework and four stages: input, pre-processing, feature computation, and output. Each stage contains one or more swappable components, allowing run-time customization. We benchmarked the engine using various levels of parallelization on a cohort of CT scans presenting 108 lung tumors. Two versions of the QIFE have been released: (1) the open-source MATLAB code posted to Github, (2) a compiled version loaded in a Docker container, posted to DockerHub, which can be easily deployed on any computer. The QIFE processed 108 objects (tumors) in 2:12 (h/mm) using 1 core, and 1:04 (h/mm) hours using four cores with object-level parallelization. We developed the Quantitative Image Feature Engine (QIFE), an open-source feature-extraction framework that focuses on modularity, standards, parallelism, provenance, and integration. Researchers can easily integrate it with their existing segmentation and imaging workflows by creating input and output components that implement their existing interfaces. Computational efficiency can be improved by parallelizing execution at the cost of memory usage. Different parallelization levels provide different trade-offs, and the optimal setting will depend on the size and composition of the dataset to be processed.

Feature extraction from mammographic images using fast marching methods

International Nuclear Information System (INIS)

Bottigli, U.; Golosio, B.

2002-01-01

Features extraction from medical images represents a fundamental step for shape recognition and diagnostic support. The present work faces the problem of the detection of large features, such as massive lesions and organ contours, from mammographic images. The regions of interest are often characterized by an average grayness intensity that is different from the surrounding. In most cases, however, the desired features cannot be extracted by simple gray level thresholding, because of image noise and non-uniform density of the surrounding tissue. In this work, edge detection is achieved through the fast marching method (Level Set Methods and Fast Marching Methods, Cambridge University Press, Cambridge, 1999), which is based on the theory of interface evolution. Starting from a seed point in the shape of interest, a front is generated which evolves according to an appropriate speed function. Such function is expressed in terms of geometric properties of the evolving interface and of image properties, and should become zero when the front reaches the desired boundary. Some examples of application of such method to mammographic images from the CALMA database (Nucl. Instr. and Meth. A 460 (2001) 107) are presented here and discussed

Imaging features of juxtacortical chondroma in children

International Nuclear Information System (INIS)

Miller, Stephen F.

2014-01-01

Juxtacortical chondroma is a rare benign bone lesion in children. Children usually present with a mildly painful mass, which prompts diagnostic imaging studies. The rarity of this condition often presents a diagnostic challenge. Correct diagnosis is crucial in guiding surgical management. To describe the characteristic imaging findings of juxtacortical chondroma in children. We identified all children who were diagnosed with juxtacortical chondroma between 1998 and 2012. A single experienced pediatric radiologist reviewed all diagnostic imaging studies, including plain radiographs, CT, MR and bone scans. Seven children (5 boys and 2 girls) with juxtacortical chondroma were identified, ranging in age from 6 years to 16 years (mean 12.3 years). Mild pain and a palpable mass were present in all seven children. Plain radiographs were available in 6/7, MR in 7/7, CT in 4/7 and skeletal scintigraphy in 5/7 children. Three lesions were located in the proximal humerus, with one each in the distal radius, distal femur, proximal tibia and scapula. Radiographic and CT features deemed highly suggestive of juxtacortical chondroma included cortical scalloping, underlying cortical sclerosis and overhanging margins. MRI features consistent with juxtacortical chondroma included isointensity to skeletal muscle on T1, marked hyperintensity on T2 and peripheral rim enhancement after contrast agent administration. One of seven lesions demonstrated intramedullary extension, and 2/7 showed adjacent soft-tissue edema. Juxtacortical chondroma is an uncommon benign lesion in children with characteristic features on plain radiographs, CT and MR. Recognition of these features is invaluable in guiding appropriate surgical management. (orig.)

Imaging features of juxtacortical chondroma in children

Energy Technology Data Exchange (ETDEWEB)

Miller, Stephen F. [St. Jude Children' s Research Hospital, Department of Radiological Sciences, Memphis, TN (United States)

2014-01-15

Juxtacortical chondroma is a rare benign bone lesion in children. Children usually present with a mildly painful mass, which prompts diagnostic imaging studies. The rarity of this condition often presents a diagnostic challenge. Correct diagnosis is crucial in guiding surgical management. To describe the characteristic imaging findings of juxtacortical chondroma in children. We identified all children who were diagnosed with juxtacortical chondroma between 1998 and 2012. A single experienced pediatric radiologist reviewed all diagnostic imaging studies, including plain radiographs, CT, MR and bone scans. Seven children (5 boys and 2 girls) with juxtacortical chondroma were identified, ranging in age from 6 years to 16 years (mean 12.3 years). Mild pain and a palpable mass were present in all seven children. Plain radiographs were available in 6/7, MR in 7/7, CT in 4/7 and skeletal scintigraphy in 5/7 children. Three lesions were located in the proximal humerus, with one each in the distal radius, distal femur, proximal tibia and scapula. Radiographic and CT features deemed highly suggestive of juxtacortical chondroma included cortical scalloping, underlying cortical sclerosis and overhanging margins. MRI features consistent with juxtacortical chondroma included isointensity to skeletal muscle on T1, marked hyperintensity on T2 and peripheral rim enhancement after contrast agent administration. One of seven lesions demonstrated intramedullary extension, and 2/7 showed adjacent soft-tissue edema. Juxtacortical chondroma is an uncommon benign lesion in children with characteristic features on plain radiographs, CT and MR. Recognition of these features is invaluable in guiding appropriate surgical management. (orig.)

Regional neuro axonal injury detected by 1H 3 Tesla spectroscopic imaging in late onset Tay sachs

International Nuclear Information System (INIS)

Gagoski, Borjan Aleksandar; Eichler, Florian S.

2010-01-01

Late-onset Tay Sachs (LOTS) is a rare lysosomal storage disorder resulting from mutations of the subunit of the lysosomal enzyme β-hexosaminidase A, which catalyzes the degradation of GM2 ganglioside. We have applied the fast encoding spectroscopic imaging technique to LOTS patients to further investigate the neuro degenerative consequences of this disease.(Author)

Evaluation of heterogeneous metabolic profile in an orthotopic human glioblastoma xenograft model using compressed sensing hyperpolarized 3D 13C magnetic resonance spectroscopic imaging.

Science.gov (United States)

Park, Ilwoo; Hu, Simon; Bok, Robert; Ozawa, Tomoko; Ito, Motokazu; Mukherjee, Joydeep; Phillips, Joanna J; James, C David; Pieper, Russell O; Ronen, Sabrina M; Vigneron, Daniel B; Nelson, Sarah J

2013-07-01

High resolution compressed sensing hyperpolarized (13)C magnetic resonance spectroscopic imaging was applied in orthotopic human glioblastoma xenografts for quantitative assessment of spatial variations in (13)C metabolic profiles and comparison with histopathology. A new compressed sensing sampling design with a factor of 3.72 acceleration was implemented to enable a factor of 4 increase in spatial resolution. Compressed sensing 3D (13)C magnetic resonance spectroscopic imaging data were acquired from a phantom and 10 tumor-bearing rats following injection of hyperpolarized [1-(13)C]-pyruvate using a 3T scanner. The (13)C metabolic profiles were compared with hematoxylin and eosin staining and carbonic anhydrase 9 staining. The high-resolution compressed sensing (13)C magnetic resonance spectroscopic imaging data enabled the differentiation of distinct (13)C metabolite patterns within abnormal tissues with high specificity in similar scan times compared to the fully sampled method. The results from pathology confirmed the different characteristics of (13)C metabolic profiles between viable, non-necrotic, nonhypoxic tumor, and necrotic, hypoxic tissue. Copyright © 2012 Wiley Periodicals, Inc.

The DEIMOS 10K Spectroscopic Survey Catalog of the COSMOS Field

Science.gov (United States)

Hasinger, G.; Capak, P.; Salvato, M.; Barger, A. J.; Cowie, L. L.; Faisst, A.; Hemmati, S.; Kakazu, Y.; Kartaltepe, J.; Masters, D.; Mobasher, B.; Nayyeri, H.; Sanders, D.; Scoville, N. Z.; Suh, H.; Steinhardt, C.; Yang, Fengwei

2018-05-01

We present a catalog of 10,718 objects in the COSMOS field, observed through multi-slit spectroscopy with the Deep Imaging Multi-Object Spectrograph (DEIMOS) on the Keck II telescope in the wavelength range ∼5500–9800 Å. The catalog contains 6617 objects with high-quality spectra (two or more spectral features), and 1798 objects with a single spectroscopic feature confirmed by the photometric redshift. For 2024 typically faint objects, we could not obtain reliable redshifts. The objects have been selected from a variety of input catalogs based on multi-wavelength observations in the field, and thus have a diverse selection function, which enables the study of the diversity in the galaxy population. The magnitude distribution of our objects is peaked at I AB ∼ 23 and K AB ∼ 21, with a secondary peak at K AB ∼ 24. We sample a broad redshift distribution in the range 0 0.65 with chance probabilities 10 Mpc. An object-to-object comparison with a multitude of other spectroscopic samples in the same field shows that our DEIMOS sample is among the best in terms of fraction of spectroscopic failures and relative redshift accuracy. We have determined the fraction of spectroscopic blends to about 0.8% in our sample. This is likely a lower limit and at any rate well below the most pessimistic expectations. Interestingly, we find evidence for strong lensing of Lyα background emitters within the slits of 12 of our target galaxies, increasing their apparent density by about a factor of 4. The data presented herein were obtained at the W. M. Keck Observatory, which is operated as a scientific partnership among the California Institute of Technology, the University of California and the National Aeronautics and Space Administration. The Observatory was made possible by the generous financial support of the W. M. Keck Foundation.

COLOR IMAGE RETRIEVAL BASED ON FEATURE FUSION THROUGH MULTIPLE LINEAR REGRESSION ANALYSIS

Directory of Open Access Journals (Sweden)

K. Seetharaman

2015-08-01

Full Text Available This paper proposes a novel technique based on feature fusion using multiple linear regression analysis, and the least-square estimation method is employed to estimate the parameters. The given input query image is segmented into various regions according to the structure of the image. The color and texture features are extracted on each region of the query image, and the features are fused together using the multiple linear regression model. The estimated parameters of the model, which is modeled based on the features, are formed as a vector called a feature vector. The Canberra distance measure is adopted to compare the feature vectors of the query and target images. The F-measure is applied to evaluate the performance of the proposed technique. The obtained results expose that the proposed technique is comparable to the other existing techniques.

Characterization of cytochrome c as marker for retinal cell degeneration by uv/vis spectroscopic imaging

Science.gov (United States)

Hollmach, Julia; Schweizer, Julia; Steiner, Gerald; Knels, Lilla; Funk, Richard H. W.; Thalheim, Silko; Koch, Edmund

2011-07-01

Retinal diseases like age-related macular degeneration have become an important cause of visual loss depending on increasing life expectancy and lifestyle habits. Due to the fact that no satisfying treatment exists, early diagnosis and prevention are the only possibilities to stop the degeneration. The protein cytochrome c (cyt c) is a suitable marker for degeneration processes and apoptosis because it is a part of the respiratory chain and involved in the apoptotic pathway. The determination of the local distribution and oxidative state of cyt c in living cells allows the characterization of cell degeneration processes. Since cyt c exhibits characteristic absorption bands between 400 and 650 nm wavelength, uv/vis in situ spectroscopic imaging was used for its characterization in retinal ganglion cells. The large amount of data, consisting of spatial and spectral information, was processed by multivariate data analysis. The challenge consists in the identification of the molecular information of cyt c. Baseline correction, principle component analysis (PCA) and cluster analysis (CA) were performed in order to identify cyt c within the spectral dataset. The combination of PCA and CA reveals cyt c and its oxidative state. The results demonstrate that uv/vis spectroscopic imaging in conjunction with sophisticated multivariate methods is a suitable tool to characterize cyt c under in situ conditions.

Imaging features of benign adrenal cysts

International Nuclear Information System (INIS)

Sanal, Hatice Tuba; Kocaoglu, Murat; Yildirim, Duzgun; Bulakbasi, Nail; Guvenc, Inanc; Tayfun, Cem; Ucoz, Taner

2006-01-01

Benign adrenal gland cysts (BACs) are rare lesions with a variable histological spectrum and may mimic not only each other but also malignant ones. We aimed to review imaging features of BACs which can be helpful in distinguishing each entity and determining the subsequent appropriate management

Emotional textile image classification based on cross-domain convolutional sparse autoencoders with feature selection

Science.gov (United States)

Li, Zuhe; Fan, Yangyu; Liu, Weihua; Yu, Zeqi; Wang, Fengqin

2017-01-01

We aim to apply sparse autoencoder-based unsupervised feature learning to emotional semantic analysis for textile images. To tackle the problem of limited training data, we present a cross-domain feature learning scheme for emotional textile image classification using convolutional autoencoders. We further propose a correlation-analysis-based feature selection method for the weights learned by sparse autoencoders to reduce the number of features extracted from large size images. First, we randomly collect image patches on an unlabeled image dataset in the source domain and learn local features with a sparse autoencoder. We then conduct feature selection according to the correlation between different weight vectors corresponding to the autoencoder's hidden units. We finally adopt a convolutional neural network including a pooling layer to obtain global feature activations of textile images in the target domain and send these global feature vectors into logistic regression models for emotional image classification. The cross-domain unsupervised feature learning method achieves 65% to 78% average accuracy in the cross-validation experiments corresponding to eight emotional categories and performs better than conventional methods. Feature selection can reduce the computational cost of global feature extraction by about 50% while improving classification performance.

Phosphorus magnetic resonance spectroscopic imaging at 7 T in patients with prostate cancer.

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Lagemaat, Miriam W; Vos, Eline K; Maas, Marnix C; Bitz, Andreas K; Orzada, Stephan; van Uden, Mark J; Kobus, Thiele; Heerschap, Arend; Scheenen, Tom W J

2014-05-01

The aim of this study was to identify characteristics of phosphorus (P) spectra of the human prostate and to investigate changes of individual phospholipid metabolites in prostate cancer through in vivo P magnetic resonance spectroscopic imaging (MRSI) at 7 T. In this institutional review board-approved study, 15 patients with biopsy-proven prostate cancer underwent T2-weighted magnetic resonance imaging and 3-dimensional P MRSI at 7 T. Voxels were selected at the tumor location, in normal-appearing peripheral zone tissue, normal-appearing transition zone tissue, and in the base of the prostate close to the seminal vesicles. Phosphorus metabolite ratios were determined and compared between tissue types. Signals of phosphoethanolamine (PE) and phosphocholine (PC) were present and well resolved in most P spectra in the prostate. Glycerophosphocholine signals were observable in 43% of the voxels in malignant tissue, but in only 10% of the voxels in normal-appearing tissue away from the seminal vesicles. In many spectra, independent of tissue type, 2 peaks resonated in the chemical shift range of inorganic phosphate, possibly representing 2 separate pH compartments. The PC/PE ratio in the seminal vesicles was highly elevated compared with the prostate in 5 patients. A considerable overlap of P metabolite ratios was found between prostate cancer and normal-appearing prostate tissue, preventing direct discrimination of these tissues. The only 2 patients with high Gleason scores tumors (≥4+5) presented with high PC and glycerophosphocholine levels in their cancer lesions. Phosphorus MRSI at 7 T shows distinct features of phospholipid metabolites in the prostate gland and its surrounding structures. In this exploratory study, no differences in P metabolite ratios were observed between prostate cancer and normal-appearing prostate tissue possibly because of the partial volume effects of small tumor foci in large MRSI voxels.

Featured Image: Diamonds in a Meteorite

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Kohler, Susanna

2018-04-01

This unique image which measures only 60 x 80 micrometers across reveals details in the Kapoeta meteorite, an 11-kg stone that fell in South Sudan in 1942. The sparkle in the image? A cluster of nanodiamonds discovered embedded in the stone in a recent study led by Yassir Abdu (University of Sharjah, United Arab Emirates). Abdu and collaborators showed that these nanodiamonds have similar spectral features to the interiors of dense interstellar clouds and they dont show any signs of shock features. This may suggest that the nanodiamonds were formed by condensation of nebular gases early in the history of the solar system. The diamonds were trapped in the surface material of the Kapoeta meteorites parent body, thought to be the asteroid Vesta. To read more about the authors study, check out the original article below.CitationYassir A. Abdu et al 2018 ApJL 856 L9. doi:10.3847/2041-8213/aab433

Vaginal Masses: Magnetic Resonance Imaging Features with Pathologic Correlation

International Nuclear Information System (INIS)

Elsayes, K.M.; Narra, V.R.; Dillman, J.R.; Velcheti, V.; Hameed, O.; Tongdee, R.; Menias, C.O.

2007-01-01

The detection of vaginal lesions has increased with the expanding use of cross-sectional imaging. Magnetic resonance imaging (MRI) - with its high-contrast resolution and multiplanar capabilities - is often useful for characterizing vaginal masses. Vaginal masses can be classified as congenital, inflammatory, cystic (benign), and neoplastic (benign or malignant) in etiology. Recognition of the typical MR imaging features of such lesions is important because it often determines the treatment approach and may obviate surgery. Finally, vaginal MR imaging can be used to evaluate post-treatment changes related to previous surgery and radiation therapy. In this article, we will review pertinent vaginal anatomy, vaginal and pelvic MRI technique, and the MRI features of a variety of vaginal lesions with pathological correlation

No-reference image quality assessment based on statistics of convolution feature maps

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Lv, Xiaoxin; Qin, Min; Chen, Xiaohui; Wei, Guo

2018-04-01

We propose a Convolutional Feature Maps (CFM) driven approach to accurately predict image quality. Our motivation bases on the finding that the Nature Scene Statistic (NSS) features on convolution feature maps are significantly sensitive to distortion degree of an image. In our method, a Convolutional Neural Network (CNN) is trained to obtain kernels for generating CFM. We design a forward NSS layer which performs on CFM to better extract NSS features. The quality aware features derived from the output of NSS layer is effective to describe the distortion type and degree an image suffered. Finally, a Support Vector Regression (SVR) is employed in our No-Reference Image Quality Assessment (NR-IQA) model to predict a subjective quality score of a distorted image. Experiments conducted on two public databases demonstrate the promising performance of the proposed method is competitive to state of the art NR-IQA methods.

High-pulse energy supercontinuum laser for high-resolution spectroscopic photoacoustic imaging of lipids in the 1650-1850 nm region.

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Dasa, Manoj Kumar; Markos, Christos; Maria, Michael; Petersen, Christian R; Moselund, Peter M; Bang, Ole

2018-04-01

We propose a cost-effective high-pulse energy supercontinuum (SC) source based on a telecom range diode laser-based amplifier and a few meters of standard single-mode optical fiber, with a pulse energy density as high as ~25 nJ/nm in the 1650-1850 nm regime (factor >3 times higher than any SC source ever used in this wavelength range). We demonstrate how such an SC source combined with a tunable filter allows high-resolution spectroscopic photoacoustic imaging and the spectroscopy of lipids in the first overtone transition band of C-H bonds (1650-1850 nm). We show the successful discrimination of two different lipids (cholesterol and lipid in adipose tissue) and the photoacoustic cross-sectional scan of lipid-rich adipose tissue at three different locations. The proposed high-pulse energy SC laser paves a new direction towards compact, broadband and cost-effective source for spectroscopic photoacoustic imaging.

A blur-invariant local feature for motion blurred image matching

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Tong, Qiang; Aoki, Terumasa

2017-07-01

Image matching between a blurred (caused by camera motion, out of focus, etc.) image and a non-blurred image is a critical task for many image/video applications. However, most of the existing local feature schemes fail to achieve this work. This paper presents a blur-invariant descriptor and a novel local feature scheme including the descriptor and the interest point detector based on moment symmetry - the authors' previous work. The descriptor is based on a new concept - center peak moment-like element (CPME) which is robust to blur and boundary effect. Then by constructing CPMEs, the descriptor is also distinctive and suitable for image matching. Experimental results show our scheme outperforms state of the art methods for blurred image matching

FT-IR spectroscopic imaging of reactions in multiphase flow in microfluidic channels.

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Chan, K L Andrew; Kazarian, Sergei G

2012-05-01

Rapid, in situ, and label-free chemical analysis in microfluidic devices is highly desirable. FT-IR spectroscopic imaging has previously been shown to be a powerful tool to visualize the distribution of different chemicals in flows in a microfluidic device at near video rate imaging speed without tracers or dyes. This paper demonstrates the possibility of using this imaging technology to capture the chemical information of all reactants and products at different points in time and space in a two-phase system. Differences in the rates of chemical reactions in laminar flow and segmented flow systems are also compared. Neutralization of benzoic acid in decanol with disodium phosphate in water has been used as the model reaction. Quantitative information, such as concentration profiles of reactant and products, can be extracted from the imaging data. The same feed flow rate was used in both the laminar flow and segmented flow systems. The laminar flow pattern was achieved using a plain wide T-junction, whereas the segmented flow was achieved by introducing a narrowed section and a nozzle at the T-junction. The results show that the reaction rate is limited by diffusion and is much slower with the laminar flow pattern, whereas the reaction is completed more quickly in the segmented flow due to better mixing.

Mass-like extramedullary hematopoiesis: imaging features

Energy Technology Data Exchange (ETDEWEB)

Ginzel, Andrew W. [Synergy Radiology Associates, Houston, TX (United States); Kransdorf, Mark J.; Peterson, Jeffrey J.; Garner, Hillary W. [Mayo Clinic, Department of Radiology, Jacksonville, FL (United States); Murphey, Mark D. [American Institute for Radiologic Pathology, Silver Spring, MD (United States)

2012-08-15

To report the imaging appearances of mass-like extramedullary hematopoiesis (EMH), to identify those features that are sufficiently characteristic to allow a confident diagnosis, and to recognize the clinical conditions associated with EMH and the relative incidence of mass-like disease. We retrospectively identified 44 patients with EMH; 12 of which (27%) had focal mass-like lesions and formed the study group. The study group consisted of 6 male and 6 female subjects with a mean age of 58 years (range 13-80 years). All 12 patients underwent CT imaging and 3 of the 12 patients had undergone additional MR imaging. The imaging characteristics of the extramedullary hematopoiesis lesions in the study group were analyzed and recorded. The patient's clinical presentation, including any condition associated with extramedullary hematopoiesis, was also recorded. Ten of the 12 (83%) patients had one or more masses located along the axial skeleton. Of the 10 patients with axial masses, 9 (90%) had multiple masses and 7 (70%) demonstrated internal fat. Eight patients (80%) had paraspinal masses and 4 patients (40%) had presacral masses. Seven patients (70%) had splenomegaly. Eleven of the 12 patients had a clinical history available for review. A predisposing condition for extramedullary hematopoiesis was present in 10 patients and included various anemias (5 cases; 45%), myelofibrosis/myelodysplastic syndrome (4 cases; 36%), and marrow proliferative disorder (1 case; 9%). One patient had no known predisposing condition. Mass-like extramedullary hematopoiesis most commonly presents as multiple, fat-containing lesions localized to the axial skeleton. When these imaging features are identified, extramedullary hematopoiesis should be strongly considered, particularly when occurring in the setting of a predisposing medical condition. (orig.)

A standardised protocol for texture feature analysis of endoscopic images in gynaecological cancer

Directory of Open Access Journals (Sweden)

Pattichis Marios S

2007-11-01

Full Text Available Abstract Background In the development of tissue classification methods, classifiers rely on significant differences between texture features extracted from normal and abnormal regions. Yet, significant differences can arise due to variations in the image acquisition method. For endoscopic imaging of the endometrium, we propose a standardized image acquisition protocol to eliminate significant statistical differences due to variations in: (i the distance from the tissue (panoramic vs close up, (ii difference in viewing angles and (iii color correction. Methods We investigate texture feature variability for a variety of targets encountered in clinical endoscopy. All images were captured at clinically optimum illumination and focus using 720 × 576 pixels and 24 bits color for: (i a variety of testing targets from a color palette with a known color distribution, (ii different viewing angles, (iv two different distances from a calf endometrial and from a chicken cavity. Also, human images from the endometrium were captured and analysed. For texture feature analysis, three different sets were considered: (i Statistical Features (SF, (ii Spatial Gray Level Dependence Matrices (SGLDM, and (iii Gray Level Difference Statistics (GLDS. All images were gamma corrected and the extracted texture feature values were compared against the texture feature values extracted from the uncorrected images. Statistical tests were applied to compare images from different viewing conditions so as to determine any significant differences. Results For the proposed acquisition procedure, results indicate that there is no significant difference in texture features between the panoramic and close up views and between angles. For a calibrated target image, gamma correction provided an acquired image that was a significantly better approximation to the original target image. In turn, this implies that the texture features extracted from the corrected images provided for better

Imaging features of skeletal changes in children with Gaucher disease

International Nuclear Information System (INIS)

Zhang Ningning; Duan Xiaomin; Duan Yanlong

2011-01-01

Objective: To discuss the imaging features of skeletal changes in children with Gaucher disease on X-ray and MRI images. Methods: One hundred and nine children with Gaucher disease were enrolled in this study. They all received routine X-ray for spine with anterior-posterior (A-P) and lateral view and bilateral femurs with A-P view. Among them, 18 patients received X-ray for pelvic with A-P view, 14 patients received X-ray for left wrist with A-P view, and 14 patients received MRI scan for femur. The MRI scan included T 1 -weighted imaging, T 2 -weighted imaging and fat-suppressed T 2 -weighted imaging with short tau inversion recovery (STIR) sequence. The imaging features of the X-ray and MRI images were analyzed retrospectively. Results: The most common feature is osteoporosis, which presented in 91 cases (83.5%). Besides this, decreased density of metaphysis occurred in 86 cases (78.9%), erlenmeyer flask deformity of metaphysis occurred in 89 patients (81.7%), thinner cortex occurred in 69 cases (63.3%), osteolytic destruction occurred in. 31 cases (28.4%), pathological fractures occurred in 26 cases (23.9%), osteosclerosis occurred in 12 cases (11.0%). cystic degeneration of bone occurred in 16 cases (14.7%), and dislocation of the hip occurred in 4 cases. All 14 patients received MRI presented abnormal signals. Among them, 4 patients presented low signal intensity both on T 1 -weighted and T 2 -weighted images in bone marrow, the other ten presented high signal intensity mixed in low signal intensity areas on T 2 - weighted and fat-suppressed T 2 -weighted images. Conclusions: The imaging features of skeletal changes in children with Gaucher disease are of some characteristics, which could provide useful information for the clinical treatment. (authors)

Automated prescription of oblique brain 3D magnetic resonance spectroscopic imaging.

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Ozhinsky, Eugene; Vigneron, Daniel B; Chang, Susan M; Nelson, Sarah J

2013-04-01

Two major difficulties encountered in implementing Magnetic Resonance Spectroscopic Imaging (MRSI) in a clinical setting are limited coverage and difficulty in prescription. The goal of this project was to automate completely the process of 3D PRESS MRSI prescription, including placement of the selection box, saturation bands and shim volume, while maximizing the coverage of the brain. The automated prescription technique included acquisition of an anatomical MRI image, optimization of the oblique selection box parameters, optimization of the placement of outer-volume suppression saturation bands, and loading of the calculated parameters into a customized 3D MRSI pulse sequence. To validate the technique and compare its performance with existing protocols, 3D MRSI data were acquired from six exams from three healthy volunteers. To assess the performance of the automated 3D MRSI prescription for patients with brain tumors, the data were collected from 16 exams from 8 subjects with gliomas. This technique demonstrated robust coverage of the tumor, high consistency of prescription and very good data quality within the T2 lesion. Copyright © 2012 Wiley Periodicals, Inc.

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

Energy Technology Data Exchange (ETDEWEB)

Lamichhane, N; Johnson, P; Chinea, F; Patel, V; Yang, F [University of Miami, Miami, FL (United States)

2016-06-15

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

Improved image retrieval based on fuzzy colour feature vector

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Ben-Ahmeida, Ahlam M.; Ben Sasi, Ahmed Y.

2013-03-01

One of Image indexing techniques is the Content-Based Image Retrieval which is an efficient way for retrieving images from the image database automatically based on their visual contents such as colour, texture, and shape. In this paper will be discuss how using content-based image retrieval (CBIR) method by colour feature extraction and similarity checking. By dividing the query image and all images in the database into pieces and extract the features of each part separately and comparing the corresponding portions in order to increase the accuracy in the retrieval. The proposed approach is based on the use of fuzzy sets, to overcome the problem of curse of dimensionality. The contribution of colour of each pixel is associated to all the bins in the histogram using fuzzy-set membership functions. As a result, the Fuzzy Colour Histogram (FCH), outperformed the Conventional Colour Histogram (CCH) in image retrieving, due to its speedy results, where were images represented as signatures that took less size of memory, depending on the number of divisions. The results also showed that FCH is less sensitive and more robust to brightness changes than the CCH with better retrieval recall values.

An image-processing methodology for extracting bloodstain pattern features.

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Arthur, Ravishka M; Humburg, Philomena J; Hoogenboom, Jerry; Baiker, Martin; Taylor, Michael C; de Bruin, Karla G

2017-08-01

There is a growing trend in forensic science to develop methods to make forensic pattern comparison tasks more objective. This has generally involved the application of suitable image-processing methods to provide numerical data for identification or comparison. This paper outlines a unique image-processing methodology that can be utilised by analysts to generate reliable pattern data that will assist them in forming objective conclusions about a pattern. A range of features were defined and extracted from a laboratory-generated impact spatter pattern. These features were based in part on bloodstain properties commonly used in the analysis of spatter bloodstain patterns. The values of these features were consistent with properties reported qualitatively for such patterns. The image-processing method developed shows considerable promise as a way to establish measurable discriminating pattern criteria that are lacking in current bloodstain pattern taxonomies. Copyright © 2017 Elsevier B.V. All rights reserved.

