Real-Time Adaptive Color Segmentation by Neural Networks
NASA Technical Reports Server (NTRS)
Duong, Tuan A.
2004-01-01
Artificial neural networks that would utilize the cascade error projection (CEP) algorithm have been proposed as means of autonomous, real-time, adaptive color segmentation of images that change with time. In the original intended application, such a neural network would be used to analyze digitized color video images of terrain on a remote planet as viewed from an uninhabited spacecraft approaching the planet. During descent toward the surface of the planet, information on the segmentation of the images into differently colored areas would be updated adaptively in real time to capture changes in contrast, brightness, and resolution, all in an effort to identify a safe and scientifically productive landing site and provide control feedback to steer the spacecraft toward that site. Potential terrestrial applications include monitoring images of crops to detect insect invasions and monitoring of buildings and other facilities to detect intruders. The CEP algorithm is reliable and is well suited to implementation in very-large-scale integrated (VLSI) circuitry. It was chosen over other neural-network learning algorithms because it is better suited to realtime learning: It provides a self-evolving neural-network structure, requires fewer iterations to converge and is more tolerant to low resolution (that is, fewer bits) in the quantization of neural-network synaptic weights. Consequently, a CEP neural network learns relatively quickly, and the circuitry needed to implement it is relatively simple. Like other neural networks, a CEP neural network includes an input layer, hidden units, and output units (see figure). As in other neural networks, a CEP network is presented with a succession of input training patterns, giving rise to a set of outputs that are compared with the desired outputs. Also as in other neural networks, the synaptic weights are updated iteratively in an effort to bring the outputs closer to target values. A distinctive feature of the CEP neural
Inference of segmented color and texture description by tensor voting.
Jia, Jiaya; Tang, Chi-Keung
2004-06-01
A robust synthesis method is proposed to automatically infer missing color and texture information from a damaged 2D image by (N)D tensor voting (N > 3). The same approach is generalized to range and 3D data in the presence of occlusion, missing data and noise. Our method translates texture information into an adaptive (N)D tensor, followed by a voting process that infers noniteratively the optimal color values in the (N)D texture space. A two-step method is proposed. First, we perform segmentation based on insufficient geometry, color, and texture information in the input, and extrapolate partitioning boundaries by either 2D or 3D tensor voting to generate a complete segmentation for the input. Missing colors are synthesized using (N)D tensor voting in each segment. Different feature scales in the input are automatically adapted by our tensor scale analysis. Results on a variety of difficult inputs demonstrate the effectiveness of our tensor voting approach.
Traffic sign recognition by color segmentation and neural network
NASA Astrophysics Data System (ADS)
Surinwarangkoon, Thongchai; Nitsuwat, Supot; Moore, Elvin J.
2011-12-01
An algorithm is proposed for traffic sign detection and identification based on color filtering, color segmentation and neural networks. Traffic signs in Thailand are classified by color into four types: namely, prohibitory signs (red or blue), general warning signs (yellow) and construction area warning signs (amber). A color filtering method is first used to detect traffic signs and classify them by type. Then color segmentation methods adapted for each color type are used to extract inner features, e.g., arrows, bars etc. Finally, neural networks trained to recognize signs in each color type are used to identify any given traffic sign. Experiments show that the algorithm can improve the accuracy of traffic sign detection and recognition for the traffic signs used in Thailand.
Color segmentation in the HSI color space using the K-means algorithm
NASA Astrophysics Data System (ADS)
Weeks, Arthur R.; Hague, G. Eric
1997-04-01
Segmentation of images is an important aspect of image recognition. While grayscale image segmentation has become quite a mature field, much less work has been done with regard to color image segmentation. Until recently, this was predominantly due to the lack of available computing power and color display hardware that is required to manipulate true color images (24-bit). TOday, it is not uncommon to find a standard desktop computer system with a true-color 24-bit display, at least 8 million bytes of memory, and 2 gigabytes of hard disk storage. Segmentation of color images is not as simple as segmenting each of the three RGB color components separately. The difficulty of using the RGB color space is that it doesn't closely model the psychological understanding of color. A better color model, which closely follows that of human visual perception is the hue, saturation, intensity model. This color model separates the color components in terms of chromatic and achromatic information. Strickland et al. was able to show the importance of color in the extraction of edge features form an image. His method enhances the edges that are detectable in the luminance image with information from the saturation image. Segmentation of both the saturation and intensity components is easily accomplished with any gray scale segmentation algorithm, since these spaces are linear. The modulus 2(pi) nature of the hue color component makes its segmentation difficult. For example, a hue of 0 and 2(pi) yields the same color tint. Instead of applying separate image segmentation to each of the hue, saturation, and intensity components, a better method is to segment the chromatic component separately from the intensity component because of the importance that the chromatic information plays in the segmentation of color images. This paper presents a method of using the gray scale K-means algorithm to segment 24-bit color images. Additionally, this paper will show the importance the hue
Brain MR image segmentation using NAMS in pseudo-color.
Li, Hua; Chen, Chuanbo; Fang, Shaohong; Zhao, Shengrong
2017-12-01
Image segmentation plays a crucial role in various biomedical applications. In general, the segmentation of brain Magnetic Resonance (MR) images is mainly used to represent the image with several homogeneous regions instead of pixels for surgical analyzing and planning. This paper proposes a new approach for segmenting MR brain images by using pseudo-color based segmentation with Non-symmetry and Anti-packing Model with Squares (NAMS). First of all, the NAMS model is presented. The model can represent the image with sub-patterns to keep the image content and largely reduce the data redundancy. Second, the key idea is proposed that convert the original gray-scale brain MR image into a pseudo-colored image and then segment the pseudo-colored image with NAMS model. The pseudo-colored image can enhance the color contrast in different tissues in brain MR images, which can improve the precision of segmentation as well as directly visual perceptional distinction. Experimental results indicate that compared with other brain MR image segmentation methods, the proposed NAMS based pseudo-color segmentation method performs more excellent in not only segmenting precisely but also saving storage.
BlobContours: adapting Blobworld for supervised color- and texture-based image segmentation
NASA Astrophysics Data System (ADS)
Vogel, Thomas; Nguyen, Dinh Quyen; Dittmann, Jana
2006-01-01
Extracting features is the first and one of the most crucial steps in recent image retrieval process. While the color features and the texture features of digital images can be extracted rather easily, the shape features and the layout features depend on reliable image segmentation. Unsupervised image segmentation, often used in image analysis, works on merely syntactical basis. That is, what an unsupervised segmentation algorithm can segment is only regions, but not objects. To obtain high-level objects, which is desirable in image retrieval, human assistance is needed. Supervised image segmentations schemes can improve the reliability of segmentation and segmentation refinement. In this paper we propose a novel interactive image segmentation technique that combines the reliability of a human expert with the precision of automated image segmentation. The iterative procedure can be considered a variation on the Blobworld algorithm introduced by Carson et al. from EECS Department, University of California, Berkeley. Starting with an initial segmentation as provided by the Blobworld framework, our algorithm, namely BlobContours, gradually updates it by recalculating every blob, based on the original features and the updated number of Gaussians. Since the original algorithm has hardly been designed for interactive processing we had to consider additional requirements for realizing a supervised segmentation scheme on the basis of Blobworld. Increasing transparency of the algorithm by applying usercontrolled iterative segmentation, providing different types of visualization for displaying the segmented image and decreasing computational time of segmentation are three major requirements which are discussed in detail.
Bayesian Fusion of Color and Texture Segmentations
NASA Technical Reports Server (NTRS)
Manduchi, Roberto
2000-01-01
In many applications one would like to use information from both color and texture features in order to segment an image. We propose a novel technique to combine "soft" segmentations computed for two or more features independently. Our algorithm merges models according to a mean entropy criterion, and allows to choose the appropriate number of classes for the final grouping. This technique also allows to improve the quality of supervised classification based on one feature (e.g. color) by merging information from unsupervised segmentation based on another feature (e.g., texture.)
Color adaptation induced from linguistic description of color
Zheng, Liling; Huang, Ping; Zhong, Xiao; Li, Tianfeng; Mo, Lei
2017-01-01
Recent theories propose that language comprehension can influence perception at the low level of perceptual system. Here, we used an adaptation paradigm to test whether processing language caused color adaptation in the visual system. After prolonged exposure to a color linguistic context, which depicted red, green, or non-specific color scenes, participants immediately performed a color detection task, indicating whether they saw a green color square in the middle of a white screen or not. We found that participants were more likely to perceive the green color square after listening to discourses denoting red compared to discourses denoting green or conveying non-specific color information, revealing that language comprehension caused an adaptation aftereffect at the perceptual level. Therefore, semantic representation of color may have a common neural substrate with color perception. These results are in line with the simulation view of embodied language comprehension theory, which predicts that processing language reactivates the sensorimotor systems that are engaged during real experience. PMID:28358807
Color adaptation induced from linguistic description of color.
Zheng, Liling; Huang, Ping; Zhong, Xiao; Li, Tianfeng; Mo, Lei
2017-01-01
Recent theories propose that language comprehension can influence perception at the low level of perceptual system. Here, we used an adaptation paradigm to test whether processing language caused color adaptation in the visual system. After prolonged exposure to a color linguistic context, which depicted red, green, or non-specific color scenes, participants immediately performed a color detection task, indicating whether they saw a green color square in the middle of a white screen or not. We found that participants were more likely to perceive the green color square after listening to discourses denoting red compared to discourses denoting green or conveying non-specific color information, revealing that language comprehension caused an adaptation aftereffect at the perceptual level. Therefore, semantic representation of color may have a common neural substrate with color perception. These results are in line with the simulation view of embodied language comprehension theory, which predicts that processing language reactivates the sensorimotor systems that are engaged during real experience.
Video rate color region segmentation for mobile robotic applications
NASA Astrophysics Data System (ADS)
de Cabrol, Aymeric; Bonnin, Patrick J.; Hugel, Vincent; Blazevic, Pierre; Chetto, Maryline
2005-08-01
Color Region may be an interesting image feature to extract for visual tasks in robotics, such as navigation and obstacle avoidance. But, whereas numerous methods are used for vision systems embedded on robots, only a few use this segmentation mainly because of the processing duration. In this paper, we propose a new real-time (ie. video rate) color region segmentation followed by a robust color classification and a merging of regions, dedicated to various applications such as RoboCup four-legged league or an industrial conveyor wheeled robot. Performances of this algorithm and confrontation with other methods, in terms of result quality and temporal performances are provided. For better quality results, the obtained speed up is between 2 and 4. For same quality results, the it is up to 10. We present also the outlines of the Dynamic Vision System of the CLEOPATRE Project - for which this segmentation has been developed - and the Clear Box Methodology which allowed us to create the new color region segmentation from the evaluation and the knowledge of other well known segmentations.
NASA Astrophysics Data System (ADS)
Zhang, Ka; Sheng, Yehua; Gong, Zhijun; Ye, Chun; Li, Yongqiang; Liang, Cheng
2007-06-01
As an important sub-system in intelligent transportation system (ITS), the detection and recognition of traffic signs from mobile images is becoming one of the hot spots in the international research field of ITS. Considering the problem of traffic sign automatic detection in motion images, a new self-adaptive algorithm for traffic sign detection based on color and shape features is proposed in this paper. Firstly, global statistical color features of different images are computed based on statistics theory. Secondly, some self-adaptive thresholds and special segmentation rules for image segmentation are designed according to these global color features. Then, for red, yellow and blue traffic signs, the color image is segmented to three binary images by these thresholds and rules. Thirdly, if the number of white pixels in the segmented binary image exceeds the filtering threshold, the binary image should be further filtered. Fourthly, the method of gray-value projection is used to confirm top, bottom, left and right boundaries for candidate regions of traffic signs in the segmented binary image. Lastly, if the shape feature of candidate region satisfies the need of real traffic sign, this candidate region is confirmed as the detected traffic sign region. The new algorithm is applied to actual motion images of natural scenes taken by a CCD camera of the mobile photogrammetry system in Nanjing at different time. The experimental results show that the algorithm is not only simple, robust and more adaptive to natural scene images, but also reliable and high-speed on real traffic sign detection.
Segmentation and Classification of Burn Color Images
2001-10-25
SEGMENTATION AND CLASSIFICATION OF BURN COLOR IMAGES Begoña Acha1, Carmen Serrano1, Laura Roa2 1Área de Teoría de la Señal y Comunicaciones ...2000, Las Vegas (USA), pp. 411-415. [21] G. Wyszecki and W.S. Stiles, Color Science: Concepts and Methods, Quantitative Data and Formulae (New
White blood cell segmentation by color-space-based k-means clustering.
Zhang, Congcong; Xiao, Xiaoyan; Li, Xiaomei; Chen, Ying-Jie; Zhen, Wu; Chang, Jun; Zheng, Chengyun; Liu, Zhi
2014-09-01
White blood cell (WBC) segmentation, which is important for cytometry, is a challenging issue because of the morphological diversity of WBCs and the complex and uncertain background of blood smear images. This paper proposes a novel method for the nucleus and cytoplasm segmentation of WBCs for cytometry. A color adjustment step was also introduced before segmentation. Color space decomposition and k-means clustering were combined for segmentation. A database including 300 microscopic blood smear images were used to evaluate the performance of our method. The proposed segmentation method achieves 95.7% and 91.3% overall accuracy for nucleus segmentation and cytoplasm segmentation, respectively. Experimental results demonstrate that the proposed method can segment WBCs effectively with high accuracy.
Development of a novel 2D color map for interactive segmentation of histological images.
Chaudry, Qaiser; Sharma, Yachna; Raza, Syed H; Wang, May D
2012-05-01
We present a color segmentation approach based on a two-dimensional color map derived from the input image. Pathologists stain tissue biopsies with various colored dyes to see the expression of biomarkers. In these images, because of color variation due to inconsistencies in experimental procedures and lighting conditions, the segmentation used to analyze biological features is usually ad-hoc. Many algorithms like K-means use a single metric to segment the image into different color classes and rarely provide users with powerful color control. Our 2D color map interactive segmentation technique based on human color perception information and the color distribution of the input image, enables user control without noticeable delay. Our methodology works for different staining types and different types of cancer tissue images. Our proposed method's results show good accuracy with low response and computational time making it a feasible method for user interactive applications involving segmentation of histological images.
Study of chromatic adaptation using memory color matches, Part II: colored illuminants.
Smet, Kevin A G; Zhai, Qiyan; Luo, Ming R; Hanselaer, Peter
2017-04-03
In a previous paper, 12 corresponding color data sets were derived for 4 neutral illuminants using the long-term memory colours of five familiar objects. The data were used to test several linear (one-step and two-step von Kries, RLAB) and nonlinear (Hunt and Nayatani) chromatic adaptation transforms (CAT). This paper extends that study to a total of 156 corresponding color sets by including 9 more colored illuminants: 2 with low and 2 with high correlated color temperatures as well as 5 representing high chroma adaptive conditions. As in the previous study, a two-step von Kries transform whereby the degree of adaptation D is optimized to minimize the DEu'v' prediction errors outperformed all other tested models for both memory color and literature corresponding color sets, whereby prediction errors were lower for the memory color set. Most of the transforms tested, except the two- and one-step von Kries models with optimized D, showed large errors for corresponding color subsets that contained non-neutral adaptive conditions as all of them tended to overestimate the effective degree of adaptation in this study. An analysis of the impact of the sensor space primaries in which the adaptation is performed was found to have little impact compared to that of model choice. Finally, the effective degree of adaptation for the 13 illumination conditions (4 neutral + 9 colored) was successfully modelled using a bivariate Gaussian in a Macleod-Boyton like chromaticity diagram.
Unsupervised color image segmentation using a lattice algebra clustering technique
NASA Astrophysics Data System (ADS)
Urcid, Gonzalo; Ritter, Gerhard X.
2011-08-01
In this paper we introduce a lattice algebra clustering technique for segmenting digital images in the Red-Green- Blue (RGB) color space. The proposed technique is a two step procedure. Given an input color image, the first step determines the finite set of its extreme pixel vectors within the color cube by means of the scaled min-W and max-M lattice auto-associative memory matrices, including the minimum and maximum vector bounds. In the second step, maximal rectangular boxes enclosing each extreme color pixel are found using the Chebychev distance between color pixels; afterwards, clustering is performed by assigning each image pixel to its corresponding maximal box. The two steps in our proposed method are completely unsupervised or autonomous. Illustrative examples are provided to demonstrate the color segmentation results including a brief numerical comparison with two other non-maximal variations of the same clustering technique.
Hair segmentation using adaptive threshold from edge and branch length measures.
Lee, Ian; Du, Xian; Anthony, Brian
2017-10-01
Non-invasive imaging techniques allow the monitoring of skin structure and diagnosis of skin diseases in clinical applications. However, hair in skin images hampers the imaging and classification of the skin structure of interest. Although many hair segmentation methods have been proposed for digital hair removal, a major challenge in hair segmentation remains in detecting hairs that are thin, overlapping, of similar contrast or color to underlying skin, or overlaid on highly-textured skin structure. To solve the problem, we present an automatic hair segmentation method that uses edge density (ED) and mean branch length (MBL) to measure hair. First, hair is detected by the integration of top-hat transform and modified second-order Gaussian filter. Second, we employ a robust adaptive threshold of ED and MBL to generate a hair mask. Third, the hair mask is refined by k-NN classification of hair and skin pixels. The proposed algorithm was tested using two datasets of healthy skin images and lesion images respectively. These datasets were taken from different imaging platforms in various illumination levels and varying skin colors. We compared the hair detection and segmentation results from our algorithm and six other hair segmentation methods of state of the art. Our method exhibits high value of sensitivity: 75% and specificity: 95%, which indicates significantly higher accuracy and better balance between true positive and false positive detection than the other methods. Published by Elsevier Ltd.
Color image segmentation to detect defects on fresh ham
NASA Astrophysics Data System (ADS)
Marty-Mahe, Pascale; Loisel, Philippe; Brossard, Didier
2003-04-01
We present in this paper the color segmentation methods that were used to detect appearance defects on 3 dimensional shape of fresh ham. The use of color histograms turned out to be an efficient solution to characterize the healthy skin, but a special care must be taken to choose the color components because of the 3 dimensional shape of ham.
Adaptation of human skin color in various populations.
Deng, Lian; Xu, Shuhua
2018-01-01
Skin color is a well-recognized adaptive trait and has been studied extensively in humans. Understanding the genetic basis of adaptation of skin color in various populations has many implications in human evolution and medicine. Impressive progress has been made recently to identify genes associated with skin color variation in a wide range of geographical and temporal populations. In this review, we discuss what is currently known about the genetics of skin color variation. We enumerated several cases of skin color adaptation in global modern humans and archaic hominins, and illustrated why, when, and how skin color adaptation occurred in different populations. Finally, we provided a summary of the candidate loci associated with pigmentation, which could be a valuable reference for further evolutionary and medical studies. Previous studies generally indicated a complex genetic mechanism underlying the skin color variation, expanding our understanding of the role of population demographic history and natural selection in shaping genetic and phenotypic diversity in humans. Future work is needed to dissect the genetic architecture of skin color adaptation in numerous ethnic minority groups around the world, which remains relatively obscure compared with that of major continental groups, and to unravel the exact genetic basis of skin color adaptation.
A kind of color image segmentation algorithm based on super-pixel and PCNN
NASA Astrophysics Data System (ADS)
Xu, GuangZhu; Wang, YaWen; Zhang, Liu; Zhao, JingJing; Fu, YunXia; Lei, BangJun
2018-04-01
Image segmentation is a very important step in the low-level visual computing. Although image segmentation has been studied for many years, there are still many problems. PCNN (Pulse Coupled Neural network) has biological background, when it is applied to image segmentation it can be viewed as a region-based method, but due to the dynamics properties of PCNN, many connectionless neurons will pulse at the same time, so it is necessary to identify different regions for further processing. The existing PCNN image segmentation algorithm based on region growing is used for grayscale image segmentation, cannot be directly used for color image segmentation. In addition, the super-pixel can better reserve the edges of images, and reduce the influences resulted from the individual difference between the pixels on image segmentation at the same time. Therefore, on the basis of the super-pixel, the original PCNN algorithm based on region growing is improved by this paper. First, the color super-pixel image was transformed into grayscale super-pixel image which was used to seek seeds among the neurons that hadn't been fired. And then it determined whether to stop growing by comparing the average of each color channel of all the pixels in the corresponding regions of the color super-pixel image. Experiment results show that the proposed algorithm for the color image segmentation is fast and effective, and has a certain effect and accuracy.
Do common mechanisms of adaptation mediate color discrimination and appearance? Contrast adaptation
NASA Astrophysics Data System (ADS)
Hillis, James M.; Brainard, David H.
2007-08-01
Are effects of background contrast on color appearance and sensitivity controlled by the same mechanism of adaptation? We examined the effects of background color contrast on color appearance and on color-difference sensitivity under well-matched conditions. We linked the data using Fechner's hypothesis that the rate of apparent stimulus change is proportional to sensitivity and examined a family of parametric models of adaptation. Our results show that both appearance and discrimination are consistent with the same mechanism of adaptation.
Gross, Colin A; Reddy, Chandan K; Dazzo, Frank B
2010-02-01
Quantitative microscopy and digital image analysis are underutilized in microbial ecology largely because of the laborious task to segment foreground object pixels from background, especially in complex color micrographs of environmental samples. In this paper, we describe an improved computing technology developed to alleviate this limitation. The system's uniqueness is its ability to edit digital images accurately when presented with the difficult yet commonplace challenge of removing background pixels whose three-dimensional color space overlaps the range that defines foreground objects. Image segmentation is accomplished by utilizing algorithms that address color and spatial relationships of user-selected foreground object pixels. Performance of the color segmentation algorithm evaluated on 26 complex micrographs at single pixel resolution had an overall pixel classification accuracy of 99+%. Several applications illustrate how this improved computing technology can successfully resolve numerous challenges of complex color segmentation in order to produce images from which quantitative information can be accurately extracted, thereby gain new perspectives on the in situ ecology of microorganisms. Examples include improvements in the quantitative analysis of (1) microbial abundance and phylotype diversity of single cells classified by their discriminating color within heterogeneous communities, (2) cell viability, (3) spatial relationships and intensity of bacterial gene expression involved in cellular communication between individual cells within rhizoplane biofilms, and (4) biofilm ecophysiology based on ribotype-differentiated radioactive substrate utilization. The stand-alone executable file plus user manual and tutorial images for this color segmentation computing application are freely available at http://cme.msu.edu/cmeias/ . This improved computing technology opens new opportunities of imaging applications where discriminating colors really matter most
Word segmentation by alternating colors facilitates eye guidance in Chinese reading.
Zhou, Wei; Wang, Aiping; Shu, Hua; Kliegl, Reinhold; Yan, Ming
2018-02-12
During sentence reading, low spatial frequency information afforded by spaces between words is the primary factor for eye guidance in spaced writing systems, whereas saccade generation for unspaced writing systems is less clear and under debate. In the present study, we investigated whether word-boundary information, provided by alternating colors (consistent or inconsistent with word-boundary information) influences saccade-target selection in Chinese. In Experiment 1, as compared to a baseline (i.e., uniform color) condition, word segmentation with alternating color shifted fixation location towards the center of words. In contrast, incorrect word segmentation shifted fixation location towards the beginning of words. In Experiment 2, we used a gaze-contingent paradigm to restrict the color manipulation only to the upcoming parafoveal words and replicated the results, including fixation location effects, as observed in Experiment 1. These results indicate that Chinese readers are capable of making use of parafoveal word-boundary knowledge for saccade generation, even if such information is unfamiliar to them. The present study provides novel support for the hypothesis that word segmentation is involved in the decision about where to fixate next during Chinese reading.
Multiple Auto-Adapting Color Balancing for Large Number of Images
NASA Astrophysics Data System (ADS)
Zhou, X.
2015-04-01
This paper presents a powerful technology of color balance between images. It does not only work for small number of images but also work for unlimited large number of images. Multiple adaptive methods are used. To obtain color seamless mosaic dataset, local color is adjusted adaptively towards the target color. Local statistics of the source images are computed based on the so-called adaptive dodging window. The adaptive target colors are statistically computed according to multiple target models. The gamma function is derived from the adaptive target and the adaptive source local stats. It is applied to the source images to obtain the color balanced output images. Five target color surface models are proposed. They are color point (or single color), color grid, 1st, 2nd and 3rd 2D polynomials. Least Square Fitting is used to obtain the polynomial target color surfaces. Target color surfaces are automatically computed based on all source images or based on an external target image. Some special objects such as water and snow are filtered by percentage cut or a given mask. Excellent results are achieved. The performance is extremely fast to support on-the-fly color balancing for large number of images (possible of hundreds of thousands images). Detailed algorithm and formulae are described. Rich examples including big mosaic datasets (e.g., contains 36,006 images) are given. Excellent results and performance are presented. The results show that this technology can be successfully used in various imagery to obtain color seamless mosaic. This algorithm has been successfully using in ESRI ArcGis.
Attique, Muhammad; Gilanie, Ghulam; Hafeez-Ullah; Mehmood, Malik S.; Naweed, Muhammad S.; Ikram, Masroor; Kamran, Javed A.; Vitkin, Alex
2012-01-01
Characterization of tissues like brain by using magnetic resonance (MR) images and colorization of the gray scale image has been reported in the literature, along with the advantages and drawbacks. Here, we present two independent methods; (i) a novel colorization method to underscore the variability in brain MR images, indicative of the underlying physical density of bio tissue, (ii) a segmentation method (both hard and soft segmentation) to characterize gray brain MR images. The segmented images are then transformed into color using the above-mentioned colorization method, yielding promising results for manual tracing. Our color transformation incorporates the voxel classification by matching the luminance of voxels of the source MR image and provided color image by measuring the distance between them. The segmentation method is based on single-phase clustering for 2D and 3D image segmentation with a new auto centroid selection method, which divides the image into three distinct regions (gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) using prior anatomical knowledge). Results have been successfully validated on human T2-weighted (T2) brain MR images. The proposed method can be potentially applied to gray-scale images from other imaging modalities, in bringing out additional diagnostic tissue information contained in the colorized image processing approach as described. PMID:22479421
Adaptive color demosaicing and false color removal
NASA Astrophysics Data System (ADS)
Guarnera, Mirko; Messina, Giuseppe; Tomaselli, Valeria
2010-04-01
Color interpolation solutions drastically influence the quality of the whole image generation pipeline, so they must guarantee the rendering of high quality pictures by avoiding typical artifacts such as blurring, zipper effects, and false colors. Moreover, demosaicing should avoid emphasizing typical artifacts of real sensors data, such as noise and green imbalance effect, which would be further accentuated by the subsequent steps of the processing pipeline. We propose a new adaptive algorithm that decides the interpolation technique to apply to each pixel, according to its neighborhood analysis. Edges are effectively interpolated through a directional filtering approach that interpolates the missing colors, selecting the suitable filter depending on edge orientation. Regions close to edges are interpolated through a simpler demosaicing approach. Thus flat regions are identified and low-pass filtered to eliminate some residual noise and to minimize the annoying green imbalance effect. Finally, an effective false color removal algorithm is used as a postprocessing step to eliminate residual color errors. The experimental results show how sharp edges are preserved, whereas undesired zipper effects are reduced, improving the edge resolution itself and obtaining superior image quality.
Habitual wearers of colored lenses adapt more rapidly to the color changes the lenses produce.
Engel, Stephen A; Wilkins, Arnold J; Mand, Shivraj; Helwig, Nathaniel E; Allen, Peter M
2016-08-01
The visual system continuously adapts to the environment, allowing it to perform optimally in a changing visual world. One large change occurs every time one takes off or puts on a pair of spectacles. It would be advantageous for the visual system to learn to adapt particularly rapidly to such large, commonly occurring events, but whether it can do so remains unknown. Here, we tested whether people who routinely wear spectacles with colored lenses increase how rapidly they adapt to the color shifts their lenses produce. Adaptation to a global color shift causes the appearance of a test color to change. We measured changes in the color that appeared "unique yellow", that is neither reddish nor greenish, as subjects donned and removed their spectacles. Nine habitual wearers and nine age-matched control subjects judged the color of a small monochromatic test light presented with a large, uniform, whitish surround every 5s. Red lenses shifted unique yellow to more reddish colors (longer wavelengths), and greenish lenses shifted it to more greenish colors (shorter wavelengths), consistent with adaptation "normalizing" the appearance of the world. In controls, the time course of this adaptation contained a large, rapid component and a smaller gradual one, in agreement with prior results. Critically, in habitual wearers the rapid component was significantly larger, and the gradual component significantly smaller than in controls. The total amount of adaptation was also larger in habitual wearers than in controls. These data suggest strongly that the visual system adapts with increasing rapidity and strength as environments are encountered repeatedly over time. An additional unexpected finding was that baseline unique yellow shifted in a direction opposite to that produced by the habitually worn lenses. Overall, our results represent one of the first formal reports that adjusting to putting on or taking off spectacles becomes easier over time, and may have important
Probabilistic fusion of stereo with color and contrast for bilayer segmentation.
Kolmogorov, Vladimir; Criminisi, Antonio; Blake, Andrew; Cross, Geoffrey; Rother, Carsten
2006-09-01
This paper describes models and algorithms for the real-time segmentation of foreground from background layers in stereo video sequences. Automatic separation of layers from color/contrast or from stereo alone is known to be error-prone. Here, color, contrast, and stereo matching information are fused to infer layers accurately and efficiently. The first algorithm, Layered Dynamic Programming (LDP), solves stereo in an extended six-state space that represents both foreground/background layers and occluded regions. The stereo-match likelihood is then fused with a contrast-sensitive color model that is learned on-the-fly and stereo disparities are obtained by dynamic programming. The second algorithm, Layered Graph Cut (LGC), does not directly solve stereo. Instead, the stereo match likelihood is marginalized over disparities to evaluate foreground and background hypotheses and then fused with a contrast-sensitive color model like the one used in LDP. Segmentation is solved efficiently by ternary graph cut. Both algorithms are evaluated with respect to ground truth data and found to have similar performance, substantially better than either stereo or color/ contrast alone. However, their characteristics with respect to computational efficiency are rather different. The algorithms are demonstrated in the application of background substitution and shown to give good quality composite video output.
NASA Astrophysics Data System (ADS)
Sammouda, Rachid; Niki, Noboru; Nishitani, Hiroshi; Nakamura, S.; Mori, Shinichiro
1997-04-01
The paper presents a method for automatic segmentation of sputum cells with color images, to develop an efficient algorithm for lung cancer diagnosis based on a Hopfield neural network. We formulate the segmentation problem as a minimization of an energy function constructed with two terms, the cost-term as a sum of squared errors, and the second term a temporary noise added to the network as an excitation to escape certain local minima with the result of being closer to the global minimum. To increase the accuracy in segmenting the regions of interest, a preclassification technique is used to extract the sputum cell regions within the color image and remove those of the debris cells. The former is then given with the raw image to the input of Hopfield neural network to make a crisp segmentation by assigning each pixel to label such as background, cytoplasm, and nucleus. The proposed technique has yielded correct segmentation of complex scene of sputum prepared by ordinary manual staining method in most of the tested images selected from our database containing thousands of sputum color images.
Lopez Labrousse, Maite I; Frumovitz, Michael; Guadalupe Patrono, M; Ramirez, Pedro T
2017-09-01
Sentinel lymph node mapping, alone or in combination with pelvic lymphadenectomy, is considered a standard approach in staging of patients with cervical or endometrial cancer [1-3]. The goal of this video is to demonstrate the use of indocyanine green (ICG) and color-segmented fluorescence when performing lymphatic mapping in patients with gynecologic malignancies. Injection of ICG is performed in two cervical sites using 1mL (0.5mL superficial and deep, respectively) at the 3 and 9 o'clock position. Sentinel lymph nodes are identified intraoperatively using the Pinpoint near-infrared imaging system (Novadaq, Ontario, CA). Color-segmented fluorescence is used to image different levels of ICG uptake demonstrating higher levels of perfusion. A color key on the side of the monitor shows the colors that coordinate with different levels of ICG uptake. Color-segmented fluorescence may help surgeons identify true sentinel nodes from fatty tissue that, although absorbing fluorescent dye, does not contain true nodal tissue. It is not intended to differentiate the primary sentinel node from secondary sentinel nodes. The key ranges from low levels of ICG uptake (gray) to the highest rate of ICG uptake (red). Bilateral sentinel lymph nodes are identified along the external iliac vessels using both standard and color-segmented fluorescence. No evidence of disease was noted after ultra-staging was performed in each of the sentinel nodes. Use of ICG in sentinel lymph node mapping allows for high bilateral detection rates. Color-segmented fluorescence may increase accuracy of sentinel lymph node identification over standard fluorescent imaging. The following are the supplementary data related to this article. Copyright © 2017 Elsevier Inc. All rights reserved.
Quantifying color variation: Improved formulas for calculating hue with segment classification.
Smith, Stacey D
2014-03-01
Differences in color form a major component of biological variation, and quantifying these differences is the first step to understanding their evolutionary and ecological importance. One common method for measuring color variation is segment classification, which uses three variables (chroma, hue, and brightness) to describe the height and shape of reflectance curves. This study provides new formulas for calculating hue (the variable that describes the "type" of color) to give correct values in all regions of color space. • Reflectance spectra were obtained from the literature, and chroma, hue, and brightness were computed for each spectrum using the original formulas as well as the new formulas. Only the new formulas result in correct values in the blue-green portion of color space. • Use of the new formulas for calculating hue will result in more accurate color quantification for a broad range of biological applications.
Local adaptive contrast enhancement for color images
NASA Astrophysics Data System (ADS)
Dijk, Judith; den Hollander, Richard J. M.; Schavemaker, John G. M.; Schutte, Klamer
2007-04-01
A camera or display usually has a smaller dynamic range than the human eye. For this reason, objects that can be detected by the naked eye may not be visible in recorded images. Lighting is here an important factor; improper local lighting impairs visibility of details or even entire objects. When a human is observing a scene with different kinds of lighting, such as shadows, he will need to see details in both the dark and light parts of the scene. For grey value images such as IR imagery, algorithms have been developed in which the local contrast of the image is enhanced using local adaptive techniques. In this paper, we present how such algorithms can be adapted so that details in color images are enhanced while color information is retained. We propose to apply the contrast enhancement on color images by applying a grey value contrast enhancement algorithm to the luminance channel of the color signal. The color coordinates of the signal will remain the same. Care is taken that the saturation change is not too high. Gamut mapping is performed so that the output can be displayed on a monitor. The proposed technique can for instance be used by operators monitoring movements of people in order to detect suspicious behavior. To do this effectively, specific individuals should both be easy to recognize and track. This requires optimal local contrast, and is sometimes much helped by color when tracking a person with colored clothes. In such applications, enhanced local contrast in color images leads to more effective monitoring.
Doulamis, A; Doulamis, N; Ntalianis, K; Kollias, S
2003-01-01
In this paper, an unsupervised video object (VO) segmentation and tracking algorithm is proposed based on an adaptable neural-network architecture. The proposed scheme comprises: 1) a VO tracking module and 2) an initial VO estimation module. Object tracking is handled as a classification problem and implemented through an adaptive network classifier, which provides better results compared to conventional motion-based tracking algorithms. Network adaptation is accomplished through an efficient and cost effective weight updating algorithm, providing a minimum degradation of the previous network knowledge and taking into account the current content conditions. A retraining set is constructed and used for this purpose based on initial VO estimation results. Two different scenarios are investigated. The first concerns extraction of human entities in video conferencing applications, while the second exploits depth information to identify generic VOs in stereoscopic video sequences. Human face/ body detection based on Gaussian distributions is accomplished in the first scenario, while segmentation fusion is obtained using color and depth information in the second scenario. A decision mechanism is also incorporated to detect time instances for weight updating. Experimental results and comparisons indicate the good performance of the proposed scheme even in sequences with complicated content (object bending, occlusion).
Adaptive Residual Interpolation for Color and Multispectral Image Demosaicking.
