Extensive collagen cross-linking affects the mechanical competence of articular cartilage: it can make the cartilage stiffer and more brittle. The concentrations of the best known cross-links, pyridinoline and pentosidine, can be accurately determined by destructive high-performance liquid chromatography (HPLC). We explore a nondestructive evaluation of cross-linking by using the intrinsic fluorescence of the intact cartilage. Articular cartilage samples from bovine knee joints were incubated in threose solution for 40 and 100 h to increase the collagen cross-linking. Control samples without threose were also prepared. Excitation-emission matrices at wavelengths of 220 to 950 nm were acquired from the samples, and the pentosidine and pyridinoline cross-links and the collagen concentrations were determined using HPLC. After the threose treatment, pentosidine and lysyl pyridinole (LP) concentrations increased. The intrinsic fluorescence, excited below 350 nm, decreased and was related to pentosidine [r=−0.90 , 240/325 nm (excitation/emission)] or LP (r=−0.85 , 235/285 nm ) concentrations. Due to overlapping, the changes in emission could not be linked specifically to the recorded cross-links. However, the fluorescence signal enabled a nondestructive optical estimate of changes in the pentosidine and LP cross-linking of intact articular cartilage.
Osteoarthritis (OA) is a common musculoskeletal disorder often diagnosed during arthroscopy. In OA, visual color changes of the articular cartilage surface are typically observed. We demonstrate in vitro the potential of visible light spectral imaging (420 to 720 nm) to quantificate these color changes. Intact bovine articular cartilage samples (n=26) are degraded both enzymatically using the collagenase and mechanically using the emery paper (P60 grit, 269 µm particle size). Spectral images are analyzed by using standard CIELAB color coordinates and the principal component analysis (PCA). After collagenase digestion, changes in the CIELAB coordinates and projection of the spectra to PCA eigenvector are statistically significant (p<0.05). After mechanical degradation, the grinding tracks could not be visualized in the RGB presentation, i.e., in the visual appearance of the sample to the naked eye under the D65 illumination. However, after projecting to the chosen eigenvector, the grinding tracks are revealed. The tracks are also seen by using only one wavelength, i.e., 469 nm, however, the contrast in the projection image is 1.6 to 2.5 times higher. Our results support the idea that the spectral imaging can be used for evaluation of the integrity of the cartilage surface.
The spectral reflectance of icons is measured using a measurement system developed in our previous study, and it is applied to detect metameric color areas in the icons. In this paper, a technique for detecting metameric color areas is proposed and examined by using a test chart and ten icons painted on wooden plates. In the proposed technique, a coefficient showing the degree of metamerism is proposed; based on the definition of metamerism whereby two stimuli can match in color while having different spectral reflectance functions. The experimental results can then be used to consider which parts of the icons have previously been repainted as restoration treatments. Despite the necessity of further consideration using certain chemical analyses and so on to conclude whether or not the experimental results are reliable, they demonstrate that the proposed technique has the basic ability to detect metameric color areas.
In this paper, we present results for optimizing images for an industrial show room. The light conditions are not very controllable and the projector is not a high quality one. The optimization is done using metameric reproduction and to do this we measure spectral information of the product, projector and the illumination at the show room. The spectral characteristic of the red channel of the projector was surprising: the range of possible red values was narrower than the green and blue range. This caused some limitations which needed to be taken into account in calculating the optimal images: optimal images can have either full contrast range with a reddish tint or correct hue with narrower contrast range.
The multiband video camera and display system has developed to increace quality of the video image. The multiband means that there has more than three color channels. The new applications can be created by increasing the number of camera primaries, for example an influence of illumination can be fixed accurately. Furthermore, increasing number of primaries gives a good possibility for image and video processing. However, the growed amount of data causes problems for data transform, store, and process. In this paper, we introduce six band video capturing system and our spectral video database. In the database there is a lot of different type of video clips and the size of spectral video database is more than 300 giga bytes (GB). We also have developed compression scheme for spectral video based on principal component analysis (PCA) and JPEG2000 methods. Here, we concentrate to compress spectral video sequence frame by frame.
A new, semantically meaningful technique for querying the images from a spectral image database is proposed. The technique is based on the use of both color- and texture features. The color features are calculated from spectral images by using the Self-Organizing Map (SOM) when methods of Gray Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP) are used for constructing the texture features. The importance of texture features in a querying is seen in experimental results, which are given by using a real spectral image database. Also the differences between the results gained by the use of co-occurrence matrix and LBP are introduced.