Modality prediction of biomedical literature images using multimodal feature representation

Directory of Open Access Journals (Sweden)

Pelka, Obioma

2016-08-01

Full Text Available This paper presents the modelling approaches performed to automatically predict the modality of images found in biomedical literature. Various state-of-the-art visual features such as Bag-of-Keypoints computed with dense SIFT descriptors, texture features and Joint Composite Descriptors were used for visual image representation. Text representation was obtained by vector quantisation on a Bag-of-Words dictionary generated using attribute importance derived from a χ-test. Computing the principal components separately on each feature, dimension reduction as well as computational load reduction was achieved. Various multiple feature fusions were adopted to supplement visual image information with corresponding text information. The improvement obtained when using multimodal features vs. visual or text features was detected, analysed and evaluated. Random Forest models with 100 to 500 deep trees grown by resampling, a multi class linear kernel SVM with C=0.05 and a late fusion of the two classifiers were used for modality prediction. A Random Forest classifier achieved a higher accuracy and computed Bag-of-Keypoints with dense SIFT descriptors proved to be a better approach than with Lowe SIFT.

Localized scleroderma: imaging features

International Nuclear Information System (INIS)

Liu, P.; Uziel, Y.; Chuang, S.; Silverman, E.; Krafchik, B.; Laxer, R.

1994-01-01

Localized scleroderma is distinct from the diffuse form of scleroderma and does not show Raynaud's phenomenon and visceral involvement. The imaging features in 23 patients ranging from 2 to 17 years of age (mean 11.1 years) were reviewed. Leg length discrepancy and muscle atrophy were the most common findings (five patients), with two patients also showing modelling deformity of the fibula. One patient with lower extremity involvement showed abnormal bone marrow signals on MR. Disabling joint contracture requiring orthopedic intervention was noted in one patient. In two patients with ''en coup de sabre'' facial deformity, CT and MR scans revealed intracranial calcifications and white matter abnormality in the ipsilateral frontal lobes, with one also showing migrational abnormality. In a third patient, CT revealed white matter abnormality in the ipsilateral parietal lobe. In one patient with progressive facial hemiatrophy, CT and MR scans showed the underlying hypoplastic left maxillary antrum and cheek. Imaging studies of areas of clinical concern revealed positive findings in half our patients. (orig.)

Combining low level features and visual attributes for VHR remote sensing image classification

Science.gov (United States)

Zhao, Fumin; Sun, Hao; Liu, Shuai; Zhou, Shilin

2015-12-01

Semantic classification of very high resolution (VHR) remote sensing images is of great importance for land use or land cover investigation. A large number of approaches exploiting different kinds of low level feature have been proposed in the literature. Engineers are often frustrated by their conclusions and a systematic assessment of various low level features for VHR remote sensing image classification is needed. In this work, we firstly perform an extensive evaluation of eight features including HOG, dense SIFT, SSIM, GIST, Geo color, LBP, Texton and Tiny images for classification of three public available datasets. Secondly, we propose to transfer ground level scene attributes to remote sensing images. Thirdly, we combine both low-level features and mid-level visual attributes to further improve the classification performance. Experimental results demonstrate that i) Dene SIFT and HOG features are more robust than other features for VHR scene image description. ii) Visual attribute competes with a combination of low level features. iii) Multiple feature combination achieves the best performance under different settings.

A unified framework for image retrieval using keyword and visual features.

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Jing, Feng; Li, Mingling; Zhang, Hong-Jiang; Zhang, Bo

2005-07-01

In this paper, a unified image retrieval framework based on both keyword annotations and visual features is proposed. In this framework, a set of statistical models are built based on visual features of a small set of manually labeled images to represent semantic concepts and used to propagate keywords to other unlabeled images. These models are updated periodically when more images implicitly labeled by users become available through relevance feedback. In this sense, the keyword models serve the function of accumulation and memorization of knowledge learned from user-provided relevance feedback. Furthermore, two sets of effective and efficient similarity measures and relevance feedback schemes are proposed for query by keyword scenario and query by image example scenario, respectively. Keyword models are combined with visual features in these schemes. In particular, a new, entropy-based active learning strategy is introduced to improve the efficiency of relevance feedback for query by keyword. Furthermore, a new algorithm is proposed to estimate the keyword features of the search concept for query by image example. It is shown to be more appropriate than two existing relevance feedback algorithms. Experimental results demonstrate the effectiveness of the proposed framework.

Face detection on distorted images using perceptual quality-aware features

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Gunasekar, Suriya; Ghosh, Joydeep; Bovik, Alan C.

2014-02-01

We quantify the degradation in performance of a popular and effective face detector when human-perceived image quality is degraded by distortions due to additive white gaussian noise, gaussian blur or JPEG compression. It is observed that, within a certain range of perceived image quality, a modest increase in image quality can drastically improve face detection performance. These results can be used to guide resource or bandwidth allocation in a communication/delivery system that is associated with face detection tasks. A new face detector based on QualHOG features is also proposed that augments face-indicative HOG features with perceptual quality-aware spatial Natural Scene Statistics (NSS) features, yielding improved tolerance against image distortions. The new detector provides statistically significant improvements over a strong baseline on a large database of face images representing a wide range of distortions. To facilitate this study, we created a new Distorted Face Database, containing face and non-face patches from images impaired by a variety of common distortion types and levels. This new dataset is available for download and further experimentation at www.ideal.ece.utexas.edu/˜suriya/DFD/.

3D-MR Spectroscopic Imaging at 3Tesla for Early Response Assessment of Glioblastoma Patients during External Beam Radiation Therapy

Science.gov (United States)

Muruganandham, Manickam; Clerkin, Patrick P; Smith, Brian J; Anderson, Carryn M; Morris, Ann; Capizzano, Aristides A; Magnotta, Vincent; McGuire, Sarah M; Smith, Mark C; Bayouth, John E; Buatti, John M

2014-01-01

Purpose To evaluate the utility of 3D-MR proton spectroscopic imaging for treatment planning and its implications for early response assessment in glioblastoma multiforme. Methods and Materials Eighteen patients with newly diagnosed, histologically confirmed glioblastoma had 3D-MR proton spectroscopic imaging (MRSI) along with T2 and T1 gadolinium enhanced MR images at simulation and at boost treatment planning after 17-20 fractions of radiotherapy. All patients received standard radiotherapy with temozolomide and follow-up with every two month MR scans. Progression free survival was defined using MacDonald criteria. MRSI images obtained at initial simulation were analyzed for choline / N-acetylaspartate ratios (Cho/NAA) on a voxel by voxel basis with abnormal activity defined as Cho/NAA ≥ 2. These images were compared on anatomically matched MRSI data collected after 3 weeks of radiotherapy. Changes in Cho/NAA between pre-therapy and 3rd week RT scans were tested using Wilcoxon matched-pairs signed rank tests and correlated with progression free survival, radiation dose and location of recurrence using Cox proportional hazards regression. Results After 8.6 months (median follow-up), 50% of patients had progressed based on imaging. Patients with a decreased or stable mean or median Cho/NAA values had less risk of progression (p< 0.01). Patients with an increase in mean or median Cho/NAA values at the 3rd week RT scan had a significantly greater chance of early progression (p <0.01). An increased Cho/NAA at the 3rd week MRSI scan carried a hazard ratio of 2.72 (95% confidence interval 1.10-6.71, p= 0.03). Most patients received the prescription dose of RT to the Cho/NAA ≥ 2 volume, which was where recurrence most often occurred. Conclusion Change in mean and median Cho/NAA detected at 3 weeks was a significant predictor of early progression. The potential impact for risk-adaptive therapy based on early spectroscopic findings is suggested. PMID:24986746

Efficient and robust model-to-image alignment using 3D scale-invariant features.

Science.gov (United States)

Toews, Matthew; Wells, William M

2013-04-01

This paper presents feature-based alignment (FBA), a general method for efficient and robust model-to-image alignment. Volumetric images, e.g. CT scans of the human body, are modeled probabilistically as a collage of 3D scale-invariant image features within a normalized reference space. Features are incorporated as a latent random variable and marginalized out in computing a maximum a posteriori alignment solution. The model is learned from features extracted in pre-aligned training images, then fit to features extracted from a new image to identify a globally optimal locally linear alignment solution. Novel techniques are presented for determining local feature orientation and efficiently encoding feature intensity in 3D. Experiments involving difficult magnetic resonance (MR) images of the human brain demonstrate FBA achieves alignment accuracy similar to widely-used registration methods, while requiring a fraction of the memory and computation resources and offering a more robust, globally optimal solution. Experiments on CT human body scans demonstrate FBA as an effective system for automatic human body alignment where other alignment methods break down. Copyright © 2012 Elsevier B.V. All rights reserved.

Automatic plankton image classification combining multiple view features via multiple kernel learning.

Science.gov (United States)

Zheng, Haiyong; Wang, Ruchen; Yu, Zhibin; Wang, Nan; Gu, Zhaorui; Zheng, Bing

2017-12-28

Plankton, including phytoplankton and zooplankton, are the main source of food for organisms in the ocean and form the base of marine food chain. As the fundamental components of marine ecosystems, plankton is very sensitive to environment changes, and the study of plankton abundance and distribution is crucial, in order to understand environment changes and protect marine ecosystems. This study was carried out to develop an extensive applicable plankton classification system with high accuracy for the increasing number of various imaging devices. Literature shows that most plankton image classification systems were limited to only one specific imaging device and a relatively narrow taxonomic scope. The real practical system for automatic plankton classification is even non-existent and this study is partly to fill this gap. Inspired by the analysis of literature and development of technology, we focused on the requirements of practical application and proposed an automatic system for plankton image classification combining multiple view features via multiple kernel learning (MKL). For one thing, in order to describe the biomorphic characteristics of plankton more completely and comprehensively, we combined general features with robust features, especially by adding features like Inner-Distance Shape Context for morphological representation. For another, we divided all the features into different types from multiple views and feed them to multiple classifiers instead of only one by combining different kernel matrices computed from different types of features optimally via multiple kernel learning. Moreover, we also applied feature selection method to choose the optimal feature subsets from redundant features for satisfying different datasets from different imaging devices. We implemented our proposed classification system on three different datasets across more than 20 categories from phytoplankton to zooplankton. The experimental results validated that our system

An improved feature extraction algorithm based on KAZE for multi-spectral image

Science.gov (United States)

Yang, Jianping; Li, Jun

2018-02-01

Multi-spectral image contains abundant spectral information, which is widely used in all fields like resource exploration, meteorological observation and modern military. Image preprocessing, such as image feature extraction and matching, is indispensable while dealing with multi-spectral remote sensing image. Although the feature matching algorithm based on linear scale such as SIFT and SURF performs strong on robustness, the local accuracy cannot be guaranteed. Therefore, this paper proposes an improved KAZE algorithm, which is based on nonlinear scale, to raise the number of feature and to enhance the matching rate by using the adjusted-cosine vector. The experiment result shows that the number of feature and the matching rate of the improved KAZE are remarkably than the original KAZE algorithm.

3-Dimensional magnetic resonance spectroscopic imaging at 3 Tesla for early response assessment of glioblastoma patients during external beam radiation therapy.

Science.gov (United States)

Muruganandham, Manickam; Clerkin, Patrick P; Smith, Brian J; Anderson, Carryn M; Morris, Ann; Capizzano, Aristides A; Magnotta, Vincent; McGuire, Sarah M; Smith, Mark C; Bayouth, John E; Buatti, John M

2014-09-01

To evaluate the utility of 3-dimensional magnetic resonance (3D-MR) proton spectroscopic imaging for treatment planning and its implications for early response assessment in glioblastoma multiforme. Eighteen patients with newly diagnosed, histologically confirmed glioblastoma had 3D-MR proton spectroscopic imaging (MRSI) along with T2 and T1 gadolinium-enhanced MR images at simulation and at boost treatment planning after 17 to 20 fractions of radiation therapy. All patients received standard radiation therapy (RT) with concurrent temozolomide followed by adjuvant temozolomide. Imaging for response assessment consisted of MR scans every 2 months. Progression-free survival was defined by the criteria of MacDonald et al. MRSI images obtained at initial simulation were analyzed for choline/N-acetylaspartate ratios (Cho/NAA) on a voxel-by-voxel basis with abnormal activity defined as Cho/NAA ≥2. These images were compared on anatomically matched MRSI data collected after 3 weeks of RT. Changes in Cho/NAA between pretherapy and third-week RT scans were tested using Wilcoxon matched-pairs signed rank tests and correlated with progression-free survival, radiation dose and location of recurrence using Cox proportional hazards regression. After a median follow-up time of 8.6 months, 50% of patients had experienced progression based on imaging. Patients with a decreased or stable mean or median Cho/NAA values had less risk of progression (P<.01). Patients with an increase in mean or median Cho/NAA values at the third-week RT scan had a significantly greater chance of early progression (P<.01). An increased Cho/NAA at the third-week MRSI scan carried a hazard ratio of 2.72 (95% confidence interval, 1.10-6.71; P=.03). Most patients received the prescription dose of RT to the Cho/NAA ≥2 volume, where recurrence most often occurred. Change in mean and median Cho/NAA detected at 3 weeks was a significant predictor of early progression. The potential impact for risk

Metabolic networks in epilepsy by MR spectroscopic imaging.

Science.gov (United States)

Pan, J W; Spencer, D D; Kuzniecky, R; Duckrow, R B; Hetherington, H; Spencer, S S

2012-12-01

The concept of an epileptic network has long been suggested from both animal and human studies of epilepsy. Based on the common observation that the MR spectroscopic imaging measure of NAA/Cr is sensitive to neuronal function and injury, we use this parameter to assess for the presence of a metabolic network in mesial temporal lobe epilepsy (MTLE) patients. A multivariate factor analysis is performed with controls and MTLE patients, using NAA/Cr measures from 12 loci: the bilateral hippocampi, thalami, basal ganglia, and insula. The factor analysis determines which and to what extent these loci are metabolically covarying. We extract two independent factors that explain the data's variability in control and MTLE patients. In controls, these factors characterize a 'thalamic' and 'dominant subcortical' function. The MTLE patients also exhibit a 'thalamic' factor, in addition to a second factor involving the ipsilateral insula and bilateral basal ganglia. These data suggest that MTLE patients demonstrate a metabolic network that involves the thalami, also seen in controls. The MTLE patients also display a second set of metabolically covarying regions that may be a manifestation of the epileptic network that characterizes limbic seizure propagation. © 2012 John Wiley & Sons A/S.

Magnetic resonance imaging features of fibrocystic change of the breast.

Science.gov (United States)

Chen, Jeon-Hor; Liu, Hui; Baek, Hyeon-Man; Nalcioglu, Orhan; Su, Min-Ying

2008-11-01

Studies specifically reporting MRI of fibrocystic change (FCC) of the breast are very few and its MRI features are not clearly known. The purpose of this study was to analyze the MRI features of FCC of the breast. Thirty-one patients with pathologically proven FCC of the breast were retrospectively reviewed. The MRI study was performed using a 1.5-T MR scanner with standard bilateral breast coil. The imaging protocol consisted of pre-contrast T1-weighed imaging and dynamic contrast-enhanced axial T1-weighed imaging. The MRI features were interpreted based on the morphologic and enhancement kinetic descriptors defined on ACR BIRADS-MRI lexicon. FCC of the breast had a wide spectrum of morphologic and kinetic features on MRI. Two types of FCC were found, including a more diffuse type of nonmass lesion (12/31, 39%) showing benign enhancement kinetic pattern with medium wash-in in early phase (9/10, 90%) and a focal mass-type lesion (11/31, 35%) with enhancement kinetic usually showing rapid up-slope mimicking a breast cancer (8/11, 73%). MRI is able to elaborate the diverse imaging features of FCC of the breast. Our result showed that FCC presenting as a focal mass-type lesion was usually overdiagnosed as malignancy. Understanding MRI of FCC is important to determine which cohort of patients should be followed up alone or receive aggressive management.

Feature Extraction in Sequential Multimedia Images: with Applications in Satellite Images and On-line Videos

Science.gov (United States)

Liang, Yu-Li

Multimedia data is increasingly important in scientific discovery and people's daily lives. Content of massive multimedia is often diverse and noisy, and motion between frames is sometimes crucial in analyzing those data. Among all, still images and videos are commonly used formats. Images are compact in size but do not contain motion information. Videos record motion but are sometimes too big to be analyzed. Sequential images, which are a set of continuous images with low frame rate, stand out because they are smaller than videos and still maintain motion information. This thesis investigates features in different types of noisy sequential images, and the proposed solutions that intelligently combined multiple features to successfully retrieve visual information from on-line videos and cloudy satellite images. The first task is detecting supraglacial lakes above ice sheet in sequential satellite images. The dynamics of supraglacial lakes on the Greenland ice sheet deeply affect glacier movement, which is directly related to sea level rise and global environment change. Detecting lakes above ice is suffering from diverse image qualities and unexpected clouds. A new method is proposed to efficiently extract prominent lake candidates with irregular shapes, heterogeneous backgrounds, and in cloudy images. The proposed system fully automatize the procedure that track lakes with high accuracy. We further cooperated with geoscientists to examine the tracked lakes and found new scientific findings. The second one is detecting obscene content in on-line video chat services, such as Chatroulette, that randomly match pairs of users in video chat sessions. A big problem encountered in such systems is the presence of flashers and obscene content. Because of various obscene content and unstable qualities of videos capture by home web-camera, detecting misbehaving users is a highly challenging task. We propose SafeVchat, which is the first solution that achieves satisfactory

Eta Carinae’s 2014.6 Spectroscopic Event: The Extraordinary He II and N II Features

Science.gov (United States)

Davidson, Kris; Mehner, Andrea; Humphreys, Roberta M.; Martin, John C.; Ishibashi, Kazunori

2015-03-01

Eta Carinae’s spectroscopic events (periastron passages) in 2003, 2009, and 2014 differed progressively. He ii λ4687 and nearby N ii multiplet 5 have special significance because they respond to very soft X-rays and the ionizing UV radiation field (EUV). Hubble Space Telescope (HST)/STIS observations in 2014 show dramatic increases in both features compared to the previous 2009.1 event. These results appear very consistent with a progressive decline in the primary wind density, proposed years ago on other grounds. If material falls onto the companion star near periastron, the accretion rate may now have become too low to suppress the EUV. Based on observations made with the NASA/ESA Hubble Space Telescope, which is opera ted by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555.

Tracking image features with PCA-SURF descriptors

CSIR Research Space (South Africa)

Pancham, A

2015-05-01

Full Text Available IAPR International Conference on Machine Vision Applications, May 18-22, 2015, Tokyo, JAPAN Tracking Image Features with PCA-SURF Descriptors Ardhisha Pancham CSIR, UKZN South Africa apancham@csir.co.za Daniel Withey CSIR South Africa...

A comparative study of image low level feature extraction algorithms

Directory of Open Access Journals (Sweden)

M.M. El-gayar

2013-07-01

Full Text Available Feature extraction and matching is at the base of many computer vision problems, such as object recognition or structure from motion. Current methods for assessing the performance of popular image matching algorithms are presented and rely on costly descriptors for detection and matching. Specifically, the method assesses the type of images under which each of the algorithms reviewed herein perform to its maximum or highest efficiency. The efficiency is measured in terms of the number of matches founds by the algorithm and the number of type I and type II errors encountered when the algorithm is tested against a specific pair of images. Current comparative studies asses the performance of the algorithms based on the results obtained in different criteria such as speed, sensitivity, occlusion, and others. This study addresses the limitations of the existing comparative tools and delivers a generalized criterion to determine beforehand the level of efficiency expected from a matching algorithm given the type of images evaluated. The algorithms and the respective images used within this work are divided into two groups: feature-based and texture-based. And from this broad classification only three of the most widely used algorithms are assessed: color histogram, FAST (Features from Accelerated Segment Test, SIFT (Scale Invariant Feature Transform, PCA-SIFT (Principal Component Analysis-SIFT, F-SIFT (fast-SIFT and SURF (speeded up robust features. The performance of the Fast-SIFT (F-SIFT feature detection methods are compared for scale changes, rotation, blur, illumination changes and affine transformations. All the experiments use repeatability measurement and the number of correct matches for the evaluation measurements. SIFT presents its stability in most situations although its slow. F-SIFT is the fastest one with good performance as the same as SURF, SIFT, PCA-SIFT show its advantages in rotation and illumination changes.

A Compton camera for spectroscopic imaging from 100 keV to 1 MeV

International Nuclear Information System (INIS)

Earnhart, J.R.D.

1998-01-01

A review of spectroscopic imaging issues, applications, and technology is presented. Compton cameras based on solid state semiconductor detectors stands out as the best system for the nondestructive assay of special nuclear materials. A camera for this application has been designed based on an efficient specific purpose Monte Carlo code developed for this project. Preliminary experiments have been performed which demonstrate the validity of the Compton camera concept and the accuracy of the code. Based on these results, a portable prototype system is in development. Proposed future work is addressed

Modeling multiple visual words assignment for bag-of-features based medical image retrieval

KAUST Repository

Wang, Jim Jing-Yan

2012-01-01

In this paper, we investigate the bag-of-features based medical image retrieval methods, which represent an image as a collection of local features, such as image patch and key points with SIFT descriptor. To improve the bag-of-features method, we first model the assignment of local descriptor as contribution functions, and then propose a new multiple assignment strategy. By assuming the local feature can be reconstructed by its neighboring visual words in vocabulary, we solve the reconstruction weights as a QP problem and then use the solved weights as contribution functions, which results in a new assignment method called the QP assignment. We carry our experiments on ImageCLEFmed datasets. Experiments\\' results show that our proposed method exceeds the performances of traditional solutions and works well for the bag-of-features based medical image retrieval tasks.

Modeling multiple visual words assignment for bag-of-features based medical image retrieval

KAUST Repository

Wang, Jim Jing-Yan; Almasri, Islam

2012-01-01

In this paper, we investigate the bag-of-features based medical image retrieval methods, which represent an image as a collection of local features, such as image patch and key points with SIFT descriptor. To improve the bag-of-features method, we first model the assignment of local descriptor as contribution functions, and then propose a new multiple assignment strategy. By assuming the local feature can be reconstructed by its neighboring visual words in vocabulary, we solve the reconstruction weights as a QP problem and then use the solved weights as contribution functions, which results in a new assignment method called the QP assignment. We carry our experiments on ImageCLEFmed datasets. Experiments' results show that our proposed method exceeds the performances of traditional solutions and works well for the bag-of-features based medical image retrieval tasks.

Perinatal clinical and imaging features of CLOVES syndrome

Energy Technology Data Exchange (ETDEWEB)

Fernandez-Pineda, Israel [Virgen del Rocio Children' s Hospital, Department of Pediatric Surgery, Seville (Spain); Fajardo, Manuel [Virgen del Rocio Children' s Hospital, Department of Pediatric Radiology, Seville (Spain); Chaudry, Gulraiz; Alomari, Ahmad I. [Children' s Hospital Boston and Harvard Medical School, Division of Vascular and Interventional Radiology, Boston, MA (United States)

2010-08-15

We report a neonate with antenatal imaging features suggestive of CLOVES syndrome. Postnatal clinical and imaging findings confirmed the diagnosis, with the constellation of truncal overgrowth, cutaneous capillary malformation, lymphatic and musculoskeletal anomalies. The clinical, radiological and histopathological findings noted in this particular phenotype help differentiate it from other overgrowth syndromes with complex vascular anomalies. (orig.)

US or MR Imaging Features of Polypoid Endometriosis: A Case Report

International Nuclear Information System (INIS)

Park, Jae Il; Cho, Jae Ho; Kim, Geum Rae; Kim, Mi Jin

2009-01-01

Polypoid endometriosis is a rare variant of endometriosis that is pathologically similar to an endometrial polyp. This lesion is frequently mistaken for a solid neoplasm in clinical, radiological and pathological examinations. The clinical and pathological features of the lesion have been well described in the English literature. However, its imaging features have not been reported in the Korean literature. We describe ultrasound and magnetic resonance imaging features of pathologically-confirmed polypoid endometriosis

US or MR Imaging Features of Polypoid Endometriosis: A Case Report

Energy Technology Data Exchange (ETDEWEB)

Park, Jae Il; Cho, Jae Ho; Kim, Geum Rae; Kim, Mi Jin [Yeungnam University, Gyeongsan (Korea, Republic of)

2009-12-15

Polypoid endometriosis is a rare variant of endometriosis that is pathologically similar to an endometrial polyp. This lesion is frequently mistaken for a solid neoplasm in clinical, radiological and pathological examinations. The clinical and pathological features of the lesion have been well described in the English literature. However, its imaging features have not been reported in the Korean literature. We describe ultrasound and magnetic resonance imaging features of pathologically-confirmed polypoid endometriosis.

Multi-slice echo-planar spectroscopic MR imaging provides both global and local metabolite measures in multiple sclerosis

DEFF Research Database (Denmark)

Mathiesen, Henrik Kahr; Tscherning, Thomas; Sorensen, Per Soelberg

2005-01-01

MR spectroscopy (MRS) provides information about neuronal loss or dysfunction by measuring decreases in N-acetyl aspartate (NAA), a metabolite widely believed to be a marker of neuronal viability. In multiple sclerosis (MS), whole-brain NAA (WBNAA) has been suggested as a marker of disease...... progression and treatment efficacy in treatment trials, and the ability to measure NAA loss in specific brain regions early in the evolution of this disease may have prognostic value. Most spectroscopic studies to date have been limited to single voxels or nonlocalized measurements of WBNAA only......, measurements of metabolites in specific brain areas chosen after image acquisition (e.g., normal-appearing white matter (NAWM), gray matter (GM), and lesions) can be obtained. The identification and exclusion of regions that are inadequate for spectroscopic evaluation in global assessments can significantly...

Imaging features of mycobacterium in patients with acquired immunodeficiency syndrome

International Nuclear Information System (INIS)

Yang Jun; Sun Yue; Wei Liangui; Xu Yunliang; Li Xingwang

2013-01-01

Objective: To analyze the imaging features of mycobacterium in AIDS patients. Methods: Twenty-three cases of mycobacterium tuberculosis and 13 patients of non-tuberculous mycobacteria were proved etiologically and included in this study. All patients underwent X-ray and CT examinations, imaging data were analyzed and compared. Results: The imaging findings of mycobacterium tuberculosis in AIDS patients included consolidation (n = 11), pleural effusion (n = 11), mediastinal lymphadenopathy (n = 11). Pulmonary lesions were always diffuse distribution, and 14 patients of extrapulmonary tuberculosis were found. Pulmonary lesions in non-tuberculous mycobacteria tend to be circumscribed. Conclusions: Non-tuberculous mycobacterial infection in AIDS patients is more common and usually combined with other infections. Imaging features are atypical. (authors)

Face recognition via sparse representation of SIFT feature on hexagonal-sampling image

Science.gov (United States)

Zhang, Daming; Zhang, Xueyong; Li, Lu; Liu, Huayong

2018-04-01

This paper investigates a face recognition approach based on Scale Invariant Feature Transform (SIFT) feature and sparse representation. The approach takes advantage of SIFT which is local feature other than holistic feature in classical Sparse Representation based Classification (SRC) algorithm and possesses strong robustness to expression, pose and illumination variations. Since hexagonal image has more inherit merits than square image to make recognition process more efficient, we extract SIFT keypoint in hexagonal-sampling image. Instead of matching SIFT feature, firstly the sparse representation of each SIFT keypoint is given according the constructed dictionary; secondly these sparse vectors are quantized according dictionary; finally each face image is represented by a histogram and these so-called Bag-of-Words vectors are classified by SVM. Due to use of local feature, the proposed method achieves better result even when the number of training sample is small. In the experiments, the proposed method gave higher face recognition rather than other methods in ORL and Yale B face databases; also, the effectiveness of the hexagonal-sampling in the proposed method is verified.

Convolutional deep belief network with feature encoding for classification of neuroblastoma histological images

Directory of Open Access Journals (Sweden)

Soheila Gheisari

2018-01-01

Full Text Available Background: Neuroblastoma is the most common extracranial solid tumor in children younger than 5 years old. Optimal management of neuroblastic tumors depends on many factors including histopathological classification. The gold standard for classification of neuroblastoma histological images is visual microscopic assessment. In this study, we propose and evaluate a deep learning approach to classify high-resolution digital images of neuroblastoma histology into five different classes determined by the Shimada classification. Subjects and Methods: We apply a combination of convolutional deep belief network (CDBN with feature encoding algorithm that automatically classifies digital images of neuroblastoma histology into five different classes. We design a three-layer CDBN to extract high-level features from neuroblastoma histological images and combine with a feature encoding model to extract features that are highly discriminative in the classification task. The extracted features are classified into five different classes using a support vector machine classifier. Data: We constructed a dataset of 1043 neuroblastoma histological images derived from Aperio scanner from 125 patients representing different classes of neuroblastoma tumors. Results: The weighted average F-measure of 86.01% was obtained from the selected high-level features, outperforming state-of-the-art methods. Conclusion: The proposed computer-aided classification system, which uses the combination of deep architecture and feature encoding to learn high-level features, is highly effective in the classification of neuroblastoma histological images.

Polarimetric SAR Image Classification Using Multiple-feature Fusion and Ensemble Learning

Directory of Open Access Journals (Sweden)

Sun Xun

2016-12-01

Full Text Available In this paper, we propose a supervised classification algorithm for Polarimetric Synthetic Aperture Radar (PolSAR images using multiple-feature fusion and ensemble learning. First, we extract different polarimetric features, including extended polarimetric feature space, Hoekman, Huynen, H/alpha/A, and fourcomponent scattering features of PolSAR images. Next, we randomly select two types of features each time from all feature sets to guarantee the reliability and diversity of later ensembles and use a support vector machine as the basic classifier for predicting classification results. Finally, we concatenate all prediction probabilities of basic classifiers as the final feature representation and employ the random forest method to obtain final classification results. Experimental results at the pixel and region levels show the effectiveness of the proposed algorithm.