Monno, Yusuke; Kiku, Daisuke; Tanaka, Masayuki; Okutomi, Masatoshi
2017-12-01
Color image demosaicking for the Bayer color filter array is an essential image processing operation for acquiring high-quality color images. Recently, residual interpolation (RI)-based algorithms have demonstrated superior demosaicking performance over conventional color difference interpolation-based algorithms. In this paper, we propose adaptive residual interpolation (ARI) that improves existing RI-based algorithms by adaptively combining two RI-based algorithms and selecting a suitable iteration number at each pixel. These are performed based on a unified criterion that evaluates the validity of an RI-based algorithm. Experimental comparisons using standard color image datasets demonstrate that ARI can improve existing RI-based algorithms by more than 0.6 dB in the color peak signal-to-noise ratio and can outperform state-of-the-art algorithms based on training images. We further extend ARI for a multispectral filter array, in which more than three spectral bands are arrayed, and demonstrate that ARI can achieve state-of-the-art performance also for the task of multispectral image demosaicking.
Adaptive geodesic transform for segmentation of vertebrae on CT images
NASA Astrophysics Data System (ADS)
Gaonkar, Bilwaj; Shu, Liao; Hermosillo, Gerardo; Zhan, Yiqiang
2014-03-01
Vertebral segmentation is a critical first step in any quantitative evaluation of vertebral pathology using CT images. This is especially challenging because bone marrow tissue has the same intensity profile as the muscle surrounding the bone. Thus simple methods such as thresholding or adaptive k-means fail to accurately segment vertebrae. While several other algorithms such as level sets may be used for segmentation any algorithm that is clinically deployable has to work in under a few seconds. To address these dual challenges we present here, a new algorithm based on the geodesic distance transform that is capable of segmenting the spinal vertebrae in under one second. To achieve this we extend the theory of the geodesic distance transforms proposed in1 to incorporate high level anatomical knowledge through adaptive weighting of image gradients. Such knowledge may be provided by the user directly or may be automatically generated by another algorithm. We incorporate information 'learnt' using a previously published machine learning algorithm2 to segment the L1 to L5 vertebrae. While we present a particular application here, the adaptive geodesic transform is a generic concept which can be applied to segmentation of other organs as well.
A NDVI assisted remote sensing image adaptive scale segmentation method
NASA Astrophysics Data System (ADS)
Zhang, Hong; Shen, Jinxiang; Ma, Yanmei
2018-03-01
Multiscale segmentation of images can effectively form boundaries of different objects with different scales. However, for the remote sensing image which widely coverage with complicated ground objects, the number of suitable segmentation scales, and each of the scale size is still difficult to be accurately determined, which severely restricts the rapid information extraction of the remote sensing image. A great deal of experiments showed that the normalized difference vegetation index (NDVI) can effectively express the spectral characteristics of a variety of ground objects in remote sensing images. This paper presents a method using NDVI assisted adaptive segmentation of remote sensing images, which segment the local area by using NDVI similarity threshold to iteratively select segmentation scales. According to the different regions which consist of different targets, different segmentation scale boundaries could be created. The experimental results showed that the adaptive segmentation method based on NDVI can effectively create the objects boundaries for different ground objects of remote sensing images.
Enhancing MPLS Protection Method with Adaptive Segment Repair
NASA Astrophysics Data System (ADS)
Chen, Chin-Ling
We propose a novel adaptive segment repair mechanism to improve traditional MPLS (Multi-Protocol Label Switching) failure recovery. The proposed mechanism protects one or more contiguous high failure probability links by dynamic setup of segment protection. Simulations demonstrate that the proposed mechanism reduces failure recovery time while also increasing network resource utilization.
Biological versus Electronic Adaptive Coloration: How Can One Inform the Other?
2012-01-01
Hyperspectral imaging of cuttlefish camouflage indicates good color match in the eyes of fish predators. Proc. Natl Acad. Sci. USA 108, 9148–9153. (doi...Patrick B. Dennis, Rajesh R. Naik, Eric Forsythe and inform the other? Biological versus electronic adaptive coloration : how can one References...TYPE 3. DATES COVERED 00-00-2012 to 00-00-2012 4. TITLE AND SUBTITLE Biological versus electronic adaptive coloration : how can one inform the
Fu, J C; Chen, C C; Chai, J W; Wong, S T C; Li, I C
2010-06-01
We propose an automatic hybrid image segmentation model that integrates the statistical expectation maximization (EM) model and the spatial pulse coupled neural network (PCNN) for brain magnetic resonance imaging (MRI) segmentation. In addition, an adaptive mechanism is developed to fine tune the PCNN parameters. The EM model serves two functions: evaluation of the PCNN image segmentation and adaptive adjustment of the PCNN parameters for optimal segmentation. To evaluate the performance of the adaptive EM-PCNN, we use it to segment MR brain image into gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF). The performance of the adaptive EM-PCNN is compared with that of the non-adaptive EM-PCNN, EM, and Bias Corrected Fuzzy C-Means (BCFCM) algorithms. The result is four sets of boundaries for the GM and the brain parenchyma (GM+WM), the two regions of most interest in medical research and clinical applications. Each set of boundaries is compared with the golden standard to evaluate the segmentation performance. The adaptive EM-PCNN significantly outperforms the non-adaptive EM-PCNN, EM, and BCFCM algorithms in gray mater segmentation. In brain parenchyma segmentation, the adaptive EM-PCNN significantly outperforms the BCFCM only. However, the adaptive EM-PCNN is better than the non-adaptive EM-PCNN and EM on average. We conclude that of the three approaches, the adaptive EM-PCNN yields the best results for gray matter and brain parenchyma segmentation. Copyright 2009 Elsevier Ltd. All rights reserved.
Quantifying color variation: Improved formulas for calculating hue with segment classification1
Smith, Stacey D.
2014-01-01
• Premise of the study: Differences in color form a major component of biological variation, and quantifying these differences is the first step to understanding their evolutionary and ecological importance. One common method for measuring color variation is segment classification, which uses three variables (chroma, hue, and brightness) to describe the height and shape of reflectance curves. This study provides new formulas for calculating hue (the variable that describes the “type” of color) to give correct values in all regions of color space. • Methods and Results: Reflectance spectra were obtained from the literature, and chroma, hue, and brightness were computed for each spectrum using the original formulas as well as the new formulas. Only the new formulas result in correct values in the blue-green portion of color space. • Conclusions: Use of the new formulas for calculating hue will result in more accurate color quantification for a broad range of biological applications. PMID:25202612
[Tumor segmentation of brain MRI with adaptive bandwidth mean shift].
Hou, Xiaowen; Liu, Qi
2014-10-01
In order to get the adaptive bandwidth of mean shift to make the tumor segmentation of brain magnetic resonance imaging (MRI) to be more accurate, we in this paper present an advanced mean shift method. Firstly, we made use of the space characteristics of brain image to eliminate the impact on segmentation of skull; and then, based on the characteristics of spatial agglomeration of different tissues of brain (includes tumor), we applied edge points to get the optimal initial mean value and the respectively adaptive bandwidth, in order to improve the accuracy of tumor segmentation. The results of experiment showed that, contrast to the fixed bandwidth mean shift method, the method in this paper could segment the tumor more accurately.
Study of chromatic adaptation using memory color matches, Part I: neutral illuminants.
Smet, Kevin A G; Zhai, Qiyan; Luo, Ming R; Hanselaer, Peter
2017-04-03
Twelve corresponding color data sets have been obtained using the long-term memory colors of familiar objects as target stimuli. Data were collected for familiar objects with neutral, red, yellow, green and blue hues under 4 approximately neutral illumination conditions on or near the blackbody locus. The advantages of the memory color matching method are discussed in light of other more traditional asymmetric matching techniques. Results were compared to eight corresponding color data sets available in literature. The corresponding color data was used to test several linear (von Kries, RLAB, etc.) and nonlinear (Hunt & Nayatani) chromatic adaptation transforms (CAT). It was found that a simple two-step von Kries, whereby the degree of adaptation D is optimized to minimize the DEu'v' prediction errors, outperformed all other tested models for both memory color and literature corresponding color sets, whereby prediction errors were lower for the memory color sets. The predictive errors were substantially smaller than the standard uncertainty on the average observer and were comparable to what are considered just-noticeable-differences in the CIE u'v' chromaticity diagram, supporting the use of memory color based internal references to study chromatic adaptation mechanisms.
Investigation of the effects of color on judgments of sweetness using a taste adaptation method.
Hidaka, Souta; Shimoda, Kazumasa
2014-01-01
It has been reported that color can affect the judgment of taste. For example, a dark red color enhances the subjective intensity of sweetness. However, the underlying mechanisms of the effect of color on taste have not been fully investigated; in particular, it remains unclear whether the effect is based on cognitive/decisional or perceptual processes. Here, we investigated the effect of color on sweetness judgments using a taste adaptation method. A sweet solution whose color was subjectively congruent with sweetness was judged as sweeter than an uncolored sweet solution both before and after adaptation to an uncolored sweet solution. In contrast, subjective judgment of sweetness for uncolored sweet solutions did not differ between the conditions following adaptation to a colored sweet solution and following adaptation to an uncolored one. Color affected sweetness judgment when the target solution was colored, but the colored sweet solution did not modulate the magnitude of taste adaptation. Therefore, it is concluded that the effect of color on the judgment of taste would occur mainly in cognitive/decisional domains.
Image segmentation on adaptive edge-preserving smoothing
NASA Astrophysics Data System (ADS)
He, Kun; Wang, Dan; Zheng, Xiuqing
2016-09-01
Nowadays, typical active contour models are widely applied in image segmentation. However, they perform badly on real images with inhomogeneous subregions. In order to overcome the drawback, this paper proposes an edge-preserving smoothing image segmentation algorithm. At first, this paper analyzes the edge-preserving smoothing conditions for image segmentation and constructs an edge-preserving smoothing model inspired by total variation. The proposed model has the ability to smooth inhomogeneous subregions and preserve edges. Then, a kind of clustering algorithm, which reasonably trades off edge-preserving and subregion-smoothing according to the local information, is employed to learn the edge-preserving parameter adaptively. At last, according to the confidence level of segmentation subregions, this paper constructs a smoothing convergence condition to avoid oversmoothing. Experiments indicate that the proposed algorithm has superior performance in precision, recall, and F-measure compared with other segmentation algorithms, and it is insensitive to noise and inhomogeneous-regions.
Corresponding color datasets and a chromatic adaptation model based on the OSA-UCS system.
Oleari, Claudio
2014-07-01
Today chromatic adaptation transforms (CATs) are reconsidered, since their mathematical inconsistency has been shown in Color Res. Appl.38, 188 (2013) and by the CIE technical committee TC 8-11: CIECAM02 Mathematics. In 2004-2005 the author proposed an adaptation transform based on the uniform color scale system of the Optical Society of America (OSA-UCS) [J. Opt. Soc. Am. A21, 677 (2004); Color Res. Appl. 30, 31 (2005)] that transforms the cone-activation stimuli into adapted stimuli. The present work considers all the 37 available corresponding color (CC) datasets selected by CIE and (1) shows that the adapted stimuli obtained from CC data are defined up to an unknown transformation, and an unambiguous definition of the adapted stimuli requires additional hypotheses or suitable experimental data (as it is in the OSA-UCS system); (2) produces a CAT, represented by a linear transformation between CCs, associated with any CC dataset, whose high quality measured in ΔE units discards the possibility of nonlinear transformations; (3) analyzes these color-conversion matrices in a heuristic way with a reference adaptation that is approximately that of the OSA-UCS adapted colors for the D65 illuminant and particularly shows accordance with the Hunt effect and the Bezold-Brücke hue shift; (4) proposes the measurements of CC stimuli with a reference adaptation equal to that of the visual situation of the OSA-UCS system for defining adapted colors for any considered illumination adaptation and therefore for defining a general CAT formula.
Adaptive Residual Interpolation for Color and Multispectral Image Demosaicking †
Kiku, Daisuke; Okutomi, Masatoshi
2017-01-01
Color image demosaicking for the Bayer color filter array is an essential image processing operation for acquiring high-quality color images. Recently, residual interpolation (RI)-based algorithms have demonstrated superior demosaicking performance over conventional color difference interpolation-based algorithms. In this paper, we propose adaptive residual interpolation (ARI) that improves existing RI-based algorithms by adaptively combining two RI-based algorithms and selecting a suitable iteration number at each pixel. These are performed based on a unified criterion that evaluates the validity of an RI-based algorithm. Experimental comparisons using standard color image datasets demonstrate that ARI can improve existing RI-based algorithms by more than 0.6 dB in the color peak signal-to-noise ratio and can outperform state-of-the-art algorithms based on training images. We further extend ARI for a multispectral filter array, in which more than three spectral bands are arrayed, and demonstrate that ARI can achieve state-of-the-art performance also for the task of multispectral image demosaicking. PMID:29194407
Color image segmentation with support vector machines: applications to road signs detection.
Cyganek, Bogusław
2008-08-01
In this paper we propose efficient color segmentation method which is based on the Support Vector Machine classifier operating in a one-class mode. The method has been developed especially for the road signs recognition system, although it can be used in other applications. The main advantage of the proposed method comes from the fact that the segmentation of characteristic colors is performed not in the original but in the higher dimensional feature space. By this a better data encapsulation with a linear hypersphere can be usually achieved. Moreover, the classifier does not try to capture the whole distribution of the input data which is often difficult to achieve. Instead, the characteristic data samples, called support vectors, are selected which allow construction of the tightest hypersphere that encloses majority of the input data. Then classification of a test data simply consists in a measurement of its distance to a centre of the found hypersphere. The experimental results show high accuracy and speed of the proposed method.
Aerial images visual localization on a vector map using color-texture segmentation
NASA Astrophysics Data System (ADS)
Kunina, I. A.; Teplyakov, L. M.; Gladkov, A. P.; Khanipov, T. M.; Nikolaev, D. P.
2018-04-01
In this paper we study the problem of combining UAV obtained optical data and a coastal vector map in absence of satellite navigation data. The method is based on presenting the territory as a set of segments produced by color-texture image segmentation. We then find such geometric transform which gives the best match between these segments and land and water areas of the georeferenced vector map. We calculate transform consisting of an arbitrary shift relatively to the vector map and bound rotation and scaling. These parameters are estimated using the RANSAC algorithm which matches the segments contours and the contours of land and water areas of the vector map. To implement this matching we suggest computing shape descriptors robust to rotation and scaling. We performed numerical experiments demonstrating the practical applicability of the proposed method.
Fixation light hue bias revisited: implications for using adaptive optics to study color vision.
Hofer, H J; Blaschke, J; Patolia, J; Koenig, D E
2012-03-01
Current vision science adaptive optics systems use near infrared wavefront sensor 'beacons' that appear as red spots in the visual field. Colored fixation targets are known to influence the perceived color of macroscopic visual stimuli (Jameson, D., & Hurvich, L. M. (1967). Fixation-light bias: An unwanted by-product of fixation control. Vision Research, 7, 805-809.), suggesting that the wavefront sensor beacon may also influence perceived color for stimuli displayed with adaptive optics. Despite its importance for proper interpretation of adaptive optics experiments on the fine scale interaction of the retinal mosaic and spatial and color vision, this potential bias has not yet been quantified or addressed. Here we measure the impact of the wavefront sensor beacon on color appearance for dim, monochromatic point sources in five subjects. The presence of the beacon altered color reports both when used as a fixation target as well as when displaced in the visual field with a chromatically neutral fixation target. This influence must be taken into account when interpreting previous experiments and new methods of adaptive correction should be used in future experiments using adaptive optics to study color. Copyright © 2012 Elsevier Ltd. All rights reserved.
Survey of contemporary trends in color image segmentation
NASA Astrophysics Data System (ADS)
Vantaram, Sreenath Rao; Saber, Eli
2012-10-01
In recent years, the acquisition of image and video information for processing, analysis, understanding, and exploitation of the underlying content in various applications, ranging from remote sensing to biomedical imaging, has grown at an unprecedented rate. Analysis by human observers is quite laborious, tiresome, and time consuming, if not infeasible, given the large and continuously rising volume of data. Hence the need for systems capable of automatically and effectively analyzing the aforementioned imagery for a variety of uses that span the spectrum from homeland security to elderly care. In order to achieve the above, tools such as image segmentation provide the appropriate foundation for expediting and improving the effectiveness of subsequent high-level tasks by providing a condensed and pertinent representation of image information. We provide a comprehensive survey of color image segmentation strategies adopted over the last decade, though notable contributions in the gray scale domain will also be discussed. Our taxonomy of segmentation techniques is sampled from a wide spectrum of spatially blind (or feature-based) approaches such as clustering and histogram thresholding as well as spatially guided (or spatial domain-based) methods such as region growing/splitting/merging, energy-driven parametric/geometric active contours, supervised/unsupervised graph cuts, and watersheds, to name a few. In addition, qualitative and quantitative results of prominent algorithms on several images from the Berkeley segmentation dataset are shown in order to furnish a fair indication of the current quality of the state of the art. Finally, we provide a brief discussion on our current perspective of the field as well as its associated future trends.
An adaptive multi-feature segmentation model for infrared image
NASA Astrophysics Data System (ADS)
Zhang, Tingting; Han, Jin; Zhang, Yi; Bai, Lianfa
2016-04-01
Active contour models (ACM) have been extensively applied to image segmentation, conventional region-based active contour models only utilize global or local single feature information to minimize the energy functional to drive the contour evolution. Considering the limitations of original ACMs, an adaptive multi-feature segmentation model is proposed to handle infrared images with blurred boundaries and low contrast. In the proposed model, several essential local statistic features are introduced to construct a multi-feature signed pressure function (MFSPF). In addition, we draw upon the adaptive weight coefficient to modify the level set formulation, which is formed by integrating MFSPF with local statistic features and signed pressure function with global information. Experimental results demonstrate that the proposed method can make up for the inadequacy of the original method and get desirable results in segmenting infrared images.
Adaptive color halftoning for minimum perceived error using the blue noise mask
NASA Astrophysics Data System (ADS)
Yu, Qing; Parker, Kevin J.
1997-04-01
Color halftoning using a conventional screen requires careful selection of screen angles to avoid Moire patterns. An obvious advantage of halftoning using a blue noise mask (BNM) is that there are no conventional screen angle or Moire patterns produced. However, a simple strategy of employing the same BNM on all color planes is unacceptable in case where a small registration error can cause objectionable color shifts. In a previous paper by Yao and Parker, strategies were presented for shifting or inverting the BNM as well as using mutually exclusive BNMs for different color planes. In this paper, the above schemes will be studied in CIE-LAB color space in terms of root mean square error and variance for luminance channel and chrominance channel respectively. We will demonstrate that the dot-on-dot scheme results in minimum chrominance error, but maximum luminance error and the 4-mask scheme results in minimum luminance error but maximum chrominance error, while the shift scheme falls in between. Based on this study, we proposed a new adaptive color halftoning algorithm that takes colorimetric color reproduction into account by applying 2-mutually exclusive BNMs on two different color planes and applying an adaptive scheme on other planes to reduce color error. We will show that by having one adaptive color channel, we obtain increased flexibility to manipulate the output so as to reduce colorimetric error while permitting customization to specific printing hardware.
Region-based multi-step optic disk and cup segmentation from color fundus image
NASA Astrophysics Data System (ADS)
Xiao, Di; Lock, Jane; Manresa, Javier Moreno; Vignarajan, Janardhan; Tay-Kearney, Mei-Ling; Kanagasingam, Yogesan
2013-02-01
Retinal optic cup-disk-ratio (CDR) is a one of important indicators of glaucomatous neuropathy. In this paper, we propose a novel multi-step 4-quadrant thresholding method for optic disk segmentation and a multi-step temporal-nasal segmenting method for optic cup segmentation based on blood vessel inpainted HSL lightness images and green images. The performance of the proposed methods was evaluated on a group of color fundus images and compared with the manual outlining results from two experts. Dice scores of detected disk and cup regions between the auto and manual results were computed and compared. Vertical CDRs were also compared among the three results. The preliminary experiment has demonstrated the robustness of the method for automatic optic disk and cup segmentation and its potential value for clinical application.
Shape and Color Features for Object Recognition Search
NASA Technical Reports Server (NTRS)
Duong, Tuan A.; Duong, Vu A.; Stubberud, Allen R.
2012-01-01
A bio-inspired shape feature of an object of interest emulates the integration of the saccadic eye movement and horizontal layer in vertebrate retina for object recognition search where a single object can be used one at a time. The optimal computational model for shape-extraction-based principal component analysis (PCA) was also developed to reduce processing time and enable the real-time adaptive system capability. A color feature of the object is employed as color segmentation to empower the shape feature recognition to solve the object recognition in the heterogeneous environment where a single technique - shape or color - may expose its difficulties. To enable the effective system, an adaptive architecture and autonomous mechanism were developed to recognize and adapt the shape and color feature of the moving object. The bio-inspired object recognition based on bio-inspired shape and color can be effective to recognize a person of interest in the heterogeneous environment where the single technique exposed its difficulties to perform effective recognition. Moreover, this work also demonstrates the mechanism and architecture of the autonomous adaptive system to enable the realistic system for the practical use in the future.
Adaptive distance metric learning for diffusion tensor image segmentation.
Kong, Youyong; Wang, Defeng; Shi, Lin; Hui, Steve C N; Chu, Winnie C W
2014-01-01
High quality segmentation of diffusion tensor images (DTI) is of key interest in biomedical research and clinical application. In previous studies, most efforts have been made to construct predefined metrics for different DTI segmentation tasks. These methods require adequate prior knowledge and tuning parameters. To overcome these disadvantages, we proposed to automatically learn an adaptive distance metric by a graph based semi-supervised learning model for DTI segmentation. An original discriminative distance vector was first formulated by combining both geometry and orientation distances derived from diffusion tensors. The kernel metric over the original distance and labels of all voxels were then simultaneously optimized in a graph based semi-supervised learning approach. Finally, the optimization task was efficiently solved with an iterative gradient descent method to achieve the optimal solution. With our approach, an adaptive distance metric could be available for each specific segmentation task. Experiments on synthetic and real brain DTI datasets were performed to demonstrate the effectiveness and robustness of the proposed distance metric learning approach. The performance of our approach was compared with three classical metrics in the graph based semi-supervised learning framework.
Adaptive Distance Metric Learning for Diffusion Tensor Image Segmentation
Kong, Youyong; Wang, Defeng; Shi, Lin; Hui, Steve C. N.; Chu, Winnie C. W.
2014-01-01
High quality segmentation of diffusion tensor images (DTI) is of key interest in biomedical research and clinical application. In previous studies, most efforts have been made to construct predefined metrics for different DTI segmentation tasks. These methods require adequate prior knowledge and tuning parameters. To overcome these disadvantages, we proposed to automatically learn an adaptive distance metric by a graph based semi-supervised learning model for DTI segmentation. An original discriminative distance vector was first formulated by combining both geometry and orientation distances derived from diffusion tensors. The kernel metric over the original distance and labels of all voxels were then simultaneously optimized in a graph based semi-supervised learning approach. Finally, the optimization task was efficiently solved with an iterative gradient descent method to achieve the optimal solution. With our approach, an adaptive distance metric could be available for each specific segmentation task. Experiments on synthetic and real brain DTI datasets were performed to demonstrate the effectiveness and robustness of the proposed distance metric learning approach. The performance of our approach was compared with three classical metrics in the graph based semi-supervised learning framework. PMID:24651858
NASA Astrophysics Data System (ADS)
Rulaningtyas, Riries; Suksmono, Andriyan B.; Mengko, Tati L. R.; Saptawati, Putri
2016-03-01
Pulmonary tuberculosis is a deadly infectious disease which occurs in many countries in Asia and Africa. In Indonesia, many people with tuberculosis disease are examined in the community health center. Examination of pulmonary tuberculosis is done through sputum smear with Ziehl - Neelsen staining using conventional light microscope. The results of Ziehl - Neelsen staining will give effect to the appearance of tuberculosis (TB) bacteria in red color and sputum background in blue color. The first examination is to detect the presence of TB bacteria from its color, then from the morphology of the TB bacteria itself. The results of Ziehl - Neelsen staining in sputum smear give the complex color images, so that the clinicians have difficulty when doing slide examination manually because it is time consuming and needs highly training to detect the presence of TB bacteria accurately. The clinicians have heavy workload to examine many sputum smear slides from the patients. To assist the clinicians when reading the sputum smear slide, this research built computer aided diagnose with color image segmentation, feature extraction, and classification method. This research used K-means clustering with patch technique to segment digital sputum smear images which separated the TB bacteria images from the background images. This segmentation method gave the good accuracy 97.68%. Then, feature extraction based on geometrical shape of TB bacteria was applied to this research. The last step, this research used neural network with back propagation method to classify TB bacteria and non TB bacteria images in sputum slides. The classification result of neural network back propagation are learning time (42.69±0.02) second, the number of epoch 5000, error rate of learning 15%, learning accuracy (98.58±0.01)%, and test accuracy (96.54±0.02)%.
Momonoi, Kazumi; Tsuji, Toshiaki; Kazuma, Kohei; Yoshida, Kumi
2012-01-01
Several flowers of Tulipa gesneriana exhibit a blue color in the bottom segments of the inner perianth. We have previously reported the inner-bottom tissue-specific iron accumulation and expression of the vacuolar iron transporter, TgVit1, in tulip cv. Murasakizuisho. To clarify whether the TgVit1-dependent iron accumulation and blue-color development in tulip petals are universal, we analyzed anthocyanin, its co-pigment components, iron contents and the expression of TgVit1 mRNA in 13 cultivars which show a blue color in the bottom segments of the inner perianth accompanying yellow- and white-colored inner-bottom petals. All of the blue bottom segments contained the same anthocyanin component, delphinidin 3-rutinoside. The flavonol composition varied with cultivar and tissue part. The major flavonol in the bottom segments of the inner perianth was rutin. The iron content in the upper part was less than that in the bottom segments of the inner perianth. The iron content in the yellow and white petals was higher in the bottom segment of the inner perianth than in the upper tissues. TgVit1 mRNA expression was apparent in all of the bottom tissues of the inner perianth. The result of a reproduction experiment by mixing the constituents suggests that the blue coloration in tulip petals is generally caused by iron complexation to delphinidin 3-rutinoside and that the iron complex is solubilized and stabilized by flavonol glycosides. TgVit1-dependent iron accumulation in the bottom segments of the inner perianth might be controlled by an unknown system that differentiated the upper parts and bottom segments of the inner perianth.
Image segmentation-based robust feature extraction for color image watermarking
NASA Astrophysics Data System (ADS)
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.
Acute Zonal Cone Photoreceptor Outer Segment Loss.
Aleman, Tomas S; Sandhu, Harpal S; Serrano, Leona W; Traband, Anastasia; Lau, Marisa K; Adamus, Grazyna; Avery, Robert A
2017-05-01
The diagnostic path presented narrows down the cause of acute vision loss to the cone photoreceptor outer segment and will refocus the search for the cause of similar currently idiopathic conditions. To describe the structural and functional associations found in a patient with acute zonal occult photoreceptor loss. A case report of an adolescent boy with acute visual field loss despite a normal fundus examination performed at a university teaching hospital. Results of a complete ophthalmic examination, full-field flash electroretinography (ERG) and multifocal ERG, light-adapted achromatic and 2-color dark-adapted perimetry, and microperimetry. Imaging was performed with spectral-domain optical coherence tomography (SD-OCT), near-infrared (NIR) and short-wavelength (SW) fundus autofluorescence (FAF), and NIR reflectance (REF). The patient was evaluated within a week of the onset of a scotoma in the nasal field of his left eye. Visual acuity was 20/20 OU, and color vision was normal in both eyes. Results of the fundus examination and of SW-FAF and NIR-FAF imaging were normal in both eyes, whereas NIR-REF imaging showed a region of hyporeflectance temporal to the fovea that corresponded with a dense relative scotoma noted on light-adapted static perimetry in the left eye. Loss in the photoreceptor outer segment detected by SD-OCT co-localized with an area of dense cone dysfunction detected on light-adapted perimetry and multifocal ERG but with near-normal rod-mediated vision according to results of 2-color dark-adapted perimetry. Full-field flash ERG findings were normal in both eyes. The outer nuclear layer and inner retinal thicknesses were normal. Localized, isolated cone dysfunction may represent the earliest photoreceptor abnormality or a distinct entity within the acute zonal occult outer retinopathy complex. Acute zonal occult outer retinopathy should be considered in patients with acute vision loss and abnormalities on NIR-REF imaging, especially if
Relationship between neural response and adaptation selectivity to form and color: an ERP study.
Rentzeperis, Ilias; Nikolaev, Andrey R; Kiper, Daniel C; van Leeuwen, Cees
2012-01-01
Adaptation is widely used as a tool for studying selectivity to visual features. In these studies it is usually assumed that the loci of feature selective neural responses and adaptation coincide. We used an adaptation paradigm to investigate the relationship between response and adaptation selectivity in event-related potentials (ERPs). ERPs were evoked by the presentation of colored Glass patterns in a form discrimination task. Response selectivities to form and, to some extent, color of the patterns were reflected in the C1 and N1 ERP components. Adaptation selectivity to color was reflected in N1 and was followed by a late (300-500 ms after stimulus onset) effect of form adaptation. Thus for form, response and adaptation selectivity were manifested in non-overlapping intervals. These results indicate that adaptation and response selectivity can be associated with different processes. Therefore, inferring selectivity from an adaptation paradigm requires analysis of both adaptation and neural response data.
Segmentation by fusion of histogram-based k-means clusters in different color spaces.
Mignotte, Max
2008-05-01
This paper presents a new, simple, and efficient segmentation approach, based on a fusion procedure which aims at combining several segmentation maps associated to simpler partition models in order to finally get a more reliable and accurate segmentation result. The different label fields to be fused in our application are given by the same and simple (K-means based) clustering technique on an input image expressed in different color spaces. Our fusion strategy aims at combining these segmentation maps with a final clustering procedure using as input features, the local histogram of the class labels, previously estimated and associated to each site and for all these initial partitions. This fusion framework remains simple to implement, fast, general enough to be applied to various computer vision applications (e.g., motion detection and segmentation), and has been successfully applied on the Berkeley image database. The experiments herein reported in this paper illustrate the potential of this approach compared to the state-of-the-art segmentation methods recently proposed in the literature.
Wavelet-based adaptive thresholding method for image segmentation
NASA Astrophysics Data System (ADS)
Chen, Zikuan; Tao, Yang; Chen, Xin; Griffis, Carl
2001-05-01
A nonuniform background distribution may cause a global thresholding method to fail to segment objects. One solution is using a local thresholding method that adapts to local surroundings. In this paper, we propose a novel local thresholding method for image segmentation, using multiscale threshold functions obtained by wavelet synthesis with weighted detail coefficients. In particular, the coarse-to- fine synthesis with attenuated detail coefficients produces a threshold function corresponding to a high-frequency- reduced signal. This wavelet-based local thresholding method adapts to both local size and local surroundings, and its implementation can take advantage of the fast wavelet algorithm. We applied this technique to physical contaminant detection for poultry meat inspection using x-ray imaging. Experiments showed that inclusion objects in deboned poultry could be extracted at multiple resolutions despite their irregular sizes and uneven backgrounds.
Color Image Segmentation Based on Statistics of Location and Feature Similarity
NASA Astrophysics Data System (ADS)
Mori, Fumihiko; Yamada, Hiromitsu; Mizuno, Makoto; Sugano, Naotoshi
The process of “image segmentation and extracting remarkable regions” is an important research subject for the image understanding. However, an algorithm based on the global features is hardly found. The requisite of such an image segmentation algorism is to reduce as much as possible the over segmentation and over unification. We developed an algorithm using the multidimensional convex hull based on the density as the global feature. In the concrete, we propose a new algorithm in which regions are expanded according to the statistics of the region such as the mean value, standard deviation, maximum value and minimum value of pixel location, brightness and color elements and the statistics are updated. We also introduced a new concept of conspicuity degree and applied it to the various 21 images to examine the effectiveness. The remarkable object regions, which were extracted by the presented system, highly coincided with those which were pointed by the sixty four subjects who attended the psychological experiment.
Robust crop and weed segmentation under uncontrolled outdoor illumination
USDA-ARS?s Scientific Manuscript database
A new machine vision for weed detection was developed from RGB color model images. Processes included in the algorithm for the detection were excessive green conversion, threshold value computation by statistical analysis, adaptive image segmentation by adjusting the threshold value, median filter, ...
A dynamic fuzzy genetic algorithm for natural image segmentation using adaptive mean shift
NASA Astrophysics Data System (ADS)
Arfan Jaffar, M.
2017-01-01
In this paper, a colour image segmentation approach based on hybridisation of adaptive mean shift (AMS), fuzzy c-mean and genetic algorithms (GAs) is presented. Image segmentation is the perceptual faction of pixels based on some likeness measure. GA with fuzzy behaviour is adapted to maximise the fuzzy separation and minimise the global compactness among the clusters or segments in spatial fuzzy c-mean (sFCM). It adds diversity to the search process to find the global optima. A simple fusion method has been used to combine the clusters to overcome the problem of over segmentation. The results show that our technique outperforms state-of-the-art methods.
NASA Astrophysics Data System (ADS)
Srinivasan, Yeshwanth; Hernes, Dana; Tulpule, Bhakti; Yang, Shuyu; Guo, Jiangling; Mitra, Sunanda; Yagneswaran, Sriraja; Nutter, Brian; Jeronimo, Jose; Phillips, Benny; Long, Rodney; Ferris, Daron
2005-04-01
Automated segmentation and classification of diagnostic markers in medical imagery are challenging tasks. Numerous algorithms for segmentation and classification based on statistical approaches of varying complexity are found in the literature. However, the design of an efficient and automated algorithm for precise classification of desired diagnostic markers is extremely image-specific. The National Library of Medicine (NLM), in collaboration with the National Cancer Institute (NCI), is creating an archive of 60,000 digitized color images of the uterine cervix. NLM is developing tools for the analysis and dissemination of these images over the Web for the study of visual features correlated with precancerous neoplasia and cancer. To enable indexing of images of the cervix, it is essential to develop algorithms for the segmentation of regions of interest, such as acetowhitened regions, and automatic identification and classification of regions exhibiting mosaicism and punctation. Success of such algorithms depends, primarily, on the selection of relevant features representing the region of interest. We present color and geometric features based statistical classification and segmentation algorithms yielding excellent identification of the regions of interest. The distinct classification of the mosaic regions from the non-mosaic ones has been obtained by clustering multiple geometric and color features of the segmented sections using various morphological and statistical approaches. Such automated classification methodologies will facilitate content-based image retrieval from the digital archive of uterine cervix and have the potential of developing an image based screening tool for cervical cancer.
Color encryption scheme based on adapted quantum logistic map
NASA Astrophysics Data System (ADS)
Zaghloul, Alaa; Zhang, Tiejun; Amin, Mohamed; Abd El-Latif, Ahmed A.
2014-04-01
This paper presents a new color image encryption scheme based on quantum chaotic system. In this scheme, a new encryption scheme is accomplished by generating an intermediate chaotic key stream with the help of quantum chaotic logistic map. Then, each pixel is encrypted by the cipher value of the previous pixel and the adapted quantum logistic map. The results show that the proposed scheme has adequate security for the confidentiality of color images.
Object Recognition using Feature- and Color-Based Methods
NASA Technical Reports Server (NTRS)
Duong, Tuan; Duong, Vu; Stubberud, Allen
2008-01-01
An improved adaptive method of processing image data in an artificial neural network has been developed to enable automated, real-time recognition of possibly moving objects under changing (including suddenly changing) conditions of illumination and perspective. The method involves a combination of two prior object-recognition methods one based on adaptive detection of shape features and one based on adaptive color segmentation to enable recognition in situations in which either prior method by itself may be inadequate. The chosen prior feature-based method is known as adaptive principal-component analysis (APCA); the chosen prior color-based method is known as adaptive color segmentation (ACOSE). These methods are made to interact with each other in a closed-loop system to obtain an optimal solution of the object-recognition problem in a dynamic environment. One of the results of the interaction is to increase, beyond what would otherwise be possible, the accuracy of the determination of a region of interest (containing an object that one seeks to recognize) within an image. Another result is to provide a minimized adaptive step that can be used to update the results obtained by the two component methods when changes of color and apparent shape occur. The net effect is to enable the neural network to update its recognition output and improve its recognition capability via an adaptive learning sequence. In principle, the improved method could readily be implemented in integrated circuitry to make a compact, low-power, real-time object-recognition system. It has been proposed to demonstrate the feasibility of such a system by integrating a 256-by-256 active-pixel sensor with APCA, ACOSE, and neural processing circuitry on a single chip. It has been estimated that such a system on a chip would have a volume no larger than a few cubic centimeters, could operate at a rate as high as 1,000 frames per second, and would consume in the order of milliwatts of power.
