In this work, we present a fast and robust method for lesions detection, primarily, a non-linear image enhancement is performed on T1 weighted magnetic resonance (MR) images in order to facilitate an effective segmentation that enables the lesion detection. First a dynamic system that performs the intensity transformation through the Modified sigmoid function contrast stretching is established, then, the enhanced image is used to classify different brain structures including the lesion using constrained fuzzy clustering, and finally, the lesion contour is outlined through the level set evolution. Through experiments, validation of the algorithm was carried out using both clinical and synthetic brain lesion datasets and an 84%–93% overlap performance of the proposed algorithm was obtained with an emphasis on robustness with respect to different lesion types.
In this paper, we present a novel algorithm to motion detection in video sequences. The proposed algorithm is based in
the use of the median of the absolute deviations from the median (MAD) as a measure of statistical dispersion of pixels
in a video sequence to provide the robustness needed to detect motion in a frame of video sequence. By using the MAD,
the proposed algorithm is able to detect small or big objects, the size of the detected objects depend of the size of kernel
used in the analysis of the video sequence. Experimental results in the human motion detection are presented showing
that the proposed algorithm can be used in security applications.
It is presented a robust three dimensional scheme using fuzzy and directional techniques in denoising video color images
contaminated by impulsive random noise. This scheme estimates a noise and movement level in local area, detecting
edges and fine details in an image video sequence. The proposed approach cares the chromaticity properties in
multidimensional and multichannel images. The algorithm was specially designed to reduce computational charge, and
its performance is quantified using objective criteria, such as Pick Signal Noise Relation, Mean Absolute Error and
Normalized Color Difference, as well visual subjective views. Novel filter shows superiority rendering against other well
known algorithms found in the literature. Real-time analysis is realized on Digital Signal Processor to outperform
processing capability. The DSP was designed by Texas Instruments for multichannel processing in the multitask process,
and permits to improve the performance of several tasks, and at the same time enhancing processing time and reducing
computational charge in such a dedicated hardware.
In literature, numerous algorithms in image denoising in case of a noise of different nature were implemented. One of
the principal noises is impulsive one companioning any transmission process. This paper presents novel approach
unificating two most powerful techniques used during last years: directional processing and fuzzy-set techniques. Novel
method permits the detection of noisy pixels and local movements (edges and fine details) in a static image or in an
image sequence. The proposed algorithm realizes the noise suppression preserving fine details and edges, as so as color
chromaticity properties in the multichannel image. We present applications of proposed algorithm in color imaging and
in multichannel remote sensing from several bands. Finally, hardware requirements are evaluated permitting real time
implementation on DSP of Texas Instruments using a Reference Framework defined as RF5. It was implemented on
DSP the multichannel algorithms in a multitask process that permits to improve the performance of several tasks, and at
the same time enhancing the time processing and reducing computational charge in a dedicated hardware. Numerous
experimental results in the processing the color images/sequences and satellite remote sensing data show the superiority
of proposed approach as in objective criteria (PSNR, MAE, NCD), as in visual subjective way. The needed processing
times and visual characteristics are exposed in the paper demonstrating accepted performance of the approach.
The usage of spatial-temporal information is more efficient than just their usage in a separate way. It has been designed a
new fuzzy logic adaptive scheme applying directional and fuzzy processing technique with motion detection and spatialtemporal
filtering of video sequences. The proposed method can distinguish the uniform regions, edges and details
features in the images decreasing the processing time charges, taking only in account the samples, which demonstrate
high level of corruption or motion. The algorithm runs adapting spatial-temporal information to smooth an additive
noise. The non-stationary noise, which left after temporal algorithm, is removed employing a magnitude algorithm that is
adapted using parameters obtained during the filtering. The designed algorithm is compared with other filters found in
literature, showing the effectiveness of proposed fuzzy logic filtering approach.
Processing of the vector image information is seemed very important because multichannel sensors used in different
applications. We introduce novel algorithms to process color images that are based on order statistics and vectorial
processing techniques: Video Adaptive Vector Directional (VAVDF) and the Vector Median M-type K-Nearest
Neighbour (VMMKNN) Filters presented in this paper. It has been demonstrated that novel algorithms suppress
effectively an impulsive noise in comparison with different other methods in 3D video color sequences. Simulation
results have been obtained using video sequences "Miss America" and "Flowers", which were corrupted by noise.
The filters: KNNF, VGVDF, VMMKNN, and, finally the proposed VAVDATM have been investigated. The
criteria PSNR, MAE and NCD demonstrate that the VAVDATM filter has shown the best performances in each a
criterion when intensity of noise is more that 7-10%. An attempt to realize the real-time processing on the DSP is
presented for median type algorithms techniques.