Pleasant/Unpleasant Filtering for Affective Image Retrieval Based on Cross-Correlation of EEG Features

Directory of Open Access Journals (Sweden)

Keranmu Xielifuguli

2014-01-01

Full Text Available People often make decisions based on sensitivity rather than rationality. In the field of biological information processing, methods are available for analyzing biological information directly based on electroencephalogram: EEG to determine the pleasant/unpleasant reactions of users. In this study, we propose a sensitivity filtering technique for discriminating preferences (pleasant/unpleasant for images using a sensitivity image filtering system based on EEG. Using a set of images retrieved by similarity retrieval, we perform the sensitivity-based pleasant/unpleasant classification of images based on the affective features extracted from images with the maximum entropy method: MEM. In the present study, the affective features comprised cross-correlation features obtained from EEGs produced when an individual observed an image. However, it is difficult to measure the EEG when a subject visualizes an unknown image. Thus, we propose a solution where a linear regression method based on canonical correlation is used to estimate the cross-correlation features from image features. Experiments were conducted to evaluate the validity of sensitivity filtering compared with image similarity retrieval methods based on image features. We found that sensitivity filtering using color correlograms was suitable for the classification of preferred images, while sensitivity filtering using local binary patterns was suitable for the classification of unpleasant images. Moreover, sensitivity filtering using local binary patterns for unpleasant images had a 90% success rate. Thus, we conclude that the proposed method is efficient for filtering unpleasant images.

3D-2D Deformable Image Registration Using Feature-Based Nonuniform Meshes.

Science.gov (United States)

Zhong, Zichun; Guo, Xiaohu; Cai, Yiqi; Yang, Yin; Wang, Jing; Jia, Xun; Mao, Weihua

2016-01-01

By using prior information of planning CT images and feature-based nonuniform meshes, this paper demonstrates that volumetric images can be efficiently registered with a very small portion of 2D projection images of a Cone-Beam Computed Tomography (CBCT) scan. After a density field is computed based on the extracted feature edges from planning CT images, nonuniform tetrahedral meshes will be automatically generated to better characterize the image features according to the density field; that is, finer meshes are generated for features. The displacement vector fields (DVFs) are specified at the mesh vertices to drive the deformation of original CT images. Digitally reconstructed radiographs (DRRs) of the deformed anatomy are generated and compared with corresponding 2D projections. DVFs are optimized to minimize the objective function including differences between DRRs and projections and the regularity. To further accelerate the above 3D-2D registration, a procedure to obtain good initial deformations by deforming the volume surface to match 2D body boundary on projections has been developed. This complete method is evaluated quantitatively by using several digital phantoms and data from head and neck cancer patients. The feature-based nonuniform meshing method leads to better results than either uniform orthogonal grid or uniform tetrahedral meshes.

3D-2D Deformable Image Registration Using Feature-Based Nonuniform Meshes

Directory of Open Access Journals (Sweden)

Zichun Zhong

2016-01-01

Full Text Available By using prior information of planning CT images and feature-based nonuniform meshes, this paper demonstrates that volumetric images can be efficiently registered with a very small portion of 2D projection images of a Cone-Beam Computed Tomography (CBCT scan. After a density field is computed based on the extracted feature edges from planning CT images, nonuniform tetrahedral meshes will be automatically generated to better characterize the image features according to the density field; that is, finer meshes are generated for features. The displacement vector fields (DVFs are specified at the mesh vertices to drive the deformation of original CT images. Digitally reconstructed radiographs (DRRs of the deformed anatomy are generated and compared with corresponding 2D projections. DVFs are optimized to minimize the objective function including differences between DRRs and projections and the regularity. To further accelerate the above 3D-2D registration, a procedure to obtain good initial deformations by deforming the volume surface to match 2D body boundary on projections has been developed. This complete method is evaluated quantitatively by using several digital phantoms and data from head and neck cancer patients. The feature-based nonuniform meshing method leads to better results than either uniform orthogonal grid or uniform tetrahedral meshes.

Simultenious binary hash and features learning for image retrieval

Science.gov (United States)

Frantc, V. A.; Makov, S. V.; Voronin, V. V.; Marchuk, V. I.; Semenishchev, E. A.; Egiazarian, K. O.; Agaian, S.

2016-05-01

Content-based image retrieval systems have plenty of applications in modern world. The most important one is the image search by query image or by semantic description. Approaches to this problem are employed in personal photo-collection management systems, web-scale image search engines, medical systems, etc. Automatic analysis of large unlabeled image datasets is virtually impossible without satisfactory image-retrieval technique. It's the main reason why this kind of automatic image processing has attracted so much attention during recent years. Despite rather huge progress in the field, semantically meaningful image retrieval still remains a challenging task. The main issue here is the demand to provide reliable results in short amount of time. This paper addresses the problem by novel technique for simultaneous learning of global image features and binary hash codes. Our approach provide mapping of pixel-based image representation to hash-value space simultaneously trying to save as much of semantic image content as possible. We use deep learning methodology to generate image description with properties of similarity preservation and statistical independence. The main advantage of our approach in contrast to existing is ability to fine-tune retrieval procedure for very specific application which allow us to provide better results in comparison to general techniques. Presented in the paper framework for data- dependent image hashing is based on use two different kinds of neural networks: convolutional neural networks for image description and autoencoder for feature to hash space mapping. Experimental results confirmed that our approach has shown promising results in compare to other state-of-the-art methods.

Sparse spectral deconvolution algorithm for noncartesian MR spectroscopic imaging.

Science.gov (United States)

Bhave, Sampada; Eslami, Ramin; Jacob, Mathews

2014-02-01

To minimize line shape distortions and spectral leakage artifacts in MR spectroscopic imaging (MRSI). A spatially and spectrally regularized non-Cartesian MRSI algorithm that uses the line shape distortion priors, estimated from water reference data, to deconvolve the spectra is introduced. Sparse spectral regularization is used to minimize noise amplification associated with deconvolution. A spiral MRSI sequence that heavily oversamples the central k-space regions is used to acquire the MRSI data. The spatial regularization term uses the spatial supports of brain and extracranial fat regions to recover the metabolite spectra and nuisance signals at two different resolutions. Specifically, the nuisance signals are recovered at the maximum resolution to minimize spectral leakage, while the point spread functions of metabolites are controlled to obtain acceptable signal-to-noise ratio. The comparisons of the algorithm against Tikhonov regularized reconstructions demonstrates considerably reduced line-shape distortions and improved metabolite maps. The proposed sparsity constrained spectral deconvolution scheme is effective in minimizing the line-shape distortions. The dual resolution reconstruction scheme is capable of minimizing spectral leakage artifacts. Copyright © 2013 Wiley Periodicals, Inc.

The fuzzy Hough Transform-feature extraction in medical images

International Nuclear Information System (INIS)

Philip, K.P.; Dove, E.L.; Stanford, W.; Chandran, K.B.; McPherson, D.D.; Gotteiner, N.L.

1994-01-01

Identification of anatomical features is a necessary step for medical image analysis. Automatic methods for feature identification using conventional pattern recognition techniques typically classify an object as a member of a predefined class of objects, but do not attempt to recover the exact or approximate shape of that object. For this reason, such techniques are usually not sufficient to identify the borders of organs when individual geometry varies in local detail, even though the general geometrical shape is similar. The authors present an algorithm that detects features in an image based on approximate geometrical models. The algorithm is based on the traditional and generalized Hough Transforms but includes notions from fuzzy set theory. The authors use the new algorithm to roughly estimate the actual locations of boundaries of an internal organ, and from this estimate, to determine a region of interest around the organ. Based on this rough estimate of the border location, and the derived region of interest, the authors find the final estimate of the true borders with other image processing techniques. The authors present results that demonstrate that the algorithm was successfully used to estimate the approximate location of the chest wall in humans, and of the left ventricular contours of a dog heart obtained from cine-computed tomographic images. The authors use this fuzzy Hough Transform algorithm as part of a larger procedures to automatically identify the myocardial contours of the heart. This algorithm may also allow for more rapid image processing and clinical decision making in other medical imaging applications

Localized scleroderma: imaging features

Energy Technology Data Exchange (ETDEWEB)

Liu, P. (Dept. of Diagnostic Imaging, Hospital for Sick Children, Toronto, ON (Canada)); Uziel, Y. (Div. of Rheumatology, Hospital for Sick Children, Toronto, ON (Canada)); Chuang, S. (Dept. of Diagnostic Imaging, Hospital for Sick Children, Toronto, ON (Canada)); Silverman, E. (Div. of Rheumatology, Hospital for Sick Children, Toronto, ON (Canada)); Krafchik, B. (Div. of Dermatology, Dept. of Pediatrics, Hospital for Sick Children, Toronto, ON (Canada)); Laxer, R. (Div. of Rheumatology, Hospital for Sick Children, Toronto, ON (Canada))

1994-06-01

Localized scleroderma is distinct from the diffuse form of scleroderma and does not show Raynaud's phenomenon and visceral involvement. The imaging features in 23 patients ranging from 2 to 17 years of age (mean 11.1 years) were reviewed. Leg length discrepancy and muscle atrophy were the most common findings (five patients), with two patients also showing modelling deformity of the fibula. One patient with lower extremity involvement showed abnormal bone marrow signals on MR. Disabling joint contracture requiring orthopedic intervention was noted in one patient. In two patients with ''en coup de sabre'' facial deformity, CT and MR scans revealed intracranial calcifications and white matter abnormality in the ipsilateral frontal lobes, with one also showing migrational abnormality. In a third patient, CT revealed white matter abnormality in the ipsilateral parietal lobe. In one patient with progressive facial hemiatrophy, CT and MR scans showed the underlying hypoplastic left maxillary antrum and cheek. Imaging studies of areas of clinical concern revealed positive findings in half our patients. (orig.)

Automatic brain MR image denoising based on texture feature-based artificial neural networks.

Science.gov (United States)

Chang, Yu-Ning; Chang, Herng-Hua

2015-01-01

Noise is one of the main sources of quality deterioration not only for visual inspection but also in computerized processing in brain magnetic resonance (MR) image analysis such as tissue classification, segmentation and registration. Accordingly, noise removal in brain MR images is important for a wide variety of subsequent processing applications. However, most existing denoising algorithms require laborious tuning of parameters that are often sensitive to specific image features and textures. Automation of these parameters through artificial intelligence techniques will be highly beneficial. In the present study, an artificial neural network associated with image texture feature analysis is proposed to establish a predictable parameter model and automate the denoising procedure. In the proposed approach, a total of 83 image attributes were extracted based on four categories: 1) Basic image statistics. 2) Gray-level co-occurrence matrix (GLCM). 3) Gray-level run-length matrix (GLRLM) and 4) Tamura texture features. To obtain the ranking of discrimination in these texture features, a paired-samples t-test was applied to each individual image feature computed in every image. Subsequently, the sequential forward selection (SFS) method was used to select the best texture features according to the ranking of discrimination. The selected optimal features were further incorporated into a back propagation neural network to establish a predictable parameter model. A wide variety of MR images with various scenarios were adopted to evaluate the performance of the proposed framework. Experimental results indicated that this new automation system accurately predicted the bilateral filtering parameters and effectively removed the noise in a number of MR images. Comparing to the manually tuned filtering process, our approach not only produced better denoised results but also saved significant processing time.

Neural network-based feature point descriptors for registration of optical and SAR images

Science.gov (United States)

Abulkhanov, Dmitry; Konovalenko, Ivan; Nikolaev, Dmitry; Savchik, Alexey; Shvets, Evgeny; Sidorchuk, Dmitry

2018-04-01

Registration of images of different nature is an important technique used in image fusion, change detection, efficient information representation and other problems of computer vision. Solving this task using feature-based approaches is usually more complex than registration of several optical images because traditional feature descriptors (SIFT, SURF, etc.) perform poorly when images have different nature. In this paper we consider the problem of registration of SAR and optical images. We train neural network to build feature point descriptors and use RANSAC algorithm to align found matches. Experimental results are presented that confirm the method's effectiveness.

WE-G-207-05: Relationship Between CT Image Quality, Segmentation Performance, and Quantitative Image Feature Analysis

Energy Technology Data Exchange (ETDEWEB)

Lee, J; Nishikawa, R [University of Pittsburgh, Pittsburgh, PA (United States); Reiser, I [The University of Chicago, Chicago, IL (United States); Boone, J [UC Davis Medical Center, Sacramento, CA (United States)

2015-06-15

Purpose: Segmentation quality can affect quantitative image feature analysis. The objective of this study is to examine the relationship between computed tomography (CT) image quality, segmentation performance, and quantitative image feature analysis. Methods: A total of 90 pathology proven breast lesions in 87 dedicated breast CT images were considered. An iterative image reconstruction (IIR) algorithm was used to obtain CT images with different quality. With different combinations of 4 variables in the algorithm, this study obtained a total of 28 different qualities of CT images. Two imaging tasks/objectives were considered: 1) segmentation and 2) classification of the lesion as benign or malignant. Twenty-three image features were extracted after segmentation using a semi-automated algorithm and 5 of them were selected via a feature selection technique. Logistic regression was trained and tested using leave-one-out-cross-validation and its area under the ROC curve (AUC) was recorded. The standard deviation of a homogeneous portion and the gradient of a parenchymal portion of an example breast were used as an estimate of image noise and sharpness. The DICE coefficient was computed using a radiologist’s drawing on the lesion. Mean DICE and AUC were used as performance metrics for each of the 28 reconstructions. The relationship between segmentation and classification performance under different reconstructions were compared. Distributions (median, 95% confidence interval) of DICE and AUC for each reconstruction were also compared. Results: Moderate correlation (Pearson’s rho = 0.43, p-value = 0.02) between DICE and AUC values was found. However, the variation between DICE and AUC values for each reconstruction increased as the image sharpness increased. There was a combination of IIR parameters that resulted in the best segmentation with the worst classification performance. Conclusion: There are certain images that yield better segmentation or classification

Image features for misalignment correction in medical flat-detector CT

International Nuclear Information System (INIS)

Wicklein, Julia; Kunze, Holger; Kalender, Willi A.; Kyriakou, Yiannis

2012-01-01

Purpose: Misalignment artifacts are a serious problem in medical flat-detector computed tomography. Generally, the geometrical parameters, which are essential for reconstruction, are provided by preceding calibration routines. These procedures are time consuming and the later use of stored parameters is sensitive toward external impacts or patient movement. The method of choice in a clinical environment would be a markerless online-calibration procedure that allows flexible scan trajectories and simultaneously corrects misalignment and motion artifacts during the reconstruction process. Therefore, different image features were evaluated according to their capability of quantifying misalignment. Methods: Projections of the FORBILD head and thorax phantoms were simulated. Additionally, acquisitions of a head phantom and patient data were used for evaluation. For the reconstruction different sources and magnitudes of misalignment were introduced in the geometry description. The resulting volumes were analyzed by entropy (based on the gray-level histogram), total variation, Gabor filter texture features, Haralick co-occurrence features, and Tamura texture features. The feature results were compared to the back-projection mismatch of the disturbed geometry. Results: The evaluations demonstrate the ability of several well-established image features to classify misalignment. The authors elaborated the particular suitability of the gray-level histogram-based entropy on identifying misalignment artifacts, after applying an appropriate window level (bone window). Conclusions: Some of the proposed feature extraction algorithms show a strong correlation with the misalignment level. Especially, entropy-based methods showed very good correspondence, with the best of these being the type that uses the gray-level histogram for calculation. This makes it a suitable image feature for online-calibration.

The algorithm of fast image stitching based on multi-feature extraction

Science.gov (United States)

Yang, Chunde; Wu, Ge; Shi, Jing

2018-05-01

This paper proposed an improved image registration method combining Hu-based invariant moment contour information and feature points detection, aiming to solve the problems in traditional image stitching algorithm, such as time-consuming feature points extraction process, redundant invalid information overload and inefficiency. First, use the neighborhood of pixels to extract the contour information, employing the Hu invariant moment as similarity measure to extract SIFT feature points in those similar regions. Then replace the Euclidean distance with Hellinger kernel function to improve the initial matching efficiency and get less mismatching points, further, estimate affine transformation matrix between the images. Finally, local color mapping method is adopted to solve uneven exposure, using the improved multiresolution fusion algorithm to fuse the mosaic images and realize seamless stitching. Experimental results confirm high accuracy and efficiency of method proposed in this paper.

Improved Feature Detection in Fused Intensity-Range Images with Complex SIFT (ℂSIFT

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Boris Jutzi

2011-09-01

Full Text Available The real and imaginary parts are proposed as an alternative to the usual Polar representation of complex-valued images. It is proven that the transformation from Polar to Cartesian representation contributes to decreased mutual information, and hence to greater distinctiveness. The Complex Scale-Invariant Feature Transform (ℂSIFT detects distinctive features in complex-valued images. An evaluation method for estimating the uniformity of feature distributions in complex-valued images derived from intensity-range images is proposed. In order to experimentally evaluate the proposed methodology on intensity-range images, three different kinds of active sensing systems were used: Range Imaging, Laser Scanning, and Structured Light Projection devices (PMD CamCube 2.0, Z+F IMAGER 5003, Microsoft Kinect.

Apertureless near-field/far-field CW two-photon microscope for biological and material imaging and spectroscopic applications.

Science.gov (United States)

Nowak, Derek B; Lawrence, A J; Sánchez, Erik J

2010-12-10

We present the development of a versatile spectroscopic imaging tool to allow for imaging with single-molecule sensitivity and high spatial resolution. The microscope allows for near-field and subdiffraction-limited far-field imaging by integrating a shear-force microscope on top of a custom inverted microscope design. The instrument has the ability to image in ambient conditions with optical resolutions on the order of tens of nanometers in the near field. A single low-cost computer controls the microscope with a field programmable gate array data acquisition card. High spatial resolution imaging is achieved with an inexpensive CW multiphoton excitation source, using an apertureless probe and simplified optical pathways. The high-resolution, combined with high collection efficiency and single-molecule sensitive optical capabilities of the microscope, are demonstrated with a low-cost CW laser source as well as a mode-locked laser source.

Accelerated proton echo planar spectroscopic imaging (PEPSI) using GRAPPA with a 32-channel phased-array coil.

Science.gov (United States)

Tsai, Shang-Yueh; Otazo, Ricardo; Posse, Stefan; Lin, Yi-Ru; Chung, Hsiao-Wen; Wald, Lawrence L; Wiggins, Graham C; Lin, Fa-Hsuan

2008-05-01

Parallel imaging has been demonstrated to reduce the encoding time of MR spectroscopic imaging (MRSI). Here we investigate up to 5-fold acceleration of 2D proton echo planar spectroscopic imaging (PEPSI) at 3T using generalized autocalibrating partial parallel acquisition (GRAPPA) with a 32-channel coil array, 1.5 cm(3) voxel size, TR/TE of 15/2000 ms, and 2.1 Hz spectral resolution. Compared to an 8-channel array, the smaller RF coil elements in this 32-channel array provided a 3.1-fold and 2.8-fold increase in signal-to-noise ratio (SNR) in the peripheral region and the central region, respectively, and more spatial modulated information. Comparison of sensitivity-encoding (SENSE) and GRAPPA reconstruction using an 8-channel array showed that both methods yielded similar quantitative metabolite measures (P > 0.1). Concentration values of N-acetyl-aspartate (NAA), total creatine (tCr), choline (Cho), myo-inositol (mI), and the sum of glutamate and glutamine (Glx) for both methods were consistent with previous studies. Using the 32-channel array coil the mean Cramer-Rao lower bounds (CRLB) were less than 8% for NAA, tCr, and Cho and less than 15% for mI and Glx at 2-fold acceleration. At 4-fold acceleration the mean CRLB for NAA, tCr, and Cho was less than 11%. In conclusion, the use of a 32-channel coil array and GRAPPA reconstruction can significantly reduce the measurement time for mapping brain metabolites. (c) 2008 Wiley-Liss, Inc.

Imaging features of foot osteoid osteoma

Energy Technology Data Exchange (ETDEWEB)

Shukla, Satyen; Clarke, Andrew W.; Saifuddin, Asif [Royal National Orthopaedic Hospital NHS Trust, Department of Radiology, Stanmore, Middlesex (United Kingdom)

2010-07-15

We performed a retrospective review of the imaging of nine patients with a diagnosis of foot osteoid osteoma (OO). Radiographs, computed tomography (CT) and magnetic resonance imaging (MRI) had been performed in all patients. Radiographic features evaluated were the identification of a nidus and cortical thickening. CT features noted were nidus location (affected bone - intramedullary, intracortical, subarticular) and nidus calcification. MRI features noted were the presence of an identifiable nidus, presence and grade of bone oedema and whether a joint effusion was identified. Of the nine patients, three were female and six male, with a mean age of 21 years (range 11-39 years). Classical symptoms of OO (night pain, relief with aspirin) were identified in five of eight (62.5%) cases (in one case, the medical records could not be retrieved). In five patients the lesion was located in the hindfoot (four calcaneus, one talus), while four were in the mid- or forefoot (two metatarsal and two phalangeal). Radiographs were normal in all patients with hindfoot OO. CT identified the nidus in all cases (89%) except one terminal phalanx lesion, while MRI demonstrated a nidus in six of nine cases (67%). The nidus was of predominantly intermediate signal intensity on T1-weighted (T1W) sequences, with intermediate to high signal intensity on T2-weighted (T2W) sequences. High-grade bone marrow oedema, limited to the affected bone and adjacent soft tissue oedema was identified in all cases. In a young patient with chronic hindfoot pain and a normal radiograph, MRI features suggestive of possible OO include extensive bone marrow oedema limited to one bone, with a possible nidus demonstrated in two-thirds of cases. The presence or absence of a nidus should be confirmed with high-resolution CT. (orig.)

Oriented Edge-Based Feature Descriptor for Multi-Sensor Image Alignment and Enhancement

Directory of Open Access Journals (Sweden)

Myung-Ho Ju

2013-10-01

Full Text Available In this paper, we present an efficient image alignment and enhancement method for multi-sensor images. The shape of the object captured in a multi-sensor images can be determined by comparing variability of contrast using corresponding edges across multi-sensor image. Using this cue, we construct a robust feature descriptor based on the magnitudes of the oriented edges. Our proposed method enables fast image alignment by identifying matching features in multi-sensor images. We enhance the aligned multi-sensor images through the fusion of the salient regions from each image. The results of stitching the multi-sensor images and their enhancement demonstrate that our proposed method can align and enhance multi-sensor images more efficiently than previous methods.

Automatic detection of diabetic retinopathy features in ultra-wide field retinal images

Science.gov (United States)

Levenkova, Anastasia; Sowmya, Arcot; Kalloniatis, Michael; Ly, Angelica; Ho, Arthur

2017-03-01

Diabetic retinopathy (DR) is a major cause of irreversible vision loss. DR screening relies on retinal clinical signs (features). Opportunities for computer-aided DR feature detection have emerged with the development of Ultra-WideField (UWF) digital scanning laser technology. UWF imaging covers 82% greater retinal area (200°), against 45° in conventional cameras3 , allowing more clinically relevant retinopathy to be detected4 . UWF images also provide a high resolution of 3078 x 2702 pixels. Currently DR screening uses 7 overlapping conventional fundus images, and the UWF images provide similar results1,4. However, in 40% of cases, more retinopathy was found outside the 7-field ETDRS) fields by UWF and in 10% of cases, retinopathy was reclassified as more severe4 . This is because UWF imaging allows examination of both the central retina and more peripheral regions, with the latter implicated in DR6 . We have developed an algorithm for automatic recognition of DR features, including bright (cotton wool spots and exudates) and dark lesions (microaneurysms and blot, dot and flame haemorrhages) in UWF images. The algorithm extracts features from grayscale (green "red-free" laser light) and colour-composite UWF images, including intensity, Histogram-of-Gradient and Local binary patterns. Pixel-based classification is performed with three different classifiers. The main contribution is the automatic detection of DR features in the peripheral retina. The method is evaluated by leave-one-out cross-validation on 25 UWF retinal images with 167 bright lesions, and 61 other images with 1089 dark lesions. The SVM classifier performs best with AUC of 94.4% / 95.31% for bright / dark lesions.

Immunocytochemistry by electron spectroscopic imaging using well defined boronated monovalent antibody fragments.

Science.gov (United States)

Kessels, M M; Qualmann, B; Sierralta, W D

1996-01-01

Contributing to the rapidly developing field of immunoelectron microscopy a new kind of markers has been created. The element boron, incorporated as very stable carborane clusters into different kinds of peptides, served as a marker detectable by electron spectroscopic imaging (ESI)--an electron microscopic technique with high-resolution potential. Covalently linked immunoreagents conspicuous by the small size of both antigen recognizing part and marker moiety are accessible by using peptide concepts for label construction and their conjugation with Fab' fragments. Due to a specific labeling of the free thiol groups of the Fab' fragments, the antigen binding capacity was not affected by the attachment of the markers and the resulting immunoprobes exhibited an elongated shape with the antigen combining site and the label located at opposite ends. The labeling densities observed with these reagents were found to be significantly higher than those obtained by using conventional colloidal gold methods. Combined with digital image processing and analysis systems, boron-based ESI proved to be a powerful approach in ultrastructural immunocytochemistry employing pre- and post-embedding methods.

Learning Rich Features from RGB-D Images for Object Detection and Segmentation

OpenAIRE

Gupta, Saurabh; Girshick, Ross; Arbeláez, Pablo; Malik, Jitendra

2014-01-01

In this paper we study the problem of object detection for RGB-D images using semantically rich image and depth features. We propose a new geocentric embedding for depth images that encodes height above ground and angle with gravity for each pixel in addition to the horizontal disparity. We demonstrate that this geocentric embedding works better than using raw depth images for learning feature representations with convolutional neural networks. Our final object detection system achieves an av...

Barrett's esophagus: clinical features, obesity, and imaging.

LENUS (Irish Health Repository)

Quigley, Eamonn M M

2011-09-01

The following includes commentaries on clinical features and imaging of Barrett\\'s esophagus (BE); the clinical factors that influence the development of BE; the influence of body fat distribution and central obesity; the role of adipocytokines and proinflammatory markers in carcinogenesis; the role of body mass index (BMI) in healing of Barrett\\'s epithelium; the role of surgery in prevention of carcinogenesis in BE; the importance of double-contrast esophagography and cross-sectional images of the esophagus; and the value of positron emission tomography\\/computed tomography.

Application of second derivative spectroscopy for increasing molecular specificity of Fourier transform infrared spectroscopic imaging of articular cartilage.

Science.gov (United States)

Rieppo, L; Saarakkala, S; Närhi, T; Helminen, H J; Jurvelin, J S; Rieppo, J

2012-05-01

Fourier transform infrared (FT-IR) spectroscopic imaging is a promising method that enables the analysis of spatial distribution of biochemical components within histological sections. However, analysis of FT-IR spectroscopic data is complicated since absorption peaks often overlap with each other. Second derivative spectroscopy is a technique which enhances the separation of overlapping peaks. The objective of this study was to evaluate the specificity of the second derivative peaks for the main tissue components of articular cartilage (AC), i.e., collagen and proteoglycans (PGs). Histological bovine AC sections were measured before and after enzymatic removal of PGs. Both formalin-fixed sections (n = 10) and cryosections (n = 6) were investigated. Relative changes in the second derivative peak heights caused by the removal of PGs were calculated for both sample groups. The results showed that numerous peaks, e.g., peaks located at 1202 cm(-1) and 1336 cm(-1), altered less than 5% in the experiment. These peaks were assumed to be specific for collagen. In contrast, two peaks located at 1064 cm(-1) and 1376 cm(-1) were seen to alter notably, approximately 50% or more. These peaks were regarded to be specific for PGs. The changes were greater in cryosections than formalin-fixed sections. The results of this study suggest that the second derivative spectroscopy offers a practical and more specific method than routinely used absorption spectrum analysis methods to obtain compositional information on AC with FT-IR spectroscopic imaging. Copyright © 2012 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.

Tissue feature-based intra-fractional motion tracking for stereoscopic x-ray image guided radiotherapy

Science.gov (United States)

Xie, Yaoqin; Xing, Lei; Gu, Jia; Liu, Wu

2013-06-01

Real-time knowledge of tumor position during radiation therapy is essential to overcome the adverse effect of intra-fractional organ motion. The goal of this work is to develop a tumor tracking strategy by effectively utilizing the inherent image features of stereoscopic x-ray images acquired during dose delivery. In stereoscopic x-ray image guided radiation delivery, two orthogonal x-ray images are acquired either simultaneously or sequentially. The essence of markerless tumor tracking is the reliable identification of inherent points with distinct tissue features on each projection image and their association between two images. The identification of the feature points on a planar x-ray image is realized by searching for points with high intensity gradient. The feature points are associated by using the scale invariance features transform descriptor. The performance of the proposed technique is evaluated by using images of a motion phantom and four archived clinical cases acquired using either a CyberKnife equipped with a stereoscopic x-ray imaging system, or a LINAC equipped with an onboard kV imager and an electronic portal imaging device. In the phantom study, the results obtained using the proposed method agree with the measurements to within 2 mm in all three directions. In the clinical study, the mean error is 0.48 ± 0.46 mm for four patient data with 144 sequential images. In this work, a tissue feature-based tracking method for stereoscopic x-ray image guided radiation therapy is developed. The technique avoids the invasive procedure of fiducial implantation and may greatly facilitate the clinical workflow.