Color constancy: enhancing von Kries adaption via sensor transformations
NASA Astrophysics Data System (ADS)
Finlayson, Graham D.; Drew, Mark S.; Funt, Brian V.
1993-09-01
Von Kries adaptation has long been considered a reasonable vehicle for color constancy. Since the color constancy performance attainable via the von Kries rule strongly depends on the spectral response characteristics of the human cones, we consider the possibility of enhancing von Kries performance by constructing new `sensors' as linear combinations of the fixed cone sensitivity functions. We show that if surface reflectances are well-modeled by 3 basis functions and illuminants by 2 basis functions then there exists a set of new sensors for which von Kries adaptation can yield perfect color constancy. These new sensors can (like the cones) be described as long-, medium-, and short-wave sensitive; however, both the new long- and medium-wave sensors have sharpened sensitivities -- their support is more concentrated. The new short-wave sensor remains relatively unchanged. A similar sharpening of cone sensitivities has previously been observed in test and field spectral sensitivities measured for the human eye. We present simulation results demonstrating improved von Kries performance using the new sensors even when the restrictions on the illumination and reflectance are relaxed.
NASA Astrophysics Data System (ADS)
Beretta, Giordano
2007-01-01
The words in a document are often supported, illustrated, and enriched by visuals. When color is used, some of it is used to define the document's identity and is therefore strictly controlled in the design process. The result of this design process is a "color specification sheet," which must be created for every background color. While in traditional publishing there are only a few backgrounds, in variable data publishing a larger number of backgrounds can be used. We present an algorithm that nudges the colors in a visual to be distinct from a background while preserving the visual's general color character.
Acute Zonal Cone Photoreceptor Outer Segment Loss
Sandhu, Harpal S.; Serrano, Leona W.; Traband, Anastasia; Lau, Marisa K.; Adamus, Grazyna; Avery, Robert A.
2017-01-01
Importance The diagnostic path presented narrows down the cause of acute vision loss to the cone photoreceptor outer segment and will refocus the search for the cause of similar currently idiopathic conditions. Objective To describe the structural and functional associations found in a patient with acute zonal occult photoreceptor loss. Design, Setting, and Participants A case report of an adolescent boy with acute visual field loss despite a normal fundus examination performed at a university teaching hospital. Main Outcomes and Measures Results of a complete ophthalmic examination, full-field flash electroretinography (ERG) and multifocal ERG, light-adapted achromatic and 2-color dark-adapted perimetry, and microperimetry. Imaging was performed with spectral-domain optical coherence tomography (SD-OCT), near-infrared (NIR) and short-wavelength (SW) fundus autofluorescence (FAF), and NIR reflectance (REF). Results The patient was evaluated within a week of the onset of a scotoma in the nasal field of his left eye. Visual acuity was 20/20 OU, and color vision was normal in both eyes. Results of the fundus examination and of SW-FAF and NIR-FAF imaging were normal in both eyes, whereas NIR-REF imaging showed a region of hyporeflectance temporal to the fovea that corresponded with a dense relative scotoma noted on light-adapted static perimetry in the left eye. Loss in the photoreceptor outer segment detected by SD-OCT co-localized with an area of dense cone dysfunction detected on light-adapted perimetry and multifocal ERG but with near-normal rod-mediated vision according to results of 2-color dark-adapted perimetry. Full-field flash ERG findings were normal in both eyes. The outer nuclear layer and inner retinal thicknesses were normal. Conclusions and Relevance Localized, isolated cone dysfunction may represent the earliest photoreceptor abnormality or a distinct entity within the acute zonal occult outer retinopathy complex. Acute zonal occult outer retinopathy
Segmenting texts from outdoor images taken by mobile phones using color features
NASA Astrophysics Data System (ADS)
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.
Unsupervised motion-based object segmentation refined by color
NASA Astrophysics Data System (ADS)
Piek, Matthijs C.; Braspenning, Ralph; Varekamp, Chris
2003-06-01
for its ability to estimate motion vectors which closely resemble the true motion. BLOCK-BASED MOTION SEGMENTATION As mentioned above we start with a block-resolution segmentation based on motion vectors. The presented method is inspired by the well-known K-means segmentation method te{K-means}. Several other methods (e.g. te{kmeansc}) adapt K-means for connectedness by adding a weighted shape-error. This adds the additional difficulty of finding the correct weights for the shape-parameters. Also, these methods often bias one particular pre-defined shape. The presented method, which we call K-regions, encourages connectedness because only blocks at the edges of segments may be assigned to another segment. This constrains the segmentation method to such a degree that it allows the method to use least squares for the robust fitting of affine motion models for each segment. Contrary to te{parmkm}, the segmentation step still operates on vectors instead of model parameters. To make sure the segmentation is temporally consistent, the segmentation of the previous frame will be used as initialisation for every new frame. We also present a scheme which makes the algorithm independent of the initially chosen amount of segments. COLOUR-BASED INTRA-BLOCK SEGMENTATION The block resolution motion-based segmentation forms the starting point for the pixel resolution segmentation. The pixel resolution segmentation is obtained from the block resolution segmentation by reclassifying pixels only at the edges of clusters. We assume that an edge between two objects can be found in either one of two neighbouring blocks that belong to different clusters. This assumption allows us to do the pixel resolution segmentation on each pair of such neighbouring blocks separately. Because of the local nature of the segmentation, it largely avoids problems with heterogeneously coloured areas. Because no new segments are introduced in this step, it also does not suffer from oversegmentation problems
Jiang, Hongquan; Zhao, Yalin; Gao, Jianmin; Gao, Zhiyong
2017-06-01
The radiographic testing (RT) image of a steam turbine manufacturing enterprise has the characteristics of low gray level, low contrast, and blurriness, which lead to a substandard image quality. Moreover, it is not conducive for human eyes to detect and evaluate defects. This study proposes an adaptive pseudo-color enhancement method for weld radiographic images based on the hue, saturation, and intensity (HSI) color space and the self-transformation of pixels to solve these problems. First, the pixel's self-transformation is performed to the pixel value of the original RT image. The function value after the pixel's self-transformation is assigned to the HSI components in the HSI color space. Thereafter, the average intensity of the enhanced image is adaptively adjusted to 0.5 according to the intensity of the original image. Moreover, the hue range and interval can be adjusted according to personal habits. Finally, the HSI components after the adaptive adjustment can be transformed to display in the red, green, and blue color space. Numerous weld radiographic images from a steam turbine manufacturing enterprise are used to validate the proposed method. The experimental results show that the proposed pseudo-color enhancement method can improve image definition and make the target and background areas distinct in weld radiographic images. The enhanced images will be more conducive for defect recognition. Moreover, the image enhanced using the proposed method conforms to the human eye visual properties, and the effectiveness of defect recognition and evaluation can be ensured.
NASA Astrophysics Data System (ADS)
Jiang, Hongquan; Zhao, Yalin; Gao, Jianmin; Gao, Zhiyong
2017-06-01
The radiographic testing (RT) image of a steam turbine manufacturing enterprise has the characteristics of low gray level, low contrast, and blurriness, which lead to a substandard image quality. Moreover, it is not conducive for human eyes to detect and evaluate defects. This study proposes an adaptive pseudo-color enhancement method for weld radiographic images based on the hue, saturation, and intensity (HSI) color space and the self-transformation of pixels to solve these problems. First, the pixel's self-transformation is performed to the pixel value of the original RT image. The function value after the pixel's self-transformation is assigned to the HSI components in the HSI color space. Thereafter, the average intensity of the enhanced image is adaptively adjusted to 0.5 according to the intensity of the original image. Moreover, the hue range and interval can be adjusted according to personal habits. Finally, the HSI components after the adaptive adjustment can be transformed to display in the red, green, and blue color space. Numerous weld radiographic images from a steam turbine manufacturing enterprise are used to validate the proposed method. The experimental results show that the proposed pseudo-color enhancement method can improve image definition and make the target and background areas distinct in weld radiographic images. The enhanced images will be more conducive for defect recognition. Moreover, the image enhanced using the proposed method conforms to the human eye visual properties, and the effectiveness of defect recognition and evaluation can be ensured.
Blood vessel segmentation in color fundus images based on regional and Hessian features.
Shah, Syed Ayaz Ali; Tang, Tong Boon; Faye, Ibrahima; Laude, Augustinus
2017-08-01
To propose a new algorithm of blood vessel segmentation based on regional and Hessian features for image analysis in retinal abnormality diagnosis. Firstly, color fundus images from the publicly available database DRIVE were converted from RGB to grayscale. To enhance the contrast of the dark objects (blood vessels) against the background, the dot product of the grayscale image with itself was generated. To rectify the variation in contrast, we used a 5 × 5 window filter on each pixel. Based on 5 regional features, 1 intensity feature and 2 Hessian features per scale using 9 scales, we extracted a total of 24 features. A linear minimum squared error (LMSE) classifier was trained to classify each pixel into a vessel or non-vessel pixel. The DRIVE dataset provided 20 training and 20 test color fundus images. The proposed algorithm achieves a sensitivity of 72.05% with 94.79% accuracy. Our proposed algorithm achieved higher accuracy (0.9206) at the peripapillary region, where the ocular manifestations in the microvasculature due to glaucoma, central retinal vein occlusion, etc. are most obvious. This supports the proposed algorithm as a strong candidate for automated vessel segmentation.
Patterns of Transfer of Adaptation Among Body Segments
NASA Technical Reports Server (NTRS)
Seidler, R. D.; Bloomberg, J. J.; Stelmach, George E.
2000-01-01
Two experiments were conducted in order to determine the patterns of transfer of visuomotor adaptation between arm and head pointing. An altered gain of display of pointing movements was used to induce a conflict between visual and somatosensory representations. Two subject groups participated in Experiment One: group 1 adapted shoulder pointing movements, and group 2 adapted wrist pointing movements to a 0.5 gain of display. Following the adaptation regimen, subjects performed a transfer test in which the shoulder group performed wrist movements and the wrist group performed shoulder movements. The results demonstrated that both groups displayed typical adaptation curves, initially undershooting the target followed by a return to baseline performance. Transfer tests revealed that both groups had high transfer of the acquired adaptation to the other joint. Experiment Two followed a similar design except that group 1 adapted head pointing movements and group 2 adapted arm pointing movements. The arm adaptation had high transfer to head pointing while the head adaptation had very little transfer to arm pointing. These results imply that, while the arm segments may share a common target representation for goal-directed actions, individual but functionally dependent target representations may exist for the control of head and arm movements.
Locally adaptive MR intensity models and MRF-based segmentation of multiple sclerosis lesions
NASA Astrophysics Data System (ADS)
Galimzianova, Alfiia; Lesjak, Žiga; Likar, Boštjan; Pernuš, Franjo; Špiclin, Žiga
2015-03-01
Neuroimaging biomarkers are an important paraclinical tool used to characterize a number of neurological diseases, however, their extraction requires accurate and reliable segmentation of normal and pathological brain structures. For MR images of healthy brains the intensity models of normal-appearing brain tissue (NABT) in combination with Markov random field (MRF) models are known to give reliable and smooth NABT segmentation. However, the presence of pathology, MR intensity bias and natural tissue-dependent intensity variability altogether represent difficult challenges for a reliable estimation of NABT intensity model based on MR images. In this paper, we propose a novel method for segmentation of normal and pathological structures in brain MR images of multiple sclerosis (MS) patients that is based on locally-adaptive NABT model, a robust method for the estimation of model parameters and a MRF-based segmentation framework. Experiments on multi-sequence brain MR images of 27 MS patients show that, compared to whole-brain model and compared to the widely used Expectation-Maximization Segmentation (EMS) method, the locally-adaptive NABT model increases the accuracy of MS lesion segmentation.
NASA Astrophysics Data System (ADS)
Soetedjo, Aryuanto; Yamada, Koichi
This paper describes a new color segmentation based on a normalized RGB chromaticity diagram for face detection. Face skin is extracted from color images using a coarse skin region with fixed boundaries followed by a fine skin region with variable boundaries. Two newly developed histograms that have prominent peaks of skin color and non-skin colors are employed to adjust the boundaries of the skin region. The proposed approach does not need a skin color model, which depends on a specific camera parameter and is usually limited to a particular environment condition, and no sample images are required. The experimental results using color face images of various races under varying lighting conditions and complex backgrounds, obtained from four different resources on the Internet, show a high detection rate of 87%. The results of the detection rate and computation time are comparable to the well known real-time face detection method proposed by Viola-Jones [11], [12].
On Adapting the Tensor Voting Framework to Robust Color Image Denoising
NASA Astrophysics Data System (ADS)
Moreno, Rodrigo; Garcia, Miguel Angel; Puig, Domenec; Julià, Carme
This paper presents an adaptation of the tensor voting framework for color image denoising, while preserving edges. Tensors are used in order to encode the CIELAB color channels, the uniformity and the edginess of image pixels. A specific voting process is proposed in order to propagate color from a pixel to its neighbors by considering the distance between pixels, the perceptual color difference (by using an optimized version of CIEDE2000), a uniformity measurement and the likelihood of the pixels being impulse noise. The original colors are corrected with those encoded by the tensors obtained after the voting process. Peak to noise ratios and visual inspection show that the proposed methodology has a better performance than state-of-the-art techniques.
New adaptive color quantization method based on self-organizing maps.
Chang, Chip-Hong; Xu, Pengfei; Xiao, Rui; Srikanthan, Thambipillai
2005-01-01
Color quantization (CQ) is an image processing task popularly used to convert true color images to palletized images for limited color display devices. To minimize the contouring artifacts introduced by the reduction of colors, a new competitive learning (CL) based scheme called the frequency sensitive self-organizing maps (FS-SOMs) is proposed to optimize the color palette design for CQ. FS-SOM harmonically blends the neighborhood adaptation of the well-known self-organizing maps (SOMs) with the neuron dependent frequency sensitive learning model, the global butterfly permutation sequence for input randomization, and the reinitialization of dead neurons to harness effective utilization of neurons. The net effect is an improvement in adaptation, a well-ordered color palette, and the alleviation of underutilization problem, which is the main cause of visually perceivable artifacts of CQ. Extensive simulations have been performed to analyze and compare the learning behavior and performance of FS-SOM against other vector quantization (VQ) algorithms. The results show that the proposed FS-SOM outperforms classical CL, Linde, Buzo, and Gray (LBG), and SOM algorithms. More importantly, FS-SOM achieves its superiority in reconstruction quality and topological ordering with a much greater robustness against variations in network parameters than the current art SOM algorithm for CQ. A most significant bit (MSB) biased encoding scheme is also introduced to reduce the number of parallel processing units. By mapping the pixel values as sign-magnitude numbers and biasing the magnitudes according to their sign bits, eight lattice points in the color space are condensed into one common point density function. Consequently, the same processing element can be used to map several color clusters and the entire FS-SOM network can be substantially scaled down without severely scarifying the quality of the displayed image. The drawback of this encoding scheme is the additional storage
Pupil-segmentation-based adaptive optics for microscopy
NASA Astrophysics Data System (ADS)
Ji, Na; Milkie, Daniel E.; Betzig, Eric
2011-03-01
Inhomogeneous optical properties of biological samples make it difficult to obtain diffraction-limited resolution in depth. Correcting the sample-induced optical aberrations needs adaptive optics (AO). However, the direct wavefront-sensing approach commonly used in astronomy is not suitable for most biological samples due to their strong scattering of light. We developed an image-based AO approach that is insensitive to sample scattering. By comparing images of the sample taken with different segments of the pupil illuminated, local tilt in the wavefront is measured from image shift. The aberrated wavefront is then obtained either by measuring the local phase directly using interference or with phase reconstruction algorithms similar to those used in astronomical AO. We implemented this pupil-segmentation-based approach in a two-photon fluorescence microscope and demonstrated that diffraction-limited resolution can be recovered from nonbiological and biological samples.
The adaptive value of primate color vision for predator detection.
Pessoa, Daniel Marques Almeida; Maia, Rafael; de Albuquerque Ajuz, Rafael Cavalcanti; De Moraes, Pedro Zurvaino Palmeira Melo Rosa; Spyrides, Maria Helena Constantino; Pessoa, Valdir Filgueiras
2014-08-01
The complex evolution of primate color vision has puzzled biologists for decades. Primates are the only eutherian mammals that evolved an enhanced capacity for discriminating colors in the green-red part of the spectrum (trichromatism). However, while Old World primates present three types of cone pigments and are routinely trichromatic, most New World primates exhibit a color vision polymorphism, characterized by the occurrence of trichromatic and dichromatic females and obligatory dichromatic males. Even though this has stimulated a prolific line of inquiry, the selective forces and relative benefits influencing color vision evolution in primates are still under debate, with current explanations focusing almost exclusively at the advantages in finding food and detecting socio-sexual signals. Here, we evaluate a previously untested possibility, the adaptive value of primate color vision for predator detection. By combining color vision modeling data on New World and Old World primates, as well as behavioral information from human subjects, we demonstrate that primates exhibiting better color discrimination (trichromats) excel those displaying poorer color visions (dichromats) at detecting carnivoran predators against the green foliage background. The distribution of color vision found in extant anthropoid primates agrees with our results, and may be explained by the advantages of trichromats and dichromats in detecting predators and insects, respectively. © 2014 Wiley Periodicals, Inc.
Adaptive Wiener filter super-resolution of color filter array images.
Karch, Barry K; Hardie, Russell C
2013-08-12
Digital color cameras using a single detector array with a Bayer color filter array (CFA) require interpolation or demosaicing to estimate missing color information and provide full-color images. However, demosaicing does not specifically address fundamental undersampling and aliasing inherent in typical camera designs. Fast non-uniform interpolation based super-resolution (SR) is an attractive approach to reduce or eliminate aliasing and its relatively low computational load is amenable to real-time applications. The adaptive Wiener filter (AWF) SR algorithm was initially developed for grayscale imaging and has not previously been applied to color SR demosaicing. Here, we develop a novel fast SR method for CFA cameras that is based on the AWF SR algorithm and uses global channel-to-channel statistical models. We apply this new method as a stand-alone algorithm and also as an initialization image for a variational SR algorithm. This paper presents the theoretical development of the color AWF SR approach and applies it in performance comparisons to other SR techniques for both simulated and real data.
Pulmonary airways tree segmentation from CT examinations using adaptive volume of interest
NASA Astrophysics Data System (ADS)
Park, Sang Cheol; Kim, Won Pil; Zheng, Bin; Leader, Joseph K.; Pu, Jiantao; Tan, Jun; Gur, David
2009-02-01
Airways tree segmentation is an important step in quantitatively assessing the severity of and changes in several lung diseases such as chronic obstructive pulmonary disease (COPD), asthma, and cystic fibrosis. It can also be used in guiding bronchoscopy. The purpose of this study is to develop an automated scheme for segmenting the airways tree structure depicted on chest CT examinations. After lung volume segmentation, the scheme defines the first cylinder-like volume of interest (VOI) using a series of images depicting the trachea. The scheme then iteratively defines and adds subsequent VOIs using a region growing algorithm combined with adaptively determined thresholds in order to trace possible sections of airways located inside the combined VOI in question. The airway tree segmentation process is automatically terminated after the scheme assesses all defined VOIs in the iteratively assembled VOI list. In this preliminary study, ten CT examinations with 1.25mm section thickness and two different CT image reconstruction kernels ("bone" and "standard") were selected and used to test the proposed airways tree segmentation scheme. The experiment results showed that (1) adopting this approach affectively prevented the scheme from infiltrating into the parenchyma, (2) the proposed method reasonably accurately segmented the airways trees with lower false positive identification rate as compared with other previously reported schemes that are based on 2-D image segmentation and data analyses, and (3) the proposed adaptive, iterative threshold selection method for the region growing step in each identified VOI enables the scheme to segment the airways trees reliably to the 4th generation in this limited dataset with successful segmentation up to the 5th generation in a fraction of the airways tree branches.
Context cue-dependent saccadic adaptation in rhesus macaques cannot be elicited using color
Smalianchuk, Ivan; Khanna, Sanjeev B.; Smith, Matthew A.; Gandhi, Neeraj J.
2015-01-01
When the head does not move, rapid movements of the eyes called saccades are used to redirect the line of sight. Saccades are defined by a series of metrical and kinematic (evolution of a movement as a function of time) relationships. For example, the amplitude of a saccade made from one visual target to another is roughly 90% of the distance between the initial fixation point (T0) and the peripheral target (T1). However, this stereotypical relationship between saccade amplitude and initial retinal error (T1-T0) may be altered, either increased or decreased, by surreptitiously displacing a visual target during an ongoing saccade. This form of motor learning (called saccadic adaptation) has been described in both humans and monkeys. Recent experiments in humans and monkeys have suggested that internal (proprioceptive) and external (target shape, color, and/or motion) cues may be used to produce context-dependent adaptation. We tested the hypothesis that an external contextual cue (target color) could be used to evoke differential gain (actual saccade/initial retinal error) states in rhesus monkeys. We did not observe differential gain states correlated with target color regardless of whether targets were displaced along the same vector as the primary saccade or perpendicular to it. Furthermore, this observation held true regardless of whether adaptation trials using various colors and intrasaccade target displacements were randomly intermixed or presented in short or long blocks of trials. These results are consistent with hypotheses that state that color cannot be used as a contextual cue and are interpreted in light of previous studies of saccadic adaptation in both humans and monkeys. PMID:25995353
Illuminant-adaptive color reproduction for mobile display
NASA Astrophysics Data System (ADS)
Kim, Jong-Man; Park, Kee-Hyon; Kwon, Oh-Seol; Cho, Yang-Ho; Ha, Yeong-Ho
2006-01-01
This paper proposes an illuminant-adaptive reproduction method using light adaptation and flare conditions for a mobile display. Mobile displays, such as PDAs and cellular phones, are viewed under various lighting conditions. In particular, images displayed in daylight are perceived as quite dark due to the light adaptation of the human visual system, as the luminance of a mobile display is considerably lower than that of an outdoor environment. In addition, flare phenomena decrease the color gamut of a mobile display by increasing the luminance of dark areas and de-saturating the chroma. Therefore, this paper presents an enhancement method composed of lightness enhancement and chroma compensation. First, the ambient light intensity is measured using a lux-sensor, then the flare is calculated based on the reflection ratio of the display device and the ambient light intensity. The relative cone response is nonlinear to the input luminance. This is also changed by the ambient light intensity. Thus, to improve the perceived image, the displayed luminance is enhanced by lightness linearization. In this paper, the image's luminance is transformed by linearization of the response to the input luminance according to the ambient light intensity. Next, the displayed image is compensated according to the physically reduced chroma, resulting from flare phenomena. The reduced chroma value is calculated according to the flare for each intensity. The chroma compensation method to maintain the original image's chroma is applied differently for each hue plane, as the flare affects each hue plane differently. At this time, the enhanced chroma also considers the gamut boundary. Based on experimental observations, the outer luminance-intensity generally ranges from 1,000 lux to 30,000 lux. Thus, in the case of an outdoor environment, i.e. greater than 1,000 lux, this study presents a color reproduction method based on an inverse cone response curve and flare condition. Consequently
Adaptive skin detection based on online training
NASA Astrophysics Data System (ADS)
Zhang, Ming; Tang, Liang; Zhou, Jie; Rong, Gang
2007-11-01
Skin is a widely used cue for porn image classification. Most conventional methods are off-line training schemes. They usually use a fixed boundary to segment skin regions in the images and are effective only in restricted conditions: e.g. good lightness and unique human race. This paper presents an adaptive online training scheme for skin detection which can handle these tough cases. In our approach, skin detection is considered as a classification problem on Gaussian mixture model. For each image, human face is detected and the face color is used to establish a primary estimation of skin color distribution. Then an adaptive online training algorithm is used to find the real boundary between skin color and background color in current image. Experimental results on 450 images showed that the proposed method is more robust in general situations than the conventional ones.
Using cystoscopy to segment bladder tumors with a multivariate approach in different color spaces.
Freitas, Nuno R; Vieira, Pedro M; Lima, Estevao; Lima, Carlos S
2017-07-01
Nowadays the diagnosis of bladder lesions relies upon cystoscopy examination and depends on the interpreter's experience. State of the art of bladder tumor identification are based on 3D reconstruction, using CT images (Virtual Cystoscopy) or images where the structures are exalted with the use of pigmentation, but none uses white light cystoscopy images. An initial attempt to automatically identify tumoral tissue was already developed by the authors and this paper will develop this idea. Traditional cystoscopy images processing has a huge potential to improve early tumor detection and allows a more effective treatment. In this paper is described a multivariate approach to do segmentation of bladder cystoscopy images, that will be used to automatically detect and improve physician diagnose. Each region can be assumed as a normal distribution with specific parameters, leading to the assumption that the distribution of intensities is a Gaussian Mixture Model (GMM). Region of high grade and low grade tumors, usually appears with higher intensity than normal regions. This paper proposes a Maximum a Posteriori (MAP) approach based on pixel intensities read simultaneously in different color channels from RGB, HSV and CIELab color spaces. The Expectation-Maximization (EM) algorithm is used to estimate the best multivariate GMM parameters. Experimental results show that the proposed method does bladder tumor segmentation into two classes in a more efficient way in RGB even in cases where the tumor shape is not well defined. Results also show that the elimination of component L from CIELab color space does not allow definition of the tumor shape.
Genomic architecture of adaptive color pattern divergence and convergence in Heliconius butterflies
Supple, Megan A.; Hines, Heather M.; Dasmahapatra, Kanchon K.; Lewis, James J.; Nielsen, Dahlia M.; Lavoie, Christine; Ray, David A.; Salazar, Camilo; McMillan, W. Owen; Counterman, Brian A.
2013-01-01
Identifying the genetic changes driving adaptive variation in natural populations is key to understanding the origins of biodiversity. The mosaic of mimetic wing patterns in Heliconius butterflies makes an excellent system for exploring adaptive variation using next-generation sequencing. In this study, we use a combination of techniques to annotate the genomic interval modulating red color pattern variation, identify a narrow region responsible for adaptive divergence and convergence in Heliconius wing color patterns, and explore the evolutionary history of these adaptive alleles. We use whole genome resequencing from four hybrid zones between divergent color pattern races of Heliconius erato and two hybrid zones of the co-mimic Heliconius melpomene to examine genetic variation across 2.2 Mb of a partial reference sequence. In the intergenic region near optix, the gene previously shown to be responsible for the complex red pattern variation in Heliconius, population genetic analyses identify a shared 65-kb region of divergence that includes several sites perfectly associated with phenotype within each species. This region likely contains multiple cis-regulatory elements that control discrete expression domains of optix. The parallel signatures of genetic differentiation in H. erato and H. melpomene support a shared genetic architecture between the two distantly related co-mimics; however, phylogenetic analysis suggests mimetic patterns in each species evolved independently. Using a combination of next-generation sequencing analyses, we have refined our understanding of the genetic architecture of wing pattern variation in Heliconius and gained important insights into the evolution of novel adaptive phenotypes in natural populations. PMID:23674305
Chouinard, Philippe A; Goodale, Melvyn A
2012-02-01
We used fMRI to identify brain areas that adapted to either animals or manipulable artifacts while participants classified highly-rendered color photographs into subcategories. Several key brain areas adapted more strongly to one class of objects compared to the other. Namely, we observed stronger adaptation for animals in the lingual gyrus bilaterally, which are known to analyze the color of objects, and in the right frontal operculum and in the anterior insular cortex bilaterally, which are known to process emotional content. In contrast, the left anterior intraparietal sulcus, which is important for configuring the hand to match the three-dimensional structure of objects during grasping, adapted more strongly to manipulable artifacts. Contrary to what a previous study has found using gray-scale photographs, we did not replicate categorical-specific adaptation in the lateral fusiform gyrus for animals and categorical-specific adaptation in the medial fusiform gyrus for manipulable artifacts. Both categories of objects adapted strongly in the fusiform gyrus without any clear preference in location along its medial-lateral axis. We think that this is because the fusiform gyrus has an important role to play in color processing and hence its responsiveness to color stimuli could be very different than its responsiveness to gray-scale photographs. Nevertheless, on the basis of what we found, we propose that the recognition and subsequent classification of animals may depend primarily on perceptual properties, such as their color, and on their emotional content whereas other factors, such as their function, may play a greater role for classifying manipulable artifacts. Copyright © 2011 Elsevier Inc. All rights reserved.
Feng, Yuan; Dong, Fenglin; Xia, Xiaolong; Hu, Chun-Hong; Fan, Qianmin; Hu, Yanle; Gao, Mingyuan; Mutic, Sasa
2017-07-01
Ultrasound (US) imaging has been widely used in breast tumor diagnosis and treatment intervention. Automatic delineation of the tumor is a crucial first step, especially for the computer-aided diagnosis (CAD) and US-guided breast procedure. However, the intrinsic properties of US images such as low contrast and blurry boundaries pose challenges to the automatic segmentation of the breast tumor. Therefore, the purpose of this study is to propose a segmentation algorithm that can contour the breast tumor in US images. To utilize the neighbor information of each pixel, a Hausdorff distance based fuzzy c-means (FCM) method was adopted. The size of the neighbor region was adaptively updated by comparing the mutual information between them. The objective function of the clustering process was updated by a combination of Euclid distance and the adaptively calculated Hausdorff distance. Segmentation results were evaluated by comparing with three experts' manual segmentations. The results were also compared with a kernel-induced distance based FCM with spatial constraints, the method without adaptive region selection, and conventional FCM. Results from segmenting 30 patient images showed the adaptive method had a value of sensitivity, specificity, Jaccard similarity, and Dice coefficient of 93.60 ± 5.33%, 97.83 ± 2.17%, 86.38 ± 5.80%, and 92.58 ± 3.68%, respectively. The region-based metrics of average symmetric surface distance (ASSD), root mean square symmetric distance (RMSD), and maximum symmetric surface distance (MSSD) were 0.03 ± 0.04 mm, 0.04 ± 0.03 mm, and 1.18 ± 1.01 mm, respectively. All the metrics except sensitivity were better than that of the non-adaptive algorithm and the conventional FCM. Only three region-based metrics were better than that of the kernel-induced distance based FCM with spatial constraints. Inclusion of the pixel neighbor information adaptively in segmenting US images improved the segmentation performance. The results demonstrate the
Medical Image Segmentation using the HSI color space and Fuzzy Mathematical Morphology
NASA Astrophysics Data System (ADS)
Gasparri, J. P.; Bouchet, A.; Abras, G.; Ballarin, V.; Pastore, J. I.
2011-12-01
Diabetic retinopathy is the most common cause of blindness among the active population in developed countries. An early ophthalmologic examination followed by proper treatment can prevent blindness. The purpose of this work is develop an automated method for segmentation the vasculature in retinal images in order to assist the expert in the evolution of a specific treatment or in the diagnosis of a potential pathology. Since the HSI space has the ability to separate the intensity of the intrinsic color information, its use is recommended for the digital processing images when they are affected by lighting changes, characteristic of the images under study. By the application of color filters, is achieved artificially change the tone of blood vessels, to better distinguish them from the bottom. This technique, combined with the application of fuzzy mathematical morphology tools as the Top-Hat transformation, creates images of the retina, where vascular branches are markedly enhanced over the original. These images provide the visualization of blood vessels by the specialist.
Liu, Hui; Zhang, Cai-Ming; Su, Zhi-Yuan; Wang, Kai; Deng, Kai
2015-01-01
The key problem of computer-aided diagnosis (CAD) of lung cancer is to segment pathologically changed tissues fast and accurately. As pulmonary nodules are potential manifestation of lung cancer, we propose a fast and self-adaptive pulmonary nodules segmentation method based on a combination of FCM clustering and classification learning. The enhanced spatial function considers contributions to fuzzy membership from both the grayscale similarity between central pixels and single neighboring pixels and the spatial similarity between central pixels and neighborhood and improves effectively the convergence rate and self-adaptivity of the algorithm. Experimental results show that the proposed method can achieve more accurate segmentation of vascular adhesion, pleural adhesion, and ground glass opacity (GGO) pulmonary nodules than other typical algorithms.
Liu, Hui; Zhang, Cai-Ming; Su, Zhi-Yuan; Wang, Kai; Deng, Kai
2015-01-01
The key problem of computer-aided diagnosis (CAD) of lung cancer is to segment pathologically changed tissues fast and accurately. As pulmonary nodules are potential manifestation of lung cancer, we propose a fast and self-adaptive pulmonary nodules segmentation method based on a combination of FCM clustering and classification learning. The enhanced spatial function considers contributions to fuzzy membership from both the grayscale similarity between central pixels and single neighboring pixels and the spatial similarity between central pixels and neighborhood and improves effectively the convergence rate and self-adaptivity of the algorithm. Experimental results show that the proposed method can achieve more accurate segmentation of vascular adhesion, pleural adhesion, and ground glass opacity (GGO) pulmonary nodules than other typical algorithms. PMID:25945120
Efficient graph-cut tattoo segmentation
NASA Astrophysics Data System (ADS)
Kim, Joonsoo; Parra, Albert; Li, He; Delp, Edward J.
2015-03-01
Law enforcement is interested in exploiting tattoos as an information source to identify, track and prevent gang-related crimes. Many tattoo image retrieval systems have been described. In a retrieval system tattoo segmentation is an important step for retrieval accuracy since segmentation removes background information in a tattoo image. Existing segmentation methods do not extract the tattoo very well when the background includes textures and color similar to skin tones. In this paper we describe a tattoo segmentation approach by determining skin pixels in regions near the tattoo. In these regions graph-cut segmentation using a skin color model and a visual saliency map is used to find skin pixels. After segmentation we determine which set of skin pixels are connected with each other that form a closed contour including a tattoo. The regions surrounded by the closed contours are considered tattoo regions. Our method segments tattoos well when the background includes textures and color similar to skin.
Lo, T Y; Sim, K S; Tso, C P; Nia, M E
2014-01-01
An improvement to the previously proposed adaptive Canny optimization technique for scanning electron microscope image colorization is reported. The additional feature, called pseudo-mapping technique, is that the grayscale markings are temporarily mapped to a set of pre-defined pseudo-color map as a mean to instill color information for grayscale colors in chrominance channels. This allows the presence of grayscale markings to be identified; hence optimization colorization of grayscale colors is made possible. This additional feature enhances the flexibility of scanning electron microscope image colorization by providing wider range of possible color enhancement. Furthermore, the nature of this technique also allows users to adjust the luminance intensities of selected region from the original image within certain extent. © 2014 Wiley Periodicals, Inc.
Xia, Yong; Eberl, Stefan; Wen, Lingfeng; Fulham, Michael; Feng, David Dagan
2012-01-01
Dual medical imaging modalities, such as PET-CT, are now a routine component of clinical practice. Medical image segmentation methods, however, have generally only been applied to single modality images. In this paper, we propose the dual-modality image segmentation model to segment brain PET-CT images into gray matter, white matter and cerebrospinal fluid. This model converts PET-CT image segmentation into an optimization process controlled simultaneously by PET and CT voxel values and spatial constraints. It is innovative in the creation and application of the modality discriminatory power (MDP) coefficient as a weighting scheme to adaptively combine the functional (PET) and anatomical (CT) information on a voxel-by-voxel basis. Our approach relies upon allowing the modality with higher discriminatory power to play a more important role in the segmentation process. We compared the proposed approach to three other image segmentation strategies, including PET-only based segmentation, combination of the results of independent PET image segmentation and CT image segmentation, and simultaneous segmentation of joint PET and CT images without an adaptive weighting scheme. Our results in 21 clinical studies showed that our approach provides the most accurate and reliable segmentation for brain PET-CT images. Copyright © 2011 Elsevier Ltd. All rights reserved.