Novel algorithms to suppress impulsive noise in 3D color images are presented. Some of them have demonstrated effectiveness in preservation of inherent characteristics in the images, such as, edges, details and chromaticity. Robust algorithm that uses order statistics, vector directional and adaptive methods is developed applying three-dimensional video processing permitting suppressing a noise. Several algorithms are extended from 2D to 3D for video processing. The results show that proposed Video Adaptive Vector Directional filter outperforms the video versions of Median M-type K-Nearest Neighbour, Vector Median, Generalized Vector Directional, K-Nearest Neighbour, α-trimmed Mean, and Median filters. All of them evaluated during simulation using PSNR, MAE and NCD criteria.
Proc. SPIE. 5298, Image Processing: Algorithms and Systems III
KEYWORDS: Optical filters, Statistical analysis, Image processing, Digital filtering, Color image processing, Image filtering, Nonlinear filtering, Color imaging, Filtering (signal processing), RGB color model
We present the analysis and simulation results for some modifications of the vectorial color imaging procedures those use at the second stage of magnitude processing the different order statistics filters.
The technique of non-parametric filtering is presented and investigated in this paper too. For unknown functional form of noise density estimated from the observations we use the gray scalar filters to provide the reference vectors needed to realize the calculations. The performances of the traditional order statistics algorithms such as, median, Vector Median, alfa-trimmed mean, Wilcoxon, other order statistics M KNN are analyzed in the paper.
For comparison analysis of the color imaging we use the following criterions: MAE; PSNR; MCRE; NCD
Numerous simulation results which characterize the impulsive noise suppression and fine detail preservation are presented in the paper using different test images) such as: Lena, Mandrill, Peppers, etc. (256x256, 24 bits, RGB space). The algorithms those demonstrated good performance results have been applied to process the video sequences: “Miss America”, “Flowers” and Foreman” corrupted by impulsive noise.
The results of the simulations presented in the paper show differences in color imaging by mentioned filtering technique and help to choose the filter that can satisfy to several criterion at dependence on noise level value.
Color image and video sequence restoration and improvement are complicated due to presence of various kinds of random noise. Impulsive noise is introduced by acquisition or broadcasting errors into communication channels. Non linear filters can provide good performance in terms of the signal-to-noise ratio in different levels of corruption as soon as minimum error chromaticity and minimum perceptual error. This paper presents the capability and real-time processing features of several processing techniques such as “directional processing”, “non parametric approaches” and “order statistics” filters. Some of such the filters were: Median Filter (MF), Vector Median Filter (VMF), -Trimmed Mean Filter (ATMF), Generalized Vector Directional Filter (GVDF), Adaptive Multichannel Non Parametric Filter (AMNF), Median M-type K-Nearest Neighbour (MM-KNN) filter, Wilcoxon M-type K-Nearest Neighbour (WM-KNN) filter, Ansari-Bradley-Siegel-Tukey M-Type K-Nearest Neighbor (ABSTM-KNN) filter, etc.
Extensive simulations in reference color RGB images “Lena”, “Mandrill”, “Peppers” and QCIF format video sequences (Miss America, Flowers and Foreman) have demonstrated that the proposed filters consistently can outperform the known nonlinear filters. The used performance criteria in color imaging were the traditional ones: PSNR, MAE and other specific for color imaging, NCD and MCRE. The real-time implementation of image filtering was realized on the DSP TMS320C6701. The processing time of proposed filters includes the duration of data acquisition, processing and store data. We simulated impulse corrupted color image QCIF sequences to demonstrate that some of the proposed and analyzing filters potentially could provide on line processing to quality video transmission of the images.
The IAI Network for the measurement of ultraviolet radiation in Chile, Argentina and Puerto Rico is composed of ten multi-channel radiometers (GUV 511, Bisopherical Instruments Inc.), which are periodically sun calibrated with a traveling reference GUV (RGUV). The RGUV is calibrated under solar light against a SUV100 spectroradiometer. This calibration is then transferred to each instrument in the network through the RGUV. A previous multi-regression model proved to be suitable to derive narrowband irradiance from broadband irradiance, ozone column and solar zenith angles (SZA). A recent modification of the existing multi-regression model improved the multi-channel instrument sun calibration against spectroradiometers. In this approach, the narrowband irradiance is the SUV spectral irradiance and the broadband is the multi-channel GUV irradiance. We included the azimuth angle as a parameter into the multi-regression equation and we applied a non-linear function, instead of a single coefficient, to correct for SZA. In this paper, the new multi-regression approach is applied to both steps of a GUV calibration: SUV - RGUV and RGUV - GUV and the results are compared with traditional calibration methods. Important improvements are observed in the calibration, in particular for SZA larger than 50°.