Tissue feature-based intra-fractional motion tracking for stereoscopic x-ray image guided radiotherapy

International Nuclear Information System (INIS)

Xie Yaoqin; Gu Jia; Xing Lei; Liu Wu

2013-01-01

Real-time knowledge of tumor position during radiation therapy is essential to overcome the adverse effect of intra-fractional organ motion. The goal of this work is to develop a tumor tracking strategy by effectively utilizing the inherent image features of stereoscopic x-ray images acquired during dose delivery. In stereoscopic x-ray image guided radiation delivery, two orthogonal x-ray images are acquired either simultaneously or sequentially. The essence of markerless tumor tracking is the reliable identification of inherent points with distinct tissue features on each projection image and their association between two images. The identification of the feature points on a planar x-ray image is realized by searching for points with high intensity gradient. The feature points are associated by using the scale invariance features transform descriptor. The performance of the proposed technique is evaluated by using images of a motion phantom and four archived clinical cases acquired using either a CyberKnife equipped with a stereoscopic x-ray imaging system, or a LINAC equipped with an onboard kV imager and an electronic portal imaging device. In the phantom study, the results obtained using the proposed method agree with the measurements to within 2 mm in all three directions. In the clinical study, the mean error is 0.48 ± 0.46 mm for four patient data with 144 sequential images. In this work, a tissue feature-based tracking method for stereoscopic x-ray image guided radiation therapy is developed. The technique avoids the invasive procedure of fiducial implantation and may greatly facilitate the clinical workflow. (paper)

RESEARCH ON FOREST FLAME RECOGNITION ALGORITHM BASED ON IMAGE FEATURE

Directory of Open Access Journals (Sweden)

Z. Wang

2017-09-01

Full Text Available In recent years, fire recognition based on image features has become a hotspot in fire monitoring. However, due to the complexity of forest environment, the accuracy of forest fireworks recognition based on image features is low. Based on this, this paper proposes a feature extraction algorithm based on YCrCb color space and K-means clustering. Firstly, the paper prepares and analyzes the color characteristics of a large number of forest fire image samples. Using the K-means clustering algorithm, the forest flame model is obtained by comparing the two commonly used color spaces, and the suspected flame area is discriminated and extracted. The experimental results show that the extraction accuracy of flame area based on YCrCb color model is higher than that of HSI color model, which can be applied in different scene forest fire identification, and it is feasible in practice.

Color Texture Image Retrieval Based on Local Extrema Features and Riemannian Distance

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Minh-Tan Pham

2017-10-01

Full Text Available A novel efficient method for content-based image retrieval (CBIR is developed in this paper using both texture and color features. Our motivation is to represent and characterize an input image by a set of local descriptors extracted from characteristic points (i.e., keypoints within the image. Then, dissimilarity measure between images is calculated based on the geometric distance between the topological feature spaces (i.e., manifolds formed by the sets of local descriptors generated from each image of the database. In this work, we propose to extract and use the local extrema pixels as our feature points. Then, the so-called local extrema-based descriptor (LED is generated for each keypoint by integrating all color, spatial as well as gradient information captured by its nearest local extrema. Hence, each image is encoded by an LED feature point cloud and Riemannian distances between these point clouds enable us to tackle CBIR. Experiments performed on several color texture databases including Vistex, STex, color Brodazt, USPtex and Outex TC-00013 using the proposed approach provide very efficient and competitive results compared to the state-of-the-art methods.

Recent Applications of Chemical Imaging to Pharmaceutical Process Monitoring and Quality Control

OpenAIRE

Gowen, A. A.; O'Donnell, Colm; Cullen, Patrick; Bell, S.

2008-01-01

Chemical Imaging (CI) is an emerging platform technology that integrates conventional imaging and spectroscopy to attain both spatial and spectral information from an object. Vibrational spectroscopic methods, such as Near Infrared (NIR) and Raman spectroscopy, combined with imaging are particularly useful for analysis of biological/pharmaceutical forms. The rapid, non-destructive and non-invasive features of CI mark its potential suitability as a process analytical tool for the pharmaceutica...

Three-dimensional magnetic resonance spectroscopic imaging in the substantia nigra of healthy controls and patients with Parkinson's disease

Energy Technology Data Exchange (ETDEWEB)

Groeger, Adriane; Godau, Jana; Berg, Daniela [University of Tuebingen, Department of Neurodegeneration, Hertie Institute for Clinical Brain Research and German Center for Neurodegenerative Disease (DZNE), Tuebingen (Germany); Chadzynski, Grzegorz; Klose, Uwe [University Hospital Tuebingen, Department of Diagnostic and Interventional Neuroradiology, Tuebingen (Germany)

2011-09-15

To investigate the substantia nigra in patients with Parkinson's disease three-dimensional magnetic resonance spectroscopic imaging with high spatial resolution at 3 Tesla was performed. Regional variations of spectroscopic data between the rostral and caudal regions of the substantia nigra as well as the midbrain tegmentum areas were evaluated in healthy controls and patients with Parkinson's disease. Nine patients with Parkinson's disease and eight age- and gender-matched healthy controls were included in this study. Data were acquired by using three-dimensional magnetic resonance spectroscopic imaging measurements. The ratios between rostral and caudal voxels of the substantia nigra as well as the midbrain tegmentum areas were calculated for the main-metabolites N-acetyl aspartate, creatine, choline, and myo-inositol. Additionally, the metabolite/creatine ratios were calculated. In all subjects spectra of acceptable quality could be obtained with a nominal voxel size of 0.252 ml. The calculated rostral-to-caudal ratios of the metabolites as well as of the metabolite/creatine ratios showed with exception of choline/creatine ratio significant differences between healthy controls and patients with Parkinson's disease. The findings from this study indicate that regional variations in N-acetyl aspartate/creatine ratios in the regions of the substantia nigra may differentiate patients with Parkinson's disease and healthy controls. (orig.)

Multi-example feature-constrained back-projection method for image super-resolution

Institute of Scientific and Technical Information of China (English)

Junlei Zhang; Dianguang Gai; Xin Zhang; Xuemei Li

2017-01-01

Example-based super-resolution algorithms,which predict unknown high-resolution image information using a relationship model learnt from known high- and low-resolution image pairs, have attracted considerable interest in the field of image processing. In this paper, we propose a multi-example feature-constrained back-projection method for image super-resolution. Firstly, we take advantage of a feature-constrained polynomial interpolation method to enlarge the low-resolution image. Next, we consider low-frequency images of different resolutions to provide an example pair. Then, we use adaptive k NN search to find similar patches in the low-resolution image for every image patch in the high-resolution low-frequency image, leading to a regression model between similar patches to be learnt. The learnt model is applied to the low-resolution high-frequency image to produce high-resolution high-frequency information. An iterative back-projection algorithm is used as the final step to determine the final high-resolution image.Experimental results demonstrate that our method improves the visual quality of the high-resolution image.

Improved medical image modality classification using a combination of visual and textual features.

Science.gov (United States)

Dimitrovski, Ivica; Kocev, Dragi; Kitanovski, Ivan; Loskovska, Suzana; Džeroski, Sašo

2015-01-01

In this paper, we present the approach that we applied to the medical modality classification tasks at the ImageCLEF evaluation forum. More specifically, we used the modality classification databases from the ImageCLEF competitions in 2011, 2012 and 2013, described by four visual and one textual types of features, and combinations thereof. We used local binary patterns, color and edge directivity descriptors, fuzzy color and texture histogram and scale-invariant feature transform (and its variant opponentSIFT) as visual features and the standard bag-of-words textual representation coupled with TF-IDF weighting. The results from the extensive experimental evaluation identify the SIFT and opponentSIFT features as the best performing features for modality classification. Next, the low-level fusion of the visual features improves the predictive performance of the classifiers. This is because the different features are able to capture different aspects of an image, their combination offering a more complete representation of the visual content in an image. Moreover, adding textual features further increases the predictive performance. Finally, the results obtained with our approach are the best results reported on these databases so far. Copyright © 2014 Elsevier Ltd. All rights reserved.

Phosphorus-31 spectroscopic imaging of the human liver

International Nuclear Information System (INIS)

Biran, M.; Raffard, G.; Canioni, P.; Kien, P.

1993-01-01

During the last decade, progresses in the field of nuclear magnetic resonance spectroscopy (M.R.S.), have allowed the metabolic studies of complex biological systems. Since the coming out of whole body magnets, clinical applications are possible; they utilize magnetic field gradients coupled with selective pulse sequences. Study of the phosphorylated metabolism of human liver can be performed with sequences as ISIS, FROGS or 1D-CSI. But they present some disadvantages (for instance contamination by phosphocreatine from muscle). In the present work, we have studied the human liver in vivo by 31 P spectroscopic imaging. Several spectra could be acquired with only one acquisition. This study has needed the building of radiofrequency coils (surface coils), specially designed for liver observation (15 cm diameter 31 P coil and 19 cm diameter proton coil, both transmitter and receiver coils). Preliminary studies have been done on a phantom followed by in vivo measurements on healthy subject livers. We have obtained localized 31 P N.M.R. spectra corresponding to different voxels within the hepatic tissue. The conditions of acquisition of spectra and the problems related to the saturation of phosphorylated metabolite signals (in particular phosphodiesters) are discussed. (author). 5 figs., 15 refs

In vivo, noninvasive functional measurements of bone sarcoma using diffuse optical spectroscopic imaging

Science.gov (United States)

Peterson, Hannah M.; Hoang, Bang H.; Geller, David; Yang, Rui; Gorlick, Richard; Berger, Jeremy; Tingling, Janet; Roth, Michael; Gill, Jonathon; Roblyer, Darren

2017-12-01

Diffuse optical spectroscopic imaging (DOSI) is an emerging near-infrared imaging technique that noninvasively measures quantitative functional information in thick tissue. This study aimed to assess the feasibility of using DOSI to measure optical contrast from bone sarcomas. These tumors are rare and pose technical and practical challenges for DOSI measurements due to the varied anatomic locations and tissue depths of presentation. Six subjects were enrolled in the study. One subject was unable to be measured due to tissue contact sensitivity. For the five remaining subjects, the signal-to-noise ratio, imaging depth, optical properties, and quantitative tissue concentrations of oxyhemoglobin, deoxyhemoglobin, water, and lipids from tumor and contralateral normal tissues were assessed. Statistical differences between tumor and contralateral normal tissue were found in chromophore concentrations and optical properties for four subjects. Low signal-to-noise was encountered during several subject's measurements, suggesting increased detector sensitivity will help to optimize DOSI for this patient population going forward. This study demonstrates that DOSI is capable of measuring optical properties and obtaining functional information in bone sarcomas. In the future, DOSI may provide a means to stratify treatment groups and monitor chemotherapy response for this disease.

Imaging features of multicentric Castleman's disease in HIV infection

International Nuclear Information System (INIS)

Hillier, J.C.; Shaw, P.; Miller, R.F.; Cartledge, J.D.; Nelson, M.; Bower, M.; Francis, N.; Padley, S.P.

2004-01-01

AIM: To describe the computed tomography (CT) features of human immunodeficiency virus (HIV)-associated Castleman's disease. MATERIALS AND METHODS: Nine HIV-positive patients with biopsy-proven Castleman's disease were studied. Clinical and demographic data, CD4 count, histological diagnosis and human herpes type 8 (HHV8) serology or immunostaining results were recorded. CT images were reviewed independently by two radiologists. RESULTS: CT findings included splenomegaly (n=7) and peripheral lymph node enlargement (axillary n=8, inguinal n=4). All nodes displayed mild to avid enhancement after intravenous administration of contrast material. Hepatomegaly was evident in seven patients. Other features included abdominal (n=6) and mediastinal (n=5) lymph node enlargement and pulmonary abnormalities (n=4). Patterns of parenchymal abnormality included bronchovascular nodularity (n=2), consolidation (n=1) and pleural effusion (n=2). On histological examination eight patients (spleen n=3, lymph node n=9, lung n=1 bone marrow n=1) had the plasma cell variant and one had mixed hyaline-vascular/plasma cell variant. The majority had either positive immunostaining for HHV8 or positive serology (n=8). CONCLUSION: Common imaging features of multicentric Castleman's disease in HIV infection are hepatosplenomegaly and peripheral lymph node enlargement. Although these imaging features may suggest the diagnosis in the appropriate clinical context, they lack specificity and so biopsy is needed for diagnosis. In distinction from multicentric Castleman's disease in other populations the plasma cell variant is most commonly encountered, splenomegaly is a universal feature and there is a strong association with Kaposi's sarcoma

Confocal spectroscopic imaging measurements of depth dependent hydration dynamics in human skin in-vivo

Science.gov (United States)

Behm, P.; Hashemi, M.; Hoppe, S.; Wessel, S.; Hagens, R.; Jaspers, S.; Wenck, H.; Rübhausen, M.

2017-11-01

We present confocal spectroscopic imaging measurements applied to in-vivo studies to determine the depth dependent hydration profiles of human skin. The observed spectroscopic signal covers the spectral range from 810 nm to 2100 nm allowing to probe relevant absorption signals that can be associated with e.g. lipid and water-absorption bands. We employ a spectrally sensitive autofocus mechanism that allows an ultrafast focusing of the measurement spot on the skin and subsequently probes the evolution of the absorption bands as a function of depth. We determine the change of the water concentration in m%. The water concentration follows a sigmoidal behavior with an increase of the water content of about 70% within 5 μm in a depth of about 14 μm. We have applied our technique to study the hydration dynamics of skin before and after treatment with different concentrations of glycerol indicating that an increase of the glycerol concentration leads to an enhanced water concentration in the stratum corneum. Moreover, in contrast to traditional corneometry we have found that the application of Aluminium Chlorohydrate has no impact to the hydration of skin.

Confocal spectroscopic imaging measurements of depth dependent hydration dynamics in human skin in-vivo

Directory of Open Access Journals (Sweden)

P. Behm

2017-11-01

Full Text Available We present confocal spectroscopic imaging measurements applied to in-vivo studies to determine the depth dependent hydration profiles of human skin. The observed spectroscopic signal covers the spectral range from 810 nm to 2100 nm allowing to probe relevant absorption signals that can be associated with e.g. lipid and water-absorption bands. We employ a spectrally sensitive autofocus mechanism that allows an ultrafast focusing of the measurement spot on the skin and subsequently probes the evolution of the absorption bands as a function of depth. We determine the change of the water concentration in m%. The water concentration follows a sigmoidal behavior with an increase of the water content of about 70% within 5 μm in a depth of about 14 μm. We have applied our technique to study the hydration dynamics of skin before and after treatment with different concentrations of glycerol indicating that an increase of the glycerol concentration leads to an enhanced water concentration in the stratum corneum. Moreover, in contrast to traditional corneometry we have found that the application of Aluminium Chlorohydrate has no impact to the hydration of skin.

Adaptive Colour Feature Identification in Image for Object Tracking

Directory of Open Access Journals (Sweden)

Feng Su

2012-01-01

Full Text Available Identification and tracking of a moving object using computer vision techniques is important in robotic surveillance. In this paper, an adaptive colour filtering method is introduced for identifying and tracking a moving object appearing in image sequences. This filter is capable of automatically identifying the most salient colour feature of the moving object in the image and using this for a robot to track the object. The method enables the selected colour feature to adapt to surrounding condition when it is changed. A method of determining the region of interest of the moving target is also developed for the adaptive colour filter to extract colour information. Experimental results show that by using a camera mounted on a robot, the proposed methods can perform robustly in tracking a randomly moving object using adaptively selected colour features in a crowded environment.

Hierarchical Recurrent Neural Hashing for Image Retrieval With Hierarchical Convolutional Features.

Science.gov (United States)

Lu, Xiaoqiang; Chen, Yaxiong; Li, Xuelong

Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep learning architectures can learn more effective image representation features. However, these methods only use semantic features to generate hash codes by shallow projection but ignore texture details. In this paper, we proposed a novel hashing method, namely hierarchical recurrent neural hashing (HRNH), to exploit hierarchical recurrent neural network to generate effective hash codes. There are three contributions of this paper. First, a deep hashing method is proposed to extensively exploit both spatial details and semantic information, in which, we leverage hierarchical convolutional features to construct image pyramid representation. Second, our proposed deep network can exploit directly convolutional feature maps as input to preserve the spatial structure of convolutional feature maps. Finally, we propose a new loss function that considers the quantization error of binarizing the continuous embeddings into the discrete binary codes, and simultaneously maintains the semantic similarity and balanceable property of hash codes. Experimental results on four widely used data sets demonstrate that the proposed HRNH can achieve superior performance over other state-of-the-art hashing methods.Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep

An adaptive clustering algorithm for image matching based on corner feature

Science.gov (United States)

Wang, Zhe; Dong, Min; Mu, Xiaomin; Wang, Song

2018-04-01

The traditional image matching algorithm always can not balance the real-time and accuracy better, to solve the problem, an adaptive clustering algorithm for image matching based on corner feature is proposed in this paper. The method is based on the similarity of the matching pairs of vector pairs, and the adaptive clustering is performed on the matching point pairs. Harris corner detection is carried out first, the feature points of the reference image and the perceived image are extracted, and the feature points of the two images are first matched by Normalized Cross Correlation (NCC) function. Then, using the improved algorithm proposed in this paper, the matching results are clustered to reduce the ineffective operation and improve the matching speed and robustness. Finally, the Random Sample Consensus (RANSAC) algorithm is used to match the matching points after clustering. The experimental results show that the proposed algorithm can effectively eliminate the most wrong matching points while the correct matching points are retained, and improve the accuracy of RANSAC matching, reduce the computation load of whole matching process at the same time.

Single nanoparticle tracking spectroscopic microscope

Science.gov (United States)

Yang, Haw [Moraga, CA; Cang, Hu [Berkeley, CA; Xu, Cangshan [Berkeley, CA; Wong, Chung M [San Gabriel, CA

2011-07-19

A system that can maintain and track the position of a single nanoparticle in three dimensions for a prolonged period has been disclosed. The system allows for continuously imaging the particle to observe any interactions it may have. The system also enables the acquisition of real-time sequential spectroscopic information from the particle. The apparatus holds great promise in performing single molecule spectroscopy and imaging on a non-stationary target.

Difet: Distributed Feature Extraction Tool for High Spatial Resolution Remote Sensing Images

Science.gov (United States)

Eken, S.; Aydın, E.; Sayar, A.

2017-11-01

In this paper, we propose distributed feature extraction tool from high spatial resolution remote sensing images. Tool is based on Apache Hadoop framework and Hadoop Image Processing Interface. Two corner detection (Harris and Shi-Tomasi) algorithms and five feature descriptors (SIFT, SURF, FAST, BRIEF, and ORB) are considered. Robustness of the tool in the task of feature extraction from LandSat-8 imageries are evaluated in terms of horizontal scalability.

Curvature histogram features for retrieval of images of smooth 3D objects

International Nuclear Information System (INIS)

Zhdanov, I; Scherbakov, O; Potapov, A; Peterson, M

2014-01-01

We consider image features on the base of histograms of oriented gradients (HOG) with addition of contour curvature histogram (HOG-CH), and also compare it with results of known scale-invariant feature transform (SIFT) approach in application to retrieval of images of smooth 3D objects.

Organ-confined prostate cancer: effect of prior transrectal biopsy on endorectal MRI and MR spectroscopic imaging

International Nuclear Information System (INIS)

Qayyum, Aliya; Coakley, F.V.; Lu, Y.; Olpin, J.D.; Wu, L.; Yeh, B.M.; Carroll, P.R.; Kurhanewicz, J.

2004-01-01

Objective: Our aim was to determine the effect of prior transrectal biopsy on endorectal MRI and MR spectroscopic imaging findings in patients with organ-confined prostate cancer. Materials and Methods: Endorectal MRI and MR spectroscopic imaging were performed in 43 patients with biopsy-proven prostate cancer before radical prostatectomy confirming organ-confined disease. For each sextant, two independent reviewers scored the degree of hemorrhage on a scale from 1 to 5 and recorded the presence or absence of capsular irregularity. A spectroscopist recorded the number of spectrally degraded voxels in the peripheral zone. The outcome variables of capsular irregularity and spectral degradation were correlated with the predictor variables of time from biopsy and degree of hemorrhage after biopsy. Results: Capsular irregularity was unrelated to time from biopsy or to degree of hemorrhage. Spectral degradation was inversely related to time from biopsy (p < 0.01); the mean percentage of degraded peripheral zone voxels was 18.5% within 8 weeks of biopsy compared with 7% after 8 weeks. Spectral degradation was unrelated to the degree of hemorrhage. Conclusion: In organ-confined prostate cancer, capsular irregularity can be seen at any time after biopsy and is independent of the degree of hemorrhage, whereas spectral degradation is seen predominantly in the first 8 weeks after biopsy. MRI staging criteria and guidelines for scheduling studies after biopsy may require appropriate modification. (author)

Hierarchical Feature Extraction With Local Neural Response for Image Recognition.

Science.gov (United States)

Li, Hong; Wei, Yantao; Li, Luoqing; Chen, C L P

2013-04-01

In this paper, a hierarchical feature extraction method is proposed for image recognition. The key idea of the proposed method is to extract an effective feature, called local neural response (LNR), of the input image with nontrivial discrimination and invariance properties by alternating between local coding and maximum pooling operation. The local coding, which is carried out on the locally linear manifold, can extract the salient feature of image patches and leads to a sparse measure matrix on which maximum pooling is carried out. The maximum pooling operation builds the translation invariance into the model. We also show that other invariant properties, such as rotation and scaling, can be induced by the proposed model. In addition, a template selection algorithm is presented to reduce computational complexity and to improve the discrimination ability of the LNR. Experimental results show that our method is robust to local distortion and clutter compared with state-of-the-art algorithms.

Bubble feature extracting based on image processing of coal flotation froth

Energy Technology Data Exchange (ETDEWEB)

Wang, F.; Wang, Y.; Lu, M.; Liu, W. [China University of Mining and Technology, Beijing (China). Dept of Chemical Engineering and Environment

2001-11-01

Using image processing the contrast ratio between the bubble on the surface of flotation froth and the image background was enhanced, and the edges of bubble were extracted. Thus a model about the relation between the statistic feature of the bubbles in the image and the cleaned coal can be established. It is feasible to extract the bubble by processing the froth image of coal flotation on the basis of analysing the shape of the bubble. By means of processing the 51 group images sampled from laboratory column, it is thought that the use of the histogram equalization of image gradation and the medium filtering can obviously improve the dynamic contrast range and the brightness of bubbles. Finally, the method of threshold value cut and the bubble edge detecting for extracting the bubble were also discussed to describe the bubble feature, such as size and shape, in the froth image and to distinguish the froth image of coal flotation. 6 refs., 3 figs.

Imaging features of maxillary osteoblastoma and its malignant transformation

International Nuclear Information System (INIS)

Ueno, Hiroshi; Ariji, Ei-ichiro; Tanaka, Takemasa; Kanda, Shigenobu; Mori, Shin-ichiro; Goto, Masaaki; Mizuno, Akio; Okabe, Haruo; Nakamura, Takashi

1994-01-01

We report two cases of osteoblastoma, one of them an unusual case in a 32-year-old woman in whom a maxillary tumor was confidently diagnosed as an osteoblastoma at the time of primary excision and subsequently transformed into an osteosarcoma 7 years after the onset of clinical symptoms. The other patient developed osteosarcoma arising in the maxilla, which was diagnosed 3 years after the primary excision and is very suggestive of malignant transformation in osteoblastoma. We present the radiological features, including computed tomographic and magnetic resonance imaging studies, of this unusual event of transformed tumor and compare imaging features of benign and dedifferentiated counterparts of this rare tumor complex. (orig.)

Endorectal coil MRI and MR-spectroscopic imaging in patients with elevated serum prostate specific antigen with negative trus transrectal ultrasound guided biopsy

Directory of Open Access Journals (Sweden)

Farooq Ahmad Ganie

2013-01-01

Conclusion: Prostatic biopsy directed with endorectal coil MRI and MR-spectroscopic imaging findings in patients with elevated serum PSA and prior negative biopsy, improves the early diagnosis of prostatic carcinoma and accurate localization of prostate cancer within the gland.

An efficient fractal image coding algorithm using unified feature and DCT

International Nuclear Information System (INIS)

Zhou Yiming; Zhang Chao; Zhang Zengke

2009-01-01

Fractal image compression is a promising technique to improve the efficiency of image storage and image transmission with high compression ratio, however, the huge time consumption for the fractal image coding is a great obstacle to the practical applications. In order to improve the fractal image coding, efficient fractal image coding algorithms using a special unified feature and a DCT coder are proposed in this paper. Firstly, based on a necessary condition to the best matching search rule during fractal image coding, the fast algorithm using a special unified feature (UFC) is addressed, and it can reduce the search space obviously and exclude most inappropriate matching subblocks before the best matching search. Secondly, on the basis of UFC algorithm, in order to improve the quality of the reconstructed image, a DCT coder is combined to construct a hybrid fractal image algorithm (DUFC). Experimental results show that the proposed algorithms can obtain good quality of the reconstructed images and need much less time than the baseline fractal coding algorithm.

SAR Data Fusion Imaging Method Oriented to Target Feature Extraction

Directory of Open Access Journals (Sweden)

Yang Wei

2015-02-01

Full Text Available To deal with the difficulty for target outlines extracting precisely due to neglect of target scattering characteristic variation during the processing of high-resolution space-borne SAR data, a novel fusion imaging method is proposed oriented to target feature extraction. Firstly, several important aspects that affect target feature extraction and SAR image quality are analyzed, including curved orbit, stop-and-go approximation, atmospheric delay, and high-order residual phase error. Furthermore, the corresponding compensation methods are addressed as well. Based on the analysis, the mathematical model of SAR echo combined with target space-time spectrum is established for explaining the space-time-frequency change rule of target scattering characteristic. Moreover, a fusion imaging strategy and method under high-resolution and ultra-large observation angle range conditions are put forward to improve SAR quality by fusion processing in range-doppler and image domain. Finally, simulations based on typical military targets are used to verify the effectiveness of the fusion imaging method.

Construction and performance of a dilution-refrigerator based spectroscopic-imaging scanning tunneling microscope.

Science.gov (United States)

Singh, U R; Enayat, M; White, S C; Wahl, P

2013-01-01

We report on the set-up and performance of a dilution-refrigerator based spectroscopic imaging scanning tunneling microscope. It operates at temperatures below 10 mK and in magnetic fields up to 14T. The system allows for sample transfer and in situ cleavage. We present first-results demonstrating atomic resolution and the multi-gap structure of the superconducting gap of NbSe(2) at base temperature. To determine the energy resolution of our system we have measured a normal metal/vacuum/superconductor tunneling junction consisting of an aluminum tip on a gold sample. Our system allows for continuous measurements at base temperature on time scales of up to ≈170 h.

DIFET: DISTRIBUTED FEATURE EXTRACTION TOOL FOR HIGH SPATIAL RESOLUTION REMOTE SENSING IMAGES

Directory of Open Access Journals (Sweden)

S. Eken

2017-11-01

Full Text Available In this paper, we propose distributed feature extraction tool from high spatial resolution remote sensing images. Tool is based on Apache Hadoop framework and Hadoop Image Processing Interface. Two corner detection (Harris and Shi-Tomasi algorithms and five feature descriptors (SIFT, SURF, FAST, BRIEF, and ORB are considered. Robustness of the tool in the task of feature extraction from LandSat-8 imageries are evaluated in terms of horizontal scalability.

Spectroscopic techniques (Moessbauer spectrometry, NMR, ESR...) as tools to resolve doubtful NMR images: Study of the craniopharyngioma tumor

International Nuclear Information System (INIS)

Rimbert, J.N.; Dumas, F.; Lafargue, C.; Kellershohn, C.; Brunelle, F.; Lallemand, D.

1990-01-01

Craniopharyngioma, an intracranial tumor, exhibits hyperintensity in the Spin-Echo-T 2 -NMR image and a hyposignal in the SE-T 1 -image. However, in some cases (15-20% cases), hypersignals are seen in both SE-T 1 and T 2 -MRI. Using spectroscopic techniques, Moessbauer spectrometry in particular, we have demonstrated that the T 1 hypersignal is due to ferritin, dissolved in the cystic liquid, after tumor cell lysis, in the course of time. Other possible reasons inducing a shortening of the T 1 relaxation time (presence of lipids, intratumoral hemorrhage) have been rejected. (orig.)

Diffusion tensor spectroscopic imaging of the human brain in children and adults.

Science.gov (United States)

Fotso, Kevin; Dager, Stephen R; Landow, Alec; Ackley, Elena; Myers, Orrin; Dixon, Mindy; Shaw, Dennis; Corrigan, Neva M; Posse, Stefan

2017-10-01

We developed diffusion tensor spectroscopic imaging (DTSI), based on proton-echo-planar-spectroscopic imaging (PEPSI), and evaluated the feasibility of mapping brain metabolite diffusion in adults and children. PRESS prelocalized DTSI at 3 Tesla (T) was performed using navigator-based correction of movement-related phase errors and cardiac gating with compensation for repetition time (TR) related variability in T 1 saturation. Mean diffusivity (MD) and fractional anisotropy (FA) of total N-acetyl-aspartate (tNAA), total creatine (tCr), and total choline (tCho) were measured in eight adults (17-60 years) and 10 children (3-24 months) using b max  = 1734 s/mm 2 , 1 cc and 4.5 cc voxel sizes, with nominal scan times of 17 min and 8:24 min. Residual movement-related phase encoding ghosting (PEG) was used as a regressor across scans to correct overestimation of MD. After correction for PEG, metabolite slice-averaged MD estimated at 20% PEG were lower (P < 0.042) for adults (0.17/0.20/0.18 × 10 -3 mm 2 /s) than for children (0.26/0.27/0.24 × 10 -3 mm 2 /s). Extrapolated to 0% PEG, the MD estimates decreased further (0.09/0.11/0.11 × 10 -3 mm 2 /s versus 0.15/0.16/0.15 × 10 -3 mm 2 /s). Slice-averaged FA of tNAA (P = 0.049), tCr (P = 0.067), and tCho (P = 0.003) were higher in children. This high-speed DTSI approach with PEG regression allows for estimation of metabolite MD and FA with improved tolerance to movement. Our preliminary data suggesting age-related changes support DTSI as a sensitive technique for investigating intracellular markers of biological processes. Magn Reson Med 78:1246-1256, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Research on Remote Sensing Image Classification Based on Feature Level Fusion

Science.gov (United States)

Yuan, L.; Zhu, G.