NASA Astrophysics Data System (ADS)
Xu, Chao; Zhou, Dongxiang; Zhai, Yongping; Liu, Yunhui
2015-12-01
This paper realizes the automatic segmentation and classification of Mycobacterium tuberculosis with conventional light microscopy. First, the candidate bacillus objects are segmented by the marker-based watershed transform. The markers are obtained by an adaptive threshold segmentation based on the adaptive scale Gaussian filter. The scale of the Gaussian filter is determined according to the color model of the bacillus objects. Then the candidate objects are extracted integrally after region merging and contaminations elimination. Second, the shape features of the bacillus objects are characterized by the Hu moments, compactness, eccentricity, and roughness, which are used to classify the single, touching and non-bacillus objects. We evaluated the logistic regression, random forest, and intersection kernel support vector machines classifiers in classifying the bacillus objects respectively. Experimental results demonstrate that the proposed method yields to high robustness and accuracy. The logistic regression classifier performs best with an accuracy of 91.68%.
Color-Blindness Study: Color Discrimination on the TICCIT System.
ERIC Educational Resources Information Center
Asay, Calvin S.; Schneider, Edward W.
The question studied whether the specific seven TICCIT system colors used within color coding schemes can be a source of confusion, or not seen at all, by the color-blind segment of target populations. Subjects were 11 color-blind and three normally sighted students at Brigham Young University. After a preliminary training exercise to acquaint the…
Magnet system optimization for segmented adaptive-gap in-vacuum undulator
DOE Office of Scientific and Technical Information (OSTI.GOV)
Kitegi, C., E-mail: ckitegi@bnl.gov; Chubar, O.; Eng, C.
2016-07-27
Segmented Adaptive Gap in-vacuum Undulator (SAGU), in which different segments have different gaps and periods, promises a considerable spectral performance gain over a conventional undulator with uniform gap and period. According to calculations, this gain can be comparable to the gain achievable with a superior undulator technology (e.g. a room-temperature in-vacuum hybrid SAGU would perform as a cryo-cooled hybrid in-vacuum undulator with uniform gap and period). However, for reaching the high spectral performance, SAGU magnetic design has to include compensation of kicks experienced by the electron beam at segment junctions because of different deflection parameter values in the segments. Wemore » show that such compensation to large extent can be accomplished by using a passive correction, however, simple correction coils are nevertheless required as well to reach perfect compensation over a whole SAGU tuning range. Magnetic optimizations performed with Radia code, and the resulting undulator radiation spectra calculated using SRW code, demonstrating a possibility of nearly perfect correction, are presented.« less
MRI Brain Tumor Segmentation and Necrosis Detection Using Adaptive Sobolev Snakes.
Nakhmani, Arie; Kikinis, Ron; Tannenbaum, Allen
2014-03-21
Brain tumor segmentation in brain MRI volumes is used in neurosurgical planning and illness staging. It is important to explore the tumor shape and necrosis regions at different points of time to evaluate the disease progression. We propose an algorithm for semi-automatic tumor segmentation and necrosis detection. Our algorithm consists of three parts: conversion of MRI volume to a probability space based on the on-line learned model, tumor probability density estimation, and adaptive segmentation in the probability space. We use manually selected acceptance and rejection classes on a single MRI slice to learn the background and foreground statistical models. Then, we propagate this model to all MRI slices to compute the most probable regions of the tumor. Anisotropic 3D diffusion is used to estimate the probability density. Finally, the estimated density is segmented by the Sobolev active contour (snake) algorithm to select smoothed regions of the maximum tumor probability. The segmentation approach is robust to noise and not very sensitive to the manual initialization in the volumes tested. Also, it is appropriate for low contrast imagery. The irregular necrosis regions are detected by using the outliers of the probability distribution inside the segmented region. The necrosis regions of small width are removed due to a high probability of noisy measurements. The MRI volume segmentation results obtained by our algorithm are very similar to expert manual segmentation.
MRI brain tumor segmentation and necrosis detection using adaptive Sobolev snakes
NASA Astrophysics Data System (ADS)
Nakhmani, Arie; Kikinis, Ron; Tannenbaum, Allen
2014-03-01
Brain tumor segmentation in brain MRI volumes is used in neurosurgical planning and illness staging. It is important to explore the tumor shape and necrosis regions at di erent points of time to evaluate the disease progression. We propose an algorithm for semi-automatic tumor segmentation and necrosis detection. Our algorithm consists of three parts: conversion of MRI volume to a probability space based on the on-line learned model, tumor probability density estimation, and adaptive segmentation in the probability space. We use manually selected acceptance and rejection classes on a single MRI slice to learn the background and foreground statistical models. Then, we propagate this model to all MRI slices to compute the most probable regions of the tumor. Anisotropic 3D di usion is used to estimate the probability density. Finally, the estimated density is segmented by the Sobolev active contour (snake) algorithm to select smoothed regions of the maximum tumor probability. The segmentation approach is robust to noise and not very sensitive to the manual initialization in the volumes tested. Also, it is appropriate for low contrast imagery. The irregular necrosis regions are detected by using the outliers of the probability distribution inside the segmented region. The necrosis regions of small width are removed due to a high probability of noisy measurements. The MRI volume segmentation results obtained by our algorithm are very similar to expert manual segmentation.
Ma, Ruiqing; Kawamoto, Ken-Ichiro; Shinomori, Keizo
2016-03-01
We explored the color constancy mechanisms of color-deficient observers under red, green, blue, and yellow illuminations. The red and green illuminations were defined individually by the longer axis of the color discrimination ellipsoid measured by the Cambridge Colour Test. Four dichromats (3 protanopes and 1 deuteranope), two anomalous trichromats (2 deuteranomalous observers), and five color-normal observers were asked to complete the color constancy task by making a simultaneous paper match under asymmetrical illuminations in haploscopic view on a monitor. The von Kries adaptation model was applied to estimate the cone responses. The model fits showed that for all color-deficient observers under all illuminations, the adjustment of the S-cone response or blue-yellow chromatically opponent responses modeled with the simple assumption of cone deletion in a certain type (S-M, S-L or S-(L+M)) was consistent with the principle of the von Kries model. The degree of adaptation was similar to that of color-normal observers. The results indicate that the color constancy of color-deficient observers is mediated by the simplified blue-yellow color system with a von Kries-type adaptation effect, even in the case of brightness match, as well as by a possible cone-level adaptation to the S-cone when the illumination produces a strong S-cone stimulation, such as blue illumination.
Luminance contours can gate afterimage colors and "real" colors.
Anstis, Stuart; Vergeer, Mark; Van Lier, Rob
2012-09-06
It has long been known that colored images may elicit afterimages in complementary colors. We have already shown (Van Lier, Vergeer, & Anstis, 2009) that one and the same adapting image may result in different afterimage colors, depending on the test contours presented after the colored image. The color of the afterimage depends on two adapting colors, those both inside and outside the test. Here, we further explore this phenomenon and show that the color-contour interactions shown for afterimage colors also occur for "real" colors. We argue that similar mechanisms apply for both types of stimulation.
NASA Astrophysics Data System (ADS)
He, Wenda; Juette, Arne; Denton, Erica R. E.; Zwiggelaar, Reyer
2015-03-01
Breast cancer is the most frequently diagnosed cancer in women. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective ways to overcome the disease. Successful mammographic density segmentation is a key aspect in deriving correct tissue composition, ensuring an accurate mammographic risk assessment. However, mammographic densities have not yet been fully incorporated with non-image based risk prediction models, (e.g. the Gail and the Tyrer-Cuzick model), because of unreliable segmentation consistency and accuracy. This paper presents a novel multiresolution mammographic density segmentation, a concept of stack representation is proposed, and 3D texture features were extracted by adapting techniques based on classic 2D first-order statistics. An unsupervised clustering technique was employed to achieve mammographic segmentation, in which two improvements were made; 1) consistent segmentation by incorporating an optimal centroids initialisation step, and 2) significantly reduced the number of missegmentation by using an adaptive cluster merging technique. A set of full field digital mammograms was used in the evaluation. Visual assessment indicated substantial improvement on segmented anatomical structures and tissue specific areas, especially in low mammographic density categories. The developed method demonstrated an ability to improve the quality of mammographic segmentation via clustering, and results indicated an improvement of 26% in segmented image with good quality when compared with the standard clustering approach. This in turn can be found useful in early breast cancer detection, risk-stratified screening, and aiding radiologists in the process of decision making prior to surgery and/or treatment.
Functional photoreceptor loss revealed with adaptive optics: an alternate cause of color blindness.
Carroll, Joseph; Neitz, Maureen; Hofer, Heidi; Neitz, Jay; Williams, David R
2004-06-01
There is enormous variation in the X-linked L/M (long/middle wavelength sensitive) gene array underlying "normal" color vision in humans. This variability has been shown to underlie individual variation in color matching behavior. Recently, red-green color blindness has also been shown to be associated with distinctly different genotypes. This has opened the possibility that there may be important phenotypic differences within classically defined groups of color blind individuals. Here, adaptive optics retinal imaging has revealed a mechanism for producing dichromatic color vision in which the expression of a mutant cone photopigment gene leads to the loss of the entire corresponding class of cone photoreceptor cells. Previously, the theory that common forms of inherited color blindness could be caused by the loss of photoreceptor cells had been discounted. We confirm that remarkably, this loss of one-third of the cones does not impair any aspect of vision other than color.
Adaptive optics using a MEMS deformable mirror for a segmented mirror telescope
NASA Astrophysics Data System (ADS)
Miyamura, Norihide
2017-09-01
For small satellite remote sensing missions, a large aperture telescope more than 400mm is required to realize less than 1m GSD observations. However, it is difficult or expensive to realize the large aperture telescope using a monolithic primary mirror with high surface accuracy. A segmented mirror telescope should be studied especially for small satellite missions. Generally, not only high accuracy of optical surface but also high accuracy of optical alignment is required for large aperture telescopes. For segmented mirror telescopes, the alignment is more difficult and more important. For conventional systems, the optical alignment is adjusted before launch to achieve desired imaging performance. However, it is difficult to adjust the alignment for large sized optics in high accuracy. Furthermore, thermal environment in orbit and vibration in a launch vehicle cause the misalignments of the optics. We are developing an adaptive optics system using a MEMS deformable mirror for an earth observing remote sensing sensor. An image based adaptive optics system compensates the misalignments and wavefront aberrations of optical elements using the deformable mirror by feedback of observed images. We propose the control algorithm of the deformable mirror for a segmented mirror telescope by using of observed image. The numerical simulation results and experimental results show that misalignment and wavefront aberration of the segmented mirror telescope are corrected and image quality is improved.
An adaptive segment method for smoothing lidar signal based on noise estimation
NASA Astrophysics Data System (ADS)
Wang, Yuzhao; Luo, Pingping
2014-10-01
An adaptive segmentation smoothing method (ASSM) is introduced in the paper to smooth the signal and suppress the noise. In the ASSM, the noise is defined as the 3σ of the background signal. An integer number N is defined for finding the changing positions in the signal curve. If the difference of adjacent two points is greater than 3Nσ, the position is recorded as an end point of the smoothing segment. All the end points detected as above are recorded and the curves between them will be smoothed separately. In the traditional method, the end points of the smoothing windows in the signals are fixed. The ASSM creates changing end points in different signals and the smoothing windows could be set adaptively. The windows are always set as the half of the segmentations and then the average smoothing method will be applied in the segmentations. The Iterative process is required for reducing the end-point aberration effect in the average smoothing method and two or three times are enough. In ASSM, the signals are smoothed in the spacial area nor frequent area, that means the frequent disturbance will be avoided. A lidar echo was simulated in the experimental work. The echo was supposed to be created by a space-born lidar (e.g. CALIOP). And white Gaussian noise was added to the echo to act as the random noise resulted from environment and the detector. The novel method, ASSM, was applied to the noisy echo to filter the noise. In the test, N was set to 3 and the Iteration time is two. The results show that, the signal could be smoothed adaptively by the ASSM, but the N and the Iteration time might be optimized when the ASSM is applied in a different lidar.
Gene loss, adaptive evolution and the co-evolution of plumage coloration genes with opsins in birds.
Borges, Rui; Khan, Imran; Johnson, Warren E; Gilbert, M Thomas P; Zhang, Guojie; Jarvis, Erich D; O'Brien, Stephen J; Antunes, Agostinho
2015-10-06
The wide range of complex photic systems observed in birds exemplifies one of their key evolutionary adaptions, a well-developed visual system. However, genomic approaches have yet to be used to disentangle the evolutionary mechanisms that govern evolution of avian visual systems. We performed comparative genomic analyses across 48 avian genomes that span extant bird phylogenetic diversity to assess evolutionary changes in the 17 representatives of the opsin gene family and five plumage coloration genes. Our analyses suggest modern birds have maintained a repertoire of up to 15 opsins. Synteny analyses indicate that PARA and PARIE pineal opsins were lost, probably in conjunction with the degeneration of the parietal organ. Eleven of the 15 avian opsins evolved in a non-neutral pattern, confirming the adaptive importance of vision in birds. Visual conopsins sw1, sw2 and lw evolved under negative selection, while the dim-light RH1 photopigment diversified. The evolutionary patterns of sw1 and of violet/ultraviolet sensitivity in birds suggest that avian ancestors had violet-sensitive vision. Additionally, we demonstrate an adaptive association between the RH2 opsin and the MC1R plumage color gene, suggesting that plumage coloration has been photic mediated. At the intra-avian level we observed some unique adaptive patterns. For example, barn owl showed early signs of pseudogenization in RH2, perhaps in response to nocturnal behavior, and penguins had amino acid deletions in RH2 sites responsible for the red shift and retinal binding. These patterns in the barn owl and penguins were convergent with adaptive strategies in nocturnal and aquatic mammals, respectively. We conclude that birds have evolved diverse opsin adaptations through gene loss, adaptive selection and coevolution with plumage coloration, and that differentiated selective patterns at the species level suggest novel photic pressures to influence evolutionary patterns of more-recent lineages.
Chromostereopsis in "virtual reality" adapters with electrically tuneable liquid lens oculars
NASA Astrophysics Data System (ADS)
Ozolinsh, Maris; Muizniece, Kristine; Berzinsh, Janis
2016-10-01
Chromostereopsis can be sight and feel in "Virtual Reality" adapters, that induces the appearance of color dependant depth sense and, finally, combines this sense with the source conceived depth scenario. Present studies are devoted to investigation the induced chromastereopsis when using adapted "Virtual Reality" frame together with mobile devices as smartphones. We did observation of composite visual stimuli presented on the high spatial resolution screen of the mobile phone placed inside a portable "Virtual Reality" adapter. Separated for the left and right eyes stimuli consisted of two areas: a) identical for both eyes color chromostereopsis part, and b) additional conventional color neutral random-dot stereopsis part with a stereodisparity based on the horizontal shift of a random-dot segment in images for the left and right eyes, correspondingly. The observer task was to equalize the depth sense for neutral and colored stimuli areas. Such scheme allows to determine actual observed chromostereopsis disparity value versus eye stimuli color difference. At standard observation conditions for adapter with +2D ocular lenses for mobile red-blue stimuli, the perceptual chromostereopsis depth sensitivity on color difference was linearly approximated with a slope SChS ≈ 2.1[arcmin/(Labcolor difference)] for red-blue pairs. Additional to standard application in adapter the tuneable "Varioptic" liquid lens oculars were incorporated, that allowed stimuli eye magnification, vergence and disparity values control electrically.
Merabet, Youssef El; Meurie, Cyril; Ruichek, Yassine; Sbihi, Abderrahmane; Touahni, Raja
2015-01-01
In this paper, we present a novel strategy for roof segmentation from aerial images (orthophotoplans) based on the cooperation of edge- and region-based segmentation methods. The proposed strategy is composed of three major steps. The first one, called the pre-processing step, consists of simplifying the acquired image with an appropriate couple of invariant and gradient, optimized for the application, in order to limit illumination changes (shadows, brightness, etc.) affecting the images. The second step is composed of two main parallel treatments: on the one hand, the simplified image is segmented by watershed regions. Even if the first segmentation of this step provides good results in general, the image is often over-segmented. To alleviate this problem, an efficient region merging strategy adapted to the orthophotoplan particularities, with a 2D modeling of roof ridges technique, is applied. On the other hand, the simplified image is segmented by watershed lines. The third step consists of integrating both watershed segmentation strategies into a single cooperative segmentation scheme in order to achieve satisfactory segmentation results. Tests have been performed on orthophotoplans containing 100 roofs with varying complexity, and the results are evaluated with the VINETcriterion using ground-truth image segmentation. A comparison with five popular segmentation techniques of the literature demonstrates the effectiveness and the reliability of the proposed approach. Indeed, we obtain a good segmentation rate of 96% with the proposed method compared to 87.5% with statistical region merging (SRM), 84% with mean shift, 82% with color structure code (CSC), 80% with efficient graph-based segmentation algorithm (EGBIS) and 71% with JSEG. PMID:25648706
NASA Astrophysics Data System (ADS)
Luiza Bondar, M.; Hoogeman, Mischa; Schillemans, Wilco; Heijmen, Ben
2013-08-01
For online adaptive radiotherapy of cervical cancer, fast and accurate image segmentation is required to facilitate daily treatment adaptation. Our aim was twofold: (1) to test and compare three intra-patient automated segmentation methods for the cervix-uterus structure in CT-images and (2) to improve the segmentation accuracy by including prior knowledge on the daily bladder volume or on the daily coordinates of implanted fiducial markers. The tested methods were: shape deformation (SD) and atlas-based segmentation (ABAS) using two non-rigid registration methods: demons and a hierarchical algorithm. Tests on 102 CT-scans of 13 patients demonstrated that the segmentation accuracy significantly increased by including the bladder volume predicted with a simple 1D model based on a manually defined bladder top. Moreover, manually identified implanted fiducial markers significantly improved the accuracy of the SD method. For patients with large cervix-uterus volume regression, the use of CT-data acquired toward the end of the treatment was required to improve segmentation accuracy. Including prior knowledge, the segmentation results of SD (Dice similarity coefficient 85 ± 6%, error margin 2.2 ± 2.3 mm, average time around 1 min) and of ABAS using hierarchical non-rigid registration (Dice 82 ± 10%, error margin 3.1 ± 2.3 mm, average time around 30 s) support their use for image guided online adaptive radiotherapy of cervical cancer.
Bondar, M Luiza; Hoogeman, Mischa; Schillemans, Wilco; Heijmen, Ben
2013-08-07
For online adaptive radiotherapy of cervical cancer, fast and accurate image segmentation is required to facilitate daily treatment adaptation. Our aim was twofold: (1) to test and compare three intra-patient automated segmentation methods for the cervix-uterus structure in CT-images and (2) to improve the segmentation accuracy by including prior knowledge on the daily bladder volume or on the daily coordinates of implanted fiducial markers. The tested methods were: shape deformation (SD) and atlas-based segmentation (ABAS) using two non-rigid registration methods: demons and a hierarchical algorithm. Tests on 102 CT-scans of 13 patients demonstrated that the segmentation accuracy significantly increased by including the bladder volume predicted with a simple 1D model based on a manually defined bladder top. Moreover, manually identified implanted fiducial markers significantly improved the accuracy of the SD method. For patients with large cervix-uterus volume regression, the use of CT-data acquired toward the end of the treatment was required to improve segmentation accuracy. Including prior knowledge, the segmentation results of SD (Dice similarity coefficient 85 ± 6%, error margin 2.2 ± 2.3 mm, average time around 1 min) and of ABAS using hierarchical non-rigid registration (Dice 82 ± 10%, error margin 3.1 ± 2.3 mm, average time around 30 s) support their use for image guided online adaptive radiotherapy of cervical cancer.
Color Vision in Color Display Night Vision Goggles.
Liggins, Eric P; Serle, William P
2017-05-01
Aircrew viewing eyepiece-injected symbology on color display night vision goggles (CDNVGs) are performing a visual task involving color under highly unnatural viewing conditions. Their performance in discriminating different colors and responding to color cues is unknown. Experimental laboratory measurements of 1) color discrimination and 2) visual search performance are reported under adaptation conditions representative of a CDNVG. Color discrimination was measured using a two-alternative forced choice (2AFC) paradigm that probes color space uniformly around a white point. Search times in the presence of different degrees of clutter (distractors in the scene) are measured for different potential symbology colors. The discrimination data support previous data suggesting that discrimination is best for colors close to the adapting point in color space (P43 phosphor in this case). There were highly significant effects of background adaptation (white or green) and test color. The search time data show that saturated colors with the greatest chromatic contrast with respect to the background lead to the shortest search times, associated with the greatest saliency. Search times for the green background were around 150 ms longer than for the white. Desaturated colors, along with those close to a typical CDNVG display phosphor in color space, should be avoided by CDNVG designers if the greatest conspicuity of symbology is desired. The results can be used by CDNVG symbology designers to optimize aircrew performance subject to wider constraints arising from the way color is used in the existing conventional cockpit instruments and displays.Liggins EP, Serle WP. Color vision in color display night vision goggles. Aerosp Med Hum Perform. 2017; 88(5):448-456.
Oleari, Claudio; Melgosa, Manuel; Huertas, Rafael
2011-11-01
The most widely used color-difference formulas are based on color-difference data obtained under D65 illumination or similar and for a 10° visual field; i.e., these formulas hold true for the CIE 1964 observer adapted to D65 illuminant. This work considers the psychometric color-vision model based on the Optical Society of America-Uniform Color Scales (OSA-UCS) system previously published by the first author [J. Opt. Soc. Am. A 21, 677 (2004); Color Res. Appl. 30, 31 (2005)] with the additional hypothesis that complete illuminant adaptation with perfect color constancy exists in the visual evaluation of color differences. In this way a computational procedure is defined for color conversion between different illuminant adaptations, which is an alternative to the current chromatic adaptation transforms. This color conversion allows the passage between different observers, e.g., CIE 1964 and CIE 1931. An application of this color conversion is here made in the color-difference evaluation for any observer and in any illuminant adaptation: these transformations convert tristimulus values related to any observer and illuminant adaptation to those related to the observer and illuminant adaptation of the definition of the color-difference formulas, i.e., to the CIE 1964 observer adapted to the D65 illuminant, and then the known color-difference formulas can be applied. The adaptations to the illuminants A, C, F11, D50, Planckian and daylight at any color temperature and for CIE 1931 and CIE 1964 observers are considered as examples, and all the corresponding transformations are given for practical use.
An automatic segmentation method of a parameter-adaptive PCNN for medical images.
Lian, Jing; Shi, Bin; Li, Mingcong; Nan, Ziwei; Ma, Yide
2017-09-01
Since pre-processing and initial segmentation steps in medical images directly affect the final segmentation results of the regions of interesting, an automatic segmentation method of a parameter-adaptive pulse-coupled neural network is proposed to integrate the above-mentioned two segmentation steps into one. This method has a low computational complexity for different kinds of medical images and has a high segmentation precision. The method comprises four steps. Firstly, an optimal histogram threshold is used to determine the parameter [Formula: see text] for different kinds of images. Secondly, we acquire the parameter [Formula: see text] according to a simplified pulse-coupled neural network (SPCNN). Thirdly, we redefine the parameter V of the SPCNN model by sub-intensity distribution range of firing pixels. Fourthly, we add an offset [Formula: see text] to improve initial segmentation precision. Compared with the state-of-the-art algorithms, the new method achieves a comparable performance by the experimental results from ultrasound images of the gallbladder and gallstones, magnetic resonance images of the left ventricle, and mammogram images of the left and the right breast, presenting the overall metric UM of 0.9845, CM of 0.8142, TM of 0.0726. The algorithm has a great potential to achieve the pre-processing and initial segmentation steps in various medical images. This is a premise for assisting physicians to detect and diagnose clinical cases.
Adaptive reptile color variation and the evolution of the Mc1r gene.
Rosenblum, Erica Bree; Hoekstra, Hopi E; Nachman, Michael W
2004-08-01
The wealth of information on the genetics of pigmentation and the clear fitness consequences of many pigmentation phenotypes provide an opportunity to study the molecular basis of an ecologically important trait. The melanocortin-1 receptor (Mc1r) is responsible for intraspecific color variation in mammals and birds. Here, we study the molecular evolution of Mc1r and investigate its role in adaptive intraspecific color differences in reptiles. We sequenced the complete Mc1r locus in seven phylogenetically diverse squamate species with melanic or blanched forms associated with different colored substrates or thermal environments. We found that patterns of amino acid substitution across different regions of the receptor are similar to the patterns seen in mammals, suggesting comparable levels of constraint and probably a conserved function for Mc1r in mammals and reptiles. We also found high levels of silent-site heterozygosity in all species, consistent with a high mutation rate or large long-term effective population size. Mc1r polymorphisms were strongly associated with color differences in Holbrookia maculata and Aspidoscelis inornata. In A. inornata, several observations suggest that Mc1r mutations may contribute to differences in color: (1) a strong association is observed between one Mc1r amino acid substitution and dorsal color; (2) no significant population structure was detected among individuals from these populations at the mitochondrial ND4 gene; (3) the distribution of allele frequencies at Mc1r deviates from neutral expectations; and (4) patterns of linkage disequilibrium at Mc1r are consistent with recent selection. This study provides comparative data on a nuclear gene in reptiles and highlights the utility of a candidate-gene approach for understanding the evolution of genes involved in vertebrate adaptation.
Automatic segmentation of psoriasis lesions
NASA Astrophysics Data System (ADS)
Ning, Yang; Shi, Chenbo; Wang, Li; Shu, Chang
2014-10-01
The automatic segmentation of psoriatic lesions is widely researched these years. It is an important step in Computer-aid methods of calculating PASI for estimation of lesions. Currently those algorithms can only handle single erythema or only deal with scaling segmentation. In practice, scaling and erythema are often mixed together. In order to get the segmentation of lesions area - this paper proposes an algorithm based on Random forests with color and texture features. The algorithm has three steps. The first step, the polarized light is applied based on the skin's Tyndall-effect in the imaging to eliminate the reflection and Lab color space are used for fitting the human perception. The second step, sliding window and its sub windows are used to get textural feature and color feature. In this step, a feature of image roughness has been defined, so that scaling can be easily separated from normal skin. In the end, Random forests will be used to ensure the generalization ability of the algorithm. This algorithm can give reliable segmentation results even the image has different lighting conditions, skin types. In the data set offered by Union Hospital, more than 90% images can be segmented accurately.
A lane line segmentation algorithm based on adaptive threshold and connected domain theory
NASA Astrophysics Data System (ADS)
Feng, Hui; Xu, Guo-sheng; Han, Yi; Liu, Yang
2018-04-01
Before detecting cracks and repairs on road lanes, it's necessary to eliminate the influence of lane lines on the recognition result in road lane images. Aiming at the problems caused by lane lines, an image segmentation algorithm based on adaptive threshold and connected domain is proposed. First, by analyzing features like grey level distribution and the illumination of the images, the algorithm uses Hough transform to divide the images into different sections and convert them into binary images separately. It then uses the connected domain theory to amend the outcome of segmentation, remove noises and fill the interior zone of lane lines. Experiments have proved that this method could eliminate the influence of illumination and lane line abrasion, removing noises thoroughly while maintaining high segmentation precision.
A novel adaptive scoring system for segmentation validation with multiple reference masks
NASA Astrophysics Data System (ADS)
Moltz, Jan H.; Rühaak, Jan; Hahn, Horst K.; Peitgen, Heinz-Otto
2011-03-01
The development of segmentation algorithms for different anatomical structures and imaging protocols is an important task in medical image processing. The validation of these methods, however, is often treated as a subordinate task. Since manual delineations, which are widely used as a surrogate for the ground truth, exhibit an inherent uncertainty, it is preferable to use multiple reference segmentations for an objective validation. This requires a consistent framework that should fulfill three criteria: 1) it should treat all reference masks equally a priori and not demand consensus between the experts; 2) it should evaluate the algorithmic performance in relation to the inter-reference variability, i.e., be more tolerant where the experts disagree about the true segmentation; 3) it should produce results that are comparable for different test data. We show why current state-of-the-art frameworks as the one used at several MICCAI segmentation challenges do not fulfill these criteria and propose a new validation methodology. A score is computed in an adaptive way for each individual segmentation problem, using a combination of volume- and surface-based comparison metrics. These are transformed into the score by relating them to the variability between the reference masks which can be measured by comparing the masks with each other or with an estimated ground truth. We present examples from a study on liver tumor segmentation in CT scans where our score shows a more adequate assessment of the segmentation results than the MICCAI framework.
Adaptive deformable model for colonic polyp segmentation and measurement on CT colonography
DOE Office of Scientific and Technical Information (OSTI.GOV)
Yao Jianhua; Summers, Ronald M.
2007-05-15
Polyp size is one important biomarker for the malignancy risk of a polyp. This paper presents an improved approach for colonic polyp segmentation and measurement on CT colonography images. The method is based on a combination of knowledge-guided intensity adjustment, fuzzy clustering, and adaptive deformable model. Since polyps on haustral folds are the most difficult to be segmented, we propose a dual-distance algorithm to first identify voxels on the folds, and then introduce a counter-force to control the model evolution. We derive linear and volumetric measurements from the segmentation. The experiment was conducted on 395 patients with 83 polyps, ofmore » which 43 polyps were on haustral folds. The results were validated against manual measurement from the optical colonoscopy and the CT colonography. The paired t-test showed no significant difference, and the R{sup 2} correlation was 0.61 for the linear measurement and 0.98 for the volumetric measurement. The mean Dice coefficient for volume overlap between automatic and manual segmentation was 0.752 (standard deviation 0.154)« less
An Illumination-Adaptive Colorimetric Measurement Using Color Image Sensor
NASA Astrophysics Data System (ADS)
Lee, Sung-Hak; Lee, Jong-Hyub; Sohng, Kyu-Ik
An image sensor for a use of colorimeter is characterized based on the CIE standard colorimetric observer. We use the method of least squares to derive a colorimetric characterization matrix between RGB output signals and CIE XYZ tristimulus values. This paper proposes an adaptive measuring method to obtain the chromaticity of colored scenes and illumination through a 3×3 camera transfer matrix under a certain illuminant. Camera RGB outputs, sensor status values, and photoelectric characteristic are used to obtain the chromaticity. Experimental results show that the proposed method is valid in the measuring performance.
NASA Astrophysics Data System (ADS)
Huang, Jian; Liu, Gui-xiong
2016-09-01
The identification of targets varies in different surge tests. A multi-color space threshold segmentation and self-learning k-nearest neighbor algorithm ( k-NN) for equipment under test status identification was proposed after using feature matching to identify equipment status had to train new patterns every time before testing. First, color space (L*a*b*, hue saturation lightness (HSL), hue saturation value (HSV)) to segment was selected according to the high luminance points ratio and white luminance points ratio of the image. Second, the unknown class sample S r was classified by the k-NN algorithm with training set T z according to the feature vector, which was formed from number of pixels, eccentricity ratio, compactness ratio, and Euler's numbers. Last, while the classification confidence coefficient equaled k, made S r as one sample of pre-training set T z '. The training set T z increased to T z+1 by T z ' if T z ' was saturated. In nine series of illuminant, indicator light, screen, and disturbances samples (a total of 21600 frames), the algorithm had a 98.65%identification accuracy, also selected five groups of samples to enlarge the training set from T 0 to T 5 by itself.
Color normalization for robust evaluation of microscopy images
NASA Astrophysics Data System (ADS)
Švihlík, Jan; Kybic, Jan; Habart, David
2015-09-01
This paper deals with color normalization of microscopy images of Langerhans islets in order to increase robustness of the islet segmentation to illumination changes. The main application is automatic quantitative evaluation of the islet parameters, useful for determining the feasibility of islet transplantation in diabetes. First, background illumination inhomogeneity is compensated and a preliminary foreground/background segmentation is performed. The color normalization itself is done in either lαβ or logarithmic RGB color spaces, by comparison with a reference image. The color-normalized images are segmented using color-based features and pixel-wise logistic regression, trained on manually labeled images. Finally, relevant statistics such as the total islet area are evaluated in order to determine the success likelihood of the transplantation.
DOE Office of Scientific and Technical Information (OSTI.GOV)
Park, Sang Hyun; Gao, Yaozong, E-mail: yzgao@cs.unc.edu; Shi, Yinghuan, E-mail: syh@nju.edu.cn
Purpose: Accurate prostate segmentation is necessary for maximizing the effectiveness of radiation therapy of prostate cancer. However, manual segmentation from 3D CT images is very time-consuming and often causes large intra- and interobserver variations across clinicians. Many segmentation methods have been proposed to automate this labor-intensive process, but tedious manual editing is still required due to the limited performance. In this paper, the authors propose a new interactive segmentation method that can (1) flexibly generate the editing result with a few scribbles or dots provided by a clinician, (2) fast deliver intermediate results to the clinician, and (3) sequentially correctmore » the segmentations from any type of automatic or interactive segmentation methods. Methods: The authors formulate the editing problem as a semisupervised learning problem which can utilize a priori knowledge of training data and also the valuable information from user interactions. Specifically, from a region of interest near the given user interactions, the appropriate training labels, which are well matched with the user interactions, can be locally searched from a training set. With voting from the selected training labels, both confident prostate and background voxels, as well as unconfident voxels can be estimated. To reflect informative relationship between voxels, location-adaptive features are selected from the confident voxels by using regression forest and Fisher separation criterion. Then, the manifold configuration computed in the derived feature space is enforced into the semisupervised learning algorithm. The labels of unconfident voxels are then predicted by regularizing semisupervised learning algorithm. Results: The proposed interactive segmentation method was applied to correct automatic segmentation results of 30 challenging CT images. The correction was conducted three times with different user interactions performed at different time periods, in
NASA Astrophysics Data System (ADS)
Botter Martins, Samuel; Vallin Spina, Thiago; Yasuda, Clarissa; Falcão, Alexandre X.
2017-02-01
Statistical Atlases have played an important role towards automated medical image segmentation. However, a challenge has been to make the atlas more adaptable to possible errors in deformable registration of anomalous images, given that the body structures of interest for segmentation might present significant differences in shape and texture. Recently, deformable registration errors have been accounted by a method that locally translates the statistical atlas over the test image, after registration, and evaluates candidate objects from a delineation algorithm in order to choose the best one as final segmentation. In this paper, we improve its delineation algorithm and extend the model to be a multi-object statistical atlas, built from control images and adaptable to anomalous images, by incorporating a texture classifier. In order to provide a first proof of concept, we instantiate the new method for segmenting, object-by-object and all objects simultaneously, the left and right brain hemispheres, and the cerebellum, without the brainstem, and evaluate it on MRT1-images of epilepsy patients before and after brain surgery, which removed portions of the temporal lobe. The results show efficiency gain with statistically significant higher accuracy, using the mean Average Symmetric Surface Distance, with respect to the original approach.
Superpixel-based segmentation of muscle fibers in multi-channel microscopy.
Nguyen, Binh P; Heemskerk, Hans; So, Peter T C; Tucker-Kellogg, Lisa
2016-12-05
Confetti fluorescence and other multi-color genetic labelling strategies are useful for observing stem cell regeneration and for other problems of cell lineage tracing. One difficulty of such strategies is segmenting the cell boundaries, which is a very different problem from segmenting color images from the real world. This paper addresses the difficulties and presents a superpixel-based framework for segmentation of regenerated muscle fibers in mice. We propose to integrate an edge detector into a superpixel algorithm and customize the method for multi-channel images. The enhanced superpixel method outperforms the original and another advanced superpixel algorithm in terms of both boundary recall and under-segmentation error. Our framework was applied to cross-section and lateral section images of regenerated muscle fibers from confetti-fluorescent mice. Compared with "ground-truth" segmentations, our framework yielded median Dice similarity coefficients of 0.92 and higher. Our segmentation framework is flexible and provides very good segmentations of multi-color muscle fibers. We anticipate our methods will be useful for segmenting a variety of tissues in confetti fluorecent mice and in mice with similar multi-color labels.