A co-occurrence matrix and Self-Organizing Map (SOM) based technique for searching images from a spectral image database is proposed. At first the SOM is trained and the Best Matching Unit (BMU) histogram is created for every spectral image of a database. Next, the texture-histogram is calculated from the co-occurrence matrices, generated using the 1st inner product images of the spectral images. BMU-histogram and the texture-histogram are combined to one feature histogram and these histograms, generated for each spectral image of a database, are saved to a histogram database. The dissimilarities between the histogram of the query image and the histograms of the database are calculated using different distance measures, more precisely Euclidean distance, dynamic partial distance and Jeffrey divergence. Finally, the images are ordered according to the histogram dissimilarity. The results using a real spectral image database are given.
We propose an experimental setup for an imaging system using a liquid crystal tunable filter (LCTF) to implement rewritable transparent broad-band color filters with arbitrary spectral transmittances. Holding-time for each transmitting wavelength of the LCTF is controlled corresponding to a computationally designed filter function, and a time-integrated intensity image is taken with a monochrome CCD camera through the LCTF. The averaged norm error between the implemented and the expected filter functions was about 10%. The system can be applied to spectral estimation, spectral based classification and spectral based parametr estimation.
We present techniques for representing spectral images in data communications. The spectral domain of the images is represented by a low-dimensional component image set, which is used to obtain an efficient compression of the high- dimensional spectral data. The component images are compressed using a similar technique as the JPEG- and MPEG- type compressions use to subsample the chrominance channels. The spectral compression is based on Principal Component Analysis (PCA) combined with a color image transmission coding technique of chromatic channel subsampling of the component images. The component images are subsampled using 4:2:2, 4:2:0, and 4:1:1-based compressions. In addition, we extended the test for larger block sizes and larger number of component images than in the original JPEG- and MPEG- standards.
Proc. SPIE. 4300, Color Imaging: Device-Independent Color, Color Hardcopy, and Graphic Arts VI
KEYWORDS: Signal to noise ratio, Principal component analysis, Independent component analysis, Image compression, Chromium, Colorimetry, Image transmission, Data communications, 3D image processing, RGB color model
We report a technique for spectral image compression to be used in the field of data communications. The spectral domain of the images is represented by a low-dimensional component image set, which is used to obtain an efficient compression of the high-dimensional spectral data. The component images are compressed using a similar technique as the JPEG- and MPEG-type compressions use to subsample the chrominance channels. The spectral compression is based on Principal Component Analysis (PCA) combined with color image transmission coding technique of 'chromatic channel subsampling' of the component images. The component images are subsampled using 4:2:2, 4:2:0, and 4:1:1-based compressions. In addition, we extended the test for larger block sizes and larger number of component images than in the original JPEG- and MPEG-standards. Totally 50 natural spectral images were used as test material in our experiments. Several error measures of the compression are reported. The same compressions are done using Independent Component Analysis and the results are compared with PCA. These methods give a good compression ratio while keeping visual quality of color still good. Quantitative comparisons between the original and reconstructed spectral images are presented.
In this study, we propose an optical transparent broad-band filter system which can be used to measure a color spectrum and two-dimensional spectral images. The filter function of this system can be changed and rewritten arbitrarily. Spectral distribution of an object color can be represented by a set of inner products between optimized filter functions and the spectral distribution of a sample. In our system, a test image is observed through the filter part consists of a liquid crystal spatial light modulator (LCSLM) and a linear variable filter (LVF) attached together. The intensity image of a sample is taken while the joint device (LCSLM and LVF) is moving just in front of the lens aperture of the CCD-camera. The spectral distribution of the intensity image through the proposed filter almost coincided with the expected filter functions. From the detected intensity images correspond to the inner products between the color filters and a sample, the color spectra of the sample were reconstructed by the use of inverse matrix. The data obtained from the filtering process is only four monochrome images. It is convenient for storing and transmitting the spectral image. The experimental results of measuring a color spectrum and two-dimensional spectral images are presented.
In this work we propose a prototype of the spectral vision system, which can be used to measure a color spectrum and two- dimensional spectral images. We first designed a low- dimensional broad band color filter set with a constraint of positive spectral values by the unsupervised neural network. Then we constructed a compact size optical setup for the spectral synthesizer, which can be used to synthesize the light corresponding to the spectral characteristics of the color filter. In the optical setup we implemented the color filters by the use of the liquid crystal spatial light modulator (LCSLM). In our experiments we illuminated a sample of a real world scene by the synthesized lights and detected the intensity images of the filtering process by the CCD- camera. The intensity images correspond to the optically calculated inner products between the color filters and a sample. The data obtained from the filtering process is only a few monochrome images and therefore convenient for storing and transmitting spectral images. From the detected inner products we reconstructed the sample's color spectra by the use of inverse matrix. We present experimental results of measuring a single color spectrum and two-dimensional spectral images.