2018-04-01

Remote sensing image classification, as an important direction of remote sensing image processing and application, has been widely studied. However, in the process of existing classification algorithms, there still exists the phenomenon of misclassification and missing points, which leads to the final classification accuracy is not high. In this paper, we selected Sentinel-1A and Landsat8 OLI images as data sources, and propose a classification method based on feature level fusion. Compare three kind of feature level fusion algorithms (i.e., Gram-Schmidt spectral sharpening, Principal Component Analysis transform and Brovey transform), and then select the best fused image for the classification experimental. In the classification process, we choose four kinds of image classification algorithms (i.e. Minimum distance, Mahalanobis distance, Support Vector Machine and ISODATA) to do contrast experiment. We use overall classification precision and Kappa coefficient as the classification accuracy evaluation criteria, and the four classification results of fused image are analysed. The experimental results show that the fusion effect of Gram-Schmidt spectral sharpening is better than other methods. In four kinds of classification algorithms, the fused image has the best applicability to Support Vector Machine classification, the overall classification precision is 94.01 % and the Kappa coefficients is 0.91. The fused image with Sentinel-1A and Landsat8 OLI is not only have more spatial information and spectral texture characteristics, but also enhances the distinguishing features of the images. The proposed method is beneficial to improve the accuracy and stability of remote sensing image classification.

Research of image retrieval technology based on color feature

Science.gov (United States)

Fu, Yanjun; Jiang, Guangyu; Chen, Fengying

2009-10-01

Recently, with the development of the communication and the computer technology and the improvement of the storage technology and the capability of the digital image equipment, more and more image resources are given to us than ever. And thus the solution of how to locate the proper image quickly and accurately is wanted.The early method is to set up a key word for searching in the database, but now the method has become very difficult when we search much more picture that we need. In order to overcome the limitation of the traditional searching method, content based image retrieval technology was aroused. Now, it is a hot research subject.Color image retrieval is the important part of it. Color is the most important feature for color image retrieval. Three key questions on how to make use of the color characteristic are discussed in the paper: the expression of color, the abstraction of color characteristic and the measurement of likeness based on color. On the basis, the extraction technology of the color histogram characteristic is especially discussed. Considering the advantages and disadvantages of the overall histogram and the partition histogram, a new method based the partition-overall histogram is proposed. The basic thought of it is to divide the image space according to a certain strategy, and then calculate color histogram of each block as the color feature of this block. Users choose the blocks that contain important space information, confirming the right value. The system calculates the distance between the corresponding blocks that users choosed. Other blocks merge into part overall histograms again, and the distance should be calculated. Then accumulate all the distance as the real distance between two pictures. The partition-overall histogram comprehensive utilizes advantages of two methods above, by choosing blocks makes the feature contain more spatial information which can improve performance; the distances between partition-overall histogram

Illumination invariant feature point matching for high-resolution planetary remote sensing images

Science.gov (United States)

Wu, Bo; Zeng, Hai; Hu, Han

2018-03-01

Despite its success with regular close-range and remote-sensing images, the scale-invariant feature transform (SIFT) algorithm is essentially not invariant to illumination differences due to the use of gradients for feature description. In planetary remote sensing imagery, which normally lacks sufficient textural information, salient regions are generally triggered by the shadow effects of keypoints, reducing the matching performance of classical SIFT. Based on the observation of dual peaks in a histogram of the dominant orientations of SIFT keypoints, this paper proposes an illumination-invariant SIFT matching method for high-resolution planetary remote sensing images. First, as the peaks in the orientation histogram are generally aligned closely with the sub-solar azimuth angle at the time of image collection, an adaptive suppression Gaussian function is tuned to level the histogram and thereby alleviate the differences in illumination caused by a changing solar angle. Next, the suppression function is incorporated into the original SIFT procedure for obtaining feature descriptors, which are used for initial image matching. Finally, as the distribution of feature descriptors changes after anisotropic suppression, and the ratio check used for matching and outlier removal in classical SIFT may produce inferior results, this paper proposes an improved matching procedure based on cross-checking and template image matching. The experimental results for several high-resolution remote sensing images from both the Moon and Mars, with illumination differences of 20°-180°, reveal that the proposed method retrieves about 40%-60% more matches than the classical SIFT method. The proposed method is of significance for matching or co-registration of planetary remote sensing images for their synergistic use in various applications. It also has the potential to be useful for flyby and rover images by integrating with the affine invariant feature detectors.

Diabetic mastopathy: Imaging features and the role of image-guided biopsy in its diagnosis

Energy Technology Data Exchange (ETDEWEB)

Kim, Jong Hyeon; Kim, Eun Kyung; Kim, Min Jung; Moon, Hee Jung; Yoon, Jung Hyun [Dept. of Radiology, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul (Korea, Republic of)

2016-03-15

The goal of this study was to evaluate the imaging features of diabetic mastopathy (DMP) and the role of image-guided biopsy in its diagnosis. Two experienced radiologists retrospectively reviewed the mammographic and sonographic images of 19 pathologically confirmed DMP patients. The techniques and results of the biopsies performed in each patient were also reviewed. Mammograms showed negative findings in 78% of the patients. On ultrasonography (US), 13 lesions were seen as masses and six as non-mass lesions. The US features of the mass lesions were as follows: irregular shape (69%), oval shape (31%), indistinct margin (69%), angular margin (15%), microlobulated margin (8%), well-defined margin (8%), heterogeneous echogenicity (62%), hypoechoic echogenicity (38%), posterior shadowing (92%), parallel orientation (100%), the absence of calcifications (100%), and the absence of vascularity (100%). Based on the US findings, 17 lesions (89%) were classified as Breast Imaging Reporting and Data System category 4 and two (11%) as category 3. US-guided core biopsy was performed in 18 patients, and 10 (56%) were diagnosed with DMP on that basis. An additional vacuum-assisted biopsy was performed in seven patients and all were diagnosed with DMP. The US features of DMP were generally suspicious for malignancy, whereas the mammographic findings were often negative or showed only focal asymmetry. Core biopsy is an adequate method for initial pathological diagnosis. However, since it yields non-diagnostic results in a considerable number of cases, the evaluation of correlations between imaging and pathology plays an important role in the diagnostic process.

Diabetic mastopathy: Imaging features and the role of image-guided biopsy in its diagnosis

International Nuclear Information System (INIS)

Kim, Jong Hyeon; Kim, Eun Kyung; Kim, Min Jung; Moon, Hee Jung; Yoon, Jung Hyun

2016-01-01

The goal of this study was to evaluate the imaging features of diabetic mastopathy (DMP) and the role of image-guided biopsy in its diagnosis. Two experienced radiologists retrospectively reviewed the mammographic and sonographic images of 19 pathologically confirmed DMP patients. The techniques and results of the biopsies performed in each patient were also reviewed. Mammograms showed negative findings in 78% of the patients. On ultrasonography (US), 13 lesions were seen as masses and six as non-mass lesions. The US features of the mass lesions were as follows: irregular shape (69%), oval shape (31%), indistinct margin (69%), angular margin (15%), microlobulated margin (8%), well-defined margin (8%), heterogeneous echogenicity (62%), hypoechoic echogenicity (38%), posterior shadowing (92%), parallel orientation (100%), the absence of calcifications (100%), and the absence of vascularity (100%). Based on the US findings, 17 lesions (89%) were classified as Breast Imaging Reporting and Data System category 4 and two (11%) as category 3. US-guided core biopsy was performed in 18 patients, and 10 (56%) were diagnosed with DMP on that basis. An additional vacuum-assisted biopsy was performed in seven patients and all were diagnosed with DMP. The US features of DMP were generally suspicious for malignancy, whereas the mammographic findings were often negative or showed only focal asymmetry. Core biopsy is an adequate method for initial pathological diagnosis. However, since it yields non-diagnostic results in a considerable number of cases, the evaluation of correlations between imaging and pathology plays an important role in the diagnostic process

TU-F-CAMPUS-J-05: Effect of Uncorrelated Noise Texture On Computed Tomography Quantitative Image Features

International Nuclear Information System (INIS)

Oliver, J; Budzevich, M; Moros, E; Zhang, G; Hunt, D

2015-01-01

Purpose: To investigate the relationship between quantitative image features (i.e. radiomics) and statistical fluctuations (i.e. electronic noise) in clinical Computed Tomography (CT) using the standardized American College of Radiology (ACR) CT accreditation phantom and patient images. Methods: Three levels of uncorrelated Gaussian noise were added to CT images of phantom and patients (20) acquired in static mode and respiratory tracking mode. We calculated the noise-power spectrum (NPS) of the original CT images of the phantom, and of the phantom images with added Gaussian noise with means of 50, 80, and 120 HU. Concurrently, on patient images (original and noise-added images), image features were calculated: 14 shape, 19 intensity (1st order statistics from intensity volume histograms), 18 GLCM features (2nd order statistics from grey level co-occurrence matrices) and 11 RLM features (2nd order statistics from run-length matrices). These features provide the underlying structural information of the images. GLCM (size 128x128) was calculated with a step size of 1 voxel in 13 directions and averaged. RLM feature calculation was performed in 13 directions with grey levels binning into 128 levels. Results: Adding the electronic noise to the images modified the quality of the NPS, shifting the noise from mostly correlated to mostly uncorrelated voxels. The dramatic increase in noise texture did not affect image structure/contours significantly for patient images. However, it did affect the image features and textures significantly as demonstrated by GLCM differences. Conclusion: Image features are sensitive to acquisition factors (simulated by adding uncorrelated Gaussian noise). We speculate that image features will be more difficult to detect in the presence of electronic noise (an uncorrelated noise contributor) or, for that matter, any other highly correlated image noise. This work focuses on the effect of electronic, uncorrelated, noise and future work shall

Spectroscopic and photoacoustic characterization of encapsulated iron oxide super-paramagnetic nanoparticles as a new multiplatform contrast agent

Science.gov (United States)

Armanetti, Paolo; Flori, Alessandra; Avigo, Cinzia; Conti, Luca; Valtancoli, Barbara; Petroni, Debora; Doumett, Saer; Cappiello, Laura; Ravagli, Costanza; Baldi, Giovanni; Bencini, Andrea; Menichetti, Luca

2018-06-01

Recently, a number of photoacoustic (PA) agents with increased tissue penetration and fine spatial resolution have been developed for molecular imaging and mapping of pathophysiological features at the molecular level. Here, we present bio-conjugated near-infrared light-absorbing magnetic nanoparticles as a new agent for PA imaging. These nanoparticles exhibit suitable absorption in the near-infrared region, with good photoacoustic signal generation efficiency and high photo-stability. Furthermore, these encapsulated iron oxide nanoparticles exhibit strong super-paramagnetic behavior and nuclear relaxivities that make them useful as magnetic resonance imaging (MRI) contrast media as well. Their simple bio-conjugation strategy, optical and chemical stability, and straightforward manipulation could enable the development of a PA probe with magnetic and spectroscopic properties suitable for in vitro and in vivo real-time imaging of relevant biological targets.

Feature Recognition of Froth Images Based on Energy Distribution Characteristics

Directory of Open Access Journals (Sweden)

WU Yanpeng

2014-09-01

Full Text Available This paper proposes a determining algorithm for froth image features based on the amplitude spectrum energy statistics by applying Fast Fourier Transformation to analyze the energy distribution of various-sized froth. The proposed algorithm has been used to do a froth feature analysis of the froth images from the alumina flotation processing site, and the results show that the consistency rate reaches 98.1 % and the usability rate 94.2 %; with its good robustness and high efficiency, the algorithm is quite suitable for flotation processing state recognition.

Solar Flares Observed with the Ramaty High Energy Solar Spectroscopic Imager (RHESSI)

Science.gov (United States)

Holman, Gordon D.

2004-01-01

Solar flares are impressive examples of explosive energy release in unconfined, magnetized plasma. It is generally believed that the flare energy is derived from the coronal magnetic field. However, we have not been able to establish the specific energy release mechanism(s) or the relative partitioning of the released energy between heating, particle acceleration (electrons and ions), and mass motions. NASA's RHESSI Mission was designed to study the acceleration and evolution of electrons and ions in flares by observing the X-ray and gamma-ray emissions these energetic particles produce. This is accomplished through the combination of high-resolution spectroscopy and spectroscopic imaging, including the first images of flares in gamma rays. RHESSI has observed over 12,000 solar flares since its launch on February 5, 2002. I will demonstrate how we use the RHESSI spectra to deduce physical properties of accelerated electrons and hot plasma in flares. Using images to estimate volumes, w e typically find that the total energy in accelerated electrons is comparable to that in the thermal plasma. I will also present flare observations that provide strong support for the presence of magnetic reconnection in a large-scale, vertical current sheet in the solar corona. RHESSI observations such as these are allowing us to probe more deeply into the physics of solar flares.

Edge enhancement and noise suppression for infrared image based on feature analysis

Science.gov (United States)

Jiang, Meng

2018-06-01

Infrared images are often suffering from background noise, blurred edges, few details and low signal-to-noise ratios. To improve infrared image quality, it is essential to suppress noise and enhance edges simultaneously. To realize it in this paper, we propose a novel algorithm based on feature analysis in shearlet domain. Firstly, as one of multi-scale geometric analysis (MGA), we introduce the theory and superiority of shearlet transform. Secondly, after analyzing the defects of traditional thresholding technique to suppress noise, we propose a novel feature extraction distinguishing image structures from noise well and use it to improve the traditional thresholding technique. Thirdly, with computing the correlations between neighboring shearlet coefficients, the feature attribute maps identifying the weak detail and strong edges are completed to improve the generalized unsharped masking (GUM). At last, experiment results with infrared images captured in different scenes demonstrate that the proposed algorithm suppresses noise efficiently and enhances image edges adaptively.

Feature selection from a facial image for distinction of sasang constitution.

Science.gov (United States)

Koo, Imhoi; Kim, Jong Yeol; Kim, Myoung Geun; Kim, Keun Ho

2009-09-01

Recently, oriental medicine has received attention for providing personalized medicine through consideration of the unique nature and constitution of individual patients. With the eventual goal of globalization, the current trend in oriental medicine research is the standardization by adopting western scientific methods, which could represent a scientific revolution. The purpose of this study is to establish methods for finding statistically significant features in a facial image with respect to distinguishing constitution and to show the meaning of those features. From facial photo images, facial elements are analyzed in terms of the distance, angle and the distance ratios, for which there are 1225, 61 250 and 749 700 features, respectively. Due to the very large number of facial features, it is quite difficult to determine truly meaningful features. We suggest a process for the efficient analysis of facial features including the removal of outliers, control for missing data to guarantee data confidence and calculation of statistical significance by applying ANOVA. We show the statistical properties of selected features according to different constitutions using the nine distances, 10 angles and 10 rates of distance features that are finally established. Additionally, the Sasang constitutional meaning of the selected features is shown here.

Image feature extraction based on the camouflage effectiveness evaluation

Science.gov (United States)

Yuan, Xin; Lv, Xuliang; Li, Ling; Wang, Xinzhu; Zhang, Zhi

2018-04-01

The key step of camouflage effectiveness evaluation is how to combine the human visual physiological features, psychological features to select effectively evaluation indexes. Based on the predecessors' camo comprehensive evaluation method, this paper chooses the suitable indexes combining with the image quality awareness, and optimizes those indexes combining with human subjective perception. Thus, it perfects the theory of index extraction.

Thin plate spline feature point matching for organ surfaces in minimally invasive surgery imaging

Science.gov (United States)

Lin, Bingxiong; Sun, Yu; Qian, Xiaoning

2013-03-01

Robust feature point matching for images with large view angle changes in Minimally Invasive Surgery (MIS) is a challenging task due to low texture and specular reflections in these images. This paper presents a new approach that can improve feature matching performance by exploiting the inherent geometric property of the organ surfaces. Recently, intensity based template image tracking using a Thin Plate Spline (TPS) model has been extended for 3D surface tracking with stereo cameras. The intensity based tracking is also used here for 3D reconstruction of internal organ surfaces. To overcome the small displacement requirement of intensity based tracking, feature point correspondences are used for proper initialization of the nonlinear optimization in the intensity based method. Second, we generate simulated images from the reconstructed 3D surfaces under all potential view positions and orientations, and then extract feature points from these simulated images. The obtained feature points are then filtered and re-projected to the common reference image. The descriptors of the feature points under different view angles are stored to ensure that the proposed method can tolerate a large range of view angles. We evaluate the proposed method with silicon phantoms and in vivo images. The experimental results show that our method is much more robust with respect to the view angle changes than other state-of-the-art methods.

Spectroscopic databases - A tool for structure elucidation

Energy Technology Data Exchange (ETDEWEB)

Luksch, P [Fachinformationszentrum Karlsruhe, Gesellschaft fuer Wissenschaftlich-Technische Information mbH, Eggenstein-Leopoldshafen (Germany)

1990-05-01

Spectroscopic databases have developed to useful tools in the process of structure elucidation. Besides the conventional library searches, new intelligent programs have been added, that are able to predict structural features from measured spectra or to simulate for a given structure. The example of the C13NMR/IR database developed at BASF and available on STN is used to illustrate the present capabilities of online database. New developments in the field of spectrum simulation and methods for the prediction of complete structures from spectroscopic information are reviewed. (author). 10 refs, 5 figs.

Skull base chordoid meningioma: Imaging features and pathology

International Nuclear Information System (INIS)

Soo, Mark Y.S.; Gomes, Lavier; Ng, Thomas; Cruz, Malville Da; Dexter, Mark

2004-01-01

The clinical, imaging and pathological features of a skull base chordoid meningioma (CM) are described. The huge tumour resulted in obstructive hydrocephalus and partial erosion of the clivus such that a chordoma was suspected. The lesion's MRI findings were similar to those of a meningioma. Light microscopic, immunohistochemistry and ultrastructural features were diagnostic of CM. Chordoid meningioma is a rare subtype of meningioma and has a great tendency to recur should surgical resection be incomplete Copyright (2004) Blackwell Publishing Asia Pty Ltd

Estimating perception of scene layout properties from global image features.

Science.gov (United States)

Ross, Michael G; Oliva, Aude

2010-01-08

The relationship between image features and scene structure is central to the study of human visual perception and computer vision, but many of the specifics of real-world layout perception remain unknown. We do not know which image features are relevant to perceiving layout properties, or whether those features provide the same information for every type of image. Furthermore, we do not know the spatial resolutions required for perceiving different properties. This paper describes an experiment and a computational model that provides new insights on these issues. Humans perceive the global spatial layout properties such as dominant depth, openness, and perspective, from a single image. This work describes an algorithm that reliably predicts human layout judgments. This model's predictions are general, not specific to the observers it trained on. Analysis reveals that the optimal spatial resolutions for determining layout vary with the content of the space and the property being estimated. Openness is best estimated at high resolution, depth is best estimated at medium resolution, and perspective is best estimated at low resolution. Given the reliability and simplicity of estimating the global layout of real-world environments, this model could help resolve perceptual ambiguities encountered by more detailed scene reconstruction schemas.

The hippocampus in patients treated with electroconvulsive therapy: a proton magnetic resonance spectroscopic imaging study.

Science.gov (United States)

Ende, G; Braus, D F; Walter, S; Weber-Fahr, W; Henn, F A

2000-10-01

We monitored the effect of electroconvulsive therapy (ECT) on the nuclear magnetic resonance-detectable metabolites N-acetylaspartate, creatine and phosphocreatine, and choline-containing compounds in the hippocampus by means of hydrogen 1 magnetic resonance spectroscopic imaging. We hypothesized that if ECT-induced memory deterioration was associated with neuronal loss in the hippocampus, the N-acetylaspartate signal would decrease after ECT and any increased membrane turnover would result in an increase in the signal from choline-containing compounds. Seventeen patients received complete courses of ECT, during which repeated proton magnetic resonance spectroscopic imaging studies of the hippocampal region were performed. Individual changes during the course of ECT were compared with values obtained in 24 healthy control subjects and 6 patients remitted from major depression without ECT. No changes in the hippocampal N-acetylaspartate signals were detected after ECT. A significant mean increase of 16% of the signal from choline-containing compounds after 5 or more ECT treatments was observed. Despite the mostly unilateral ECT application (14 of 17 patients), the increase in the choline-containing compound signal was observed bilaterally. Lactate or elevated lipid signals were not detected. All patients showed clinical amelioration of depression after ECT. Electroconvulsive therapy is not likely to induce hippocampal atrophy or cell death, which would be reflected by a decrease in the N-acetylaspartate signal. Compared with an age-matched control group, the choline-containing compounds signal in patients with a major depressive episode was significantly lower than normal, before ECT and normalized during ECT.

3-Dimensional Magnetic Resonance Spectroscopic Imaging at 3 Tesla for Early Response Assessment of Glioblastoma Patients During External Beam Radiation Therapy

International Nuclear Information System (INIS)

Muruganandham, Manickam; Clerkin, Patrick P.; Smith, Brian J.; Anderson, Carryn M.; Morris, Ann; Capizzano, Aristides A.; Magnotta, Vincent; McGuire, Sarah M.; Smith, Mark C.; Bayouth, John E.; Buatti, John M.

2014-01-01

Purpose: To evaluate the utility of 3-dimensional magnetic resonance (3D-MR) proton spectroscopic imaging for treatment planning and its implications for early response assessment in glioblastoma multiforme. Methods and Materials: Eighteen patients with newly diagnosed, histologically confirmed glioblastoma had 3D-MR proton spectroscopic imaging (MRSI) along with T2 and T1 gadolinium-enhanced MR images at simulation and at boost treatment planning after 17 to 20 fractions of radiation therapy. All patients received standard radiation therapy (RT) with concurrent temozolomide followed by adjuvant temozolomide. Imaging for response assessment consisted of MR scans every 2 months. Progression-free survival was defined by the criteria of MacDonald et al. MRSI images obtained at initial simulation were analyzed for choline/N-acetylaspartate ratios (Cho/NAA) on a voxel-by-voxel basis with abnormal activity defined as Cho/NAA ≥2. These images were compared on anatomically matched MRSI data collected after 3 weeks of RT. Changes in Cho/NAA between pretherapy and third-week RT scans were tested using Wilcoxon matched-pairs signed rank tests and correlated with progression-free survival, radiation dose and location of recurrence using Cox proportional hazards regression. Results: After a median follow-up time of 8.6 months, 50% of patients had experienced progression based on imaging. Patients with a decreased or stable mean or median Cho/NAA values had less risk of progression (P<.01). Patients with an increase in mean or median Cho/NAA values at the third-week RT scan had a significantly greater chance of early progression (P<.01). An increased Cho/NAA at the third-week MRSI scan carried a hazard ratio of 2.72 (95% confidence interval, 1.10-6.71; P=.03). Most patients received the prescription dose of RT to the Cho/NAA ≥2 volume, where recurrence most often occurred. Conclusion: Change in mean and median Cho/NAA detected at 3 weeks was a significant predictor of

3-Dimensional Magnetic Resonance Spectroscopic Imaging at 3 Tesla for Early Response Assessment of Glioblastoma Patients During External Beam Radiation Therapy

Energy Technology Data Exchange (ETDEWEB)

Muruganandham, Manickam; Clerkin, Patrick P. [Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa City, Iowa (United States); Smith, Brian J. [Department of Biostatistics, University of Iowa Hospitals and Clinics, Iowa City, Iowa (United States); Anderson, Carryn M.; Morris, Ann [Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa City, Iowa (United States); Capizzano, Aristides A.; Magnotta, Vincent [Department of Radiology, University of Iowa Hospitals and Clinics, Iowa City, Iowa (United States); McGuire, Sarah M.; Smith, Mark C.; Bayouth, John E. [Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa City, Iowa (United States); Buatti, John M., E-mail: john-buatti@uiowa.edu [Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa City, Iowa (United States)

2014-09-01

Purpose: To evaluate the utility of 3-dimensional magnetic resonance (3D-MR) proton spectroscopic imaging for treatment planning and its implications for early response assessment in glioblastoma multiforme. Methods and Materials: Eighteen patients with newly diagnosed, histologically confirmed glioblastoma had 3D-MR proton spectroscopic imaging (MRSI) along with T2 and T1 gadolinium-enhanced MR images at simulation and at boost treatment planning after 17 to 20 fractions of radiation therapy. All patients received standard radiation therapy (RT) with concurrent temozolomide followed by adjuvant temozolomide. Imaging for response assessment consisted of MR scans every 2 months. Progression-free survival was defined by the criteria of MacDonald et al. MRSI images obtained at initial simulation were analyzed for choline/N-acetylaspartate ratios (Cho/NAA) on a voxel-by-voxel basis with abnormal activity defined as Cho/NAA ≥2. These images were compared on anatomically matched MRSI data collected after 3 weeks of RT. Changes in Cho/NAA between pretherapy and third-week RT scans were tested using Wilcoxon matched-pairs signed rank tests and correlated with progression-free survival, radiation dose and location of recurrence using Cox proportional hazards regression. Results: After a median follow-up time of 8.6 months, 50% of patients had experienced progression based on imaging. Patients with a decreased or stable mean or median Cho/NAA values had less risk of progression (P<.01). Patients with an increase in mean or median Cho/NAA values at the third-week RT scan had a significantly greater chance of early progression (P<.01). An increased Cho/NAA at the third-week MRSI scan carried a hazard ratio of 2.72 (95% confidence interval, 1.10-6.71; P=.03). Most patients received the prescription dose of RT to the Cho/NAA ≥2 volume, where recurrence most often occurred. Conclusion: Change in mean and median Cho/NAA detected at 3 weeks was a significant predictor of

Profiles of US and CT imaging features with a high probability of appendicitis

International Nuclear Information System (INIS)

Randen, A. van; Lameris, W.; Es, H.W. van; Hove, W. ten; Bouma, W.H.; Leeuwen, M.S. van; Keulen, E.M. van; Hulst, V.P.M. van der; Henneman, O.D.; Bossuyt, P.M.; Boermeester, M.A.; Stoker, J.

2010-01-01

To identify and evaluate profiles of US and CT features associated with acute appendicitis. Consecutive patients presenting with acute abdominal pain at the emergency department were invited to participate in this study. All patients underwent US and CT. Imaging features known to be associated with appendicitis, and an imaging diagnosis were prospectively recorded by two independent radiologists. A final diagnosis was assigned after 6 months. Associations between appendiceal imaging features and a final diagnosis of appendicitis were evaluated with logistic regression analysis. Appendicitis was assigned to 284 of 942 evaluated patients (30%). All evaluated features were associated with appendicitis. Imaging profiles were created after multivariable logistic regression analysis. Of 147 patients with a thickened appendix, local transducer tenderness and peri-appendiceal fat infiltration on US, 139 (95%) had appendicitis. On CT, 119 patients in whom the appendix was completely visualised, thickened, with peri-appendiceal fat infiltration and appendiceal enhancement, 114 had a final diagnosis of appendicitis (96%). When at least two of these essential features were present on US or CT, sensitivity was 92% (95% CI 89-96%) and 96% (95% CI 93-98%), respectively. Most patients with appendicitis can be categorised within a few imaging profiles on US and CT. When two of the essential features are present the diagnosis of appendicitis can be made accurately. (orig.)

Profiles of US and CT imaging features with a high probability of appendicitis

Energy Technology Data Exchange (ETDEWEB)

Randen, A. van; Lameris, W. [University of Amsterdam, Department of Radiology, Academic Medical Center, Amsterdam (Netherlands); University of Amsterdam, Department of Surgery, Academic Medical Center, Amsterdam (Netherlands); Es, H.W. van [St Antonius Hospital, Department of Radiology, Nieuwegein (Netherlands); Hove, W. ten; Bouma, W.H. [Gelre Hospitals, Department of Surgery, Apeldoorn (Netherlands); Leeuwen, M.S. van [University Medical Centre, Department of Radiology, Utrecht (Netherlands); Keulen, E.M. van [Tergooi Hospitals, Department of Radiology, Hilversum (Netherlands); Hulst, V.P.M. van der [Onze Lieve Vrouwe Gasthuis, Department of Radiology, Amsterdam (Netherlands); Henneman, O.D. [Bronovo Hospital, Department of Radiology, The Hague (Netherlands); Bossuyt, P.M. [University of Amsterdam, Department of Clinical Epidemiology, Biostatistics, and Bioinformatics, Academic Medical Center, Amsterdam (Netherlands); Boermeester, M.A. [University of Amsterdam, Department of Surgery, Academic Medical Center, Amsterdam (Netherlands); Stoker, J. [University of Amsterdam, Department of Radiology, Academic Medical Center, Amsterdam (Netherlands)

2010-07-15

To identify and evaluate profiles of US and CT features associated with acute appendicitis. Consecutive patients presenting with acute abdominal pain at the emergency department were invited to participate in this study. All patients underwent US and CT. Imaging features known to be associated with appendicitis, and an imaging diagnosis were prospectively recorded by two independent radiologists. A final diagnosis was assigned after 6 months. Associations between appendiceal imaging features and a final diagnosis of appendicitis were evaluated with logistic regression analysis. Appendicitis was assigned to 284 of 942 evaluated patients (30%). All evaluated features were associated with appendicitis. Imaging profiles were created after multivariable logistic regression analysis. Of 147 patients with a thickened appendix, local transducer tenderness and peri-appendiceal fat infiltration on US, 139 (95%) had appendicitis. On CT, 119 patients in whom the appendix was completely visualised, thickened, with peri-appendiceal fat infiltration and appendiceal enhancement, 114 had a final diagnosis of appendicitis (96%). When at least two of these essential features were present on US or CT, sensitivity was 92% (95% CI 89-96%) and 96% (95% CI 93-98%), respectively. Most patients with appendicitis can be categorised within a few imaging profiles on US and CT. When two of the essential features are present the diagnosis of appendicitis can be made accurately. (orig.)