Chameleon-like elastomers with molecularly encoded strain-adaptive stiffening and coloration
NASA Astrophysics Data System (ADS)
Vatankhah-Varnosfaderani, Mohammad; Keith, Andrew N.; Cong, Yidan; Liang, Heyi; Rosenthal, Martin; Sztucki, Michael; Clair, Charles; Magonov, Sergei; Ivanov, Dimitri A.; Dobrynin, Andrey V.; Sheiko, Sergei S.
2018-03-01
Active camouflage is widely recognized as a soft-tissue feature, and yet the ability to integrate adaptive coloration and tissuelike mechanical properties into synthetic materials remains elusive. We provide a solution to this problem by uniting these functions in moldable elastomers through the self-assembly of linear-bottlebrush-linear triblock copolymers. Microphase separation of the architecturally distinct blocks results in physically cross-linked networks that display vibrant color, extreme softness, and intense strain stiffening on par with that of skin tissue. Each of these functional properties is regulated by the structure of one macromolecule, without the need for chemical cross-linking or additives. These materials remain stable under conditions characteristic of internal bodily environments and under ambient conditions, neither swelling in bodily fluids nor drying when exposed to air.
A Fully Automated Method to Detect and Segment a Manufactured Object in an Underwater Color Image
NASA Astrophysics Data System (ADS)
Barat, Christian; Phlypo, Ronald
2010-12-01
We propose a fully automated active contours-based method for the detection and the segmentation of a moored manufactured object in an underwater image. Detection of objects in underwater images is difficult due to the variable lighting conditions and shadows on the object. The proposed technique is based on the information contained in the color maps and uses the visual attention method, combined with a statistical approach for the detection and an active contour for the segmentation of the object to overcome the above problems. In the classical active contour method the region descriptor is fixed and the convergence of the method depends on the initialization. With our approach, this dependence is overcome with an initialization using the visual attention results and a criterion to select the best region descriptor. This approach improves the convergence and the processing time while providing the advantages of a fully automated method.
Development of an adaptive bilateral filter for evaluating color image difference
NASA Astrophysics Data System (ADS)
Wang, Zhaohui; Hardeberg, Jon Yngve
2012-04-01
Spatial filtering, which aims to mimic the contrast sensitivity function (CSF) of the human visual system (HVS), has previously been combined with color difference formulae for measuring color image reproduction errors. These spatial filters attenuate imperceptible information in images, unfortunately including high frequency edges, which are believed to be crucial in the process of scene analysis by the HVS. The adaptive bilateral filter represents a novel approach, which avoids the undesirable loss of edge information introduced by CSF-based filtering. The bilateral filter employs two Gaussian smoothing filters in different domains, i.e., spatial domain and intensity domain. We propose a method to decide the parameters, which are designed to be adaptive to the corresponding viewing conditions, and the quantity and homogeneity of information contained in an image. Experiments and discussions are given to support the proposal. A series of perceptual experiments were conducted to evaluate the performance of our approach. The experimental sample images were reproduced with variations in six image attributes: lightness, chroma, hue, compression, noise, and sharpness/blurriness. The Pearson's correlation values between the model-predicted image difference and the observed difference were employed to evaluate the performance, and compare it with that of spatial CIELAB and image appearance model.
Biological versus electronic adaptive coloration: how can one inform the other?
Kreit, Eric; Mäthger, Lydia M.; Hanlon, Roger T.; Dennis, Patrick B.; Naik, Rajesh R.; Forsythe, Eric; Heikenfeld, Jason
2013-01-01
Adaptive reflective surfaces have been a challenge for both electronic paper (e-paper) and biological organisms. Multiple colours, contrast, polarization, reflectance, diffusivity and texture must all be controlled simultaneously without optical losses in order to fully replicate the appearance of natural surfaces and vividly communicate information. This review merges the frontiers of knowledge for both biological adaptive coloration, with a focus on cephalopods, and synthetic reflective e-paper within a consistent framework of scientific metrics. Currently, the highest performance approach for both nature and technology uses colourant transposition. Three outcomes are envisioned from this review: reflective display engineers may gain new insights from millions of years of natural selection and evolution; biologists will benefit from understanding the types of mechanisms, characterization and metrics used in synthetic reflective e-paper; all scientists will gain a clearer picture of the long-term prospects for capabilities such as adaptive concealment and signalling. PMID:23015522
Jain, Veena; Das, Taposh K; Pruthi, Gunjan; Shah, Naseem; Rajendiran, Suresh
2015-01-01
Change in color and loss of marginal adaptation of tooth colored restorative materials is not acceptable. Bleaching is commonly used for treating discolored teeth. However, the literature is scanty regarding its effect on color and marginal adaptation of direct and indirect composite laminate veneers (CLVs) under in vivo conditions. Purpose of the study was to determine the effect of bleaching on color change and marginal adaptation of direct and indirect CLVs over a period of time when exposed to the oral environment. For this purpose, a total of 14 subjects irrespective of age and sex indicated for CLV restorations on maxillary anterior teeth were selected following the inclusion and exclusion criteria. For each subject, indirect CLVs were fabricated and looted in the first quadrant (Group 1) and direct CLV's (Group 2), were given in the second quadrant. Color change was assessed clinically using intra-oral digital spectrophotometer and marginal adaptation was assessed on epoxy resin replica of the tooth-restoration interface under scanning electron microscope. After 6 months, the subjects underwent a home bleaching regimen for 14 days using 10% carbamide peroxide. The assessment of color change and marginal adaptation was done at 6 months after veneering (0-180 days), immediately after the bleaching regimen (0-194 days) and 3 months after the bleaching regimen (0-284 days). The difference in median color change (ΔE) between the groups was tested using Wilcoxon rank sum test while the median color change with time within the groups was tested using Wilcoxon signed rank test. The difference in the rates of marginal adaptation was tested between the groups using Chi-square/Fisher's exact test. Bleaching led to statistically significant color change at cervical (CE), middle and incisal (IE) regions when direct and indirect composites were compared (P < 0.05). During intra-group comparison, direct CLV's showed significant color change at CE and IE regions when
Robust visual object tracking with interleaved segmentation
NASA Astrophysics Data System (ADS)
Abel, Peter; Kieritz, Hilke; Becker, Stefan; Arens, Michael
2017-10-01
In this paper we present a new approach for tracking non-rigid, deformable objects by means of merging an on-line boosting-based tracker and a fast foreground background segmentation. We extend an on-line boosting- based tracker, which uses axes-aligned bounding boxes with fixed aspect-ratio as tracking states. By constructing a confidence map from the on-line boosting-based tracker and unifying this map with a confidence map, which is obtained from a foreground background segmentation algorithm, we build a superior confidence map. For constructing a rough confidence map of a new frame based on on-line boosting, we employ the responses of the strong classifier as well as the single weak classifier responses that were built before during the updating step. This confidence map provides a rough estimation of the object's position and dimension. In order to refine this confidence map, we build a fine, pixel-wisely segmented confidence map and merge both maps together. Our segmentation method is color-histogram-based and provides a fine and fast image segmentation. By means of back-projection and the Bayes' rule, we obtain a confidence value for every pixel. The rough and the fine confidence maps are merged together by building an adaptively weighted sum of both maps. The weights are obtained by utilizing the variances of both confidence maps. Further, we apply morphological operators in the merged confidence map in order to reduce the noise. In the resulting map we estimate the object localization and dimension via continuous adaptive mean shift. Our approach provides a rotated rectangle as tracking states, which enables a more precise description of non-rigid, deformable objects than axes-aligned bounding boxes. We evaluate our tracker on the visual object tracking (VOT) benchmark dataset 2016.
Boosting the discriminative power of color models for feature detection
NASA Astrophysics Data System (ADS)
Stokman, Harro M. G.; Gevers, Theo
2005-01-01
We consider the well-known problem of segmenting a color image into foreground-background pixels. Such result can be obtained by segmenting the red, green and blue channels directly. Alternatively, the result may be obtained through the transformation of the color image into other color spaces, such as HSV or normalized colors. The problem then is how to select the color space or color channel that produces the best segmentation result. Furthermore, if more than one channels are equally good candidates, the next problem is how to combine the results. In this article, we investigate if the principles of the formal model for diversification of Markowitz (1952) can be applied to solve the problem. We verify, in theory and in practice, that the proposed diversification model can be applied effectively to determine the most appropriate combination of color spaces for the application at hand.
Adaptation and visual salience
McDermott, Kyle C.; Malkoc, Gokhan; Mulligan, Jeffrey B.; Webster, Michael A.
2011-01-01
We examined how the salience of color is affected by adaptation to different color distributions. Observers searched for a color target on a dense background of distractors varying along different directions in color space. Prior adaptation to the backgrounds enhanced search on the same background while adaptation to orthogonal background directions slowed detection. Advantages of adaptation were seen for both contrast adaptation (to different color axes) and chromatic adaptation (to different mean chromaticities). Control experiments, including analyses of eye movements during the search, suggest that these aftereffects are unlikely to reflect simple learning or changes in search strategies on familiar backgrounds, and instead result from how adaptation alters the relative salience of the target and background colors. Comparable effects were observed along different axes in the chromatic plane or for axes defined by different combinations of luminance and chromatic contrast, consistent with visual search and adaptation mediated by multiple color mechanisms. Similar effects also occurred for color distributions characteristic of natural environments with strongly selective color gamuts. Our results are consistent with the hypothesis that adaptation may play an important functional role in highlighting the salience of novel stimuli by discounting ambient properties of the visual environment. PMID:21106682
Gaylord-Harden, Noni K.; So, Suzanna; Bai, Grace J.; Henry, David B.; Tolan, Patrick H.
2017-01-01
The current study examined a model of desensitization to community violence exposure—the pathologic adaptation model—in male adolescents of color. The current study included 285 African American (61%) and Latino (39%) male adolescents (W1 M age = 12.41) from the Chicago Youth Development Study to examine the longitudinal associations between community violence exposure, depressive symptoms, and violent behavior. Consistent with the pathologic adaptation model, results indicated a linear, positive association between community violence exposure in middle adolescence and violent behavior in late adolescence, as well as a curvilinear association between community violence exposure in middle adolescence and depressive symptoms in late adolescence, suggesting emotional desensitization. Further, these effects were specific to cognitive-affective symptoms of depression and not somatic symptoms. Emotional desensitization outcomes, as assessed by depressive symptoms, can occur in male adolescents of color exposed to community violence and these effects extend from middle adolescence to late adolescence. PMID:27653968
CT-based manual segmentation and evaluation of paranasal sinuses.
Pirner, S; Tingelhoff, K; Wagner, I; Westphal, R; Rilk, M; Wahl, F M; Bootz, F; Eichhorn, Klaus W G
2009-04-01
Manual segmentation of computed tomography (CT) datasets was performed for robot-assisted endoscope movement during functional endoscopic sinus surgery (FESS). Segmented 3D models are needed for the robots' workspace definition. A total of 50 preselected CT datasets were each segmented in 150-200 coronal slices with 24 landmarks being set. Three different colors for segmentation represent diverse risk areas. Extension and volumetric measurements were performed. Three-dimensional reconstruction was generated after segmentation. Manual segmentation took 8-10 h for each CT dataset. The mean volumes were: right maxillary sinus 17.4 cm(3), left side 17.9 cm(3), right frontal sinus 4.2 cm(3), left side 4.0 cm(3), total frontal sinuses 7.9 cm(3), sphenoid sinus right side 5.3 cm(3), left side 5.5 cm(3), total sphenoid sinus volume 11.2 cm(3). Our manually segmented 3D-models present the patient's individual anatomy with a special focus on structures in danger according to the diverse colored risk areas. For safe robot assistance, the high-accuracy models represent an average of the population for anatomical variations, extension and volumetric measurements. They can be used as a database for automatic model-based segmentation. None of the segmentation methods so far described provide risk segmentation. The robot's maximum distance to the segmented border can be adjusted according to the differently colored areas.
Global Binary Continuity for Color Face Detection With Complex Background
NASA Astrophysics Data System (ADS)
Belavadi, Bhaskar; Mahendra Prashanth, K. V.; Joshi, Sujay S.; Suprathik, N.
2017-08-01
In this paper, we propose a method to detect human faces in color images, with complex background. The proposed algorithm makes use of basically two color space models, specifically HSV and YCgCr. The color segmented image is filled uniformly with a single color (binary) and then all unwanted discontinuous lines are removed to get the final image. Experimental results on Caltech database manifests that the purported model is able to accomplish far better segmentation for faces of varying orientations, skin color and background environment.
Segregating animals in naturalistic surroundings: interaction of color distributions and mechanisms.
Jansen, Michael; Giesel, Martin; Zaidi, Qasim
2016-03-01
Humans have been shown to rapidly detect animals in naturalistic scenes, but the role of color in this task is unclear. We first analyze the color information contained in a large number of images of salient and camouflaged animals in generic backgrounds. We found that color distributions of most animals and of their immediate backgrounds were oriented along other than the cardinal directions of color space. In addition, the maximum distances between animals and background distributions also tended to be along noncardinal directions, suggesting a role for higher-order cortical color mechanisms whose preferred axes are distributed widely in color space. We measured temporal thresholds for segmenting animal color distributions from background distributions in the absence of spatial cues. Combined over all observers and all images in our sample, thresholds for segmenting isoluminant projections of these distributions were lower than for segmenting the original distributions and considerably lower than for segmenting achromatic projections. Color information is thus likely to be useful in segregating animals in generic views, i.e., views not purposely chosen by the photographer to enhance the visibility of the animal. However, a comparison of thresholds with distances between distributions failed to reveal any advantage conferred by higher-order color mechanisms.
Adaptations in rod outer segment disc membranes in response to environmental lighting conditions.
Rakshit, Tatini; Senapati, Subhadip; Parmar, Vipul M; Sahu, Bhubanananda; Maeda, Akiko; Park, Paul S-H
2017-10-01
The light-sensing rod photoreceptor cell exhibits several adaptations in response to the lighting environment. While adaptations to short-term changes in lighting conditions have been examined in depth, adaptations to long-term changes in lighting conditions are less understood. Atomic force microscopy was used to characterize the structure of rod outer segment disc membranes, the site of photon absorption by the pigment rhodopsin, to better understand how photoreceptor cells respond to long-term lighting changes. Structural properties of the disc membrane changed in response to housing mice in constant dark or light conditions and these adaptive changes required output from the phototransduction cascade initiated by rhodopsin. Among these were changes in the packing density of rhodopsin in the membrane, which was independent of rhodopsin synthesis and specifically affected scotopic visual function as assessed by electroretinography. Studies here support the concept of photostasis, which maintains optimal photoreceptor cell function with implications in retinal degenerations. Copyright © 2017 Elsevier B.V. All rights reserved.
Adaptive local linear regression with application to printer color management.
Gupta, Maya R; Garcia, Eric K; Chin, Erika
2008-06-01
Local learning methods, such as local linear regression and nearest neighbor classifiers, base estimates on nearby training samples, neighbors. Usually, the number of neighbors used in estimation is fixed to be a global "optimal" value, chosen by cross validation. This paper proposes adapting the number of neighbors used for estimation to the local geometry of the data, without need for cross validation. The term enclosing neighborhood is introduced to describe a set of neighbors whose convex hull contains the test point when possible. It is proven that enclosing neighborhoods yield bounded estimation variance under some assumptions. Three such enclosing neighborhood definitions are presented: natural neighbors, natural neighbors inclusive, and enclosing k-NN. The effectiveness of these neighborhood definitions with local linear regression is tested for estimating lookup tables for color management. Significant improvements in error metrics are shown, indicating that enclosing neighborhoods may be a promising adaptive neighborhood definition for other local learning tasks as well, depending on the density of training samples.
Reactive power and voltage control strategy based on dynamic and adaptive segment for DG inverter
NASA Astrophysics Data System (ADS)
Zhai, Jianwei; Lin, Xiaoming; Zhang, Yongjun
2018-03-01
The inverter of distributed generation (DG) can support reactive power to help solve the problem of out-of-limit voltage in active distribution network (ADN). Therefore, a reactive voltage control strategy based on dynamic and adaptive segment for DG inverter is put forward to actively control voltage in this paper. The proposed strategy adjusts the segmented voltage threshold of Q(U) droop curve dynamically and adaptively according to the voltage of grid-connected point and the power direction of adjacent downstream line. And then the reactive power reference of DG inverter can be got through modified Q(U) control strategy. The reactive power of inverter is controlled to trace the reference value. The proposed control strategy can not only control the local voltage of grid-connected point but also help to maintain voltage within qualified range considering the terminal voltage of distribution feeder and the reactive support for adjacent downstream DG. The scheme using the proposed strategy is compared with the scheme without the reactive support of DG inverter and the scheme using the Q(U) control strategy with constant segmented voltage threshold. The simulation results suggest that the proposed method has a significant improvement on solving the problem of out-of-limit voltage, restraining voltage variation and improving voltage quality.
Local adaptation and matching habitat choice in female barn owls with respect to melanic coloration.
Dreiss, A N; Antoniazza, S; Burri, R; Fumagalli, L; Sonnay, C; Frey, C; Goudet, J; Roulin, Alexandre
2012-01-01
Local adaptation is a major mechanism underlying the maintenance of phenotypic variation in spatially heterogeneous environments. In the barn owl (Tyto alba), dark and pale reddish-pheomelanic individuals are adapted to conditions prevailing in northern and southern Europe, respectively. Using a long-term dataset from Central Europe, we report results consistent with the hypothesis that the different pheomelanic phenotypes are adapted to specific local conditions in females, but not in males. Compared to whitish females, reddish females bred in sites surrounded by more arable fields and less forests. Colour-dependent habitat choice was apparently beneficial. First, whitish females produced more fledglings when breeding in wooded areas, whereas reddish females when breeding in sites with more arable fields. Second, cross-fostering experiments showed that female nestlings grew wings more rapidly when both their foster and biological mothers were of similar colour. The latter result suggests that mothers should particularly produce daughters in environments that best match their own coloration. Accordingly, whiter females produced fewer daughters in territories with more arable fields. In conclusion, females displaying alternative melanic phenotypes bred in habitats providing them with the highest fitness benefits. Although small in magnitude, matching habitat selection and local adaptation may help maintain variation in pheomelanin coloration in the barn owl. © 2011 The Authors. Journal of Evolutionary Biology © 2011 European Society For Evolutionary Biology.
Visual enhancement of unmixed multispectral imagery using adaptive smoothing
Lemeshewsky, G.P.; Rahman, Z.-U.; Schowengerdt, R.A.; Reichenbach, S.E.
2004-01-01
Adaptive smoothing (AS) has been previously proposed as a method to smooth uniform regions of an image, retain contrast edges, and enhance edge boundaries. The method is an implementation of the anisotropic diffusion process which results in a gray scale image. This paper discusses modifications to the AS method for application to multi-band data which results in a color segmented image. The process was used to visually enhance the three most distinct abundance fraction images produced by the Lagrange constraint neural network learning-based unmixing of Landsat 7 Enhanced Thematic Mapper Plus multispectral sensor data. A mutual information-based method was applied to select the three most distinct fraction images for subsequent visualization as a red, green, and blue composite. A reported image restoration technique (partial restoration) was applied to the multispectral data to reduce unmixing error, although evaluation of the performance of this technique was beyond the scope of this paper. The modified smoothing process resulted in a color segmented image with homogeneous regions separated by sharpened, coregistered multiband edges. There was improved class separation with the segmented image, which has importance to subsequent operations involving data classification.
Wavelength-Adaptive Dehazing Using Histogram Merging-Based Classification for UAV Images
Yoon, Inhye; Jeong, Seokhwa; Jeong, Jaeheon; Seo, Doochun; Paik, Joonki
2015-01-01
Since incoming light to an unmanned aerial vehicle (UAV) platform can be scattered by haze and dust in the atmosphere, the acquired image loses the original color and brightness of the subject. Enhancement of hazy images is an important task in improving the visibility of various UAV images. This paper presents a spatially-adaptive dehazing algorithm that merges color histograms with consideration of the wavelength-dependent atmospheric turbidity. Based on the wavelength-adaptive hazy image acquisition model, the proposed dehazing algorithm consists of three steps: (i) image segmentation based on geometric classes; (ii) generation of the context-adaptive transmission map; and (iii) intensity transformation for enhancing a hazy UAV image. The major contribution of the research is a novel hazy UAV image degradation model by considering the wavelength of light sources. In addition, the proposed transmission map provides a theoretical basis to differentiate visually important regions from others based on the turbidity and merged classification results. PMID:25808767
Wavelength-adaptive dehazing using histogram merging-based classification for UAV images.
Yoon, Inhye; Jeong, Seokhwa; Jeong, Jaeheon; Seo, Doochun; Paik, Joonki
2015-03-19
Since incoming light to an unmanned aerial vehicle (UAV) platform can be scattered by haze and dust in the atmosphere, the acquired image loses the original color and brightness of the subject. Enhancement of hazy images is an important task in improving the visibility of various UAV images. This paper presents a spatially-adaptive dehazing algorithm that merges color histograms with consideration of the wavelength-dependent atmospheric turbidity. Based on the wavelength-adaptive hazy image acquisition model, the proposed dehazing algorithm consists of three steps: (i) image segmentation based on geometric classes; (ii) generation of the context-adaptive transmission map; and (iii) intensity transformation for enhancing a hazy UAV image. The major contribution of the research is a novel hazy UAV image degradation model by considering the wavelength of light sources. In addition, the proposed transmission map provides a theoretical basis to differentiate visually important regions from others based on the turbidity and merged classification results.
NASA Astrophysics Data System (ADS)
Clausing, Eric; Vielhauer, Claus
2014-02-01
Locksmith forensics is an important area in crime scene forensics. Due to new optical, contactless, nanometer range sensing technology, such traces can be captured, digitized and analyzed more easily allowing a complete digital forensic investigation. In this paper we present a significantly improved approach for the detection and segmentation of toolmarks on surfaces of locking cylinder components (using the example of the locking cylinder component 'key pin') acquired by a 3D Confocal Laser Scanning Microscope. This improved approach is based on our prior work1 using a block-based classification approach with textural features. In this prior work1 we achieve a solid detection rate of 75-85% for the detection of toolmarks originating from illegal opening methods. Here, in this paper we improve, expand and fuse this prior approach with additional features from acquired surface topography data, color data and an image processing approach using adapted Gabor filters. In particular we are able of raising the detection and segmentation rates above 90% with our test set of 20 key pins with approximately 700 single toolmark traces of four different opening methods. We can provide a precise pixel- based segmentation as opposed to the rather imprecise segmentation of our prior block-based approach and as the use of the two additional data types (color and especially topography) require a specific pre-processing, we furthermore propose an adequate approach for this purpose.
Memari, Nogol; Ramli, Abd Rahman; Bin Saripan, M Iqbal; Mashohor, Syamsiah; Moghbel, Mehrdad
2017-01-01
The structure and appearance of the blood vessel network in retinal fundus images is an essential part of diagnosing various problems associated with the eyes, such as diabetes and hypertension. In this paper, an automatic retinal vessel segmentation method utilizing matched filter techniques coupled with an AdaBoost classifier is proposed. The fundus image is enhanced using morphological operations, the contrast is increased using contrast limited adaptive histogram equalization (CLAHE) method and the inhomogeneity is corrected using Retinex approach. Then, the blood vessels are enhanced using a combination of B-COSFIRE and Frangi matched filters. From this preprocessed image, different statistical features are computed on a pixel-wise basis and used in an AdaBoost classifier to extract the blood vessel network inside the image. Finally, the segmented images are postprocessed to remove the misclassified pixels and regions. The proposed method was validated using publicly accessible Digital Retinal Images for Vessel Extraction (DRIVE), Structured Analysis of the Retina (STARE) and Child Heart and Health Study in England (CHASE_DB1) datasets commonly used for determining the accuracy of retinal vessel segmentation methods. The accuracy of the proposed segmentation method was comparable to other state of the art methods while being very close to the manual segmentation provided by the second human observer with an average accuracy of 0.972, 0.951 and 0.948 in DRIVE, STARE and CHASE_DB1 datasets, respectively.
Support for context effects on segmentation and segments depends on the context.
Heffner, Christopher C; Newman, Rochelle S; Idsardi, William J
2017-04-01
Listeners must adapt to differences in speech rate across talkers and situations. Speech rate adaptation effects are strong for adjacent syllables (i.e., proximal syllables). For studies that have assessed adaptation effects on speech rate information more than one syllable removed from a point of ambiguity in speech (i.e., distal syllables), the difference in strength between different types of ambiguity is stark. Studies of word segmentation have shown large shifts in perception as a result of distal rate manipulations, while studies of segmental perception have shown only weak, or even nonexistent, effects. However, no study has standardized methods and materials to study context effects for both types of ambiguity simultaneously. Here, a set of sentences was created that differed as minimally as possible except for whether the sentences were ambiguous to the voicing of a consonant or ambiguous to the location of a word boundary. The sentences were then rate-modified to slow down the distal context speech rate to various extents, dependent on three different definitions of distal context that were adapted from previous experiments, along with a manipulation of proximal context to assess whether proximal effects were comparable across ambiguity types. The results indicate that the definition of distal influenced the extent of distal rate effects strongly for both segments and segmentation. They also establish the presence of distal rate effects on word-final segments for the first time. These results were replicated, with some caveats regarding the perception of individual segments, in an Internet-based sample recruited from Mechanical Turk.
Yu, Kai; Shi, Fei; Gao, Enting; Zhu, Weifang; Chen, Haoyu; Chen, Xinjian
2018-01-01
Optic nerve head (ONH) is a crucial region for glaucoma detection and tracking based on spectral domain optical coherence tomography (SD-OCT) images. In this region, the existence of a “hole” structure makes retinal layer segmentation and analysis very challenging. To improve retinal layer segmentation, we propose a 3D method for ONH centered SD-OCT image segmentation, which is based on a modified graph search algorithm with a shared-hole and locally adaptive constraints. With the proposed method, both the optic disc boundary and nine retinal surfaces can be accurately segmented in SD-OCT images. An overall mean unsigned border positioning error of 7.27 ± 5.40 µm was achieved for layer segmentation, and a mean Dice coefficient of 0.925 ± 0.03 was achieved for optic disc region detection. PMID:29541497
A fast and efficient segmentation scheme for cell microscopic image.
Lebrun, G; Charrier, C; Lezoray, O; Meurie, C; Cardot, H
2007-04-27
Microscopic cellular image segmentation schemes must be efficient for reliable analysis and fast to process huge quantity of images. Recent studies have focused on improving segmentation quality. Several segmentation schemes have good quality but processing time is too expensive to deal with a great number of images per day. For segmentation schemes based on pixel classification, the classifier design is crucial since it is the one which requires most of the processing time necessary to segment an image. The main contribution of this work is focused on how to reduce the complexity of decision functions produced by support vector machines (SVM) while preserving recognition rate. Vector quantization is used in order to reduce the inherent redundancy present in huge pixel databases (i.e. images with expert pixel segmentation). Hybrid color space design is also used in order to improve data set size reduction rate and recognition rate. A new decision function quality criterion is defined to select good trade-off between recognition rate and processing time of pixel decision function. The first results of this study show that fast and efficient pixel classification with SVM is possible. Moreover posterior class pixel probability estimation is easy to compute with Platt method. Then a new segmentation scheme using probabilistic pixel classification has been developed. This one has several free parameters and an automatic selection must dealt with, but criteria for evaluate segmentation quality are not well adapted for cell segmentation, especially when comparison with expert pixel segmentation must be achieved. Another important contribution in this paper is the definition of a new quality criterion for evaluation of cell segmentation. The results presented here show that the selection of free parameters of the segmentation scheme by optimisation of the new quality cell segmentation criterion produces efficient cell segmentation.
Automated segmentation of retinal pigment epithelium cells in fluorescence adaptive optics images.
Rangel-Fonseca, Piero; Gómez-Vieyra, Armando; Malacara-Hernández, Daniel; Wilson, Mario C; Williams, David R; Rossi, Ethan A
2013-12-01
Adaptive optics (AO) imaging methods allow the histological characteristics of retinal cell mosaics, such as photoreceptors and retinal pigment epithelium (RPE) cells, to be studied in vivo. The high-resolution images obtained with ophthalmic AO imaging devices are rich with information that is difficult and/or tedious to quantify using manual methods. Thus, robust, automated analysis tools that can provide reproducible quantitative information about the cellular mosaics under examination are required. Automated algorithms have been developed to detect the position of individual photoreceptor cells; however, most of these methods are not well suited for characterizing the RPE mosaic. We have developed an algorithm for RPE cell segmentation and show its performance here on simulated and real fluorescence AO images of the RPE mosaic. Algorithm performance was compared to manual cell identification and yielded better than 91% correspondence. This method can be used to segment RPE cells for morphometric analysis of the RPE mosaic and speed the analysis of both healthy and diseased RPE mosaics.
Adaptive region-growing with maximum curvature strategy for tumor segmentation in 18F-FDG PET
NASA Astrophysics Data System (ADS)
Tan, Shan; Li, Laquan; Choi, Wookjin; Kang, Min Kyu; D'Souza, Warren D.; Lu, Wei
2017-07-01
Accurate tumor segmentation in PET is crucial in many oncology applications. We developed an adaptive region-growing (ARG) algorithm with a maximum curvature strategy (ARG_MC) for tumor segmentation in PET. The ARG_MC repeatedly applied a confidence connected region-growing algorithm with increasing relaxing factor f. The optimal relaxing factor (ORF) was then determined at the transition point on the f-volume curve, where the volume just grew from the tumor into the surrounding normal tissues. The ARG_MC along with five widely used algorithms were tested on a phantom with 6 spheres at different signal to background ratios and on two clinic datasets including 20 patients with esophageal cancer and 11 patients with non-Hodgkin lymphoma (NHL). The ARG_MC did not require any phantom calibration or any a priori knowledge of the tumor or PET scanner. The identified ORF varied with tumor types (mean ORF = 9.61, 3.78 and 2.55 respectively for the phantom, esophageal cancer, and NHL datasets), and varied from one tumor to another. For the phantom, the ARG_MC ranked the second in segmentation accuracy with an average Dice similarity index (DSI) of 0.86, only slightly worse than Daisne’s adaptive thresholding method (DSI = 0.87), which required phantom calibration. For both the esophageal cancer dataset and the NHL dataset, the ARG_MC had the highest accuracy with an average DSI of 0.87 and 0.84, respectively. The ARG_MC was robust to parameter settings and region of interest selection, and it did not depend on scanners, imaging protocols, or tumor types. Furthermore, the ARG_MC made no assumption about the tumor size or tumor uptake distribution, making it suitable for segmenting tumors with heterogeneous FDG uptake. In conclusion, the ARG_MC was accurate, robust and easy to use, it provides a highly potential tool for PET tumor segmentation in clinic.
Adaptive Region-Growing with Maximum Curvature Strategy for Tumor Segmentation in 18F-FDG PET
Tan, Shan; Li, Laquan; Choi, Wookjin; Kang, Min Kyu; D’Souza, Warren D.; Lu, Wei
2017-01-01
Accurate tumor segmentation in PET is crucial in many oncology applications. We developed an adaptive region-growing (ARG) algorithm with a maximum curvature strategy (ARG_MC) for tumor segmentation in PET. The ARG_MC repeatedly applied a confidence connected region-growing (CCRG) algorithm with increasing relaxing factor f. The optimal relaxing factor (ORF) was then determined at the transition point on the f-volume curve, where the volume just grew from the tumor into the surrounding normal tissues. The ARG_MC along with five widely used algorithms were tested on a phantom with 6 spheres at different signal to background ratios and on two clinic datasets including 20 patients with esophageal cancer and 11 patients with non-Hodgkin lymphoma (NHL). The ARG_MC did not require any phantom calibration or any a priori knowledge of the tumor or PET scanner. The identified ORF varied with tumor types (mean ORF = 9.61, 3.78 and 2.55 respectively for the phantom, esophageal cancer, and NHL datasets), and varied from one tumor to another. For the phantom, the ARG_MC ranked the second in segmentation accuracy with an average Dice similarity index (DSI) of 0.86, only slightly worse than Daisne’s adaptive thresholding method (DSI=0.87), which required phantom calibration. For both the esophageal cancer dataset and the NHL dataset, the ARG_MC had the highest accuracy with an average DSI of 0.87 and 0.84, respectively. The ARG_MC was robust to parameter settings and region of interest selection, and it did not depend on scanners, imaging protocols, or tumor types. Furthermore, the ARG_MC made no assumption about the tumor size or tumor uptake distribution, making it suitable for segmenting tumors with heterogeneous FDG uptake. In conclusion, the ARG_MC was accurate, robust and easy to use, it provides a highly potential tool for PET tumor segmentation in clinic. PMID:28604372
Elleithy, Khaled; Elleithy, Abdelrahman
2018-01-01
Eye exam can be as efficacious as physical one in determining health concerns. Retina screening can be the very first clue for detecting a variety of hidden health issues including pre-diabetes and diabetes. Through the process of clinical diagnosis and prognosis; ophthalmologists rely heavily on the binary segmented version of retina fundus image; where the accuracy of segmented vessels, optic disc, and abnormal lesions extremely affects the diagnosis accuracy which in turn affect the subsequent clinical treatment steps. This paper proposes an automated retinal fundus image segmentation system composed of three segmentation subsystems follow same core segmentation algorithm. Despite of broad difference in features and characteristics; retinal vessels, optic disc, and exudate lesions are extracted by each subsystem without the need for texture analysis or synthesis. For sake of compact diagnosis and complete clinical insight, our proposed system can detect these anatomical structures in one session with high accuracy even in pathological retina images. The proposed system uses a robust hybrid segmentation algorithm combines adaptive fuzzy thresholding and mathematical morphology. The proposed system is validated using four benchmark datasets: DRIVE and STARE (vessels), DRISHTI-GS (optic disc), and DIARETDB1 (exudates lesions). Competitive segmentation performance is achieved, outperforming a variety of up-to-date systems and demonstrating the capacity to deal with other heterogeneous anatomical structures. PMID:29888146
Automated skin lesion segmentation with kernel density estimation
NASA Astrophysics Data System (ADS)
Pardo, A.; Real, E.; Fernandez-Barreras, G.; Madruga, F. J.; López-Higuera, J. M.; Conde, O. M.
2017-07-01
Skin lesion segmentation is a complex step for dermoscopy pathological diagnosis. Kernel density estimation is proposed as a segmentation technique based on the statistic distribution of color intensities in the lesion and non-lesion regions.
Adaptive skin segmentation via feature-based face detection
NASA Astrophysics Data System (ADS)
Taylor, Michael J.; Morris, Tim
2014-05-01
Variations in illumination can have significant effects on the apparent colour of skin, which can be damaging to the efficacy of any colour-based segmentation approach. We attempt to overcome this issue by presenting a new adaptive approach, capable of generating skin colour models at run-time. Our approach adopts a Viola-Jones feature-based face detector, in a moderate-recall, high-precision configuration, to sample faces within an image, with an emphasis on avoiding potentially detrimental false positives. From these samples, we extract a set of pixels that are likely to be from skin regions, filter them according to their relative luma values in an attempt to eliminate typical non-skin facial features (eyes, mouths, nostrils, etc.), and hence establish a set of pixels that we can be confident represent skin. Using this representative set, we train a unimodal Gaussian function to model the skin colour in the given image in the normalised rg colour space - a combination of modelling approach and colour space that benefits us in a number of ways. A generated function can subsequently be applied to every pixel in the given image, and, hence, the probability that any given pixel represents skin can be determined. Segmentation of the skin, therefore, can be as simple as applying a binary threshold to the calculated probabilities. In this paper, we touch upon a number of existing approaches, describe the methods behind our new system, present the results of its application to arbitrary images of people with detectable faces, which we have found to be extremely encouraging, and investigate its potential to be used as part of real-time systems.