Volumetric Spectroscopic Imaging of Glioblastoma Multiforme Radiation Treatment Volumes

Energy Technology Data Exchange (ETDEWEB)

Parra, N. Andres [Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, Florida (United States); Maudsley, Andrew A. [Department of Radiology, University of Miami Miller School of Medicine, Miami, Florida (United States); Gupta, Rakesh K. [Department of Radiology and Imaging, Fortis Memorial Research Institute, Gurgaon, Haryana (India); Ishkanian, Fazilat; Huang, Kris [Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, Florida (United States); Walker, Gail R. [Biostatistics and Bioinformatics Core Resource, Sylvester Cancer Center, University of Miami Miller School of Medicine, Miami, Florida (United States); Padgett, Kyle [Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, Florida (United States); Department of Radiology, University of Miami Miller School of Medicine, Miami, Florida (United States); Roy, Bhaswati [Department of Radiology and Imaging, Fortis Memorial Research Institute, Gurgaon, Haryana (India); Panoff, Joseph; Markoe, Arnold [Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, Florida (United States); Stoyanova, Radka, E-mail: RStoyanova@med.miami.edu [Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, Florida (United States)

2014-10-01

Purpose: Magnetic resonance (MR) imaging and computed tomography (CT) are used almost exclusively in radiation therapy planning of glioblastoma multiforme (GBM), despite their well-recognized limitations. MR spectroscopic imaging (MRSI) can identify biochemical patterns associated with normal brain and tumor, predominantly by observation of choline (Cho) and N-acetylaspartate (NAA) distributions. In this study, volumetric 3-dimensional MRSI was used to map these compounds over a wide region of the brain and to evaluate metabolite-defined treatment targets (metabolic tumor volumes [MTV]). Methods and Materials: Volumetric MRSI with effective voxel size of ∼1.0 mL and standard clinical MR images were obtained from 19 GBM patients. Gross tumor volumes and edema were manually outlined, and clinical target volumes (CTVs) receiving 46 and 60 Gy were defined (CTV{sub 46} and CTV{sub 60}, respectively). MTV{sub Cho} and MTV{sub NAA} were constructed based on volumes with high Cho and low NAA relative to values estimated from normal-appearing tissue. Results: The MRSI coverage of the brain was between 70% and 76%. The MTV{sub NAA} were almost entirely contained within the edema, and the correlation between the 2 volumes was significant (r=0.68, P=.001). In contrast, a considerable fraction of MTV{sub Cho} was outside of the edema (median, 33%) and for some patients it was also outside of the CTV{sub 46} and CTV{sub 60}. These untreated volumes were greater than 10% for 7 patients (37%) in the study, and on average more than one-third (34.3%) of the MTV{sub Cho} for these patients were outside of CTV{sub 60}. Conclusions: This study demonstrates the potential usefulness of whole-brain MRSI for radiation therapy planning of GBM and revealed that areas of metabolically active tumor are not covered by standard RT volumes. The described integration of MTV into the RT system will pave the way to future clinical trials investigating outcomes in patients treated based on

Multimodal Image Alignment via Linear Mapping between Feature Modalities.

Science.gov (United States)

Jiang, Yanyun; Zheng, Yuanjie; Hou, Sujuan; Chang, Yuchou; Gee, James

2017-01-01

We propose a novel landmark matching based method for aligning multimodal images, which is accomplished uniquely by resolving a linear mapping between different feature modalities. This linear mapping results in a new measurement on similarity of images captured from different modalities. In addition, our method simultaneously solves this linear mapping and the landmark correspondences by minimizing a convex quadratic function. Our method can estimate complex image relationship between different modalities and nonlinear nonrigid spatial transformations even in the presence of heavy noise, as shown in our experiments carried out by using a variety of image modalities.

Hyperspectral Image Classification Based on the Combination of Spatial-spectral Feature and Sparse Representation

Directory of Open Access Journals (Sweden)

YANG Zhaoxia

2015-07-01

Full Text Available In order to avoid the problem of being over-dependent on high-dimensional spectral feature in the traditional hyperspectral image classification, a novel approach based on the combination of spatial-spectral feature and sparse representation is proposed in this paper. Firstly, we extract the spatial-spectral feature by reorganizing the local image patch with the first d principal components(PCs into a vector representation, followed by a sorting scheme to make the vector invariant to local image rotation. Secondly, we learn the dictionary through a supervised method, and use it to code the features from test samples afterwards. Finally, we embed the resulting sparse feature coding into the support vector machine(SVM for hyperspectral image classification. Experiments using three hyperspectral data show that the proposed method can effectively improve the classification accuracy comparing with traditional classification methods.

Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models

International Nuclear Information System (INIS)

Khalvati, Farzad; Wong, Alexander; Haider, Masoom A.

2015-01-01

Prostate cancer is the most common form of cancer and the second leading cause of cancer death in North America. Auto-detection of prostate cancer can play a major role in early detection of prostate cancer, which has a significant impact on patient survival rates. While multi-parametric magnetic resonance imaging (MP-MRI) has shown promise in diagnosis of prostate cancer, the existing auto-detection algorithms do not take advantage of abundance of data available in MP-MRI to improve detection accuracy. The goal of this research was to design a radiomics-based auto-detection method for prostate cancer via utilizing MP-MRI data. In this work, we present new MP-MRI texture feature models for radiomics-driven detection of prostate cancer. In addition to commonly used non-invasive imaging sequences in conventional MP-MRI, namely T2-weighted MRI (T2w) and diffusion-weighted imaging (DWI), our proposed MP-MRI texture feature models incorporate computed high-b DWI (CHB-DWI) and a new diffusion imaging modality called correlated diffusion imaging (CDI). Moreover, the proposed texture feature models incorporate features from individual b-value images. A comprehensive set of texture features was calculated for both the conventional MP-MRI and new MP-MRI texture feature models. We performed feature selection analysis for each individual modality and then combined best features from each modality to construct the optimized texture feature models. The performance of the proposed MP-MRI texture feature models was evaluated via leave-one-patient-out cross-validation using a support vector machine (SVM) classifier trained on 40,975 cancerous and healthy tissue samples obtained from real clinical MP-MRI datasets. The proposed MP-MRI texture feature models outperformed the conventional model (i.e., T2w+DWI) with regard to cancer detection accuracy. Comprehensive texture feature models were developed for improved radiomics-driven detection of prostate cancer using MP-MRI. Using a

High-definition Fourier Transform Infrared (FT-IR) Spectroscopic Imaging of Human Tissue Sections towards Improving Pathology

Science.gov (United States)

Nguyen, Peter L.; Davidson, Bennett; Akkina, Sanjeev; Guzman, Grace; Setty, Suman; Kajdacsy-Balla, Andre; Walsh, Michael J.

2015-01-01

High-definition Fourier Transform Infrared (FT-IR) spectroscopic imaging is an emerging approach to obtain detailed images that have associated biochemical information. FT-IR imaging of tissue is based on the principle that different regions of the mid-infrared are absorbed by different chemical bonds (e.g., C=O, C-H, N-H) within cells or tissue that can then be related to the presence and composition of biomolecules (e.g., lipids, DNA, glycogen, protein, collagen). In an FT-IR image, every pixel within the image comprises an entire Infrared (IR) spectrum that can give information on the biochemical status of the cells that can then be exploited for cell-type or disease-type classification. In this paper, we show: how to obtain IR images from human tissues using an FT-IR system, how to modify existing instrumentation to allow for high-definition imaging capabilities, and how to visualize FT-IR images. We then present some applications of FT-IR for pathology using the liver and kidney as examples. FT-IR imaging holds exciting applications in providing a novel route to obtain biochemical information from cells and tissue in an entirely label-free non-perturbing route towards giving new insight into biomolecular changes as part of disease processes. Additionally, this biochemical information can potentially allow for objective and automated analysis of certain aspects of disease diagnosis. PMID:25650759

Feature Tracking for High Speed AFM Imaging of Biopolymers.

Science.gov (United States)

Hartman, Brett; Andersson, Sean B

2018-03-31

The scanning speed of atomic force microscopes continues to advance with some current commercial microscopes achieving on the order of one frame per second and at least one reaching 10 frames per second. Despite the success of these instruments, even higher frame rates are needed with scan ranges larger than are currently achievable. Moreover, there is a significant installed base of slower instruments that would benefit from algorithmic approaches to increasing their frame rate without requiring significant hardware modifications. In this paper, we present an experimental demonstration of high speed scanning on an existing, non-high speed instrument, through the use of a feedback-based, feature-tracking algorithm that reduces imaging time by focusing on features of interest to reduce the total imaging area. Experiments on both circular and square gratings, as well as silicon steps and DNA strands show a reduction in imaging time by a factor of 3-12 over raster scanning, depending on the parameters chosen.

Constraining reconnection region conditions using imaging and spectroscopic analysis of a coronal jet

Science.gov (United States)

Brannon, Sean; Kankelborg, Charles

2017-08-01

Coronal jets typically appear as thin, collimated structures in EUV and X-ray wavelengths, and are understood to be initiated by magnetic reconnection in the lower corona or upper chromosphere. Plasma that is heated and accelerated upward into coronal jets may therefore carry indirect information on conditions in the reconnection region and current sheet located at the jet base. On 2017 October 14, the Interface Region Imaging Spectrograph (IRIS) and Solar Dynamics Observatory Atmospheric Imaging Assembly (SDO/AIA) observed a series of jet eruptions originating from NOAA AR 12599. The jet structure has a length-to-width ratio that exceeds 50, and remains remarkably straight throughout its evolution. Several times during the observation bright blobs of plasma are seen to erupt upward, ascending and subsequently descending along the structure. These blobs are cotemporal with footpoint and arcade brightenings, which we believe indicates multiple episodes of reconnection at the structure base. Through imaging and spectroscopic analysis of jet and footpoint plasma we determine a number of properties, including the line-of-sight inclination, the temperature and density structure, and lift-off velocities and accelerations of jet eruptions. We use these properties to constrain the geometry of the jet structure and conditions in reconnection region.

Features and limitations of mobile tablet devices for viewing radiological images.

Science.gov (United States)

Grunert, J H

2015-03-01

Mobile radiological image display systems are becoming increasingly common, necessitating a comparison of the features of these systems, specifically the operating system employed, connection to stationary PACS, data security and rang of image display and image analysis functions. In the fall of 2013, a total of 17 PACS suppliers were surveyed regarding the technical features of 18 mobile radiological image display systems using a standardized questionnaire. The study also examined to what extent the technical specifications of the mobile image display systems satisfy the provisions of the Germany Medical Devices Act as well as the provisions of the German X-ray ordinance (RöV). There are clear differences in terms of how the mobile systems connected to the stationary PACS. Web-based solutions allow the mobile image display systems to function independently of their operating systems. The examined systems differed very little in terms of image display and image analysis functions. Mobile image display systems complement stationary PACS and can be used to view images. The impacts of the new quality assurance guidelines (QS-RL) as well as the upcoming new standard DIN 6868 - 157 on the acceptance testing of mobile image display units for the purpose of image evaluation are discussed. © Georg Thieme Verlag KG Stuttgart · New York.

1H-MR-spectroscopic imaging in patients with Alzheimer's disease

International Nuclear Information System (INIS)

Block, W.; Traeber, F.; Kuhl, C.K.; Fric, M.; Keller, E.; Lamerichs, R.; Rink, H.; Moeller, H.J.; Schild, H.H.

1995-01-01

To detect regional differences in accompanying metabolic changes, 1 H-Magnetic Resonance Spectroscopic Imaging was performed in 16 patients with Alzheimer's disease (AD); the clinical diagnosis was based upon DSM-III-R and NINCDS-ADRDA guidelines. In the hippocampal region metabolic maps of the local distribution of N-acetylaspartate (NAA), choline (Cho), creatine compounds (P(Cr)) and lactate were determined. Ratios of Cho/NAA, (P)Cr/NAA and Cho/(P)Cr calculated from selected hippocampal spectra were compared to those from healthy volunteers (n=17). AD patients demonstrated an increase of Cho/NAA and (P)Cr/NAA ratios caused by increased choline compounds and decreased NAA. These alterations were observed in 11/12 cases in the hippocampal and in 7/12 in the temporo-occipital region. Hippocampal Cho/NAA ratios (0.56±0.19) were significantly elevated compared with controls. The observed elevation of choline compounds in the hippocampus supports the hypothesis that alterations in the cholinergic system play an important role in Alzheimer's disease. The observed reduction of NAA is due to neuronal degeneration. (orig./MG) [de

Image features dependant correlation-weighting function for efficient PRNU based source camera identification.

Science.gov (United States)

Tiwari, Mayank; Gupta, Bhupendra

2018-04-01

For source camera identification (SCI), photo response non-uniformity (PRNU) has been widely used as the fingerprint of the camera. The PRNU is extracted from the image by applying a de-noising filter then taking the difference between the original image and the de-noised image. However, it is observed that intensity-based features and high-frequency details (edges and texture) of the image, effect quality of the extracted PRNU. This effects correlation calculation and creates problems in SCI. For solving this problem, we propose a weighting function based on image features. We have experimentally identified image features (intensity and high-frequency contents) effect on the estimated PRNU, and then develop a weighting function which gives higher weights to image regions which give reliable PRNU and at the same point it gives comparatively less weights to the image regions which do not give reliable PRNU. Experimental results show that the proposed weighting function is able to improve the accuracy of SCI up to a great extent. Copyright © 2018 Elsevier B.V. All rights reserved.

Robust and efficient method for matching features in omnidirectional images

Science.gov (United States)

Zhu, Qinyi; Zhang, Zhijiang; Zeng, Dan

2018-04-01

Binary descriptors have been widely used in many real-time applications due to their efficiency. These descriptors are commonly designed for perspective images but perform poorly on omnidirectional images, which are severely distorted. To address this issue, this paper proposes tangent plane BRIEF (TPBRIEF) and adapted log polar grid-based motion statistics (ALPGMS). TPBRIEF projects keypoints to a unit sphere and applies the fixed test set in BRIEF descriptor on the tangent plane of the unit sphere. The fixed test set is then backprojected onto the original distorted images to construct the distortion invariant descriptor. TPBRIEF directly enables keypoint detecting and feature describing on original distorted images, whereas other approaches correct the distortion through image resampling, which introduces artifacts and adds time cost. With ALPGMS, omnidirectional images are divided into circular arches named adapted log polar grids. Whether a match is true or false is then determined by simply thresholding the match numbers in a grid pair where the two matched points located. Experiments show that TPBRIEF greatly improves the feature matching accuracy and ALPGMS robustly removes wrong matches. Our proposed method outperforms the state-of-the-art methods.

MR imaging features of hemispherical spondylosclerosis

Energy Technology Data Exchange (ETDEWEB)

Vicentini, Joao R.T.; Martinez-Salazar, Edgar L.; Chang, Connie Y.; Bredella, Miriam A.; Rosenthal, Daniel I.; Torriani, Martin [Massachusetts General Hospital and Harvard Medical School, Division of Musculoskeletal Imaging and Intervention, Department of Radiology, Boston, MA (United States)

2017-10-15

Hemispherical spondylosclerosis (HS) is a rare degenerative entity characterized by dome-shaped sclerosis of a single vertebral body that may pose a diagnostic dilemma. The goal of this study was to describe the MR imaging features of HS. We identified spine radiographs and CT examinations of subjects with HS who also had MR imaging for correlation. Two musculoskeletal radiologists independently assessed sclerosis characteristics, presence of endplate erosions, marrow signal intensity, and disk degeneration (Pfirrmann scale). We identified 11 subjects (six males, five females, mean 48 ± 10 years) with radiographic/CT findings of HS. The most commonly affected vertebral body was L4 (6/11; 55%). On MR imaging, variable signal intensity was noted, being most commonly low on T1 (8/11, 73%) and high on fat-suppressed T2-weighted (8/11, 73%) images. In two subjects, diffuse post-contrast enhancement was seen in the lesion. Moderate disk degeneration and endplate bone erosions adjacent to sclerosis were present in all subjects. Erosions of the opposite endplate were present in two subjects (2/11, 18%). CT data from nine subjects showed the mean attenuation value of HS was 472 ± 96 HU. HS appearance on MR imaging is variable and may not correlate with the degree of sclerosis seen on radiographs or CT. Disk degenerative changes and asymmetric endplate erosions are consistent markers of HS. (orig.)

Feature Detection of Curve Traffic Sign Image on The Bandung - Jakarta Highway

Science.gov (United States)

Naseer, M.; Supriadi, I.; Supangkat, S. H.

2018-03-01

Unsealed roadside and problems with the road surface are common causes of road crashes, particularly when those are combined with curves. Curve traffic sign is an important component for giving early warning to driver on traffic, especially on high-speed traffic like on the highway. Traffic sign detection has became a very interesting research now, and in this paper will be discussed about the detection of curve traffic sign. There are two types of curve signs are discussed, namely the curve turn to the left and the curve turn to the right and the all data sample used are the curves taken / recorded from some signs on the Bandung - Jakarta Highway. Feature detection of the curve signs use Speed Up Robust Feature (SURF) method, where the detected scene image is 800x450. From 45 curve turn to the right images, the system can detect the feature well to 35 images, where the success rate is 77,78%, while from the 45 curve turn to the left images, the system can detect the feature well to 34 images and the success rate is 75,56%, so the average accuracy in the detection process is 76,67%. While the average time for the detection process is 0.411 seconds.

The Feature Extraction Based on Texture Image Information for Emotion Sensing in Speech

Directory of Open Access Journals (Sweden)

Kun-Ching Wang

2014-09-01

Full Text Available In this paper, we present a novel texture image feature for Emotion Sensing in Speech (ESS. This idea is based on the fact that the texture images carry emotion-related information. The feature extraction is derived from time-frequency representation of spectrogram images. First, we transform the spectrogram as a recognizable image. Next, we use a cubic curve to enhance the image contrast. Then, the texture image information (TII derived from the spectrogram image can be extracted by using Laws’ masks to characterize emotional state. In order to evaluate the effectiveness of the proposed emotion recognition in different languages, we use two open emotional databases including the Berlin Emotional Speech Database (EMO-DB and eNTERFACE corpus and one self-recorded database (KHUSC-EmoDB, to evaluate the performance cross-corpora. The results of the proposed ESS system are presented using support vector machine (SVM as a classifier. Experimental results show that the proposed TII-based feature extraction inspired by visual perception can provide significant classification for ESS systems. The two-dimensional (2-D TII feature can provide the discrimination between different emotions in visual expressions except for the conveyance pitch and formant tracks. In addition, the de-noising in 2-D images can be more easily completed than de-noising in 1-D speech.

Prior-knowledge Fitting of Accelerated Five-dimensional Echo Planar J-resolved Spectroscopic Imaging: Effect of Nonlinear Reconstruction on Quantitation.

Science.gov (United States)

Iqbal, Zohaib; Wilson, Neil E; Thomas, M Albert

2017-07-24

1 H Magnetic Resonance Spectroscopic imaging (SI) is a powerful tool capable of investigating metabolism in vivo from mul- tiple regions. However, SI techniques are time consuming, and are therefore difficult to implement clinically. By applying non-uniform sampling (NUS) and compressed sensing (CS) reconstruction, it is possible to accelerate these scans while re- taining key spectral information. One recently developed method that utilizes this type of acceleration is the five-dimensional echo planar J-resolved spectroscopic imaging (5D EP-JRESI) sequence, which is capable of obtaining two-dimensional (2D) spectra from three spatial dimensions. The prior-knowledge fitting (ProFit) algorithm is typically used to quantify 2D spectra in vivo, however the effects of NUS and CS reconstruction on the quantitation results are unknown. This study utilized a simulated brain phantom to investigate the errors introduced through the acceleration methods. Errors (normalized root mean square error >15%) were found between metabolite concentrations after twelve-fold acceleration for several low concentra- tion (OGM) human brain matter were quantified in vivo using the 5D EP-JRESI sequence with eight-fold acceleration.

Significance of the impact of motion compensation on the variability of PET image features

Science.gov (United States)

Carles, M.; Bach, T.; Torres-Espallardo, I.; Baltas, D.; Nestle, U.; Martí-Bonmatí, L.

2018-03-01

In lung cancer, quantification by positron emission tomography/computed tomography (PET/CT) imaging presents challenges due to respiratory movement. Our primary aim was to study the impact of motion compensation implied by retrospectively gated (4D)-PET/CT on the variability of PET quantitative parameters. Its significance was evaluated by comparison with the variability due to (i) the voxel size in image reconstruction and (ii) the voxel size in image post-resampling. The method employed for feature extraction was chosen based on the analysis of (i) the effect of discretization of the standardized uptake value (SUV) on complementarity between texture features (TF) and conventional indices, (ii) the impact of the segmentation method on the variability of image features, and (iii) the variability of image features across the time-frame of 4D-PET. Thirty-one PET-features were involved. Three SUV discretization methods were applied: a constant width (SUV resolution) of the resampling bin (method RW), a constant number of bins (method RN) and RN on the image obtained after histogram equalization (method EqRN). The segmentation approaches evaluated were 40% of SUVmax and the contrast oriented algorithm (COA). Parameters derived from 4D-PET images were compared with values derived from the PET image obtained for (i) the static protocol used in our clinical routine (3D) and (ii) the 3D image post-resampled to the voxel size of the 4D image and PET image derived after modifying the reconstruction of the 3D image to comprise the voxel size of the 4D image. Results showed that TF complementarity with conventional indices was sensitive to the SUV discretization method. In the comparison of COA and 40% contours, despite the values not being interchangeable, all image features showed strong linear correlations (r  >  0.91, p\\ll 0.001 ). Across the time-frames of 4D-PET, all image features followed a normal distribution in most patients. For our patient cohort, the

Comparison on imaging features of central serous chorioretinopathy fundus

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Ji-Jin Zhang

2014-10-01

Full Text Available AIM: To explore and analyze the image features, diagnosis and treatment of the central serous chorioretinopathy(CSCRfundus. METHODS: From May 2008 to May 2014, 97 cases of 121 eyes with central serous chorioretinopathy were treated in in our hospital. The imaging features were compared and analyzed through different methods. RESULTS: Sixty-one cases(61 eyeswere ≤45 years, including 13 case with disease in both eyes, single stove leak accounted for 48.6%, multifocal leakage(25.7%, atypical leakage accounted for 25.7%. Thirty-six cases(47 eyeswere >45 years, 11 cases with disease in both eyes, single focal leakage(8.5%, multifocal leakage(48.9%, atypical leakage accounted for 42.6%. FFA results showed acute hairstyle at the beginning of 89 eyes, chronic deferment type 32 eyes. OCT examination showed that the main features were neuroepithelial detachment, as well as the change of the retinal pigment epithelium(RPElayer, which was divided into RPE layer detachment 93 eyes, accounting for 76.9%, rough and RPE little ridges in 28 cases, accounting for 23.1%. The average thickness of macular center concave on the cortex of microns was 137.87±19.21μm, and there was no significant difference conpared with normal(137.32±4.98μmmicrons(t=0.30, P>0.05. The closer leakage area to macular fovea, the worse of eyesight.. CONCLUSION: Different imaging examination on central serous chorioretinopathy can show different features. For clinical diagnosis and treatment it had different and complementary roles, but were given significant help for diseases treatment.

Feature Extraction and Classification on Esophageal X-Ray Images of Xinjiang Kazak Nationality

Directory of Open Access Journals (Sweden)

Fang Yang

2017-01-01

Full Text Available Esophageal cancer is one of the fastest rising types of cancers in China. The Kazak nationality is the highest-risk group in Xinjiang. In this work, an effective computer-aided diagnostic system is developed to assist physicians in interpreting digital X-ray image features and improving the quality of diagnosis. The modules of the proposed system include image preprocessing, feature extraction, feature selection, image classification, and performance evaluation. 300 original esophageal X-ray images were resized to a region of interest and then enhanced by the median filter and histogram equalization method. 37 features from textural, frequency, and complexity domains were extracted. Both sequential forward selection and principal component analysis methods were employed to select the discriminative features for classification. Then, support vector machine and K-nearest neighbors were applied to classify the esophageal cancer images with respect to their specific types. The classification performance was evaluated in terms of the area under the receiver operating characteristic curve, accuracy, precision, and recall, respectively. Experimental results show that the classification performance of the proposed system outperforms the conventional visual inspection approaches in terms of diagnostic quality and processing time. Therefore, the proposed computer-aided diagnostic system is promising for the diagnostics of esophageal cancer.

SPECTROSCOPIC ANALYSIS OF AN EIT WAVE/DIMMING OBSERVED BY HINODE/EIS

International Nuclear Information System (INIS)

Chen, F.; Ding, M. D.; Chen, P. F.

2010-01-01

EUV Imaging Telescope (EIT) waves are a wavelike phenomenon propagating outward from the coronal mass ejection source region, with expanding dimmings following behind. We present a spectroscopic study of an EIT wave/dimming event observed by the Hinode/Extreme-ultraviolet Imaging Spectrometer. Although the identification of the wave front is somewhat affected by the pre-existing loop structures, the expanding dimming is well defined. We investigate the line intensity, width, and Doppler velocity for four EUV lines. In addition to the significant blueshift implying plasma outflows in the dimming region as revealed in previous studies, we find that the widths of all four spectral lines increase at the outer edge of the dimmings. We illustrate that this feature can be well explained by the field line stretching model, which claims that EIT waves are apparently moving brightenings that are generated by the successive stretching of the closed field lines.

Medical Image Fusion Algorithm Based on Nonlinear Approximation of Contourlet Transform and Regional Features

Directory of Open Access Journals (Sweden)

Hui Huang

2017-01-01

Full Text Available According to the pros and cons of contourlet transform and multimodality medical imaging, here we propose a novel image fusion algorithm that combines nonlinear approximation of contourlet transform with image regional features. The most important coefficient bands of the contourlet sparse matrix are retained by nonlinear approximation. Low-frequency and high-frequency regional features are also elaborated to fuse medical images. The results strongly suggested that the proposed algorithm could improve the visual effects of medical image fusion and image quality, image denoising, and enhancement.

Feature Selection from a Facial Image for Distinction of Sasang Constitution

Directory of Open Access Journals (Sweden)

Imhoi Koo

2009-01-01

Full Text Available Recently, oriental medicine has received attention for providing personalized medicine through consideration of the unique nature and constitution of individual patients. With the eventual goal of globalization, the current trend in oriental medicine research is the standardization by adopting western scientific methods, which could represent a scientific revolution. The purpose of this study is to establish methods for finding statistically significant features in a facial image with respect to distinguishing constitution and to show the meaning of those features. From facial photo images, facial elements are analyzed in terms of the distance, angle and the distance ratios, for which there are 1225, 61 250 and 749 700 features, respectively. Due to the very large number of facial features, it is quite difficult to determine truly meaningful features. We suggest a process for the efficient analysis of facial features including the removal of outliers, control for missing data to guarantee data confidence and calculation of statistical significance by applying ANOVA. We show the statistical properties of selected features according to different constitutions using the nine distances, 10 angles and 10 rates of distance features that are finally established. Additionally, the Sasang constitutional meaning of the selected features is shown here.

Feature Selection from a Facial Image for Distinction of Sasang Constitution

Science.gov (United States)

Koo, Imhoi; Kim, Jong Yeol; Kim, Myoung Geun

2009-01-01

Recently, oriental medicine has received attention for providing personalized medicine through consideration of the unique nature and constitution of individual patients. With the eventual goal of globalization, the current trend in oriental medicine research is the standardization by adopting western scientific methods, which could represent a scientific revolution. The purpose of this study is to establish methods for finding statistically significant features in a facial image with respect to distinguishing constitution and to show the meaning of those features. From facial photo images, facial elements are analyzed in terms of the distance, angle and the distance ratios, for which there are 1225, 61 250 and 749 700 features, respectively. Due to the very large number of facial features, it is quite difficult to determine truly meaningful features. We suggest a process for the efficient analysis of facial features including the removal of outliers, control for missing data to guarantee data confidence and calculation of statistical significance by applying ANOVA. We show the statistical properties of selected features according to different constitutions using the nine distances, 10 angles and 10 rates of distance features that are finally established. Additionally, the Sasang constitutional meaning of the selected features is shown here. PMID:19745013

Fast hybrid fractal image compression using an image feature and neural network

International Nuclear Information System (INIS)

Zhou Yiming; Zhang Chao; Zhang Zengke

2008-01-01

Since fractal image compression could maintain high-resolution reconstructed images at very high compression ratio, it has great potential to improve the efficiency of image storage and image transmission. On the other hand, fractal image encoding is time consuming for the best matching search between range blocks and domain blocks, which limits the algorithm to practical application greatly. In order to solve this problem, two strategies are adopted to improve the fractal image encoding algorithm in this paper. Firstly, based on the definition of an image feature, a necessary condition of the best matching search and FFC algorithm are proposed, and it could reduce the search space observably and exclude most inappropriate domain blocks according to each range block before the best matching search. Secondly, on the basis of FFC algorithm, in order to reduce the mapping error during the best matching search, a special neural network is constructed to modify the mapping scheme for the subblocks, in which the pixel values fluctuate greatly (FNFC algorithm). Experimental results show that the proposed algorithms could obtain good quality of the reconstructed images and need much less time than the baseline encoding algorithm

High throughput assessment of cells and tissues: Bayesian classification of spectral metrics from infrared vibrational spectroscopic imaging data.

Science.gov (United States)

Bhargava, Rohit; Fernandez, Daniel C; Hewitt, Stephen M; Levin, Ira W

2006-07-01

Vibrational spectroscopy allows a visualization of tissue constituents based on intrinsic chemical composition and provides a potential route to obtaining diagnostic markers of diseases. Characterizations utilizing infrared vibrational spectroscopy, in particular, are conventionally low throughput in data acquisition, generally lacking in spatial resolution with the resulting data requiring intensive numerical computations to extract information. These factors impair the ability of infrared spectroscopic measurements to represent accurately the spatial heterogeneity in tissue, to incorporate robustly the diversity introduced by patient cohorts or preparative artifacts and to validate developed protocols in large population studies. In this manuscript, we demonstrate a combination of Fourier transform infrared (FTIR) spectroscopic imaging, tissue microarrays (TMAs) and fast numerical analysis as a paradigm for the rapid analysis, development and validation of high throughput spectroscopic characterization protocols. We provide an extended description of the data treatment algorithm and a discussion of various factors that may influence decision-making using this approach. Finally, a number of prostate tissue biopsies, arranged in an array modality, are employed to examine the efficacy of this approach in histologic recognition of epithelial cell polarization in patients displaying a variety of normal, malignant and hyperplastic conditions. An index of epithelial cell polarization, derived from a combined spectral and morphological analysis, is determined to be a potentially useful diagnostic marker.

Extended feature-fusion guidelines to improve image-based multi-modal biometrics

CSIR Research Space (South Africa)

Brown, Dane

2016-09-01

Full Text Available The feature-level, unlike the match score-level, lacks multi-modal fusion guidelines. This work demonstrates a practical approach for improved image-based biometric feature-fusion. The approach extracts and combines the face, fingerprint...

Primary Neuroendocrine Tumor of the Breast: Imaging Features

International Nuclear Information System (INIS)

Chang, Eun Deok; Kim, Min Kyun; Kim, Jeong Soo; Whang, In Yong

2013-01-01

Focal neuroendocrine differentiation can be found in diverse histological types of breast tumors. However, the term, neuroendocrine breast tumor, indicates the diffuse expression of neuroendocrine markers in more than 50% of the tumor cell population. The imaging features of neuroendocrine breast tumor have not been accurately described due to extreme rarity of this tumor type. We present a case of a pathologically confirmed, primary neuroendocrine breast tumor in a 42-year-old woman, with imaging findings difficult to be differentiated from that of invasive ductal carcinoma

a Statistical Texture Feature for Building Collapse Information Extraction of SAR Image

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Li, L.; Yang, H.; Chen, Q.; Liu, X.

2018-04-01

Synthetic Aperture Radar (SAR) has become one of the most important ways to extract post-disaster collapsed building information, due to its extreme versatility and almost all-weather, day-and-night working capability, etc. In view of the fact that the inherent statistical distribution of speckle in SAR images is not used to extract collapsed building information, this paper proposed a novel texture feature of statistical models of SAR images to extract the collapsed buildings. In the proposed feature, the texture parameter of G0 distribution from SAR images is used to reflect the uniformity of the target to extract the collapsed building. This feature not only considers the statistical distribution of SAR images, providing more accurate description of the object texture, but also is applied to extract collapsed building information of single-, dual- or full-polarization SAR data. The RADARSAT-2 data of Yushu earthquake which acquired on April 21, 2010 is used to present and analyze the performance of the proposed method. In addition, the applicability of this feature to SAR data with different polarizations is also analysed, which provides decision support for the data selection of collapsed building information extraction.

Optimization of wavelet decomposition for image compression and feature preservation.

Science.gov (United States)

Lo, Shih-Chung B; Li, Huai; Freedman, Matthew T

2003-09-01

A neural-network-based framework has been developed to search for an optimal wavelet kernel that can be used for a specific image processing task. In this paper, a linear convolution neural network was employed to seek a wavelet that minimizes errors and maximizes compression efficiency for an image or a defined image pattern such as microcalcifications in mammograms and bone in computed tomography (CT) head images. We have used this method to evaluate the performance of tap-4 wavelets on mammograms, CTs, magnetic resonance images, and Lena images. We found that the Daubechies wavelet or those wavelets with similar filtering characteristics can produce the highest compression efficiency with the smallest mean-square-error for many image patterns including general image textures as well as microcalcifications in digital mammograms. However, the Haar wavelet produces the best results on sharp edges and low-noise smooth areas. We also found that a special wavelet whose low-pass filter coefficients are 0.32252136, 0.85258927, 1.38458542, and -0.14548269) produces the best preservation outcomes in all tested microcalcification features including the peak signal-to-noise ratio, the contrast and the figure of merit in the wavelet lossy compression scheme. Having analyzed the spectrum of the wavelet filters, we can find the compression outcomes and feature preservation characteristics as a function of wavelets. This newly developed optimization approach can be generalized to other image analysis applications where a wavelet decomposition is employed.

Computer-aided breast MR image feature analysis for prediction of tumor response to chemotherapy

International Nuclear Information System (INIS)

Aghaei, Faranak; Tan, Maxine; Liu, Hong; Zheng, Bin; Hollingsworth, Alan B.; Qian, Wei

2015-01-01

Purpose: To identify a new clinical marker based on quantitative kinetic image features analysis and assess its feasibility to predict tumor response to neoadjuvant chemotherapy. Methods: The authors assembled a dataset involving breast MR images acquired from 68 cancer patients before undergoing neoadjuvant chemotherapy. Among them, 25 patients had complete response (CR) and 43 had partial and nonresponse (NR) to chemotherapy based on the response evaluation criteria in solid tumors. The authors developed a computer-aided detection scheme to segment breast areas and tumors depicted on the breast MR images and computed a total of 39 kinetic image features from both tumor and background parenchymal enhancement regions. The authors then applied and tested two approaches to classify between CR and NR cases. The first one analyzed each individual feature and applied a simple feature fusion method that combines classification results from multiple features. The second approach tested an attribute selected classifier that integrates an artificial neural network (ANN) with a wrapper subset evaluator, which was optimized using a leave-one-case-out validation method. Results: In the pool of 39 features, 10 yielded relatively higher classification performance with the areas under receiver operating characteristic curves (AUCs) ranging from 0.61 to 0.78 to classify between CR and NR cases. Using a feature fusion method, the maximum AUC = 0.85 ± 0.05. Using the ANN-based classifier, AUC value significantly increased to 0.96 ± 0.03 (p < 0.01). Conclusions: This study demonstrated that quantitative analysis of kinetic image features computed from breast MR images acquired prechemotherapy has potential to generate a useful clinical marker in predicting tumor response to chemotherapy

Computer-aided breast MR image feature analysis for prediction of tumor response to chemotherapy

Energy Technology Data Exchange (ETDEWEB)

Aghaei, Faranak; Tan, Maxine; Liu, Hong; Zheng, Bin, E-mail: Bin.Zheng-1@ou.edu [School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma 73019 (United States); Hollingsworth, Alan B. [Mercy Women’s Center, Mercy Health Center, Oklahoma City, Oklahoma 73120 (United States); Qian, Wei [Department of Electrical and Computer Engineering, University of Texas, El Paso, Texas 79968 (United States)

2015-11-15

Purpose: To identify a new clinical marker based on quantitative kinetic image features analysis and assess its feasibility to predict tumor response to neoadjuvant chemotherapy. Methods: The authors assembled a dataset involving breast MR images acquired from 68 cancer patients before undergoing neoadjuvant chemotherapy. Among them, 25 patients had complete response (CR) and 43 had partial and nonresponse (NR) to chemotherapy based on the response evaluation criteria in solid tumors. The authors developed a computer-aided detection scheme to segment breast areas and tumors depicted on the breast MR images and computed a total of 39 kinetic image features from both tumor and background parenchymal enhancement regions. The authors then applied and tested two approaches to classify between CR and NR cases. The first one analyzed each individual feature and applied a simple feature fusion method that combines classification results from multiple features. The second approach tested an attribute selected classifier that integrates an artificial neural network (ANN) with a wrapper subset evaluator, which was optimized using a leave-one-case-out validation method. Results: In the pool of 39 features, 10 yielded relatively higher classification performance with the areas under receiver operating characteristic curves (AUCs) ranging from 0.61 to 0.78 to classify between CR and NR cases. Using a feature fusion method, the maximum AUC = 0.85 ± 0.05. Using the ANN-based classifier, AUC value significantly increased to 0.96 ± 0.03 (p < 0.01). Conclusions: This study demonstrated that quantitative analysis of kinetic image features computed from breast MR images acquired prechemotherapy has potential to generate a useful clinical marker in predicting tumor response to chemotherapy.

Enhancement and feature extraction of RS images from seismic area and seismic disaster recognition technologies

Science.gov (United States)

Zhang, Jingfa; Qin, Qiming

2003-09-01

Many types of feature extracting of RS image are analyzed, and the work procedure of pattern recognizing in RS images of seismic disaster is proposed. The aerial RS image of Tangshan Great Earthquake is processed, and the digital features of various typical seismic disaster on the RS image is calculated.

Change Detection of High-Resolution Remote Sensing Images Based on Adaptive Fusion of Multiple Features

Science.gov (United States)

Wang, G. H.; Wang, H. B.; Fan, W. F.; Liu, Y.; Chen, C.

2018-04-01

In view of the traditional change detection algorithm mainly depends on the spectral information image spot, failed to effectively mining and fusion of multi-image feature detection advantage, the article borrows the ideas of object oriented analysis proposed a multi feature fusion of remote sensing image change detection algorithm. First by the multi-scale segmentation of image objects based; then calculate the various objects of color histogram and linear gradient histogram; utilizes the color distance and edge line feature distance between EMD statistical operator in different periods of the object, using the adaptive weighted method, the color feature distance and edge in a straight line distance of combination is constructed object heterogeneity. Finally, the curvature histogram analysis image spot change detection results. The experimental results show that the method can fully fuse the color and edge line features, thus improving the accuracy of the change detection.

Human gait recognition by pyramid of HOG feature on silhouette images

Science.gov (United States)

Yang, Guang; Yin, Yafeng; Park, Jeanrok; Man, Hong

2013-03-01

As a uncommon biometric modality, human gait recognition has a great advantage of identify people at a distance without high resolution images. It has attracted much attention in recent years, especially in the fields of computer vision and remote sensing. In this paper, we propose a human gait recognition framework that consists of a reliable background subtraction method followed by the pyramid of Histogram of Gradient (pHOG) feature extraction on the silhouette image, and a Hidden Markov Model (HMM) based classifier. Through background subtraction, the silhouette of human gait in each frame is extracted and normalized from the raw video sequence. After removing the shadow and noise in each region of interest (ROI), pHOG feature is computed on the silhouettes images. Then the pHOG features of each gait class will be used to train a corresponding HMM. In the test stage, pHOG feature will be extracted from each test sequence and used to calculate the posterior probability toward each trained HMM model. Experimental results on the CASIA Gait Dataset B1 demonstrate that with our proposed method can achieve very competitive recognition rate.

THE YOUNG SOLAR ANALOGS PROJECT. I. SPECTROSCOPIC AND PHOTOMETRIC METHODS AND MULTI-YEAR TIMESCALE SPECTROSCOPIC RESULTS

Energy Technology Data Exchange (ETDEWEB)

Gray, R. O.; Briley, M. M.; Lambert, R. A.; Fuller, V. A.; Newsome, I. M.; Seeds, M. F. [Department of Physics and Astronomy, Appalachian State University, Boone, NC 26808 (United States); Saken, J. M.; Kahvaz, Y. [Department of Physics and Physical Science, Marshall University, Huntington, WV 25755 (United States); Corbally, C. J. [Vatican Observatory Research Group, Steward Observatory, Tucson, AZ 85721-0065 (United States)

2015-12-15

This is the first in a series of papers presenting methods and results from the Young Solar Analogs Project, which began in 2007. This project monitors both spectroscopically and photometrically a set of 31 young (300–1500 Myr) solar-type stars with the goal of gaining insight into the space environment of the Earth during the period when life first appeared. From our spectroscopic observations we derive the Mount Wilson S chromospheric activity index (S{sub MW}), and describe the method we use to transform our instrumental indices to S{sub MW} without the need for a color term. We introduce three photospheric indices based on strong absorption features in the blue-violet spectrum—the G-band, the Ca i resonance line, and the Hydrogen-γ line—with the expectation that these indices might prove to be useful in detecting variations in the surface temperatures of active solar-type stars. We also describe our photometric program, and in particular our “Superstar technique” for differential photometry which, instead of relying on a handful of comparison stars, uses the photon flux in the entire star field in the CCD image to derive the program star magnitude. This enables photometric errors on the order of 0.005–0.007 magnitude. We present time series plots of our spectroscopic data for all four indices, and carry out extensive statistical tests on those time series demonstrating the reality of variations on timescales of years in all four indices. We also statistically test for and discover correlations and anti-correlations between the four indices. We discuss the physical basis of those correlations. As it turns out, the “photospheric” indices appear to be most strongly affected by emission in the Paschen continuum. We thus anticipate that these indices may prove to be useful proxies for monitoring emission in the ultraviolet Balmer continuum. Future papers in this series will discuss variability of the program stars on medium (days–months) and short

Principal component and spatial correlation analysis of spectroscopic-imaging data in scanning probe microscopy

International Nuclear Information System (INIS)

Jesse, Stephen; Kalinin, Sergei V

2009-01-01

An approach for the analysis of multi-dimensional, spectroscopic-imaging data based on principal component analysis (PCA) is explored. PCA selects and ranks relevant response components based on variance within the data. It is shown that for examples with small relative variations between spectra, the first few PCA components closely coincide with results obtained using model fitting, and this is achieved at rates approximately four orders of magnitude faster. For cases with strong response variations, PCA allows an effective approach to rapidly process, de-noise, and compress data. The prospects for PCA combined with correlation function analysis of component maps as a universal tool for data analysis and representation in microscopy are discussed.

Breast cancer mitosis detection in histopathological images with spatial feature extraction

Science.gov (United States)

Albayrak, Abdülkadir; Bilgin, Gökhan

2013-12-01

In this work, cellular mitosis detection in histopathological images has been investigated. Mitosis detection is very expensive and time consuming process. Development of digital imaging in pathology has enabled reasonable and effective solution to this problem. Segmentation of digital images provides easier analysis of cell structures in histopathological data. To differentiate normal and mitotic cells in histopathological images, feature extraction step is very crucial step for the system accuracy. A mitotic cell has more distinctive textural dissimilarities than the other normal cells. Hence, it is important to incorporate spatial information in feature extraction or in post-processing steps. As a main part of this study, Haralick texture descriptor has been proposed with different spatial window sizes in RGB and La*b* color spaces. So, spatial dependencies of normal and mitotic cellular pixels can be evaluated within different pixel neighborhoods. Extracted features are compared with various sample sizes by Support Vector Machines using k-fold cross validation method. According to the represented results, it has been shown that separation accuracy on mitotic and non-mitotic cellular pixels gets better with the increasing size of spatial window.

Image Classification Using Biomimetic Pattern Recognition with Convolutional Neural Networks Features

Science.gov (United States)

Huo, Guanying

2017-01-01

As a typical deep-learning model, Convolutional Neural Networks (CNNs) can be exploited to automatically extract features from images using the hierarchical structure inspired by mammalian visual system. For image classification tasks, traditional CNN models employ the softmax function for classification. However, owing to the limited capacity of the softmax function, there are some shortcomings of traditional CNN models in image classification. To deal with this problem, a new method combining Biomimetic Pattern Recognition (BPR) with CNNs is proposed for image classification. BPR performs class recognition by a union of geometrical cover sets in a high-dimensional feature space and therefore can overcome some disadvantages of traditional pattern recognition. The proposed method is evaluated on three famous image classification benchmarks, that is, MNIST, AR, and CIFAR-10. The classification accuracies of the proposed method for the three datasets are 99.01%, 98.40%, and 87.11%, respectively, which are much higher in comparison with the other four methods in most cases. PMID:28316614

Abdominal tuberculosis: Imaging features

International Nuclear Information System (INIS)

Pereira, Jose M.; Madureira, Antonio J.; Vieira, Alberto; Ramos, Isabel

2005-01-01

Radiological findings of abdominal tuberculosis can mimic those of many different diseases. A high level of suspicion is required, especially in high-risk population. In this article, we will describe barium studies, ultrasound (US) and computed tomography (CT) findings of abdominal tuberculosis (TB), with emphasis in the latest. We will illustrate CT findings that can help in the diagnosis of abdominal tuberculosis and describe imaging features that differentiate it from other inflammatory and neoplastic diseases, particularly lymphoma and Crohn's disease. As tuberculosis can affect any organ in the abdomen, emphasis is placed to ileocecal involvement, lymphadenopathy, peritonitis and solid organ disease (liver, spleen and pancreas). A positive culture or hystologic analysis of biopsy is still required in many patients for definitive diagnosis. Learning objectives:1.To review the relevant pathophysiology of abdominal tuberculosis. 2.Illustrate CT findings that can help in the diagnosis

Spectroscopic Characterization of GEO Satellites with Gunma LOW Resolution Spectrograph

Science.gov (United States)

Endo, T.; Ono, H.; Hosokawa, M.; Ando, T.; Takanezawa, T.; Hashimoto, O.

The spectroscopic observation is potentially a powerful tool for understanding the Geostationary Earth Orbit (GEO) objects. We present here the results of an investigation of energy spectra of GEO satellites obtained from a groundbased optical telescope. The spectroscopic observations were made from April to June 2016 with the Gunma LOW resolution Spectrograph and imager (GLOWS) at the Gunma Astronomical Observatory (GAO) in JAPAN. The observation targets consist of eleven different satellites: two weather satellites, four communications satellites, and five broadcasting satellites. All the spectra of those GEO satellites are inferred to be solar-like. A number of well-known absorption features such as H-alpha, H-beta, Na-D,water vapor and oxygen molecules are clearly seen in thewavelength range of 4,000 - 8,000 Å. For comparison, we calculated the intensity ratio of the spectra of GEO satellites to that of the Moon which is the natural satellite of the earth. As a result, the following characteristics were obtained. 1) Some variations are seen in the strength of absorption features of water vapor and oxygen originated by the telluric atmosphere, but any other characteristic absorption features were not found. 2) For all observed satellites, the intensity ratio of the spectrum of GEO satellites decrease as a function of wavelength or to be flat. It means that the spectral reflectance of satellite materials is bluer than that of the Moon. 3) A characteristic dip at around 4,800 Å is found in all observed spectra of a weather satellite. Based on these observations, it is indicated that the characteristics of the spectrum are mainly derived from the solar panels because the apparent area of the solar cell is probably larger than that of the satellite body.

Accelerated high-resolution 3D magnetic resonance spectroscopic imaging in the brain At 7 T

International Nuclear Information System (INIS)

Hangel, G.

2015-01-01

With the announcement of the first series of magnetic resonance (MR) scanners with a field strength of 7 Tesla (T) intended for clinical practice, the development of high-performance sequences for higher field strengths has gained importance. Magnetic resonance spectroscopic imaging (MRSI) in the brain currently offers the unique ability to spatially resolve the distribution of multiple metabolites simultaneously. Its big diagnostic potential could be applied to many clinical protocols, for example the assessment of tumour treatment or progress of Multiple Sclerosis. Moving to ultra-high fields like 7 T has the main benefits of increased signal-to-noise ratio (SNR) and improved spectral quality, but brings its own challenges due to stronger field inhomogeneities. Necessary for a robust, flexible and useful MRSI sequence in the brain are high resolutions, shortened measurement times, the possibility for 3D-MRSI and the suppression of spectral contamination by trans-cranial lipids. This thesis addresses these limitations and proposes Hadamard spectroscopic imaging (HSI) as solution for multi-slice MRSI, the application of generalized autocalibrating partially parallel acquisition (GRAPPA) and spiral trajectories for measurement acceleration, non-selective inversion recovery (IR) lipid-suppression as well as combinations of these methods. Further, the optimisation of water suppression for 7 T systems and the acquisition of ultra-high resolution (UHR)-MRSI are discussed. In order to demonstrate the clinical feasibility of these approaches, MRSI measurement results of a glioma patient are presented. The discussion of the obtained results in the context of the state-of-art in 7 T MRSI in the brain, possible future applications as well as potential further improvements of the MRSI sequences conclude this thesis. (author) [de

The ship edge feature detection based on high and low threshold for remote sensing image

Science.gov (United States)

Li, Xuan; Li, Shengyang

2018-05-01

In this paper, a method based on high and low threshold is proposed to detect the ship edge feature due to the low accuracy rate caused by the noise. Analyze the relationship between human vision system and the target features, and to determine the ship target by detecting the edge feature. Firstly, using the second-order differential method to enhance the quality of image; Secondly, to improvement the edge operator, we introduction of high and low threshold contrast to enhancement image edge and non-edge points, and the edge as the foreground image, non-edge as a background image using image segmentation to achieve edge detection, and remove the false edges; Finally, the edge features are described based on the result of edge features detection, and determine the ship target. The experimental results show that the proposed method can effectively reduce the number of false edges in edge detection, and has the high accuracy of remote sensing ship edge detection.

Feature Extraction and Simplification from colour images based on Colour Image Segmentation and Skeletonization using the Quad-Edge data structure

DEFF Research Database (Denmark)

Sharma, Ojaswa; Mioc, Darka; Anton, François

2007-01-01

Region features in colour images are of interest in applications such as mapping, GIS, climatology, change detection, medicine, etc. This research work is an attempt to automate the process of extracting feature boundaries from colour images. This process is an attempt to eventually replace manua...

Identification of natural images and computer-generated graphics based on statistical and textural features.

Science.gov (United States)

Peng, Fei; Li, Jiao-ting; Long, Min

2015-03-01

To discriminate the acquisition pipelines of digital images, a novel scheme for the identification of natural images and computer-generated graphics is proposed based on statistical and textural features. First, the differences between them are investigated from the view of statistics and texture, and 31 dimensions of feature are acquired for identification. Then, LIBSVM is used for the classification. Finally, the experimental results are presented. The results show that it can achieve an identification accuracy of 97.89% for computer-generated graphics, and an identification accuracy of 97.75% for natural images. The analyses also demonstrate the proposed method has excellent performance, compared with some existing methods based only on statistical features or other features. The method has a great potential to be implemented for the identification of natural images and computer-generated graphics. © 2014 American Academy of Forensic Sciences.

Segmenting texts from outdoor images taken by mobile phones using color features

Science.gov (United States)

Liu, Zongyi; Zhou, Hanning

2011-01-01

Recognizing texts from images taken by mobile phones with low resolution has wide applications. It has been shown that a good image binarization can substantially improve the performances of OCR engines. In this paper, we present a framework to segment texts from outdoor images taken by mobile phones using color features. The framework consists of three steps: (i) the initial process including image enhancement, binarization and noise filtering, where we binarize the input images in each RGB channel, and apply component level noise filtering; (ii) grouping components into blocks using color features, where we compute the component similarities by dynamically adjusting the weights of RGB channels, and merge groups hierachically, and (iii) blocks selection, where we use the run-length features and choose the Support Vector Machine (SVM) as the classifier. We tested the algorithm using 13 outdoor images taken by an old-style LG-64693 mobile phone with 640x480 resolution. We compared the segmentation results with Tsar's algorithm, a state-of-the-art camera text detection algorithm, and show that our algorithm is more robust, particularly in terms of the false alarm rates. In addition, we also evaluated the impacts of our algorithm on the Abbyy's FineReader, one of the most popular commercial OCR engines in the market.

Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction.

Science.gov (United States)

Nguyen, Dat Tien; Kim, Ki Wan; Hong, Hyung Gil; Koo, Ja Hyung; Kim, Min Cheol; Park, Kang Ryoung

2017-03-20

Extracting powerful image features plays an important role in computer vision systems. Many methods have previously been proposed to extract image features for various computer vision applications, such as the scale-invariant feature transform (SIFT), speed-up robust feature (SURF), local binary patterns (LBP), histogram of oriented gradients (HOG), and weighted HOG. Recently, the convolutional neural network (CNN) method for image feature extraction and classification in computer vision has been used in various applications. In this research, we propose a new gender recognition method for recognizing males and females in observation scenes of surveillance systems based on feature extraction from visible-light and thermal camera videos through CNN. Experimental results confirm the superiority of our proposed method over state-of-the-art recognition methods for the gender recognition problem using human body images.

Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction

Science.gov (United States)

Nguyen, Dat Tien; Kim, Ki Wan; Hong, Hyung Gil; Koo, Ja Hyung; Kim, Min Cheol; Park, Kang Ryoung

2017-01-01

Extracting powerful image features plays an important role in computer vision systems. Many methods have previously been proposed to extract image features for various computer vision applications, such as the scale-invariant feature transform (SIFT), speed-up robust feature (SURF), local binary patterns (LBP), histogram of oriented gradients (HOG), and weighted HOG. Recently, the convolutional neural network (CNN) method for image feature extraction and classification in computer vision has been used in various applications. In this research, we propose a new gender recognition method for recognizing males and females in observation scenes of surveillance systems based on feature extraction from visible-light and thermal camera videos through CNN. Experimental results confirm the superiority of our proposed method over state-of-the-art recognition methods for the gender recognition problem using human body images. PMID:28335510

Feature and Contrast Enhancement of Mammographic Image Based on Multiscale Analysis and Morphology

Directory of Open Access Journals (Sweden)

Shibin Wu

2013-01-01

Full Text Available A new algorithm for feature and contrast enhancement of mammographic images is proposed in this paper. The approach bases on multiscale transform and mathematical morphology. First of all, the Laplacian Gaussian pyramid operator is applied to transform the mammography into different scale subband images. In addition, the detail or high frequency subimages are equalized by contrast limited adaptive histogram equalization (CLAHE and low-pass subimages are processed by mathematical morphology. Finally, the enhanced image of feature and contrast is reconstructed from the Laplacian Gaussian pyramid coefficients modified at one or more levels by contrast limited adaptive histogram equalization and mathematical morphology, respectively. The enhanced image is processed by global nonlinear operator. The experimental results show that the presented algorithm is effective for feature and contrast enhancement of mammogram. The performance evaluation of the proposed algorithm is measured by contrast evaluation criterion for image, signal-noise-ratio (SNR, and contrast improvement index (CII.

Comparison of clustering methods for tracking features in RGB-D images

CSIR Research Space (South Africa)

Pancham, Ardhisha

2016-10-01

Full Text Available difficult to track individually over an image sequence. Clustering techniques have been recommended and used to cluster image features to improve tracking results. New and affordable RGB-D cameras, provide both color and depth information. This paper...

Imaging features of intracranial solitary fibrous tumors

International Nuclear Information System (INIS)

Yu Shuilian; Man Yuping; Ma Longbai; Liu Ying; Wei Qiang; Zhu Youkai

2012-01-01

Objective: To summarize the imaging features of intracranial solitary fibrous tumors (ISFT). Methods: Ten patients with ISFT proven histopathologically were collected. Four cases had CT data and all cases had MR data. The imaging features and pathological results were retrospectively analyzed. Results: All cases were misdiagnosed as meningioma at pre-operation. All lesions arose from intracranial meninges including 5 lesions above the tentorium, 4 lesions beneath the tentorium and 1 lesion growing around the tentorium. The margins of all the masses were well defined, and 8 lesions presented multilobular shape. CT demonstrated hyerattenuated masses in all 4 lesions, smooth erosion of the basicranial skull in 1 lesion, and punctiform calcification of the capsule in 1 lesion. T 1 WI showed most lesions with isointense or slight hyperintense signals including homogeneous in 4 lesions and heterogeneous in 6 lesions. T 2 WI demonstrated isointense or slight hyperintense in 2 lesions, mixed hypointense and hyperintense signals in 4, cystic portion in 2, and two distinct portion of hyperintense and hypointense signal, so called 'yin-yang' pattern, in 2. Strong enhanced was found in all lesions, especially in 8 lesion with heterogeneous with the low T 2 signal. 'Dural tail' was found in 4 lesions. Conclusions: ISFI has some specific CT and MR features including heterogeneous signal intensity on T 2 WI, strong enhancement of areas with low T 2 signal intensity, slight or no 'dural tail', without skull thickening, and the typical 'yin-yang' pattern. (authors)

A new and fast image feature selection method for developing an optimal mammographic mass detection scheme.

Science.gov (United States)

Tan, Maxine; Pu, Jiantao; Zheng, Bin

2014-08-01

Selecting optimal features from a large image feature pool remains a major challenge in developing computer-aided detection (CAD) schemes of medical images. The objective of this study is to investigate a new approach to significantly improve efficacy of image feature selection and classifier optimization in developing a CAD scheme of mammographic masses. An image dataset including 1600 regions of interest (ROIs) in which 800 are positive (depicting malignant masses) and 800 are negative (depicting CAD-generated false positive regions) was used in this study. After segmentation of each suspicious lesion by a multilayer topographic region growth algorithm, 271 features were computed in different feature categories including shape, texture, contrast, isodensity, spiculation, local topological features, as well as the features related to the presence and location of fat and calcifications. Besides computing features from the original images, the authors also computed new texture features from the dilated lesion segments. In order to select optimal features from this initial feature pool and build a highly performing classifier, the authors examined and compared four feature selection methods to optimize an artificial neural network (ANN) based classifier, namely: (1) Phased Searching with NEAT in a Time-Scaled Framework, (2) A sequential floating forward selection (SFFS) method, (3) A genetic algorithm (GA), and (4) A sequential forward selection (SFS) method. Performances of the four approaches were assessed using a tenfold cross validation method. Among these four methods, SFFS has highest efficacy, which takes 3%-5% of computational time as compared to GA approach, and yields the highest performance level with the area under a receiver operating characteristic curve (AUC) = 0.864 ± 0.034. The results also demonstrated that except using GA, including the new texture features computed from the dilated mass segments improved the AUC results of the ANNs optimized

Imaging features of nontumorous conditions involving the trachea and main-stem bronchi

International Nuclear Information System (INIS)

Jeon, Kyung Nyeo; Kang, Duk Sik; Bae, Kyung Soo

2002-01-01

A number of nontumorous diseases may affect the trachea and main-stem bronchi, and their nonspecific symptoms may include coughing, dyspnea, wheezing and stridor. The clinical course is often long-term and a misdiagnosis of bronchial asthma is common. The imaging findings of these nontumorous conditions are, however, relatively characteristic, and diagnosis either without or in conjunction with clinical information is often possible. For specific diagnosis, recognition of their imaging features is therefore of prime importance. In this pictorial essay, we illustrate the imaging features of various nontumorous conditions involving the trachea and main-stem bronchi

Cryptogenic organizing pneumonia: typical and atypical imaging features on computed tomography

International Nuclear Information System (INIS)

Hamer, O.W.; Silva, C.I.; Mueller, N.L.