Cavina-Pratesi, C; Kentridge, R W; Heywood, C A; Milner, A D
2010-10-01
Previous neuroimaging research suggests that although object shape is analyzed in the lateral occipital cortex, surface properties of objects, such as color and texture, are dealt with in more medial areas, close to the collateral sulcus (CoS). The present study sought to determine whether there is a single medial region concerned with surface properties in general or whether instead there are multiple foci independently extracting different surface properties. We used stimuli varying in their shape, texture, or color, and tested healthy participants and 2 object-agnosic patients, in both a discrimination task and a functional MR adaptation paradigm. We found a double dissociation between medial and lateral occipitotemporal cortices in processing surface (texture or color) versus geometric (shape) properties, respectively. In Experiment 2, we found that the medial occipitotemporal cortex houses separate foci for color (within anterior CoS and lingual gyrus) and texture (caudally within posterior CoS). In addition, we found that areas selective for shape, texture, and color individually were quite distinct from those that respond to all of these features together (shape and texture and color). These latter areas appear to correspond to those associated with the perception of complex stimuli such as faces and places.
The activation of segmental and tonal information in visual word recognition.
Li, Chuchu; Lin, Candise Y; Wang, Min; Jiang, Nan
2013-08-01
Mandarin Chinese has a logographic script in which graphemes map onto syllables and morphemes. It is not clear whether Chinese readers activate phonological information during lexical access, although phonological information is not explicitly represented in Chinese orthography. In the present study, we examined the activation of phonological information, including segmental and tonal information in Chinese visual word recognition, using the Stroop paradigm. Native Mandarin speakers named the presentation color of Chinese characters in Mandarin. The visual stimuli were divided into five types: color characters (e.g., , hong2, "red"), homophones of the color characters (S+T+; e.g., , hong2, "flood"), different-tone homophones (S+T-; e.g., , hong1, "boom"), characters that shared the same tone but differed in segments with the color characters (S-T+; e.g., , ping2, "bottle"), and neutral characters (S-T-; e.g., , qian1, "leading through"). Classic Stroop facilitation was shown in all color-congruent trials, and interference was shown in the incongruent trials. Furthermore, the Stroop effect was stronger for S+T- than for S-T+ trials, and was similar between S+T+ and S+T- trials. These findings suggested that both tonal and segmental forms of information play roles in lexical constraints; however, segmental information has more weight than tonal information. We proposed a revised visual word recognition model in which the functions of both segmental and suprasegmental types of information and their relative weights are taken into account.
Adaptive Ambient Illumination Based on Color Harmony Model
NASA Astrophysics Data System (ADS)
Kikuchi, Ayano; Hirai, Keita; Nakaguchi, Toshiya; Tsumura, Norimichi; Miyake, Yoichi
We investigated the relationship between ambient illumination and psychological effect by applying a modified color harmony model. We verified the proposed model by analyzing correlation between psychological value and modified color harmony score. Experimental results showed the possibility to obtain the best color for illumination using this model.
Space Adaptation of Active Mirror Segment Concepts
NASA Technical Reports Server (NTRS)
Ames, Gregory H.
1999-01-01
This report summarizes the results of a three year effort by Blue Line Engineering Co. to advance the state of segmented mirror systems in several separate but related areas. The initial set of tasks were designed to address the issues of system level architecture, digital processing system, cluster level support structures, and advanced mirror fabrication concepts. Later in the project new tasks were added to provide support to the existing segmented mirror testbed at Marshall Space Flight Center (MSFC) in the form of upgrades to the 36 subaperture wavefront sensor. Still later, tasks were added to build and install a new system processor based on the results of the new system architecture. The project was successful in achieving a number of important results. These include the following most notable accomplishments: 1) The creation of a new modular digital processing system that is extremely capable and may be applied to a wide range of segmented mirror systems as well as many classes of Multiple Input Multiple Output (MIMO) control systems such as active structures or industrial automation. 2) A new graphical user interface was created for operation of segmented mirror systems. 3) The development of a high bit rate serial data loop that permits bi-directional flow of data to and from as many as 39 segments daisy-chained to form a single cluster of segments. 4) Upgrade of the 36 subaperture Hartmann type Wave Front Sensor (WFS) of the Phased Array Mirror, Extendible Large Aperture (PAMELA) testbed at MSFC resulting in a 40 to 5OX improvement in SNR which in turn enabled NASA personnel to achieve many significant strides in improved closed-loop system operation in 1998. 5) A new system level processor was built and delivered to MSFC for use with the PAMELA testbed. This new system featured a new graphical user interface to replace the obsolete and non-supported menu system originally delivered with the PAMELA system. The hardware featured Blue Line's new stackable
Segmented socioeconomic adaptation of New Eastern European professionals in the United States.
Michalikova, Nina
2018-01-01
This study examines the socioeconomic adaptation of post-1991 Eastern European professionals in the United States. The data were obtained from the pooled 2006-2010 American Community Surveys. The analysis includes recent immigrants between ages of 25-65 who have at least an associate's degree. Skilled immigrants in professional or managerial occupations are compared with non-professionals or managers to examine and compare socioeconomic outcomes. The findings presented in this study support the segmented assimilation theory and reveal cross-group and cross-country disparities in socioeconomic adaptation. Despite the high amount of human capital, Eastern European skilled immigrants tend to have a lower share of professionals and managers than other groups. Their average income is lower than the income of some other groups in the analysis, especially immigrants from Northern and Western Europe, suggesting these immigrants experience difficulties in transferring human capital. Among the three largest Eastern European groups - Russia, Ukraine, and Poland - there is a clear hierarchy in socioeconomic status with Russian professionals having the highest educational attainment and income, followed by immigrants from Ukraine and Poland. Results also revealed gender differences in socioeconomic adaptation. Women from Eastern Europe are highly professional, but they tend to be concentrated in different occupations than men, leading to a significant gender-wage gap. The effect of selected individual and country-level characteristics on skilled immigrants' socioeconomic adaptation is discussed.
CFA-aware features for steganalysis of color images
NASA Astrophysics Data System (ADS)
Goljan, Miroslav; Fridrich, Jessica
2015-03-01
Color interpolation is a form of upsampling, which introduces constraints on the relationship between neighboring pixels in a color image. These constraints can be utilized to substantially boost the accuracy of steganography detectors. In this paper, we introduce a rich model formed by 3D co-occurrences of color noise residuals split according to the structure of the Bayer color filter array to further improve detection. Some color interpolation algorithms, AHD and PPG, impose pixel constraints so tight that extremely accurate detection becomes possible with merely eight features eliminating the need for model richification. We carry out experiments on non-adaptive LSB matching and the content-adaptive algorithm WOW on five different color interpolation algorithms. In contrast to grayscale images, in color images that exhibit traces of color interpolation the security of WOW is significantly lower and, depending on the interpolation algorithm, may even be lower than non-adaptive LSB matching.
A Bayesian Approach for Image Segmentation with Shape Priors
DOE Office of Scientific and Technical Information (OSTI.GOV)
Chang, Hang; Yang, Qing; Parvin, Bahram
2008-06-20
Color and texture have been widely used in image segmentation; however, their performance is often hindered by scene ambiguities, overlapping objects, or missingparts. In this paper, we propose an interactive image segmentation approach with shape prior models within a Bayesian framework. Interactive features, through mouse strokes, reduce ambiguities, and the incorporation of shape priors enhances quality of the segmentation where color and/or texture are not solely adequate. The novelties of our approach are in (i) formulating the segmentation problem in a well-de?ned Bayesian framework with multiple shape priors, (ii) ef?ciently estimating parameters of the Bayesian model, and (iii) multi-object segmentationmore » through user-speci?ed priors. We demonstrate the effectiveness of our method on a set of natural and synthetic images.« less
A Discrete Model for Color Naming
NASA Astrophysics Data System (ADS)
Menegaz, G.; Le Troter, A.; Sequeira, J.; Boi, J. M.
2006-12-01
The ability to associate labels to colors is very natural for human beings. Though, this apparently simple task hides very complex and still unsolved problems, spreading over many different disciplines ranging from neurophysiology to psychology and imaging. In this paper, we propose a discrete model for computational color categorization and naming. Starting from the 424 color specimens of the OSA-UCS set, we propose a fuzzy partitioning of the color space. Each of the 11 basic color categories identified by Berlin and Kay is modeled as a fuzzy set whose membership function is implicitly defined by fitting the model to the results of an ad hoc psychophysical experiment (Experiment 1). Each OSA-UCS sample is represented by a feature vector whose components are the memberships to the different categories. The discrete model consists of a three-dimensional Delaunay triangulation of the CIELAB color space which associates each OSA-UCS sample to a vertex of a 3D tetrahedron. Linear interpolation is used to estimate the membership values of any other point in the color space. Model validation is performed both directly, through the comparison of the predicted membership values to the subjective counterparts, as evaluated via another psychophysical test (Experiment 2), and indirectly, through the investigation of its exploitability for image segmentation. The model has proved to be successful in both cases, providing an estimation of the membership values in good agreement with the subjective measures as well as a semantically meaningful color-based segmentation map.
Optimal wavefront control for adaptive segmented mirrors
NASA Technical Reports Server (NTRS)
Downie, John D.; Goodman, Joseph W.
1989-01-01
A ground-based astronomical telescope with a segmented primary mirror will suffer image-degrading wavefront aberrations from at least two sources: (1) atmospheric turbulence and (2) segment misalignment or figure errors of the mirror itself. This paper describes the derivation of a mirror control feedback matrix that assumes the presence of both types of aberration and is optimum in the sense that it minimizes the mean-squared residual wavefront error. Assumptions of the statistical nature of the wavefront measurement errors, atmospheric phase aberrations, and segment misalignment errors are made in the process of derivation. Examples of the degree of correlation are presented for three different types of wavefront measurement data and compared to results of simple corrections.
Mishra, Ajay; Aloimonos, Yiannis
2009-01-01
The human visual system observes and understands a scene/image by making a series of fixations. Every fixation point lies inside a particular region of arbitrary shape and size in the scene which can either be an object or just a part of it. We define as a basic segmentation problem the task of segmenting that region containing the fixation point. Segmenting the region containing the fixation is equivalent to finding the enclosing contour- a connected set of boundary edge fragments in the edge map of the scene - around the fixation. This enclosing contour should be a depth boundary.We present here a novel algorithm that finds this bounding contour and achieves the segmentation of one object, given the fixation. The proposed segmentation framework combines monocular cues (color/intensity/texture) with stereo and/or motion, in a cue independent manner. The semantic robots of the immediate future will be able to use this algorithm to automatically find objects in any environment. The capability of automatically segmenting objects in their visual field can bring the visual processing to the next level. Our approach is different from current approaches. While existing work attempts to segment the whole scene at once into many areas, we segment only one image region, specifically the one containing the fixation point. Experiments with real imagery collected by our active robot and from the known databases 1 demonstrate the promise of the approach.
Automatic video segmentation and indexing
NASA Astrophysics Data System (ADS)
Chahir, Youssef; Chen, Liming
1999-08-01
Indexing is an important aspect of video database management. Video indexing involves the analysis of video sequences, which is a computationally intensive process. However, effective management of digital video requires robust indexing techniques. The main purpose of our proposed video segmentation is twofold. Firstly, we develop an algorithm that identifies camera shot boundary. The approach is based on the use of combination of color histograms and block-based technique. Next, each temporal segment is represented by a color reference frame which specifies the shot similarities and which is used in the constitution of scenes. Experimental results using a variety of videos selected in the corpus of the French Audiovisual National Institute are presented to demonstrate the effectiveness of performing shot detection, the content characterization of shots and the scene constitution.
Number of discernible object colors is a conundrum.
Masaoka, Kenichiro; Berns, Roy S; Fairchild, Mark D; Moghareh Abed, Farhad
2013-02-01
Widely varying estimates of the number of discernible object colors have been made by using various methods over the past 100 years. To clarify the source of the discrepancies in the previous, inconsistent estimates, the number of discernible object colors is estimated over a wide range of color temperatures and illuminance levels using several chromatic adaptation models, color spaces, and color difference limens. Efficient and accurate models are used to compute optimal-color solids and count the number of discernible colors. A comprehensive simulation reveals limitations in the ability of current color appearance models to estimate the number of discernible colors even if the color solid is smaller than the optimal-color solid. The estimates depend on the color appearance model, color space, and color difference limen used. The fundamental problem lies in the von Kries-type chromatic adaptation transforms, which have an unknown effect on the ranking of the number of discernible colors at different color temperatures.
Color vision in children and the Lanthony New Color Test.
Ling, Barbara Y; Dain, Stephen J
2008-01-01
Much is known about color vision in infants, adolescents, and adults, but very few studies report the changes, which occur in color perception of children in their early schooling years. There is also a shortage of suitable color vision tests for children. This study investigated the changes in color vision of school students between 5-12 years old using the Lanthony New Color Test (NCT). Subjects of all ages were able to complete a shortened form of this test adequately. The Vingrys and King-Smith (1988) method of panel test analysis and Adams and Rodic (1982) color confusion score were adapted to analyze their performance of the test. This study confirmed that there are changes in color perception occurring in this age group. Color perception abilities increased as a function of age and there was also an improvement in the performance on the NCT with age. This can be attributed to both cognitive development and changes occurring to the color vision system.
White blood cell counting analysis of blood smear images using various segmentation strategies
NASA Astrophysics Data System (ADS)
Safuan, Syadia Nabilah Mohd; Tomari, Razali; Zakaria, Wan Nurshazwani Wan; Othman, Nurmiza
2017-09-01
In white blood cell (WBC) diagnosis, the most crucial measurement parameter is the WBC counting. Such information is widely used to evaluate the effectiveness of cancer therapy and to diagnose several hidden infection within human body. The current practice of manual WBC counting is laborious and a very subjective assessment which leads to the invention of computer aided system (CAS) with rigorous image processing solution. In the CAS counting work, segmentation is the crucial step to ensure the accuracy of the counted cell. The optimal segmentation strategy that can work under various blood smeared image acquisition conditions is remain a great challenge. In this paper, a comparison between different segmentation methods based on color space analysis to get the best counting outcome is elaborated. Initially, color space correction is applied to the original blood smeared image to standardize the image color intensity level. Next, white blood cell segmentation is performed by using combination of several color analysis subtraction which are RGB, CMYK and HSV, and Otsu thresholding. Noises and unwanted regions that present after the segmentation process is eliminated by applying a combination of morphological and Connected Component Labelling (CCL) filter. Eventually, Circle Hough Transform (CHT) method is applied to the segmented image to estimate the number of WBC including the one under the clump region. From the experiment, it is found that G-S yields the best performance.
Selective Pressures Explain Differences in Flower Color among Gentiana lutea Populations.
Sobral, Mar; Veiga, Tania; Domínguez, Paula; Guitián, Javier A; Guitián, Pablo; Guitián, José M
2015-01-01
Flower color variation among plant populations might reflect adaptation to local conditions such as the interacting animal community. In the northwest Iberian Peninsula, flower color of Gentiana lutea varies longitudinally among populations, ranging from orange to yellow. We explored whether flower color is locally adapted and the role of pollinators and seed predators as agents of selection by analyzing the influence of flower color on (i) pollinator visitation rate and (ii) escape from seed predation and (iii) by testing whether differences in pollinator communities correlate with flower color variation across populations. Finally, (iv) we investigated whether variation in selective pressures explains flower color variation among 12 G. lutea populations. Flower color influenced pollinator visits and differences in flower color among populations were related to variation in pollinator communities. Selective pressures on flower color vary among populations and explain part of flower color differences among populations of G. lutea. We conclude that flower color in G. lutea is locally adapted and that pollinators play a role in this adaptation.
Selective Pressures Explain Differences in Flower Color among Gentiana lutea Populations
Domínguez, Paula; Guitián, Javier A.; Guitián, Pablo; Guitián, José M.
2015-01-01
Flower color variation among plant populations might reflect adaptation to local conditions such as the interacting animal community. In the northwest Iberian Peninsula, flower color of Gentiana lutea varies longitudinally among populations, ranging from orange to yellow. We explored whether flower color is locally adapted and the role of pollinators and seed predators as agents of selection by analyzing the influence of flower color on (i) pollinator visitation rate and (ii) escape from seed predation and (iii) by testing whether differences in pollinator communities correlate with flower color variation across populations. Finally, (iv) we investigated whether variation in selective pressures explains flower color variation among 12 G. lutea populations. Flower color influenced pollinator visits and differences in flower color among populations were related to variation in pollinator communities. Selective pressures on flower color vary among populations and explain part of flower color differences among populations of G. lutea. We conclude that flower color in G. lutea is locally adapted and that pollinators play a role in this adaptation. PMID:26172378
NASA Astrophysics Data System (ADS)
Polewski, P.; Yao, W.; Heurich, M.; Krzystek, P.; Stilla, U.
2015-03-01
Standing dead trees, known as snags, are an essential factor in maintaining biodiversity in forest ecosystems. Combined with their role as carbon sinks, this makes for a compelling reason to study their spatial distribution. This paper presents an integrated method to detect and delineate individual dead tree crowns from color infrared aerial imagery. Our approach consists of two steps which incorporate statistical information about prior distributions of both the image intensities and the shapes of the target objects. In the first step, we perform a Gaussian Mixture Model clustering in the pixel color space with priors on the cluster means, obtaining up to 3 components corresponding to dead trees, living trees, and shadows. We then refine the dead tree regions using a level set segmentation method enriched with a generative model of the dead trees' shape distribution as well as a discriminative model of their pixel intensity distribution. The iterative application of the statistical shape template yields the set of delineated dead crowns. The prior information enforces the consistency of the template's shape variation with the shape manifold defined by manually labeled training examples, which makes it possible to separate crowns located in close proximity and prevents the formation of large crown clusters. Also, the statistical information built into the segmentation gives rise to an implicit detection scheme, because the shape template evolves towards an empty contour if not enough evidence for the object is present in the image. We test our method on 3 sample plots from the Bavarian Forest National Park with reference data obtained by manually marking individual dead tree polygons in the images. Our results are scenario-dependent and range from a correctness/completeness of 0.71/0.81 up to 0.77/1, with an average center-of-gravity displacement of 3-5 pixels between the detected and reference polygons.
DOT National Transportation Integrated Search
2006-03-01
The previous study showed that many colors were used in air traffic control displays. We also found that colors were used mainly for three purposes: capturing controllers immediate attention, identifying targets, and segmenting information. This r...
Color Addition and Subtraction Apps
NASA Astrophysics Data System (ADS)
Ruiz, Frances; Ruiz, Michael J.
2015-10-01
Color addition and subtraction apps in HTML5 have been developed for students as an online hands-on experience so that they can more easily master principles introduced through traditional classroom demonstrations. The evolution of the additive RGB color model is traced through the early IBM color adapters so that students can proceed step by step in understanding mathematical representations of RGB color. Finally, color addition and subtraction are presented for the X11 colors from web design to illustrate yet another real-life application of color mixing.
DOE Office of Scientific and Technical Information (OSTI.GOV)
Gupta, V; Wang, Y; Romero, A
2014-06-01
Purpose: Various studies have demonstrated that online adaptive radiotherapy by real-time re-optimization of the treatment plan can improve organs-at-risk (OARs) sparing in the abdominal region. Its clinical implementation, however, requires fast and accurate auto-segmentation of OARs in CT scans acquired just before each treatment fraction. Autosegmentation is particularly challenging in the abdominal region due to the frequently observed large deformations. We present a clinical validation of a new auto-segmentation method that uses fully automated non-rigid registration for propagating abdominal OAR contours from planning to daily treatment CT scans. Methods: OARs were manually contoured by an expert panel to obtain groundmore » truth contours for repeat CT scans (3 per patient) of 10 patients. For the non-rigid alignment, we used a new non-rigid registration method that estimates the deformation field by optimizing local normalized correlation coefficient with smoothness regularization. This field was used to propagate planning contours to repeat CTs. To quantify the performance of the auto-segmentation, we compared the propagated and ground truth contours using two widely used metrics- Dice coefficient (Dc) and Hausdorff distance (Hd). The proposed method was benchmarked against translation and rigid alignment based auto-segmentation. Results: For all organs, the auto-segmentation performed better than the baseline (translation) with an average processing time of 15 s per fraction CT. The overall improvements ranged from 2% (heart) to 32% (pancreas) in Dc, and 27% (heart) to 62% (spinal cord) in Hd. For liver, kidneys, gall bladder, stomach, spinal cord and heart, Dc above 0.85 was achieved. Duodenum and pancreas were the most challenging organs with both showing relatively larger spreads and medians of 0.79 and 2.1 mm for Dc and Hd, respectively. Conclusion: Based on the achieved accuracy and computational time we conclude that the investigated auto-segmentation
Segmentation of financial seals and its implementation on a DSP-based system
NASA Astrophysics Data System (ADS)
He, Jin; Liu, Tiegen; Guo, Jingjing; Zhang, Hao
2009-11-01
Automatic seal imprint identification is an important part of modern financial security. Accurate segmentation is the basis of correct identification. In this paper, a DSP (digital signal processor) based identification system was designed, and an adaptive algorithm was proposed to extract binary seal images from financial instruments. As the kernel of the identification system, a DSP chip of TMS320DM642 was used to implement image processing, controlling and coordinating works of each system module. The proposed algorithm consisted of three stages, including extraction of grayscale seal image, denoising and binarization. A grayscale seal image was extracted by color transform from a financial instrument image. Adaptive morphological operations were used to highlight details of the extracted grayscale seal image and smooth the background. After median filter for noise elimination, the filtered seal image was binarized by Otsu's method. The algorithm was developed based on the DSP development environment CCS and real-time operation system DSP/BIOS. To simplify the implementation of the proposed algorithm, the calibration of white balance and the coarse positioning of the seal imprint were implemented by TMS320DM642 controlling image acquisition. IMGLIB of TMS320DM642 was used for the efficiency improvement. The experiment result showed that financial seal imprints, even with intricate and dense strokes can be correctly segmented by the proposed algorithm. Adhesion and incompleteness distortions in the segmentation results were reduced, even when the original seal imprint had a poor quality.
NASA Astrophysics Data System (ADS)
Nanayakkara, Nuwan D.; Samarabandu, Jagath; Fenster, Aaron
2006-04-01
Estimation of prostate location and volume is essential in determining a dose plan for ultrasound-guided brachytherapy, a common prostate cancer treatment. However, manual segmentation is difficult, time consuming and prone to variability. In this paper, we present a semi-automatic discrete dynamic contour (DDC) model based image segmentation algorithm, which effectively combines a multi-resolution model refinement procedure together with the domain knowledge of the image class. The segmentation begins on a low-resolution image by defining a closed DDC model by the user. This contour model is then deformed progressively towards higher resolution images. We use a combination of a domain knowledge based fuzzy inference system (FIS) and a set of adaptive region based operators to enhance the edges of interest and to govern the model refinement using a DDC model. The automatic vertex relocation process, embedded into the algorithm, relocates deviated contour points back onto the actual prostate boundary, eliminating the need of user interaction after initialization. The accuracy of the prostate boundary produced by the proposed algorithm was evaluated by comparing it with a manually outlined contour by an expert observer. We used this algorithm to segment the prostate boundary in 114 2D transrectal ultrasound (TRUS) images of six patients scheduled for brachytherapy. The mean distance between the contours produced by the proposed algorithm and the manual outlines was 2.70 ± 0.51 pixels (0.54 ± 0.10 mm). We also showed that the algorithm is insensitive to variations of the initial model and parameter values, thus increasing the accuracy and reproducibility of the resulting boundaries in the presence of noise and artefacts.
2011-12-07
This color view from NASA Mars Exploration Rover Opportunity of a mineral vein called Homestake and is found to be rich in calcium and sulfur. Homestake is near the edge of the Cape York segment of the western rim of Endeavour Crater.
An improved K-means clustering algorithm in agricultural image segmentation
NASA Astrophysics Data System (ADS)
Cheng, Huifeng; Peng, Hui; Liu, Shanmei
Image segmentation is the first important step to image analysis and image processing. In this paper, according to color crops image characteristics, we firstly transform the color space of image from RGB to HIS, and then select proper initial clustering center and cluster number in application of mean-variance approach and rough set theory followed by clustering calculation in such a way as to automatically segment color component rapidly and extract target objects from background accurately, which provides a reliable basis for identification, analysis, follow-up calculation and process of crops images. Experimental results demonstrate that improved k-means clustering algorithm is able to reduce the computation amounts and enhance precision and accuracy of clustering.
Device-independent color scanning
NASA Astrophysics Data System (ADS)
Burger, Rudolph E.
1993-08-01
Color calibration technology is being incorporated into both Apple and Microsoft's operating systems. These color savvy operating systems will produce a market pull towards 'smart color' scanners and printers which, in turn, will lead towards a distributed architecture for color management systems (CMS). Today's desktop scanners produce red-green-blue color signals that do not accurately describe the color of the object being scanned. Future scanners will be self-calibrating and communicate their own 'device profile' to the operating system based CMS. This paper describes some of the key technologies required for this next generation of smart color scanners. Topics covered include a comparison of colorimetric and conventional scanning technologies, and the impact of metamerism, dye fluorescence and chromatic adaptation on device independent color scanning.
Adaptive elastic segmentation of brain MRI via shape-model-guided evolutionary programming.
Pitiot, Alain; Toga, Arthur W; Thompson, Paul M
2002-08-01
This paper presents a fully automated segmentation method for medical images. The goal is to localize and parameterize a variety of types of structure in these images for subsequent quantitative analysis. We propose a new hybrid strategy that combines a general elastic template matching approach and an evolutionary heuristic. The evolutionary algorithm uses prior statistical information about the shape of the target structure to control the behavior of a number of deformable templates. Each template, modeled in the form of a B-spline, is warped in a potential field which is itself dynamically adapted. Such a hybrid scheme proves to be promising: by maintaining a population of templates, we cover a large domain of the solution space under the global guidance of the evolutionary heuristic, and thoroughly explore interesting areas. We address key issues of automated image segmentation systems. The potential fields are initially designed based on the spatial features of the edges in the input image, and are subjected to spatially adaptive diffusion to guarantee the deformation of the template. This also improves its global consistency and convergence speed. The deformation algorithm can modify the internal structure of the templates to allow a better match. We investigate in detail the preprocessing phase that the images undergo before they can be used more effectively in the iterative elastic matching procedure: a texture classifier, trained via linear discriminant analysis of a learning set, is used to enhance the contrast of the target structure with respect to surrounding tissues. We show how these techniques interact within a statistically driven evolutionary scheme to achieve a better tradeoff between template flexibility and sensitivity to noise and outliers. We focus on understanding the features of template matching that are most beneficial in terms of the achieved match. Examples from simulated and real image data are discussed, with considerations of
Illumination adaptation with rapid-response color sensors
NASA Astrophysics Data System (ADS)
Zhang, Xinchi; Wang, Quan; Boyer, Kim L.
2014-09-01
Smart lighting solutions based on imaging sensors such as webcams or time-of-flight sensors suffer from rising privacy concerns. In this work, we use low-cost non-imaging color sensors to measure local luminous flux of different colors in an indoor space. These sensors have much higher data acquisition rate and are much cheaper than many o_-the-shelf commercial products. We have developed several applications with these sensors, including illumination feedback control and occupancy-driven lighting.
Segmentation and classification of dermatological lesions
NASA Astrophysics Data System (ADS)
Sáez, Aurora; Acha, Begoña; Serrano, Carmen
2010-03-01
Certain skin diseases are chronic, inflammatory and without cure. However, there are many treatment options that can clear them for a period of time. Measuring their severity and assessing their extent, is a fundamental issue to determine the efficacy of the treatment under test. Two of the most important parameters of severity assessment are Erythema (redness) and Scaliness. Physicians classify these parameters into several grades by visual grading method. In this paper a color image segmentation and classification algorithm is developed to obtain an assessment of erythema and scaliness of dermatological lesions. Color digital photographs taken under an acquisition protocol form the database. Difference between green band and blue band of images in RGB color space shows two modes (healthy skin and lesion) with clear separation. Otsu's method is applied to this difference in order to isolate the lesion. After the skin disease is segmented, some color and texture features are calculated and they are the inputs to a Fuzzy-ARTMAP neural network. The neural network classifies them into the five grades of erythema and the five grades of scaliness. The method has been tested with 31 images with a success percentage of 83.87 % when the images are classified in erythema, and 77.42 % for scaliness classification.
Comparison of parameter-adapted segmentation methods for fluorescence micrographs.
Held, Christian; Palmisano, Ralf; Häberle, Lothar; Hensel, Michael; Wittenberg, Thomas
2011-11-01
Interpreting images from fluorescence microscopy is often a time-consuming task with poor reproducibility. Various image processing routines that can help investigators evaluate the images are therefore useful. The critical aspect for a reliable automatic image analysis system is a robust segmentation algorithm that can perform accurate segmentation for different cell types. In this study, several image segmentation methods were therefore compared and evaluated in order to identify the most appropriate segmentation schemes that are usable with little new parameterization and robustly with different types of fluorescence-stained cells for various biological and biomedical tasks. The study investigated, compared, and enhanced four different methods for segmentation of cultured epithelial cells. The maximum-intensity linking (MIL) method, an improved MIL, a watershed method, and an improved watershed method based on morphological reconstruction were used. Three manually annotated datasets consisting of 261, 817, and 1,333 HeLa or L929 cells were used to compare the different algorithms. The comparisons and evaluations showed that the segmentation performance of methods based on the watershed transform was significantly superior to the performance of the MIL method. The results also indicate that using morphological opening by reconstruction can improve the segmentation of cells stained with a marker that exhibits the dotted surface of cells. Copyright © 2011 International Society for Advancement of Cytometry.
NASA Astrophysics Data System (ADS)
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.
Adaptive Local Linear Regression with Application to Printer Color Management
2008-01-01
values formed the test samples. This process guaranteed that the CIELAB test samples were in the gamut for each printer, but each printer had a...digital images has recently led to increased consumer demand for accurate color reproduction. Given a CIELAB color one would like to reproduce, the color...management problem is to determine what RGB color one must send the printer to minimize the error between the desired CIELAB color and the CIELAB
Color transfer algorithm in medical images
NASA Astrophysics Data System (ADS)
Wang, Weihong; Xu, Yangfa
2007-12-01
In digital virtual human project, image data acquires from the freezing slice of human body specimen. The color and brightness between a group of images of a certain organ could be quite different. The quality of these images could bring great difficulty in edge extraction, segmentation, as well as 3D reconstruction process. Thus it is necessary to unify the color of the images. The color transfer algorithm is a good algorithm to deal with this kind of problem. This paper introduces the principle of this algorithm and uses it in the medical image processing.
Digalakis, Michail; Papamichail, Michail; Glava, Chryssoula; Grammatoglou, Xanthippi; Sergentanis, Theodoros N; Papalois, Apostolos; Bramis, John
2011-12-01
Interposition of a reversed intestinal segment as a factor facilitating intestinal adaptation has been experimentally investigated. Controversy exists about its efficacy in terms of body weight improvement, direction of luminal changes, and underlying mechanisms. This study aims to provide a comprehensive approach. The pigs were randomly allocated to two groups: (1) short bowel (SB) group (n=8) and (2) short bowel reverse jejunal segment (SB-RS) group (n=8). On postoperative d 3, 30, and 60, intestinal transit time was measured; body weight and serum albumin were measured on baseline, as well as on postoperative d 30 and 60. After sacrifice, histopathologic and immunohistochemical (PCNA, activated caspase-3) evaluation followed. Transit time was numerically longer in SB-RS group at all time points; the difference reached statistical significance on d 60. No statistically significant differences were observed concerning body weight or serum albumin. In the SB-RS group, a statistically significant increase in muscle thickness, crypt depth, villus height, and PCNA immunostaining, and a decrease in caspase-3 positive (+) cell count were documented both at the jejunal and ileal level. The reversed jejunal segment seemed able to enhance intestinal adaptation at a histopathologic level, as well as to favorably modify transit time. These putatively beneficial actions were not reflected upon body weight. The decrease in apoptosis was caspase-3-dependent. Crown Copyright © 2011. Published by Elsevier Inc. All rights reserved.
Color Addition and Subtraction Apps
ERIC Educational Resources Information Center
Ruiz, Frances; Ruiz, Michael J.
2015-01-01
Color addition and subtraction apps in HTML5 have been developed for students as an online hands-on experience so that they can more easily master principles introduced through traditional classroom demonstrations. The evolution of the additive RGB color model is traced through the early IBM color adapters so that students can proceed step by step…
Adaptive Morphological Feature-Based Object Classifier for a Color Imaging System
NASA Technical Reports Server (NTRS)
McDowell, Mark; Gray, Elizabeth
2009-01-01
Utilizing a Compact Color Microscope Imaging System (CCMIS), a unique algorithm has been developed that combines human intelligence along with machine vision techniques to produce an autonomous microscope tool for biomedical, industrial, and space applications. This technique is based on an adaptive, morphological, feature-based mapping function comprising 24 mutually inclusive feature metrics that are used to determine the metrics for complex cell/objects derived from color image analysis. Some of the features include: Area (total numbers of non-background pixels inside and including the perimeter), Bounding Box (smallest rectangle that bounds and object), centerX (x-coordinate of intensity-weighted, center-of-mass of an entire object or multi-object blob), centerY (y-coordinate of intensity-weighted, center-of-mass, of an entire object or multi-object blob), Circumference (a measure of circumference that takes into account whether neighboring pixels are diagonal, which is a longer distance than horizontally or vertically joined pixels), . Elongation (measure of particle elongation given as a number between 0 and 1. If equal to 1, the particle bounding box is square. As the elongation decreases from 1, the particle becomes more elongated), . Ext_vector (extremal vector), . Major Axis (the length of a major axis of a smallest ellipse encompassing an object), . Minor Axis (the length of a minor axis of a smallest ellipse encompassing an object), . Partial (indicates if the particle extends beyond the field of view), . Perimeter Points (points that make up a particle perimeter), . Roundness [(4(pi) x area)/perimeter(squared)) the result is a measure of object roundness, or compactness, given as a value between 0 and 1. The greater the ratio, the rounder the object.], . Thin in center (determines if an object becomes thin in the center, (figure-eight-shaped), . Theta (orientation of the major axis), . Smoothness and color metrics for each component (red, green, blue
Motion Alters Color Appearance
Hong, Sang-Wook; Kang, Min-Suk
2016-01-01
Chromatic induction compellingly demonstrates that chromatic context as well as spectral lights reflected from an object determines its color appearance. Here, we show that when one colored object moves around an identical stationary object, the perceived saturation of the stationary object decreases dramatically whereas the saturation of the moving object increases. These color appearance shifts in the opposite directions suggest that normalization induced by the object’s motion may mediate the shift in color appearance. We ruled out other plausible alternatives such as local adaptation, attention, and transient neural responses that could explain the color shift without assuming interaction between color and motion processing. These results demonstrate that the motion of an object affects both its own color appearance and the color appearance of a nearby object, suggesting a tight coupling between color and motion processing. PMID:27824098
Hierarchical layered and semantic-based image segmentation using ergodicity map
NASA Astrophysics Data System (ADS)
Yadegar, Jacob; Liu, Xiaoqing
2010-04-01
Image segmentation plays a foundational role in image understanding and computer vision. Although great strides have been made and progress achieved on automatic/semi-automatic image segmentation algorithms, designing a generic, robust, and efficient image segmentation algorithm is still challenging. Human vision is still far superior compared to computer vision, especially in interpreting semantic meanings/objects in images. We present a hierarchical/layered semantic image segmentation algorithm that can automatically and efficiently segment images into hierarchical layered/multi-scaled semantic regions/objects with contextual topological relationships. The proposed algorithm bridges the gap between high-level semantics and low-level visual features/cues (such as color, intensity, edge, etc.) through utilizing a layered/hierarchical ergodicity map, where ergodicity is computed based on a space filling fractal concept and used as a region dissimilarity measurement. The algorithm applies a highly scalable, efficient, and adaptive Peano- Cesaro triangulation/tiling technique to decompose the given image into a set of similar/homogenous regions based on low-level visual cues in a top-down manner. The layered/hierarchical ergodicity map is built through a bottom-up region dissimilarity analysis. The recursive fractal sweep associated with the Peano-Cesaro triangulation provides efficient local multi-resolution refinement to any level of detail. The generated binary decomposition tree also provides efficient neighbor retrieval mechanisms for contextual topological object/region relationship generation. Experiments have been conducted within the maritime image environment where the segmented layered semantic objects include the basic level objects (i.e. sky/land/water) and deeper level objects in the sky/land/water surfaces. Experimental results demonstrate the proposed algorithm has the capability to robustly and efficiently segment images into layered semantic objects
Pfau, Maximilian; Lindner, Moritz; Müller, Philipp L; Birtel, Johannes; Finger, Robert P; Harmening, Wolf M; Fleckenstein, Monika; Holz, Frank G; Schmitz-Valckenberg, Steffen
2017-05-01
To determine the effective dynamic range (EDR), retest reliability, and number of discriminable steps (DS) for mesopic and dark-adapted two-color fundus-controlled perimetry (FCP) using the S-MAIA (Scotopic-Macular Integrity Assessment) "micro-perimeter." In this prospective cross-sectional study, each of the 52 eyes of 52 subjects with various macular diseases (mean age 62.0 ± 16.9 years; range, 19.1-90.1 years) underwent duplicate mesopic (achromatic stimuli, 400-800 nm), dark-adapted cyan (505 nm), and dark-adapted red (627 nm) FCP using a grid of 61 stimuli covering 18° of the central retina. The EDR, the number of DS, and the retest reliability for point-wise sensitivity (PWS) were analyzed. The effects of fixation stability, sensitivity, and age on retest reliability were examined using mixed-effects models. The EDR was 10 to 30 dB with five DS for mesopic and 4 to 17 dB with four DS for dark-adapted cyan and red testing. PWS retest reliability was good among all three types of retinal sensitivity assessments (coefficient of repeatability ±5.79, ±4.72, and ±4.77 dB, respectively) and did not depend on fixation stability or age. PWS had no effect on retest variability in dark-adapted cyan and dark-adapted red testing but had a minor effect in mesopic testing. Combined mesopic and dark-adapted two-color FCP allows for reliable topographic testing of cone and rod function in patients with various macular diseases with and without foveal fixation. Retest reliability is homogeneous across eccentricities and various degrees of scotoma depth, including zones at risk for disease progression. These reliability estimates can serve for the design of future clinical trials.
A dual-adaptive support-based stereo matching algorithm
NASA Astrophysics Data System (ADS)
Zhang, Yin; Zhang, Yun
2017-07-01
Many stereo matching algorithms use fixed color thresholds and a rigid cross skeleton to segment supports (viz., Cross method), which, however, does not work well for different images. To address this issue, this paper proposes a novel dual adaptive support (viz., DAS)-based stereo matching method, which uses both appearance and shape information of a local region to segment supports automatically, and, then, integrates the DAS-based cost aggregation with the absolute difference plus census transform cost, scanline optimization and disparity refinement to develop a stereo matching system. The performance of the DAS method is also evaluated in the Middlebury benchmark and by comparing with the Cross method. The results show that the average error for the DAS method 25.06% lower than that for the Cross method, indicating that the proposed method is more accurate, with fewer parameters and suitable for parallel computing.
A novel quantum steganography scheme for color images
NASA Astrophysics Data System (ADS)
Li, Panchi; Liu, Xiande
In quantum image steganography, embedding capacity and security are two important issues. This paper presents a novel quantum steganography scheme using color images as cover images. First, the secret information is divided into 3-bit segments, and then each 3-bit segment is embedded into the LSB of one color pixel in the cover image according to its own value and using Gray code mapping rules. Extraction is the inverse of embedding. We designed the quantum circuits that implement the embedding and extracting process. The simulation results on a classical computer show that the proposed scheme outperforms several other existing schemes in terms of embedding capacity and security.
Unsupervised object segmentation with a hybrid graph model (HGM).
Liu, Guangcan; Lin, Zhouchen; Yu, Yong; Tang, Xiaoou
2010-05-01
In this work, we address the problem of performing class-specific unsupervised object segmentation, i.e., automatic segmentation without annotated training images. Object segmentation can be regarded as a special data clustering problem where both class-specific information and local texture/color similarities have to be considered. To this end, we propose a hybrid graph model (HGM) that can make effective use of both symmetric and asymmetric relationship among samples. The vertices of a hybrid graph represent the samples and are connected by directed edges and/or undirected ones, which represent the asymmetric and/or symmetric relationship between them, respectively. When applied to object segmentation, vertices are superpixels, the asymmetric relationship is the conditional dependence of occurrence, and the symmetric relationship is the color/texture similarity. By combining the Markov chain formed by the directed subgraph and the minimal cut of the undirected subgraph, the object boundaries can be determined for each image. Using the HGM, we can conveniently achieve simultaneous segmentation and recognition by integrating both top-down and bottom-up information into a unified process. Experiments on 42 object classes (9,415 images in total) show promising results.
Image segmentation and 3D visualization for MRI mammography
NASA Astrophysics Data System (ADS)
Li, Lihua; Chu, Yong; Salem, Angela F.; Clark, Robert A.
2002-05-01
MRI mammography has a number of advantages, including the tomographic, and therefore three-dimensional (3-D) nature, of the images. It allows the application of MRI mammography to breasts with dense tissue, post operative scarring, and silicon implants. However, due to the vast quantity of images and subtlety of difference in MR sequence, there is a need for reliable computer diagnosis to reduce the radiologist's workload. The purpose of this work was to develop automatic breast/tissue segmentation and visualization algorithms to aid physicians in detecting and observing abnormalities in breast. Two segmentation algorithms were developed: one for breast segmentation, the other for glandular tissue segmentation. In breast segmentation, the MRI image is first segmented using an adaptive growing clustering method. Two tracing algorithms were then developed to refine the breast air and chest wall boundaries of breast. The glandular tissue segmentation was performed using an adaptive thresholding method, in which the threshold value was spatially adaptive using a sliding window. The 3D visualization of the segmented 2D slices of MRI mammography was implemented under IDL environment. The breast and glandular tissue rendering, slicing and animation were displayed.
Efficient threshold for volumetric segmentation
NASA Astrophysics Data System (ADS)
Burdescu, Dumitru D.; Brezovan, Marius; Stanescu, Liana; Stoica Spahiu, Cosmin; Ebanca, Daniel
2015-07-01
Image segmentation plays a crucial role in effective understanding of digital images. However, the research on the existence of general purpose segmentation algorithm that suits for variety of applications is still very much active. Among the many approaches in performing image segmentation, graph based approach is gaining popularity primarily due to its ability in reflecting global image properties. Volumetric image segmentation can simply result an image partition composed by relevant regions, but the most fundamental challenge in segmentation algorithm is to precisely define the volumetric extent of some object, which may be represented by the union of multiple regions. The aim in this paper is to present a new method to detect visual objects from color volumetric images and efficient threshold. We present a unified framework for volumetric image segmentation and contour extraction that uses a virtual tree-hexagonal structure defined on the set of the image voxels. The advantage of using a virtual tree-hexagonal network superposed over the initial image voxels is that it reduces the execution time and the memory space used, without losing the initial resolution of the image.
Houck, M R; Hoffman, J E
1986-05-01
According to feature-integration theory (Treisman & Gelade, 1980), separable features such as color and shape exist in separate maps in preattentive vision and can be integrated only through the use of spatial attention. Many perceptual aftereffects, however, which are also assumed to reflect the features available in preattentive vision, are sensitive to conjunctions of features. One possible resolution of these views holds that adaptation to conjunctions depends on spatial attention. We tested this proposition by presenting observers with gratings varying in color and orientation. The resulting McCollough aftereffects were independent of whether the adaptation stimuli were presented inside or outside of the focus of spatial attention. Therefore, color and shape appear to be conjoined preattentively, when perceptual aftereffects are used as the measure. These same stimuli, however, appeared to be separable in two additional experiments that required observers to search for gratings of a specified color and orientation. These results show that different experimental procedures may be tapping into different stages of preattentive vision.
Rebouças Filho, Pedro Pedrosa; Cortez, Paulo César; da Silva Barros, Antônio C; C Albuquerque, Victor Hugo; R S Tavares, João Manuel
2017-01-01
The World Health Organization estimates that 300 million people have asthma, 210 million people have Chronic Obstructive Pulmonary Disease (COPD), and, according to WHO, COPD will become the third major cause of death worldwide in 2030. Computational Vision systems are commonly used in pulmonology to address the task of image segmentation, which is essential for accurate medical diagnoses. Segmentation defines the regions of the lungs in CT images of the thorax that must be further analyzed by the system or by a specialist physician. This work proposes a novel and powerful technique named 3D Adaptive Crisp Active Contour Method (3D ACACM) for the segmentation of CT lung images. The method starts with a sphere within the lung to be segmented that is deformed by forces acting on it towards the lung borders. This process is performed iteratively in order to minimize an energy function associated with the 3D deformable model used. In the experimental assessment, the 3D ACACM is compared against three approaches commonly used in this field: the automatic 3D Region Growing, the level-set algorithm based on coherent propagation and the semi-automatic segmentation by an expert using the 3D OsiriX toolbox. When applied to 40 CT scans of the chest the 3D ACACM had an average F-measure of 99.22%, revealing its superiority and competency to segment lungs in CT images. Copyright © 2016 Elsevier B.V. All rights reserved.
Color correction pipeline optimization for digital cameras
NASA Astrophysics Data System (ADS)
Bianco, Simone; Bruna, Arcangelo R.; Naccari, Filippo; Schettini, Raimondo
2013-04-01
The processing pipeline of a digital camera converts the RAW image acquired by the sensor to a representation of the original scene that should be as faithful as possible. There are mainly two modules responsible for the color-rendering accuracy of a digital camera: the former is the illuminant estimation and correction module, and the latter is the color matrix transformation aimed to adapt the color response of the sensor to a standard color space. These two modules together form what may be called the color correction pipeline. We design and test new color correction pipelines that exploit different illuminant estimation and correction algorithms that are tuned and automatically selected on the basis of the image content. Since the illuminant estimation is an ill-posed problem, illuminant correction is not error-free. An adaptive color matrix transformation module is optimized, taking into account the behavior of the first module in order to alleviate the amplification of color errors. The proposed pipelines are tested on a publicly available dataset of RAW images. Experimental results show that exploiting the cross-talks between the modules of the pipeline can lead to a higher color-rendition accuracy.
Lee, Dong-Youn; Kim, Cho-Rok; Park, Ji-Hye; Lee, Joo-Heung
2011-08-01
In vitiligo, the melanocyte of the hair follicle is one of the major sources for repigmentation. Segmental vitiligo seems to be often associated with white hairs. However, in the case of small vellus hairs, it is often difficult or impossible to detect hair color. Thus, the real incidence of leukotrichia in segmental vitiligo has not been known. In this study, we investigated the existence of white hairs in the lesional skin of 82 patients with segmental vitiligo. When it was difficult to detect hair color with the naked eye or a magnifier, a digital microscope with 30× magnification was used. Interestingly, all 82 patients showed leukotrichia in segmental vitiligo independent of age and disease duration. Some patients had more than 90% white hairs in the lesional skin, and they showed poor response to medical treatment. Based on our results, a very high percentage of patients with segmental vitiligo may be associated with leukotrichia. Many white hairs in segmental vitiligo may contribute to the lack of response with medical treatment. The examination of hair color with a digital microscope may be very useful for the prediction of treatment outcome and decision of treatment modalities. © 2011 The International Society of Dermatology.
DOE Office of Scientific and Technical Information (OSTI.GOV)
Derksen, A; Koenig, L; Heldmann, S
Purpose: To improve results of deformable image registration (DIR) in adaptive radiotherapy for large bladder deformations in CT/CBCT pelvis imaging. Methods: A variational multi-modal DIR algorithm is incorporated in a joint iterative scheme, alternating between segmentation based bladder matching and registration. Using an initial DIR to propagate the bladder contour to the CBCT, in a segmentation step the contour is improved by discrete image gradient sampling along all surface normals and adapting the delineation to match the location of each maximum (with a search range of +−5/2mm at the superior/inferior bladder side and step size of 0.5mm). An additional graph-cutmore » based constraint limits the maximum difference between neighboring points. This improved contour is utilized in a subsequent DIR with a surface matching constraint. By calculating an euclidean distance map of the improved contour surface, the new constraint enforces the DIR to map each point of the original contour onto the improved contour. The resulting deformation is then used as a starting guess to compute a deformation update, which can again be used for the next segmentation step. The result is a dense deformation, able to capture much larger bladder deformations. The new method is evaluated on ten CT/CBCT male pelvis datasets, calculating Dice similarity coefficients (DSC) between the final propagated bladder contour and a manually delineated gold standard on the CBCT image. Results: Over all ten cases, an average DSC of 0.93±0.03 is achieved on the bladder. Compared with the initial DIR (0.88±0.05), the DSC is equal (2 cases) or improved (8 cases). Additionally, DSC accuracy of femoral bones (0.94±0.02) was not affected. Conclusion: The new approach shows that using the presented alternating segmentation/registration approach, the results of bladder DIR in the pelvis region can be greatly improved, especially for cases with large variations in bladder volume. Fraunhofer MEVIS
Gao, Wei-Wei; Shen, Jian-Xin; Wang, Yu-Liang; Liang, Chun; Zuo, Jing
2013-02-01
In order to automatically detect hemorrhages in fundus images, and develop an automated diabetic retinopathy screening system, a novel algorithm named locally adaptive region growing based on multi-template matching was established and studied. Firstly, spectral signature of major anatomical structures in fundus was studied, so that the right channel among RGB channels could be selected for different segmentation objects. Secondly, the fundus image was preprocessed by means of HSV brightness correction and contrast limited adaptive histogram equalization (CLAHE). Then, seeds of region growing were founded out by removing optic disc and vessel from the resulting image of normalized cross-correlation (NCC) template matching on the previous preprocessed image with several templates. Finally, locally adaptive region growing segmentation was used to find out the exact contours of hemorrhages, and the automated detection of the lesions was accomplished. The approach was tested on 90 different resolution fundus images with variable color, brightness and quality. Results suggest that the approach could fast and effectively detect hemorrhages in fundus images, and it is stable and robust. As a result, the approach can meet the clinical demands.
SU-C-9A-01: Parameter Optimization in Adaptive Region-Growing for Tumor Segmentation in PET
DOE Office of Scientific and Technical Information (OSTI.GOV)
Tan, S; Huazhong University of Science and Technology, Wuhan, Hubei; Xue, M
Purpose: To design a reliable method to determine the optimal parameter in the adaptive region-growing (ARG) algorithm for tumor segmentation in PET. Methods: The ARG uses an adaptive similarity criterion m - fσ ≤ I-PET ≤ m + fσ, so that a neighboring voxel is appended to the region based on its similarity to the current region. When increasing the relaxing factor f (f ≥ 0), the resulting volumes monotonically increased with a sharp increase when the region just grew into the background. The optimal f that separates the tumor from the background is defined as the first point withmore » the local maximum curvature on an Error function fitted to the f-volume curve. The ARG was tested on a tumor segmentation Benchmark that includes ten lung cancer patients with 3D pathologic tumor volume as ground truth. For comparison, the widely used 42% and 50% SUVmax thresholding, Otsu optimal thresholding, Active Contours (AC), Geodesic Active Contours (GAC), and Graph Cuts (GC) methods were tested. The dice similarity index (DSI), volume error (VE), and maximum axis length error (MALE) were calculated to evaluate the segmentation accuracy. Results: The ARG provided the highest accuracy among all tested methods. Specifically, the ARG has an average DSI, VE, and MALE of 0.71, 0.29, and 0.16, respectively, better than the absolute 42% thresholding (DSI=0.67, VE= 0.57, and MALE=0.23), the relative 42% thresholding (DSI=0.62, VE= 0.41, and MALE=0.23), the absolute 50% thresholding (DSI=0.62, VE=0.48, and MALE=0.21), the relative 50% thresholding (DSI=0.48, VE=0.54, and MALE=0.26), OTSU (DSI=0.44, VE=0.63, and MALE=0.30), AC (DSI=0.46, VE= 0.85, and MALE=0.47), GAC (DSI=0.40, VE= 0.85, and MALE=0.46) and GC (DSI=0.66, VE= 0.54, and MALE=0.21) methods. Conclusions: The results suggest that the proposed method reliably identified the optimal relaxing factor in ARG for tumor segmentation in PET. This work was supported in part by National Cancer Institute Grant R01 CA172638
Sample Training Based Wildfire Segmentation by 2D Histogram θ-Division with Minimum Error
Dong, Erqian; Sun, Mingui; Jia, Wenyan; Zhang, Dengyi; Yuan, Zhiyong
2013-01-01
A novel wildfire segmentation algorithm is proposed with the help of sample training based 2D histogram θ-division and minimum error. Based on minimum error principle and 2D color histogram, the θ-division methods were presented recently, but application of prior knowledge on them has not been explored. For the specific problem of wildfire segmentation, we collect sample images with manually labeled fire pixels. Then we define the probability function of error division to evaluate θ-division segmentations, and the optimal angle θ is determined by sample training. Performances in different color channels are compared, and the suitable channel is selected. To further improve the accuracy, the combination approach is presented with both θ-division and other segmentation methods such as GMM. Our approach is tested on real images, and the experiments prove its efficiency for wildfire segmentation. PMID:23878526
Ramsey, David J; Sunness, Janet S; Malviya, Poorva; Applegate, Carol; Hager, Gregory D; Handa, James T
2014-07-01
To develop a computer-based image segmentation method for standardizing the quantification of geographic atrophy (GA). The authors present an automated image segmentation method based on the fuzzy c-means clustering algorithm for the detection of GA lesions. The method is evaluated by comparing computerized segmentation against outlines of GA drawn by an expert grader for a longitudinal series of fundus autofluorescence images with paired 30° color fundus photographs for 10 patients. The automated segmentation method showed excellent agreement with an expert grader for fundus autofluorescence images, achieving a performance level of 94 ± 5% sensitivity and 98 ± 2% specificity on a per-pixel basis for the detection of GA area, but performed less well on color fundus photographs with a sensitivity of 47 ± 26% and specificity of 98 ± 2%. The segmentation algorithm identified 75 ± 16% of the GA border correctly in fundus autofluorescence images compared with just 42 ± 25% for color fundus photographs. The results of this study demonstrate a promising computerized segmentation method that may enhance the reproducibility of GA measurement and provide an objective strategy to assist an expert in the grading of images.
NASA Astrophysics Data System (ADS)
Diaz, Kristians; Castañeda, Benjamín; Miranda, César; Lavarello, Roberto; Llanos, Alejandro
2010-03-01
We developed a protocol for the acquisition of digital images and an algorithm for a color-based automatic segmentation of cutaneous lesions of Leishmaniasis. The protocol for image acquisition provides control over the working environment to manipulate brightness, lighting and undesirable shadows on the injury using indirect lighting. Also, this protocol was used to accurately calculate the area of the lesion expressed in mm2 even in curved surfaces by combining the information from two consecutive images. Different color spaces were analyzed and compared using ROC curves in order to determine the color layer with the highest contrast between the background and the wound. The proposed algorithm is composed of three stages: (1) Location of the wound determined by threshold and mathematical morphology techniques to the H layer of the HSV color space, (2) Determination of the boundaries of the wound by analyzing the color characteristics in the YIQ space based on masks (for the wound and the background) estimated from the first stage, and (3) Refinement of the calculations obtained on the previous stages by using the discrete dynamic contours algorithm. The segmented regions obtained with the algorithm were compared with manual segmentations made by a medical specialist. Broadly speaking, our results support that color provides useful information during segmentation and measurement of wounds of cutaneous Leishmaniasis. Results from ten images showed 99% specificity, 89% sensitivity, and 98% accuracy.
Zett, Claudio; Stina, Deborah M Rosa; Kato, Renata Tiemi; Novais, Eduardo Amorim; Allemann, Norma
2018-04-01
The aim of this study is to perform imaging of irises of different colors using spectral domain anterior segment optical coherence tomography angiography (AS-OCTA) and iris fluorescein angiography (IFA) and compare their effectiveness in examining iris vasculature. This is a cross-sectional observational clinical study. Patients with no vascular iris alterations and different pigmentation levels were recruited. Participants were imaged using OCTA adapted with an anterior segment lens and IFA with a confocal scanning laser ophthalmoscope (cSLO) adapted with an anterior segment lens. AS-OCTA and IFA images were then compared. Two blinded readers classified iris pigmentation and compared the percentage of visible vessels between OCTA and IFA images. Twenty eyes of 10 patients with different degrees of iris pigmentation were imaged using AS-OCTA and IFA. Significantly more visible iris vessels were observed using OCTA than using FA (W = 5.22; p < 0.001). Iris pigmentation was negatively correlated to the percentage of visible vessels in both imaging methods (OCTA, rho = - 0.73, p < 0.001; IFA, rho = - 0.77, p < 0.001). Unlike FA, AS-OCTA could not detect leakage of dye, delay, or impregnation. Nystagmus and inadequate fixation along with motion artifacts resulted in lower quality images in AS-OCTA than in IFA. AS-OCTA is a new imaging modality which allows analysis of iris vasculature. In both AS-OCTA and IFA, iris pigmentation caused vasculature imaging blockage, but AS-OCTA provided more detailed iris vasculature images than IFA. Additional studies including different iris pathologies are needed to determine the most optimal scanning parameters in OCTA of the anterior segment.
Color transfer between high-dynamic-range images
NASA Astrophysics Data System (ADS)
Hristova, Hristina; Cozot, Rémi; Le Meur, Olivier; Bouatouch, Kadi
2015-09-01
Color transfer methods alter the look of a source image with regards to a reference image. So far, the proposed color transfer methods have been limited to low-dynamic-range (LDR) images. Unlike LDR images, which are display-dependent, high-dynamic-range (HDR) images contain real physical values of the world luminance and are able to capture high luminance variations and finest details of real world scenes. Therefore, there exists a strong discrepancy between the two types of images. In this paper, we bridge the gap between the color transfer domain and the HDR imagery by introducing HDR extensions to LDR color transfer methods. We tackle the main issues of applying a color transfer between two HDR images. First, to address the nature of light and color distributions in the context of HDR imagery, we carry out modifications of traditional color spaces. Furthermore, we ensure high precision in the quantization of the dynamic range for histogram computations. As image clustering (based on light and colors) proved to be an important aspect of color transfer, we analyze it and adapt it to the HDR domain. Our framework has been applied to several state-of-the-art color transfer methods. Qualitative experiments have shown that results obtained with the proposed adaptation approach exhibit less artifacts and are visually more pleasing than results obtained when straightforwardly applying existing color transfer methods to HDR images.
HDR imaging and color constancy: two sides of the same coin?
NASA Astrophysics Data System (ADS)
McCann, John J.
2011-01-01
At first, we think that High Dynamic Range (HDR) imaging is a technique for improved recordings of scene radiances. Many of us think that human color constancy is a variation of a camera's automatic white balance algorithm. However, on closer inspection, glare limits the range of light we can detect in cameras and on retinas. All scene regions below middle gray are influenced, more or less, by the glare from the bright scene segments. Instead of accurate radiance reproduction, HDR imaging works well because it preserves the details in the scene's spatial contrast. Similarly, on closer inspection, human color constancy depends on spatial comparisons that synthesize appearances from all the scene segments. Can spatial image processing play similar principle roles in both HDR imaging and color constancy?
Khan, Arif Ul Maula; Torelli, Angelo; Wolf, Ivo; Gretz, Norbert
2018-05-08
In biological assays, automated cell/colony segmentation and counting is imperative owing to huge image sets. Problems occurring due to drifting image acquisition conditions, background noise and high variation in colony features in experiments demand a user-friendly, adaptive and robust image processing/analysis method. We present AutoCellSeg (based on MATLAB) that implements a supervised automatic and robust image segmentation method. AutoCellSeg utilizes multi-thresholding aided by a feedback-based watershed algorithm taking segmentation plausibility criteria into account. It is usable in different operation modes and intuitively enables the user to select object features interactively for supervised image segmentation method. It allows the user to correct results with a graphical interface. This publicly available tool outperforms tools like OpenCFU and CellProfiler in terms of accuracy and provides many additional useful features for end-users.
Human preference for individual colors
NASA Astrophysics Data System (ADS)
Palmer, Stephen E.; Schloss, Karen B.
2010-02-01
Color preference is an important aspect of human behavior, but little is known about why people like some colors more than others. Recent results from the Berkeley Color Project (BCP) provide detailed measurements of preferences among 32 chromatic colors as well as other relevant aspects of color perception. We describe the fit of several color preference models, including ones based on cone outputs, color-emotion associations, and Palmer and Schloss's ecological valence theory. The ecological valence theory postulates that color serves an adaptive "steering' function, analogous to taste preferences, biasing organisms to approach advantageous objects and avoid disadvantageous ones. It predicts that people will tend to like colors to the extent that they like the objects that are characteristically that color, averaged over all such objects. The ecological valence theory predicts 80% of the variance in average color preference ratings from the Weighted Affective Valence Estimates (WAVEs) of correspondingly colored objects, much more variance than any of the other models. We also describe how hue preferences for single colors differ as a function of gender, expertise, culture, social institutions, and perceptual experience.
Adaptive segmentation of nuclei in H&S stained tendon microscopy
NASA Astrophysics Data System (ADS)
Chuang, Bo-I.; Wu, Po-Ting; Hsu, Jian-Han; Jou, I.-Ming; Su, Fong-Chin; Sun, Yung-Nien
2015-12-01
Tendiopathy is a popular clinical issue in recent years. In most cases like trigger finger or tennis elbow, the pathology change can be observed under H and E stained tendon microscopy. However, the qualitative analysis is too subjective and thus the results heavily depend on the observers. We develop an automatic segmentation procedure which segments and counts the nuclei in H and E stained tendon microscopy fast and precisely. This procedure first determines the complexity of images and then segments the nuclei from the image. For the complex images, the proposed method adopts sampling-based thresholding to segment the nuclei. While for the simple images, the Laplacian-based thresholding is employed to re-segment the nuclei more accurately. In the experiments, the proposed method is compared with the experts outlined results. The nuclei number of proposed method is closed to the experts counted, and the processing time of proposed method is much faster than the experts'.
AdOn HDP-HMM: An Adaptive Online Model for Segmentation and Classification of Sequential Data.
Bargi, Ava; Xu, Richard Yi Da; Piccardi, Massimo
2017-09-21
Recent years have witnessed an increasing need for the automated classification of sequential data, such as activities of daily living, social media interactions, financial series, and others. With the continuous flow of new data, it is critical to classify the observations on-the-fly and without being limited by a predetermined number of classes. In addition, a model should be able to update its parameters in response to a possible evolution in the distributions of the classes. This compelling problem, however, does not seem to have been adequately addressed in the literature, since most studies focus on offline classification over predefined class sets. In this paper, we present a principled solution for this problem based on an adaptive online system leveraging Markov switching models and hierarchical Dirichlet process priors. This adaptive online approach is capable of classifying the sequential data over an unlimited number of classes while meeting the memory and delay constraints typical of streaming contexts. In this paper, we introduce an adaptive ''learning rate'' that is responsible for balancing the extent to which the model retains its previous parameters or adapts to new observations. Experimental results on stationary and evolving synthetic data and two video data sets, TUM Assistive Kitchen and collated Weizmann, show a remarkable performance in terms of segmentation and classification, particularly for sequences from evolutionary distributions and/or those containing previously unseen classes.
Automatic and quantitative measurement of collagen gel contraction using model-guided segmentation
NASA Astrophysics Data System (ADS)
Chen, Hsin-Chen; Yang, Tai-Hua; Thoreson, Andrew R.; Zhao, Chunfeng; Amadio, Peter C.; Sun, Yung-Nien; Su, Fong-Chin; An, Kai-Nan
2013-08-01
Quantitative measurement of collagen gel contraction plays a critical role in the field of tissue engineering because it provides spatial-temporal assessment (e.g., changes of gel area and diameter during the contraction process) reflecting the cell behavior and tissue material properties. So far the assessment of collagen gels relies on manual segmentation, which is time-consuming and suffers from serious intra- and inter-observer variability. In this study, we propose an automatic method combining various image processing techniques to resolve these problems. The proposed method first detects the maximal feasible contraction range of circular references (e.g., culture dish) and avoids the interference of irrelevant objects in the given image. Then, a three-step color conversion strategy is applied to normalize and enhance the contrast between the gel and background. We subsequently introduce a deformable circular model which utilizes regional intensity contrast and circular shape constraint to locate the gel boundary. An adaptive weighting scheme was employed to coordinate the model behavior, so that the proposed system can overcome variations of gel boundary appearances at different contraction stages. Two measurements of collagen gels (i.e., area and diameter) can readily be obtained based on the segmentation results. Experimental results, including 120 gel images for accuracy validation, showed high agreement between the proposed method and manual segmentation with an average dice similarity coefficient larger than 0.95. The results also demonstrated obvious improvement in gel contours obtained by the proposed method over two popular, generic segmentation methods.
Moving vehicles segmentation based on Gaussian motion model
NASA Astrophysics Data System (ADS)
Zhang, Wei; Fang, Xiang Z.; Lin, Wei Y.
2005-07-01
Moving objects segmentation is a challenge in computer vision. This paper focuses on the segmentation of moving vehicles in dynamic scene. We analyses the psychology of human vision and present a framework for segmenting moving vehicles in the highway. The proposed framework consists of two parts. Firstly, we propose an adaptive background update method in which the background is updated according to the change of illumination conditions and thus can adapt to the change of illumination sensitively. Secondly, we construct a Gaussian motion model to segment moving vehicles, in which the motion vectors of the moving pixels are modeled as a Gaussian model and an on-line EM algorithm is used to update the model. The Gaussian distribution of the adaptive model is elevated to determine which moving vectors result from moving vehicles and which from other moving objects such as waving trees. Finally, the pixels with motion vector result from the moving vehicles are segmented. Experimental results of several typical scenes show that the proposed model can detect the moving vehicles correctly and is immune from influence of the moving objects caused by the waving trees and the vibration of camera.
Improved compression technique for multipass color printers
NASA Astrophysics Data System (ADS)
Honsinger, Chris
1998-01-01
A multipass color printer prints a color image by printing one color place at a time in a prescribed order, e.g., in a four-color systems, the cyan plane may be printed first, the magenta next, and so on. It is desirable to discard the data related to each color plane once it has been printed, so that data from the next print may be downloaded. In this paper, we present a compression scheme that allows the release of a color plane memory, but still takes advantage of the correlation between the color planes. The compression scheme is based on a block adaptive technique for decorrelating the color planes followed by a spatial lossy compression of the decorrelated data. A preferred method of lossy compression is the DCT-based JPEG compression standard, as it is shown that the block adaptive decorrelation operations can be efficiently performed in the DCT domain. The result of the compression technique are compared to that of using JPEG on RGB data without any decorrelating transform. In general, the technique is shown to improve the compression performance over a practical range of compression ratios by at least 30 percent in all images, and up to 45 percent in some images.
Easy-interactive and quick psoriasis lesion segmentation
NASA Astrophysics Data System (ADS)
Ma, Guoli; He, Bei; Yang, Wenming; Shu, Chang
2013-12-01
This paper proposes an interactive psoriasis lesion segmentation algorithm based on Gaussian Mixture Model (GMM). Psoriasis is an incurable skin disease and affects large population in the world. PASI (Psoriasis Area and Severity Index) is the gold standard utilized by dermatologists to monitor the severity of psoriasis. Computer aid methods of calculating PASI are more objective and accurate than human visual assessment. Psoriasis lesion segmentation is the basis of the whole calculating. This segmentation is different from the common foreground/background segmentation problems. Our algorithm is inspired by GrabCut and consists of three main stages. First, skin area is extracted from the background scene by transforming the RGB values into the YCbCr color space. Second, a rough segmentation of normal skin and psoriasis lesion is given. This is an initial segmentation given by thresholding a single gaussian model and the thresholds are adjustable, which enables user interaction. Third, two GMMs, one for the initial normal skin and one for psoriasis lesion, are built to refine the segmentation. Experimental results demonstrate the effectiveness of the proposed algorithm.
Very-long-term and short-term chromatic adaptation: are their influences cumulative?
Belmore, Suzanne C; Shevell, Steven K
2011-02-09
Very-long-term (VLT) chromatic adaptation results from exposure to an altered chromatic environment for days or weeks. Color shifts from VLT adaptation are observed hours or days after leaving the altered environment. Short-term chromatic adaptation, on the other hand, results from exposure for a few minutes or less, with color shifts measured within seconds or a few minutes after the adapting light is extinguished; recovery to the pre-adapted state is complete in less than an hour. Here, both types of adaptation were combined. All adaptation was to reddish-appearing long-wavelength light. Shifts in unique yellow were measured following adaptation. Previous studies demonstrate shifts in unique yellow due to VLT chromatic adaptation, but shifts from short-term chromatic adaptation to comparable adapting light can be far greater than from VLT adaptation. The question considered here is whether the color shifts from VLT adaptation are cumulative with large shifts from short-term adaptation or, alternatively, does simultaneous short-term adaptation eliminate color shifts caused by VLT adaptation. The results show the color shifts from VLT and short-term adaptation together are cumulative, which indicates that both short-term and very-long-term chromatic adaptation affect color perception during natural viewing. Copyright © 2010 Elsevier Ltd. All rights reserved.
The Constancy of Colored After-Images
Zeki, Semir; Cheadle, Samuel; Pepper, Joshua; Mylonas, Dimitris
2017-01-01
We undertook psychophysical experiments to determine whether the color of the after-image produced by viewing a colored patch which is part of a complex multi-colored scene depends on the wavelength-energy composition of the light reflected from that patch. Our results show that it does not. The after-image, just like the color itself, depends on the ratio of light of different wavebands reflected from it and its surrounds. Hence, traditional accounts of after-images as being the result of retinal adaptation or the perceptual result of physiological opponency, are inadequate. We propose instead that the color of after-images is generated after colors themselves are generated in the visual brain. PMID:28539878
Lee, Joshua K; Nordahl, Christine W; Amaral, David G; Lee, Aaron; Solomon, Marjorie; Ghetti, Simona
2015-11-01
Volumetric assessments of the hippocampus and other brain structures during childhood provide useful indices of brain development and correlates of cognitive functioning in typically and atypically developing children. Automated methods such as FreeSurfer promise efficient and replicable segmentation, but may include errors which are avoided by trained manual tracers. A recently devised automated correction tool that uses a machine learning algorithm to remove systematic errors, the Automatic Segmentation Adapter Tool (ASAT), was capable of substantially improving the accuracy of FreeSurfer segmentations in an adult sample [Wang et al., 2011], but the utility of ASAT has not been examined in pediatric samples. In Study 1, the validity of FreeSurfer and ASAT corrected hippocampal segmentations were examined in 20 typically developing children and 20 children with autism spectrum disorder aged 2 and 3 years. We showed that while neither FreeSurfer nor ASAT accuracy differed by disorder or age, the accuracy of ASAT corrected segmentations were substantially better than FreeSurfer segmentations in every case, using as few as 10 training examples. In Study 2, we applied ASAT to 89 typically developing children aged 2 to 4 years to examine relations between hippocampal volume, age, sex, and expressive language. Girls had smaller hippocampi overall, and in left hippocampus this difference was larger in older than younger girls. Expressive language ability was greater in older children, and this difference was larger in those with larger hippocampi, bilaterally. Overall, this research shows that ASAT is highly reliable and useful to examinations relating behavior to hippocampal structure. © 2015 Wiley Periodicals, Inc.