2008-01-01

Organizing pneumonia (OP) occurs without any identifiable cause (''cryptogenic organizing pneumonia'') as well as secondary to a multitude of disorders of various origins (''secondary organizing pneumonia''). Possible triggers are infections, drugs, collagen vascular disease, inflammatory bowel disease, transplantations, and radiation directed to the chest. The present manuscript provides an overview of the histopathological, clinical and CT imaging features of OP. Classic CT morphologies (peripheral and peribronchovascular consolidations and ground glass opacities) and atypical imaging features (nodules, crazy paving, lines and bands, perilobular consolidations and the reversed halo sign) are discussed. (orig.)

STUDY ON SHADOW EFFECTS OF VARIOUS FEATURES ON CLOSE RANGE THERMAL IMAGES

Directory of Open Access Journals (Sweden)

C. L. Liao

2012-07-01

Full Text Available Thermal infrared data become more popular in remote sensing investigation, for it could be acquired both in day and night. The change of temperature has special characteristic in natural environment, so the thermal infrared images could be used in monitoring volcanic landform, the urban development, and disaster prevention. Heat shadow is formed by reflecting radiating capacity which followed the objects. Because of poor spatial resolution of thermal infrared images in satellite sensor, shadow effects were usually ignored. This research focus on discussing the shadow effects of various features, which include metals and nonmetallic materials. An area-based thermal sensor, FLIR-T360 was selected to acquire thermal images. Various features with different emissivity were chosen as reflective surface to obtain thermal shadow in normal atmospheric temperature. Experiments found that the shadow effects depend on the distance between sensors and features, depression angle, object temperature and emissivity of reflective surface. The causes of shadow effects have been altered in the experiment for analyzing the variance in thermal infrared images. The result shows that there were quite different impacts by shadow effects between metals and nonmetallic materials. The further research would be produced a math model to describe the shadow effects of different features in the future work.

Simultaneous PET/MRI with 13C magnetic resonance spectroscopic imaging (hyperPET): phantom-based evaluation of PET quantification

DEFF Research Database (Denmark)

Hansen, Adam E.; Andersen, Flemming L.; Henriksen, Sarah T.

2016-01-01

Background: Integrated PET/MRI with hyperpolarized 13C magnetic resonance spectroscopic imaging (13C-MRSI) offers simultaneous, dual-modality metabolic imaging. A prerequisite for the use of simultaneous imaging is the absence of interference between the two modalities. This has been documented...... for a clinical whole-body system using simultaneous 1 H-MRI and PET but never for 13C-MRSI and PET. Here, the feasibility of simultaneous PET and 13C-MRSI as well as hyperpolarized 13C-MRSI in an integrated whole-body PET/MRI hybrid scanner is evaluated using phantom experiments. Methods: Combined PET and 13C......-MRSI phantoms including a NEMA [18F]-FDG phantom, 13C-acetate and 13C-urea sources, and hyperpolarized 13C-pyruvate were imaged repeatedly with PET and/or 13C-MRSI. Measurements evaluated for interference effects included PET activity values in the largest sphere and a background region; total number of PET...

Spectroscopically Enhanced Method and System for Multi-Factor Biometric Authentication

Science.gov (United States)

Pishva, Davar

This paper proposes a spectroscopic method and system for preventing spoofing of biometric authentication. One of its focus is to enhance biometrics authentication with a spectroscopic method in a multifactor manner such that a person's unique ‘spectral signatures’ or ‘spectral factors’ are recorded and compared in addition to a non-spectroscopic biometric signature to reduce the likelihood of imposter getting authenticated. By using the ‘spectral factors’ extracted from reflectance spectra of real fingers and employing cluster analysis, it shows how the authentic fingerprint image presented by a real finger can be distinguished from an authentic fingerprint image embossed on an artificial finger, or molded on a fingertip cover worn by an imposter. This paper also shows how to augment two widely used biometrics systems (fingerprint and iris recognition devices) with spectral biometrics capabilities in a practical manner and without creating much overhead or inconveniencing their users.

Image cytometric nuclear texture features in inoperable head and neck cancer: a pilot study

International Nuclear Information System (INIS)

Strojan-Flezar, Margareta; Lavrencak, Jaka; Zganec, Mario; Strojan, Primoz

2011-01-01

Image cytometry can measure numerous nuclear features which could be considered a surrogate end-point marker of molecular genetic changes in a nucleus. The aim of the study was to analyze image cytometric nuclear features in paired samples of primary tumor and neck metastasis in patients with inoperable carcinoma of the head and neck. Image cytometric analysis of cell suspensions prepared from primary tumor tissue and fine needle aspiration biopsy cell samples of neck metastases from 21 patients treated with concomitant radiochemotherapy was performed. Nuclear features were correlated with clinical characteristics and response to therapy. Manifestation of distant metastases and new primaries was associated (p<0.05) with several chromatin characteristics from primary tumor cells, whereas the origin of index cancer and disease response in the neck was related to those in the cells from metastases. Many nuclear features of primary tumors and metastases correlated with the TNM stage. A specific pattern of correlation between well-established prognostic indicators and nuclear features of samples from primary tumors and those from neck metastases was observed. Image cytometric nuclear features represent a promising candidate marker for recognition of biologically different tumor subgroups

High Resolution SAR Imaging Employing Geometric Features for Extracting Seismic Damage of Buildings

Science.gov (United States)

Cui, L. P.; Wang, X. P.; Dou, A. X.; Ding, X.

2018-04-01

Synthetic Aperture Radar (SAR) image is relatively easy to acquire but difficult for interpretation. This paper probes how to identify seismic damage of building using geometric features of SAR. The SAR imaging geometric features of buildings, such as the high intensity layover, bright line induced by double bounce backscattering and dark shadow is analysed, and show obvious differences texture features of homogeneity, similarity and entropy in combinatorial imaging geometric regions between the un-collapsed and collapsed buildings in airborne SAR images acquired in Yushu city damaged by 2010 Ms7.1 Yushu, Qinghai, China earthquake, which implicates a potential capability to discriminate collapsed and un-collapsed buildings from SAR image. Study also shows that the proportion of highlight (layover & bright line) area (HA) is related to the seismic damage degree, thus a SAR image damage index (SARDI), which related to the ratio of HA to the building occupation are of building in a street block (SA), is proposed. While HA is identified through feature extraction with high-pass and low-pass filtering of SAR image in frequency domain. A partial region with 58 natural street blocks in the Yushu City are selected as study area. Then according to the above method, HA is extracted, SARDI is then calculated and further classified into 3 classes. The results show effective through validation check with seismic damage classes interpreted artificially from post-earthquake airborne high resolution optical image, which shows total classification accuracy 89.3 %, Kappa coefficient 0.79 and identical to the practical seismic damage distribution. The results are also compared and discussed with the building damage identified from SAR image available by other authors.

Compact Representation of High-Dimensional Feature Vectors for Large-Scale Image Recognition and Retrieval.

Science.gov (United States)

Zhang, Yu; Wu, Jianxin; Cai, Jianfei

2016-05-01

In large-scale visual recognition and image retrieval tasks, feature vectors, such as Fisher vector (FV) or the vector of locally aggregated descriptors (VLAD), have achieved state-of-the-art results. However, the combination of the large numbers of examples and high-dimensional vectors necessitates dimensionality reduction, in order to reduce its storage and CPU costs to a reasonable range. In spite of the popularity of various feature compression methods, this paper shows that the feature (dimension) selection is a better choice for high-dimensional FV/VLAD than the feature (dimension) compression methods, e.g., product quantization. We show that strong correlation among the feature dimensions in the FV and the VLAD may not exist, which renders feature selection a natural choice. We also show that, many dimensions in FV/VLAD are noise. Throwing them away using feature selection is better than compressing them and useful dimensions altogether using feature compression methods. To choose features, we propose an efficient importance sorting algorithm considering both the supervised and unsupervised cases, for visual recognition and image retrieval, respectively. Combining with the 1-bit quantization, feature selection has achieved both higher accuracy and less computational cost than feature compression methods, such as product quantization, on the FV and the VLAD image representations.

Development of estimation system of knee extension strength using image features in ultrasound images of rectus femoris

Science.gov (United States)

Murakami, Hiroki; Watanabe, Tsuneo; Fukuoka, Daisuke; Terabayashi, Nobuo; Hara, Takeshi; Muramatsu, Chisako; Fujita, Hiroshi

2016-04-01

The word "Locomotive syndrome" has been proposed to describe the state of requiring care by musculoskeletal disorders and its high-risk condition. Reduction of the knee extension strength is cited as one of the risk factors, and the accurate measurement of the strength is needed for the evaluation. The measurement of knee extension strength using a dynamometer is one of the most direct and quantitative methods. This study aims to develop a system for measuring the knee extension strength using the ultrasound images of the rectus femoris muscles obtained with non-invasive ultrasonic diagnostic equipment. First, we extract the muscle area from the ultrasound images and determine the image features, such as the thickness of the muscle. We combine these features and physical features, such as the patient's height, and build a regression model of the knee extension strength from training data. We have developed a system for estimating the knee extension strength by applying the regression model to the features obtained from test data. Using the test data of 168 cases, correlation coefficient value between the measured values and estimated values was 0.82. This result suggests that this system can estimate knee extension strength with high accuracy.

Comparison of image features calculated in different dimensions for computer-aided diagnosis of lung nodules

Science.gov (United States)

Xu, Ye; Lee, Michael C.; Boroczky, Lilla; Cann, Aaron D.; Borczuk, Alain C.; Kawut, Steven M.; Powell, Charles A.

2009-02-01

Features calculated from different dimensions of images capture quantitative information of the lung nodules through one or multiple image slices. Previously published computer-aided diagnosis (CADx) systems have used either twodimensional (2D) or three-dimensional (3D) features, though there has been little systematic analysis of the relevance of the different dimensions and of the impact of combining different dimensions. The aim of this study is to determine the importance of combining features calculated in different dimensions. We have performed CADx experiments on 125 pulmonary nodules imaged using multi-detector row CT (MDCT). The CADx system computed 192 2D, 2.5D, and 3D image features of the lesions. Leave-one-out experiments were performed using five different combinations of features from different dimensions: 2D, 3D, 2.5D, 2D+3D, and 2D+3D+2.5D. The experiments were performed ten times for each group. Accuracy, sensitivity and specificity were used to evaluate the performance. Wilcoxon signed-rank tests were applied to compare the classification results from these five different combinations of features. Our results showed that 3D image features generate the best result compared with other combinations of features. This suggests one approach to potentially reducing the dimensionality of the CADx data space and the computational complexity of the system while maintaining diagnostic accuracy.

Rough-fuzzy clustering and unsupervised feature selection for wavelet based MR image segmentation.

Directory of Open Access Journals (Sweden)

Pradipta Maji

Full Text Available Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of brain magnetic resonance (MR images. For many human experts, manual segmentation is a difficult and time consuming task, which makes an automated brain MR image segmentation method desirable. In this regard, this paper presents a new segmentation method for brain MR images, integrating judiciously the merits of rough-fuzzy computing and multiresolution image analysis technique. The proposed method assumes that the major brain tissues, namely, gray matter, white matter, and cerebrospinal fluid from the MR images are considered to have different textural properties. The dyadic wavelet analysis is used to extract the scale-space feature vector for each pixel, while the rough-fuzzy clustering is used to address the uncertainty problem of brain MR image segmentation. An unsupervised feature selection method is introduced, based on maximum relevance-maximum significance criterion, to select relevant and significant textural features for segmentation problem, while the mathematical morphology based skull stripping preprocessing step is proposed to remove the non-cerebral tissues like skull. The performance of the proposed method, along with a comparison with related approaches, is demonstrated on a set of synthetic and real brain MR images using standard validity indices.

Real-time ultrasound image classification for spine anesthesia using local directional Hadamard features.

Science.gov (United States)

Pesteie, Mehran; Abolmaesumi, Purang; Ashab, Hussam Al-Deen; Lessoway, Victoria A; Massey, Simon; Gunka, Vit; Rohling, Robert N

2015-06-01

Injection therapy is a commonly used solution for back pain management. This procedure typically involves percutaneous insertion of a needle between or around the vertebrae, to deliver anesthetics near nerve bundles. Most frequently, spinal injections are performed either blindly using palpation or under the guidance of fluoroscopy or computed tomography. Recently, due to the drawbacks of the ionizing radiation of such imaging modalities, there has been a growing interest in using ultrasound imaging as an alternative. However, the complex spinal anatomy with different wave-like structures, affected by speckle noise, makes the accurate identification of the appropriate injection plane difficult. The aim of this study was to propose an automated system that can identify the optimal plane for epidural steroid injections and facet joint injections. A multi-scale and multi-directional feature extraction system to provide automated identification of the appropriate plane is proposed. Local Hadamard coefficients are obtained using the sequency-ordered Hadamard transform at multiple scales. Directional features are extracted from local coefficients which correspond to different regions in the ultrasound images. An artificial neural network is trained based on the local directional Hadamard features for classification. The proposed method yields distinctive features for classification which successfully classified 1032 images out of 1090 for epidural steroid injection and 990 images out of 1052 for facet joint injection. In order to validate the proposed method, a leave-one-out cross-validation was performed. The average classification accuracy for leave-one-out validation was 94 % for epidural and 90 % for facet joint targets. Also, the feature extraction time for the proposed method was 20 ms for a native 2D ultrasound image. A real-time machine learning system based on the local directional Hadamard features extracted by the sequency-ordered Hadamard transform for

Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction

Directory of Open Access Journals (Sweden)

Dat Tien Nguyen

2017-03-01

Full Text Available Extracting powerful image features plays an important role in computer vision systems. Many methods have previously been proposed to extract image features for various computer vision applications, such as the scale-invariant feature transform (SIFT, speed-up robust feature (SURF, local binary patterns (LBP, histogram of oriented gradients (HOG, and weighted HOG. Recently, the convolutional neural network (CNN method for image feature extraction and classification in computer vision has been used in various applications. In this research, we propose a new gender recognition method for recognizing males and females in observation scenes of surveillance systems based on feature extraction from visible-light and thermal camera videos through CNN. Experimental results confirm the superiority of our proposed method over state-of-the-art recognition methods for the gender recognition problem using human body images.

Electron spectroscopic imaging of antigens by reaction with boronated antibodies.

Science.gov (United States)

Qualmann, B; Kessels, M M; Klobasa, F; Jungblut, P W; Sierralta, W D

1996-07-01

Two small homogeneous markers for electron spectroscopic imaging (ESI) containing eight dodecaborane cages linked to a poly-alpha, epsilon-L-lysine dendrimer were synthesized; one of these was made water soluble by the attachment of a polyether. The markers were coupled to the sulfhydryl group of (monovalent) antibody fragments (Fab') by a homobifunctional cross-linker. While the coupling ratios of the poorly water-soluble compound did not exceed 20%, the polyether-containing variant reacted quantitatively. Its suitability for immunolabelling was tested in a study of the mechanism of the transcellular transport of an administered heterologous protein (bovine serum albumin, BSA) through ileal enterocytes of newborn piglets by endocytotic vesicles in comparison to conventional immunogold reagents. The post-embedding technique was employed. The boronated Fab' gave rise to considerably higher tagging frequencies than seen with immunogold, as could be expected from its form- and size-related physical advantages and the dense packing of BSA in the vesicles. The new probe, carrying the antigen-combining cleft at one end and the boron clusters at the opposite end of the oval-shaped conjugate, add to the potential of ESI-based immunocytochemistry.

Comparative imaging features of brucellar and tuberculous spondylitis

International Nuclear Information System (INIS)

Sharif, H.S.; Aldeyan, O.; Clark, D.C.; Madkour, M.M.

1987-01-01

Images obtained with various modalities in 17 patients with Brucella spondylitis and 12 patients with tuberculous spondylitis were analyzed in order to identify distinguishing features. All patients underwent radiography, 21 underwent bone scintigraphy, and all underwent high-resolution CT and/or MR imaging. Characteristic findings in Brucella spondylitis included a predilection for the lumbar spine, bone destruction limited to the end-plates and associated with sclerosis, and disk space collapse (16 of 19) with disk vacuum phenomenon in eight and localized soft-tissue edema. MR imaging showed diffuse increased signal in vertebrae, disks, and adjacent soft tissues on long repetition time/long echo time studies (four patients). Tuberculosis spondylitis was characterized by a midthoracic predilection, diffuse vertebral destruction with gibbus deformity, severe disk collapse, and extensive paraspinal abscesses. MR imaging findings (three patients) were similar to but more severe than findings in Brucella spondylitis

Abdominal tuberculosis: Imaging features

Energy Technology Data Exchange (ETDEWEB)

Pereira, Jose M. [Department of Radiology, Hospital de S. Joao, Porto (Portugal)]. E-mail: jmpjesus@yahoo.com; Madureira, Antonio J. [Department of Radiology, Hospital de S. Joao, Porto (Portugal); Vieira, Alberto [Department of Radiology, Hospital de S. Joao, Porto (Portugal); Ramos, Isabel [Department of Radiology, Hospital de S. Joao, Porto (Portugal)

2005-08-01

Radiological findings of abdominal tuberculosis can mimic those of many different diseases. A high level of suspicion is required, especially in high-risk population. In this article, we will describe barium studies, ultrasound (US) and computed tomography (CT) findings of abdominal tuberculosis (TB), with emphasis in the latest. We will illustrate CT findings that can help in the diagnosis of abdominal tuberculosis and describe imaging features that differentiate it from other inflammatory and neoplastic diseases, particularly lymphoma and Crohn's disease. As tuberculosis can affect any organ in the abdomen, emphasis is placed to ileocecal involvement, lymphadenopathy, peritonitis and solid organ disease (liver, spleen and pancreas). A positive culture or hystologic analysis of biopsy is still required in many patients for definitive diagnosis. Learning objectives:1.To review the relevant pathophysiology of abdominal tuberculosis. 2.Illustrate CT findings that can help in the diagnosis.

Spectroscopic techniques (Mössbauer spectrometry, NMR, ESR,…) as tools to resolve doubtful NMR images: Study of the craniopharyngioma tumor

Science.gov (United States)

Rimbert, J. N.; Dumas, F.; Lafargue, C.; Kellershohn, C.; Brunelle, F.; Lallemand, D.

1990-07-01

Craniopharyngioma, an intracranial tumor, exhibits hyperintensity in the Spin-Echo-T2-NMR image and a hyposignal in the SE-T1-image. However, in some cases (15-20% cases), hypersignals are seen in both SE-T1 and T2-MRI. Using spectroscopic techniques, Mössbauer spectrometry in particular, we have demonstrated that the T1 hypersignal is due to ferritin, dissolved in the cystic liquid, after tumor cell lysis, in the course of time. Other possible reasons inducing a shortening of the T1 relaxation time (presence of lipids, intratumoral hemorrhage) have been rejected.

MR imaging features of peritoneal adenomatoid mesothelioma: a case report

International Nuclear Information System (INIS)

Lins, Cynthia Maria Coelho; Elias Junior, Jorge; Muglia, Valdair Francisco; Monteiro, Carlos Ribeiro; Feres, Omar

2009-01-01

Adenomatoid mesothelioma of the peritoneum (AMP) is a rare benign tumor originating from mesothelial cells.1 Most frequently, AMP occurs between 26 and 55 years of age, at a mean age of 41 years. In contrast to diffuse malignant mesothelioma, which has been linked to asbestos exposure, the etiology of AMP has not been established. Only a minority of patients have symptoms related to the tumor. AMP may present local recurrence, but it has no potential for malignant transformation. Although there are many case reports of abdominal mesotheliomas, to date, there have been no reports of MR imaging features of AMP. In this article, we present the MR imaging features of a case of AMP with histopathological correlation. (author)

MR imaging features of peritoneal adenomatoid mesothelioma: a case report

Energy Technology Data Exchange (ETDEWEB)

Lins, Cynthia Maria Coelho; Elias Junior, Jorge; Muglia, Valdair Francisco; Monteiro, Carlos Ribeiro [University of Sao Paulo (USP), Ribeirao Preto, SP (Brazil). School of Medicine. Dept. of Internal Medicine], e-mail: jejunior@fmrp.usp.br; Cunha, Adilson Ferreira [School of Medicine of Sao Jose do Rio Preto (FAMERP), SP (Brazil). Dept. of Gynecology and Obstetrics; Valeri, Fabio V. [Victorio Valeri Institute of Medical Diagnosis, Ribeirao Preto, SP (Brazil); Feres, Omar [University of Sao Paulo (USP), Ribeirao Preto, SP (Brazil). School of Medicine. Dept. of Surgery and Anatomy

2009-07-01

Adenomatoid mesothelioma of the peritoneum (AMP) is a rare benign tumor originating from mesothelial cells.1 Most frequently, AMP occurs between 26 and 55 years of age, at a mean age of 41 years. In contrast to diffuse malignant mesothelioma, which has been linked to asbestos exposure, the etiology of AMP has not been established. Only a minority of patients have symptoms related to the tumor. AMP may present local recurrence, but it has no potential for malignant transformation. Although there are many case reports of abdominal mesotheliomas, to date, there have been no reports of MR imaging features of AMP. In this article, we present the MR imaging features of a case of AMP with histopathological correlation. (author)

Feature extraction based on extended multi-attribute profiles and sparse autoencoder for remote sensing image classification

Science.gov (United States)

Teffahi, Hanane; Yao, Hongxun; Belabid, Nasreddine; Chaib, Souleyman

2018-02-01

The satellite images with very high spatial resolution have been recently widely used in image classification topic as it has become challenging task in remote sensing field. Due to a number of limitations such as the redundancy of features and the high dimensionality of the data, different classification methods have been proposed for remote sensing images classification particularly the methods using feature extraction techniques. This paper propose a simple efficient method exploiting the capability of extended multi-attribute profiles (EMAP) with sparse autoencoder (SAE) for remote sensing image classification. The proposed method is used to classify various remote sensing datasets including hyperspectral and multispectral images by extracting spatial and spectral features based on the combination of EMAP and SAE by linking them to kernel support vector machine (SVM) for classification. Experiments on new hyperspectral image "Huston data" and multispectral image "Washington DC data" shows that this new scheme can achieve better performance of feature learning than the primitive features, traditional classifiers and ordinary autoencoder and has huge potential to achieve higher accuracy for classification in short running time.

Role of endorectal magnetic resonance spectroscopic imaging in two different Gleason scores in prostate cancer.

Science.gov (United States)

Nagarajan, Rajakumar; Margolis, Daniel; McClure, Tim; Raman, Steve; Thomas, M Albert

2011-01-01

The major goal of the work was to record three-dimensional magnetic resonance spectroscopic imaging (MRSI) and to compare metabolite ratios between different Gleason scores (GS). MRSI localized by endorectal coil-acquired point-resolved spectroscopy was performed in 14 men with prostate cancer of GS 6 (n = 7) and 7 (n = 7) using a 1.5-tesla MRI scanner. The ratio of (choline + creatine)/citrate was increased with an increase of GS, i.e. 0.590 ± 0.171 in the target lesion and 0.321 ± 0.157 in the contralateral region of patients with a GS of 6 as opposed to 1.082 ± 0.432 in the target lesion and 0.360 ± 0.243 in the contralateral region of patients with a GS of 7. Our pilot results demonstrated that MRSI was an additional biochemical tool which is complementary to the current imaging modalities for early diagnosis and therapeutic management of prostate cancer. Copyright © 2011 S. Karger AG, Basel.

Malignant round cell tumours of bone: atypical clinical and imaging features

International Nuclear Information System (INIS)

Saifuddin, A.; Whelan, J.; Pringle, J.A.S.; Cannon, S.R.

2000-01-01

Objective. To describe the clinical, radiological and MRI features of six atypical cases of histologically proven appendicular Ewing sarcoma/ primitive neuroectodermal tumour (PNET). Design. Retrospective review of case notes and available imaging was carried out. Patients. Six patients (4 male, 2 female; mean age 27 years, range 19-44 years), presenting over a 77-month period, were identified from the Bone Tumour Register. All had unusual clinical and imaging features for Ewing sarcoma/PNET.Results and conclusions. Four tumours were centred on the distal femoral metaphysis, one in the proximal tibial metaphysis and one in the distal tibial metaphysis. Plain radiographs were available in four cases and showed minor cortical changes. MRI demonstrated a relatively small, eccentrically located intraosseous component with a large, eccentric extraosseous component. Extension into the epiphysis was seen in three cases and into the adjacent joint in two cases. Intraosseous ''skip'' metastases were present in three cases. The clinical and imaging features were atypical for conventional intraosseous Ewing sarcoma/PNET and the exact site of origin (intraosseous, periosteal or soft-tissue) was unclear. (orig.)

Learning effective color features for content based image retrieval in dermatology

NARCIS (Netherlands)

Bunte, Kerstin; Biehl, Michael; Jonkman, Marcel F.; Petkov, Nicolai

We investigate the extraction of effective color features for a content-based image retrieval (CBIR) application in dermatology. Effectiveness is measured by the rate of correct retrieval of images from four color classes of skin lesions. We employ and compare two different methods to learn

Proton magnetic spectroscopic imaging of the child's brain: the response of tumors to treatment

International Nuclear Information System (INIS)

Tzika, A.A.; Young Poussaint, T.; Astrakas, L.G.; Barnes, P.D.; Goumnerova, L.; Scott, R.M.; Black, P.McL.; Anthony, D.C.; Billett, A.L.; Tarbell, N.J.

2001-01-01

Our aim was to determine and/or predict response to treatment of brain tumors in children using proton magnetic resonance spectroscopic imaging (MRSI). We studied 24 patients aged 10 months to 24 years, using MRI and point-resolved spectroscopy (PRESS; TR 2000 TE 65 ms) with volume preselection and phase-encoding in two dimensions on a 1.5 T imager. Multiple logistic regression was used to establish independent predictors of active tumor growth. Biologically vital cell metabolites, such as N-acetyl aspartate and choline-containing compounds (Cho), were significantly different between tumor and control tissues (P<0.001). The eight brain tumors which responded to radiation or chemotherapy, exhibited lower Cho (P=0.05), higher total creatine (tCr) (P=0.02) and lower lactate and lipid (L) (P=0.04) than16 tumors which were not treated (except by surgery) or did not respond to treatment. The only significant independent predictor of active tumor growth was tCr (P<0.01). We suggest that tCr is useful in assessing response of brain tumors to treatment. (orig.)

Malignant fibrous histiocytoma of bone: conventional X-ray and MR imaging features

International Nuclear Information System (INIS)

Link, T.M.; Haeussler, M.D.; Poppek, S.; Woertler, K.; Rummeny, E.J.; Blasius, S.; Lindner, N.

1998-01-01

Objective. To evaluate the conventional X-ray and MR imaging features of malignant fibrous histiocytoma (MFH) of bone. Design. MRI examinations and conventional radiographs were reviewed in 39 patients with biopsy-proven MFH. Imaging characteristics were analyzed and the differential diagnoses assessed in a masked fashion by two experienced radiologists. Results. Typical X-ray features included aggressive, destructive tumor growth centrally located in the metaphysis of long bones. Periosteal reactions and expansive growth were rarely seen. On MR images extraosseous tumor spread was frequently noted. On T2-weighted images and contrast-enhanced T1-weighted images most of the tumors displayed an inhomogeneous, nodular signal pattern with peripheral Gd-DTPA enhancement. Conclusions. Although several MR imaging criteria were typical for MFH none of them was specific. X-ray diagnosis of MFH may also prove difficult, with the main differential diagnosis being metastasis in the older and osteosarcoma in the younger population. (orig.)

Cervical spine injury in the elderly: imaging features

Energy Technology Data Exchange (ETDEWEB)

Ehara, S. [Dept. of Radiology, Iwate Medical University School of Medicine, Morioka (Japan); Shimamura, Tadashi [Dept. of Orthopedic Surgery, Iwate Medical University School of Medicine, Morioka (Japan)

2001-01-01

An increase in the elderly population has resulted in an increased incidence of cervical spine injury in this group. No specific type of cervical spine trauma is seen in the elderly, although dens fractures are reported to be common. Hyperextension injuries due to falling and the resultant central cord syndrome in the mid and lower cervical segments due to decreased elasticity as a result of spondylosis may be also characteristic. The imaging features of cervical spine injury are often modified by associated spondylosis deformans, DISH and other systemic disorders. The value of MR imaging in such cases is emphasized. (orig.)

FRACTAL IMAGE FEATURE VECTORS WITH APPLICATIONS IN FRACTOGRAPHY

Directory of Open Access Journals (Sweden)

Hynek Lauschmann

2011-05-01

Full Text Available The morphology of fatigue fracture surface (caused by constant cycle loading is strictly related to crack growth rate. This relation may be expressed, among other methods, by means of fractal analysis. Fractal dimension as a single numerical value is not sufficient. Two types of fractal feature vectors are discussed: multifractal and multiparametric. For analysis of images, the box-counting method for 3D is applied with respect to the non-homogeneity of dimensions (two in space, one in brightness. Examples of application are shown: images of several fracture surfaces are analyzed and related to crack growth rate.