DOE Office of Scientific and Technical Information (OSTI.GOV)
Lu, Y; Chen, I; Kashani, R
Purpose: In MRI-guided online adaptive radiation therapy, re-contouring of bowel is time-consuming and can impact the overall time of patients on table. The study aims to auto-segment bowel on volumetric MR images by using an interactive multi-region labeling algorithm. Methods: 5 Patients with locally advanced pancreatic cancer underwent fractionated radiotherapy (18–25 fractions each, total 118 fractions) on an MRI-guided radiation therapy system with a 0.35 Tesla magnet and three Co-60 sources. At each fraction, a volumetric MR image of the patient was acquired when the patient was in the treatment position. An interactive two-dimensional multi-region labeling technique based on graphmore » cut solver was applied on several typical MRI images to segment the large bowel and small bowel, followed by a shape based contour interpolation for generating entire bowel contours along all image slices. The resulted contours were compared with the physician’s manual contouring by using metrics of Dice coefficient and Hausdorff distance. Results: Image data sets from the first 5 fractions of each patient were selected (total of 25 image data sets) for the segmentation test. The algorithm segmented the large and small bowel effectively and efficiently. All bowel segments were successfully identified, auto-contoured and matched with manual contours. The time cost by the algorithm for each image slice was within 30 seconds. For large bowel, the calculated Dice coefficients and Hausdorff distances (mean±std) were 0.77±0.07 and 13.13±5.01mm, respectively; for small bowel, the corresponding metrics were 0.73±0.08and 14.15±4.72mm, respectively. Conclusion: The preliminary results demonstrated the potential of the proposed algorithm in auto-segmenting large and small bowel on low field MRI images in MRI-guided adaptive radiation therapy. Further work will be focused on improving its segmentation accuracy and lessening human interaction.« less
USDA-ARS?s Scientific Manuscript database
Segmentation is the first step in image analysis to subdivide an image into meaningful regions. The segmentation result directly affects the subsequent image analysis. The objective of the research was to develop an automatic adjustable algorithm for segmentation of color images, using linear suppor...
Hassanein, Mohamed; El-Sheimy, Naser
2018-01-01
Over the last decade, the use of unmanned aerial vehicle (UAV) technology has evolved significantly in different applications as it provides a special platform capable of combining the benefits of terrestrial and aerial remote sensing. Therefore, such technology has been established as an important source of data collection for different precision agriculture (PA) applications such as crop health monitoring and weed management. Generally, these PA applications depend on performing a vegetation segmentation process as an initial step, which aims to detect the vegetation objects in collected agriculture fields’ images. The main result of the vegetation segmentation process is a binary image, where vegetations are presented in white color and the remaining objects are presented in black. Such process could easily be performed using different vegetation indexes derived from multispectral imagery. Recently, to expand the use of UAV imagery systems for PA applications, it was important to reduce the cost of such systems through using low-cost RGB cameras Thus, developing vegetation segmentation techniques for RGB images is a challenging problem. The proposed paper introduces a new vegetation segmentation methodology for low-cost UAV RGB images, which depends on using Hue color channel. The proposed methodology follows the assumption that the colors in any agriculture field image can be distributed into vegetation and non-vegetations colors. Therefore, four main steps are developed to detect five different threshold values using the hue histogram of the RGB image, these thresholds are capable to discriminate the dominant color, either vegetation or non-vegetation, within the agriculture field image. The achieved results for implementing the proposed methodology showed its ability to generate accurate and stable vegetation segmentation performance with mean accuracy equal to 87.29% and standard deviation as 12.5%. PMID:29670055
Automatic knee cartilage delineation using inheritable segmentation
NASA Astrophysics Data System (ADS)
Dries, Sebastian P. M.; Pekar, Vladimir; Bystrov, Daniel; Heese, Harald S.; Blaffert, Thomas; Bos, Clemens; van Muiswinkel, Arianne M. C.
2008-03-01
We present a fully automatic method for segmentation of knee joint cartilage from fat suppressed MRI. The method first applies 3-D model-based segmentation technology, which allows to reliably segment the femur, patella, and tibia by iterative adaptation of the model according to image gradients. Thin plate spline interpolation is used in the next step to position deformable cartilage models for each of the three bones with reference to the segmented bone models. After initialization, the cartilage models are fine adjusted by automatic iterative adaptation to image data based on gray value gradients. The method has been validated on a collection of 8 (3 left, 5 right) fat suppressed datasets and demonstrated the sensitivity of 83+/-6% compared to manual segmentation on a per voxel basis as primary endpoint. Gross cartilage volume measurement yielded an average error of 9+/-7% as secondary endpoint. For cartilage being a thin structure, already small deviations in distance result in large errors on a per voxel basis, rendering the primary endpoint a hard criterion.
Khan, Muhammad Burhan; Nisar, Humaira; Ng, Choon Aun; Yeap, Kim Ho; Lai, Koon Chun
2017-12-01
Image processing and analysis is an effective tool for monitoring and fault diagnosis of activated sludge (AS) wastewater treatment plants. The AS image comprise of flocs (microbial aggregates) and filamentous bacteria. In this paper, nine different approaches are proposed for image segmentation of phase-contrast microscopic (PCM) images of AS samples. The proposed strategies are assessed for their effectiveness from the perspective of microscopic artifacts associated with PCM. The first approach uses an algorithm that is based on the idea that different color space representation of images other than red-green-blue may have better contrast. The second uses an edge detection approach. The third strategy, employs a clustering algorithm for the segmentation and the fourth applies local adaptive thresholding. The fifth technique is based on texture-based segmentation and the sixth uses watershed algorithm. The seventh adopts a split-and-merge approach. The eighth employs Kittler's thresholding. Finally, the ninth uses a top-hat and bottom-hat filtering-based technique. The approaches are assessed, and analyzed critically with reference to the artifacts of PCM. Gold approximations of ground truth images are prepared to assess the segmentations. Overall, the edge detection-based approach exhibits the best results in terms of accuracy, and the texture-based algorithm in terms of false negative ratio. The respective scenarios are explained for suitability of edge detection and texture-based algorithms.
Local Competition-Based Superpixel Segmentation Algorithm in Remote Sensing
Liu, Jiayin; Tang, Zhenmin; Cui, Ying; Wu, Guoxing
2017-01-01
Remote sensing technologies have been widely applied in urban environments’ monitoring, synthesis and modeling. Incorporating spatial information in perceptually coherent regions, superpixel-based approaches can effectively eliminate the “salt and pepper” phenomenon which is common in pixel-wise approaches. Compared with fixed-size windows, superpixels have adaptive sizes and shapes for different spatial structures. Moreover, superpixel-based algorithms can significantly improve computational efficiency owing to the greatly reduced number of image primitives. Hence, the superpixel algorithm, as a preprocessing technique, is more and more popularly used in remote sensing and many other fields. In this paper, we propose a superpixel segmentation algorithm called Superpixel Segmentation with Local Competition (SSLC), which utilizes a local competition mechanism to construct energy terms and label pixels. The local competition mechanism leads to energy terms locality and relativity, and thus, the proposed algorithm is less sensitive to the diversity of image content and scene layout. Consequently, SSLC could achieve consistent performance in different image regions. In addition, the Probability Density Function (PDF), which is estimated by Kernel Density Estimation (KDE) with the Gaussian kernel, is introduced to describe the color distribution of superpixels as a more sophisticated and accurate measure. To reduce computational complexity, a boundary optimization framework is introduced to only handle boundary pixels instead of the whole image. We conduct experiments to benchmark the proposed algorithm with the other state-of-the-art ones on the Berkeley Segmentation Dataset (BSD) and remote sensing images. Results demonstrate that the SSLC algorithm yields the best overall performance, while the computation time-efficiency is still competitive. PMID:28604641
Local Competition-Based Superpixel Segmentation Algorithm in Remote Sensing.
Liu, Jiayin; Tang, Zhenmin; Cui, Ying; Wu, Guoxing
2017-06-12
Remote sensing technologies have been widely applied in urban environments' monitoring, synthesis and modeling. Incorporating spatial information in perceptually coherent regions, superpixel-based approaches can effectively eliminate the "salt and pepper" phenomenon which is common in pixel-wise approaches. Compared with fixed-size windows, superpixels have adaptive sizes and shapes for different spatial structures. Moreover, superpixel-based algorithms can significantly improve computational efficiency owing to the greatly reduced number of image primitives. Hence, the superpixel algorithm, as a preprocessing technique, is more and more popularly used in remote sensing and many other fields. In this paper, we propose a superpixel segmentation algorithm called Superpixel Segmentation with Local Competition (SSLC), which utilizes a local competition mechanism to construct energy terms and label pixels. The local competition mechanism leads to energy terms locality and relativity, and thus, the proposed algorithm is less sensitive to the diversity of image content and scene layout. Consequently, SSLC could achieve consistent performance in different image regions. In addition, the Probability Density Function (PDF), which is estimated by Kernel Density Estimation (KDE) with the Gaussian kernel, is introduced to describe the color distribution of superpixels as a more sophisticated and accurate measure. To reduce computational complexity, a boundary optimization framework is introduced to only handle boundary pixels instead of the whole image. We conduct experiments to benchmark the proposed algorithm with the other state-of-the-art ones on the Berkeley Segmentation Dataset (BSD) and remote sensing images. Results demonstrate that the SSLC algorithm yields the best overall performance, while the computation time-efficiency is still competitive.
Integrated segmentation of cellular structures
NASA Astrophysics Data System (ADS)
Ajemba, Peter; Al-Kofahi, Yousef; Scott, Richard; Donovan, Michael; Fernandez, Gerardo
2011-03-01
Automatic segmentation of cellular structures is an essential step in image cytology and histology. Despite substantial progress, better automation and improvements in accuracy and adaptability to novel applications are needed. In applications utilizing multi-channel immuno-fluorescence images, challenges include misclassification of epithelial and stromal nuclei, irregular nuclei and cytoplasm boundaries, and over and under-segmentation of clustered nuclei. Variations in image acquisition conditions and artifacts from nuclei and cytoplasm images often confound existing algorithms in practice. In this paper, we present a robust and accurate algorithm for jointly segmenting cell nuclei and cytoplasm using a combination of ideas to reduce the aforementioned problems. First, an adaptive process that includes top-hat filtering, Eigenvalues-of-Hessian blob detection and distance transforms is used to estimate the inverse illumination field and correct for intensity non-uniformity in the nuclei channel. Next, a minimum-error-thresholding based binarization process and seed-detection combining Laplacian-of-Gaussian filtering constrained by a distance-map-based scale selection is used to identify candidate seeds for nuclei segmentation. The initial segmentation using a local maximum clustering algorithm is refined using a minimum-error-thresholding technique. Final refinements include an artifact removal process specifically targeted at lumens and other problematic structures and a systemic decision process to reclassify nuclei objects near the cytoplasm boundary as epithelial or stromal. Segmentation results were evaluated using 48 realistic phantom images with known ground-truth. The overall segmentation accuracy exceeds 94%. The algorithm was further tested on 981 images of actual prostate cancer tissue. The artifact removal process worked in 90% of cases. The algorithm has now been deployed in a high-volume histology analysis application.
Xiao, Jingjing; Stolkin, Rustam; Gao, Yuqing; Leonardis, Ales
2017-09-06
This paper presents a novel robust method for single target tracking in RGB-D images, and also contributes a substantial new benchmark dataset for evaluating RGB-D trackers. While a target object's color distribution is reasonably motion-invariant, this is not true for the target's depth distribution, which continually varies as the target moves relative to the camera. It is therefore nontrivial to design target models which can fully exploit (potentially very rich) depth information for target tracking. For this reason, much of the previous RGB-D literature relies on color information for tracking, while exploiting depth information only for occlusion reasoning. In contrast, we propose an adaptive range-invariant target depth model, and show how both depth and color information can be fully and adaptively fused during the search for the target in each new RGB-D image. We introduce a new, hierarchical, two-layered target model (comprising local and global models) which uses spatio-temporal consistency constraints to achieve stable and robust on-the-fly target relearning. In the global layer, multiple features, derived from both color and depth data, are adaptively fused to find a candidate target region. In ambiguous frames, where one or more features disagree, this global candidate region is further decomposed into smaller local candidate regions for matching to local-layer models of small target parts. We also note that conventional use of depth data, for occlusion reasoning, can easily trigger false occlusion detections when the target moves rapidly toward the camera. To overcome this problem, we show how combining target information with contextual information enables the target's depth constraint to be relaxed. Our adaptively relaxed depth constraints can robustly accommodate large and rapid target motion in the depth direction, while still enabling the use of depth data for highly accurate reasoning about occlusions. For evaluation, we introduce a new RGB
Pet fur color and texture classification
NASA Astrophysics Data System (ADS)
Yen, Jonathan; Mukherjee, Debarghar; Lim, SukHwan; Tretter, Daniel
2007-01-01
Object segmentation is important in image analysis for imaging tasks such as image rendering and image retrieval. Pet owners have been known to be quite vocal about how important it is to render their pets perfectly. We present here an algorithm for pet (mammal) fur color classification and an algorithm for pet (animal) fur texture classification. Per fur color classification can be applied as a necessary condition for identifying the regions in an image that may contain pets much like the skin tone classification for human flesh detection. As a result of the evolution, fur coloration of all mammals is caused by a natural organic pigment called Melanin and Melanin has only very limited color ranges. We have conducted a statistical analysis and concluded that mammal fur colors can be only in levels of gray or in two colors after the proper color quantization. This pet fur color classification algorithm has been applied for peteye detection. We also present here an algorithm for animal fur texture classification using the recently developed multi-resolution directional sub-band Contourlet transform. The experimental results are very promising as these transforms can identify regions of an image that may contain fur of mammals, scale of reptiles and feather of birds, etc. Combining the color and texture classification, one can have a set of strong classifiers for identifying possible animals in an image.
Roberts, Philipp K; Nesper, Peter L; Onishi, Alex C; Skondra, Dimitra; Jampol, Lee M; Fawzi, Amani A
2018-01-01
To characterize lesions of acute posterior multifocal placoid pigment epitheliopathy (APMPPE) by multimodal imaging including adaptive optics scanning laser ophthalmoscopy (AOSLO). We included patients with APMPPE at different stages of evolution of the placoid lesions. Color fundus photography, spectral domain optical coherence tomography, infrared reflectance, fundus autofluorescence, and AOSLO images were obtained and registered to correlate microstructural changes. Eight eyes of four patients (two women) were included and analyzed by multimodal imaging. Photoreceptor reflectivity within APMPPE lesions was more heterogeneous than in adjacent healthy areas. Hyperpigmentation on color fundus photography appeared hyperreflective on infrared reflectance and on AOSLO. Irregularity of the interdigitation zone and the photoreceptor inner and outer segment junctions (IS/OS) on spectral domain optical coherence tomography was associated with photoreceptor hyporeflectivity on AOSLO. Interruption of the interdigitation zone or IS/OS was associated with loss of photoreceptor reflectivity on AOSLO. Irregularities in the reflectivity of the photoreceptor mosaic are visible on AOSLO even in inactive APMPPE lesions, where the photoreceptor bands on spectral domain optical coherence tomography have recovered. Adaptive optics scanning laser ophthalmoscopy combined with multimodal imaging has the potential to enhance our understanding of photoreceptor involvement in APMPPE.
Teixidó, Mercè; Font, Davinia; Pallejà, Tomàs; Tresanchez, Marcel; Nogués, Miquel; Palacín, Jordi
2012-01-01
This work proposes the detection of red peaches in orchard images based on the definition of different linear color models in the RGB vector color space. The classification and segmentation of the pixels of the image is then performed by comparing the color distance from each pixel to the different previously defined linear color models. The methodology proposed has been tested with images obtained in a real orchard under natural light. The peach variety in the orchard was the paraguayo (Prunus persica var. platycarpa) peach with red skin. The segmentation results showed that the area of the red peaches in the images was detected with an average error of 11.6%; 19.7% in the case of bright illumination; 8.2% in the case of low illumination; 8.6% for occlusion up to 33%; 12.2% in the case of occlusion between 34 and 66%; and 23% for occlusion above 66%. Finally, a methodology was proposed to estimate the diameter of the fruits based on an ellipsoidal fitting. A first diameter was obtained by using all the contour pixels and a second diameter was obtained by rejecting some pixels of the contour. This approach enables a rough estimate of the fruit occlusion percentage range by comparing the two diameter estimates. PMID:22969369
Teixidó, Mercè; Font, Davinia; Pallejà, Tomàs; Tresanchez, Marcel; Nogués, Miquel; Palacín, Jordi
2012-01-01
This work proposes the detection of red peaches in orchard images based on the definition of different linear color models in the RGB vector color space. The classification and segmentation of the pixels of the image is then performed by comparing the color distance from each pixel to the different previously defined linear color models. The methodology proposed has been tested with images obtained in a real orchard under natural light. The peach variety in the orchard was the paraguayo (Prunus persica var. platycarpa) peach with red skin. The segmentation results showed that the area of the red peaches in the images was detected with an average error of 11.6%; 19.7% in the case of bright illumination; 8.2% in the case of low illumination; 8.6% for occlusion up to 33%; 12.2% in the case of occlusion between 34 and 66%; and 23% for occlusion above 66%. Finally, a methodology was proposed to estimate the diameter of the fruits based on an ellipsoidal fitting. A first diameter was obtained by using all the contour pixels and a second diameter was obtained by rejecting some pixels of the contour. This approach enables a rough estimate of the fruit occlusion percentage range by comparing the two diameter estimates.
Color Afterimages in Autistic Adults
ERIC Educational Resources Information Center
Maule, John; Stanworth, Kirstie; Pellicano, Elizabeth; Franklin, Anna
2018-01-01
It has been suggested that attenuated adaptation to visual stimuli in autism is the result of atypical perceptual priors (e.g., Pellicano and Burr in "Trends Cogn Sci" 16(10):504-510, 2012. doi:10.1016/j.tics.2012.08.009). This study investigated adaptation to color in autistic adults, measuring both strength of afterimage and the…
The Eight Frame Colored Squiggle Technique
ERIC Educational Resources Information Center
Steinhardt, Lenore
2006-01-01
In this art therapy adaptation of the squiggle technique, the client draws eight colored squiggles on a paper folded into eight frames and then develops them into images utilizing a full range of color. The client is encouraged to write titles on each frame and use them to compose a story. This technique often stimulates emergence of meaningful…
The influence of color on emotional perception of natural scenes.
Codispoti, Maurizio; De Cesarei, Andrea; Ferrari, Vera
2012-01-01
Is color a critical factor when processing the emotional content of natural scenes? Under challenging perceptual conditions, such as when pictures are briefly presented, color might facilitate scene segmentation and/or function as a semantic cue via association with scene-relevant concepts (e.g., red and blood/injury). To clarify the influence of color on affective picture perception, we compared the late positive potentials (LPP) to color versus grayscale pictures, presented for very brief (24 ms) and longer (6 s) exposure durations. Results indicated that removing color information had no effect on the affective modulation of the LPP, regardless of exposure duration. These findings imply that the recognition of the emotional content of scenes, even when presented very briefly, does not critically rely on color information. Copyright © 2011 Society for Psychophysiological Research.
NASA Astrophysics Data System (ADS)
Chávez, G. Moreno; Sarocchi, D.; Santana, E. Arce; Borselli, L.
2015-12-01
The study of grain size distribution is fundamental for understanding sedimentological environments. Through these analyses, clast erosion, transport and deposition processes can be interpreted and modeled. However, grain size distribution analysis can be difficult in some outcrops due to the number and complexity of the arrangement of clasts and matrix and their physical size. Despite various technological advances, it is almost impossible to get the full grain size distribution (blocks to sand grain size) with a single method or instrument of analysis. For this reason development in this area continues to be fundamental. In recent years, various methods of particle size analysis by automatic image processing have been developed, due to their potential advantages with respect to classical ones; speed and final detailed content of information (virtually for each analyzed particle). In this framework, we have developed a novel algorithm and software for grain size distribution analysis, based on color image segmentation using an entropy-controlled quadratic Markov measure field algorithm and the Rosiwal method for counting intersections between clast and linear transects in the images. We test the novel algorithm in different sedimentary deposit types from 14 varieties of sedimentological environments. The results of the new algorithm were compared with grain counts performed manually by the same Rosiwal methods applied by experts. The new algorithm has the same accuracy as a classical manual count process, but the application of this innovative methodology is much easier and dramatically less time-consuming. The final productivity of the new software for analysis of clasts deposits after recording field outcrop images can be increased significantly.
Adaptive segmentation of cerebrovascular tree in time-of-flight magnetic resonance angiography.
Hao, J T; Li, M L; Tang, F L
2008-01-01
Accurate segmentation of the human vasculature is an important prerequisite for a number of clinical procedures, such as diagnosis, image-guided neurosurgery and pre-surgical planning. In this paper, an improved statistical approach to extracting whole cerebrovascular tree in time-of-flight magnetic resonance angiography is proposed. Firstly, in order to get a more accurate segmentation result, a localized observation model is proposed instead of defining the observation model over the entire dataset. Secondly, for the binary segmentation, an improved Iterative Conditional Model (ICM) algorithm is presented to accelerate the segmentation process. The experimental results showed that the proposed algorithm can obtain more satisfactory segmentation results and save more processing time than conventional approaches, simultaneously.
Ben Chaabane, Salim; Fnaiech, Farhat
2014-01-23
Color image segmentation has been so far applied in many areas; hence, recently many different techniques have been developed and proposed. In the medical imaging area, the image segmentation may be helpful to provide assistance to doctor in order to follow-up the disease of a certain patient from the breast cancer processed images. The main objective of this work is to rebuild and also to enhance each cell from the three component images provided by an input image. Indeed, from an initial segmentation obtained using the statistical features and histogram threshold techniques, the resulting segmentation may represent accurately the non complete and pasted cells and enhance them. This allows real help to doctors, and consequently, these cells become clear and easy to be counted. A novel method for color edges extraction based on statistical features and automatic threshold is presented. The traditional edge detector, based on the first and the second order neighborhood, describing the relationship between the current pixel and its neighbors, is extended to the statistical domain. Hence, color edges in an image are obtained by combining the statistical features and the automatic threshold techniques. Finally, on the obtained color edges with specific primitive color, a combination rule is used to integrate the edge results over the three color components. Breast cancer cell images were used to evaluate the performance of the proposed method both quantitatively and qualitatively. Hence, a visual and a numerical assessment based on the probability of correct classification (PC), the false classification (Pf), and the classification accuracy (Sens(%)) are presented and compared with existing techniques. The proposed method shows its superiority in the detection of points which really belong to the cells, and also the facility of counting the number of the processed cells. Computer simulations highlight that the proposed method substantially enhances the segmented image
Hierarchical image segmentation via recursive superpixel with adaptive regularity
NASA Astrophysics Data System (ADS)
Nakamura, Kensuke; Hong, Byung-Woo
2017-11-01
A fast and accurate segmentation algorithm in a hierarchical way based on a recursive superpixel technique is presented. We propose a superpixel energy formulation in which the trade-off between data fidelity and regularization is dynamically determined based on the local residual in the energy optimization procedure. We also present an energy optimization algorithm that allows a pixel to be shared by multiple regions to improve the accuracy and appropriate the number of segments. The qualitative and quantitative evaluations demonstrate that our algorithm, combining the proposed energy and optimization, outperforms the conventional k-means algorithm by up to 29.10% in F-measure. We also perform comparative analysis with state-of-the-art algorithms in the hierarchical segmentation. Our algorithm yields smooth regions throughout the hierarchy as opposed to the others that include insignificant details. Our algorithm overtakes the other algorithms in terms of balance between accuracy and computational time. Specifically, our method runs 36.48% faster than the region-merging approach, which is the fastest of the comparing algorithms, while achieving a comparable accuracy.
The evolution of vertebrate color vision.
Jacobs, Gerald H
2012-01-01
Color vision is conventionally defined as the ability of animals to reliably discriminate among objects and lights based solely on differences in their spectral properties. Although the nature of color vision varies widely in different animals, a large majority of all vertebrate species possess some color vision and that fact attests to the adaptive importance this capacity holds as a tool for analyzing the environment. In recent years dramatic advances have been made in our understanding of the nature of vertebrate color vision and of the evolution of the biological mechanisms underlying this capacity. In this chapter I review and comment on these advances.
Document segmentation for high-quality printing
NASA Astrophysics Data System (ADS)
Ancin, Hakan
1997-04-01
A technique to segment dark texts on light background of mixed mode color documents is presented. This process does not perceptually change graphics and photo regions. Color documents are scanned and printed from various media which usually do not have clean background. This is especially the case for the printouts generated from thin magazine samples, these printouts usually include text and figures form the back of the page, which is called bleeding. Removal of bleeding artifacts improves the perceptual quality of the printed document and reduces the color ink usage. By detecting the light background of the document, these artifacts are removed from background regions. Also detection of dark text regions enables the halftoning algorithms to use true black ink for the black text pixels instead of composite black. The processed document contains sharp black text on white background, resulting improved perceptual quality and better ink utilization. The described method is memory efficient and requires a small number of scan lines of high resolution color documents during processing.
Biological origins of color categorization.
Skelton, Alice E; Catchpole, Gemma; Abbott, Joshua T; Bosten, Jenny M; Franklin, Anna
2017-05-23
The biological basis of the commonality in color lexicons across languages has been hotly debated for decades. Prior evidence that infants categorize color could provide support for the hypothesis that color categorization systems are not purely constructed by communication and culture. Here, we investigate the relationship between infants' categorization of color and the commonality across color lexicons, and the potential biological origin of infant color categories. We systematically mapped infants' categorical recognition memory for hue onto a stimulus array used previously to document the color lexicons of 110 nonindustrialized languages. Following familiarization to a given hue, infants' response to a novel hue indicated that their recognition memory parses the hue continuum into red, yellow, green, blue, and purple categories. Infants' categorical distinctions aligned with common distinctions in color lexicons and are organized around hues that are commonly central to lexical categories across languages. The boundaries between infants' categorical distinctions also aligned, relative to the adaptation point, with the cardinal axes that describe the early stages of color representation in retinogeniculate pathways, indicating that infant color categorization may be partly organized by biological mechanisms of color vision. The findings suggest that color categorization in language and thought is partially biologically constrained and have implications for broader debate on how biology, culture, and communication interact in human cognition.
Biological origins of color categorization
Catchpole, Gemma; Abbott, Joshua T.; Bosten, Jenny M.; Franklin, Anna
2017-01-01
The biological basis of the commonality in color lexicons across languages has been hotly debated for decades. Prior evidence that infants categorize color could provide support for the hypothesis that color categorization systems are not purely constructed by communication and culture. Here, we investigate the relationship between infants’ categorization of color and the commonality across color lexicons, and the potential biological origin of infant color categories. We systematically mapped infants’ categorical recognition memory for hue onto a stimulus array used previously to document the color lexicons of 110 nonindustrialized languages. Following familiarization to a given hue, infants’ response to a novel hue indicated that their recognition memory parses the hue continuum into red, yellow, green, blue, and purple categories. Infants’ categorical distinctions aligned with common distinctions in color lexicons and are organized around hues that are commonly central to lexical categories across languages. The boundaries between infants’ categorical distinctions also aligned, relative to the adaptation point, with the cardinal axes that describe the early stages of color representation in retinogeniculate pathways, indicating that infant color categorization may be partly organized by biological mechanisms of color vision. The findings suggest that color categorization in language and thought is partially biologically constrained and have implications for broader debate on how biology, culture, and communication interact in human cognition. PMID:28484022
Li, Xingyu; Plataniotis, Konstantinos N
2015-07-01
In digital histopathology, tasks of segmentation and disease diagnosis are achieved by quantitative analysis of image content. However, color variation in image samples makes it challenging to produce reliable results. This paper introduces a complete normalization scheme to address the problem of color variation in histopathology images jointly caused by inconsistent biopsy staining and nonstandard imaging condition. Method : Different from existing normalization methods that either address partial cause of color variation or lump them together, our method identifies causes of color variation based on a microscopic imaging model and addresses inconsistency in biopsy imaging and staining by an illuminant normalization module and a spectral normalization module, respectively. In evaluation, we use two public datasets that are representative of histopathology images commonly received in clinics to examine the proposed method from the aspects of robustness to system settings, performance consistency against achromatic pixels, and normalization effectiveness in terms of histological information preservation. As the saturation-weighted statistics proposed in this study generates stable and reliable color cues for stain normalization, our scheme is robust to system parameters and insensitive to image content and achromatic colors. Extensive experimentation suggests that our approach outperforms state-of-the-art normalization methods as the proposed method is the only approach that succeeds to preserve histological information after normalization. The proposed color normalization solution would be useful to mitigate effects of color variation in pathology images on subsequent quantitative analysis.
Adaptive shell color plasticity during the early ontogeny of an intertidal keystone snail.
Manríquez, Patricio H; Lagos, Nelson A; Jara, María Elisa; Castilla, Juan Carlos
2009-09-22
We report a mechanism of crypsis present during the vulnerable early post-metamorphic ontogeny (=20 mm peristomal length) of the muricid snail Concholepas concholepas, a rocky shore keystone predator characteristic of the southeastern Pacific coast. In the field, we found a significant occurrence (>95%) of specimens bearing patterns of shell coloration (dark or light colored) that matched the background coloration provided by patches of Concholepas' most abundant prey (mussels or barnacles respectively). The variation in shell color was positively associated with the color of the most common prey (r = 0.99). In laboratory experiments, shell coloration of C. concholepas depended on the prey-substrate used to induce metamorphosis and for the post-metamorphic rearing. The snail shell color matched the color of the prey offered during rearing. Laboratory manipulation experiments, switching the prey during rearing, showed a corresponding change in snail shell color along the outermost shell edge. As individuals grew and became increasingly indistinguishable from the surrounding background, cryptic individuals had higher survival (71%) than the non cryptic ones (4%) when they were reared in the presence of the predatory crab Acanthocyclus hassleri. These results suggest that the evolution of shell color plasticity during the early ontogeny of C. concholepas, depends on the color of the more abundant of the consumed prey available in the natural habitat where settlement has taken place; this in turn has important consequences for their fitness and survivorship in the presence of visual predators.
Adaptive shell color plasticity during the early ontogeny of an intertidal keystone snail
Manríquez, Patricio H.; Lagos, Nelson A.; Jara, María Elisa; Castilla, Juan Carlos
2009-01-01
We report a mechanism of crypsis present during the vulnerable early post-metamorphic ontogeny (≤20 mm peristomal length) of the muricid snail Concholepas concholepas, a rocky shore keystone predator characteristic of the southeastern Pacific coast. In the field, we found a significant occurrence (>95%) of specimens bearing patterns of shell coloration (dark or light colored) that matched the background coloration provided by patches of Concholepas' most abundant prey (mussels or barnacles respectively). The variation in shell color was positively associated with the color of the most common prey (r = 0.99). In laboratory experiments, shell coloration of C. concholepas depended on the prey-substrate used to induce metamorphosis and for the post-metamorphic rearing. The snail shell color matched the color of the prey offered during rearing. Laboratory manipulation experiments, switching the prey during rearing, showed a corresponding change in snail shell color along the outermost shell edge. As individuals grew and became increasingly indistinguishable from the surrounding background, cryptic individuals had higher survival (71%) than the non cryptic ones (4%) when they were reared in the presence of the predatory crab Acanthocyclus hassleri. These results suggest that the evolution of shell color plasticity during the early ontogeny of C. concholepas, depends on the color of the more abundant of the consumed prey available in the natural habitat where settlement has taken place; this in turn has important consequences for their fitness and survivorship in the presence of visual predators. PMID:19805296
A novel weighted-direction color interpolation
NASA Astrophysics Data System (ADS)
Tao, Jin-you; Yang, Jianfeng; Xue, Bin; Liang, Xiaofen; Qi, Yong-hong; Wang, Feng
2013-08-01
A digital camera capture images by covering the sensor surface with a color filter array (CFA), only get a color sample at pixel location. Demosaicking is a process by estimating the missing color components of each pixel to get a full resolution image. In this paper, a new algorithm based on edge adaptive and different weighting factors is proposed. Our method can effectively suppress undesirable artifacts. Experimental results based on Kodak images show that the proposed algorithm obtain higher quality images compared to other methods in numerical and visual aspects.
Image segmentation by hierarchial agglomeration of polygons using ecological statistics
Prasad, Lakshman; Swaminarayan, Sriram
2013-04-23
A method for rapid hierarchical image segmentation based on perceptually driven contour completion and scene statistics is disclosed. The method begins with an initial fine-scale segmentation of an image, such as obtained by perceptual completion of partial contours into polygonal regions using region-contour correspondences established by Delaunay triangulation of edge pixels as implemented in VISTA. The resulting polygons are analyzed with respect to their size and color/intensity distributions and the structural properties of their boundaries. Statistical estimates of granularity of size, similarity of color, texture, and saliency of intervening boundaries are computed and formulated into logical (Boolean) predicates. The combined satisfiability of these Boolean predicates by a pair of adjacent polygons at a given segmentation level qualifies them for merging into a larger polygon representing a coarser, larger-scale feature of the pixel image and collectively obtains the next level of polygonal segments in a hierarchy of fine-to-coarse segmentations. The iterative application of this process precipitates textured regions as polygons with highly convolved boundaries and helps distinguish them from objects which typically have more regular boundaries. The method yields a multiscale decomposition of an image into constituent features that enjoy a hierarchical relationship with features at finer and coarser scales. This provides a traversable graph structure from which feature content and context in terms of other features can be derived, aiding in automated image understanding tasks. The method disclosed is highly efficient and can be used to decompose and analyze large images.
Stereo matching using census cost over cross window and segmentation-based disparity refinement
NASA Astrophysics Data System (ADS)
Li, Qingwu; Ni, Jinyan; Ma, Yunpeng; Xu, Jinxin
2018-03-01
Stereo matching is a vital requirement for many applications, such as three-dimensional (3-D) reconstruction, robot navigation, object detection, and industrial measurement. To improve the practicability of stereo matching, a method using census cost over cross window and segmentation-based disparity refinement is proposed. First, a cross window is obtained using distance difference and intensity similarity in binocular images. Census cost over the cross window and color cost are combined as the matching cost, which is aggregated by the guided filter. Then, winner-takes-all strategy is used to calculate the initial disparities. Second, a graph-based segmentation method is combined with color and edge information to achieve moderate under-segmentation. The segmented regions are classified into reliable regions and unreliable regions by consistency checking. Finally, the two regions are optimized by plane fitting and propagation, respectively, to match the ambiguous pixels. The experimental results are on Middlebury Stereo Datasets, which show that the proposed method has good performance in occluded and discontinuous regions, and it obtains smoother disparity maps with a lower average matching error rate compared with other algorithms.
Poly-Pattern Compressive Segmentation of ASTER Data for GIS
NASA Technical Reports Server (NTRS)
Myers, Wayne; Warner, Eric; Tutwiler, Richard
2007-01-01
Pattern-based segmentation of multi-band image data, such as ASTER, produces one-byte and two-byte approximate compressions. This is a dual segmentation consisting of nested coarser and finer level pattern mappings called poly-patterns. The coarser A-level version is structured for direct incorporation into geographic information systems in the manner of a raster map. GIs renderings of this A-level approximation are called pattern pictures which have the appearance of color enhanced images. The two-byte version consisting of thousands of B-level segments provides a capability for approximate restoration of the multi-band data in selected areas or entire scenes. Poly-patterns are especially useful for purposes of change detection and landscape analysis at multiple scales. The primary author has implemented the segmentation methodology in a public domain software suite.
A global/local affinity graph for image segmentation.
Xiaofang Wang; Yuxing Tang; Masnou, Simon; Liming Chen
2015-04-01
Construction of a reliable graph capturing perceptual grouping cues of an image is fundamental for graph-cut based image segmentation methods. In this paper, we propose a novel sparse global/local affinity graph over superpixels of an input image to capture both short- and long-range grouping cues, and thereby enabling perceptual grouping laws, including proximity, similarity, continuity, and to enter in action through a suitable graph-cut algorithm. Moreover, we also evaluate three major visual features, namely, color, texture, and shape, for their effectiveness in perceptual segmentation and propose a simple graph fusion scheme to implement some recent findings from psychophysics, which suggest combining these visual features with different emphases for perceptual grouping. In particular, an input image is first oversegmented into superpixels at different scales. We postulate a gravitation law based on empirical observations and divide superpixels adaptively into small-, medium-, and large-sized sets. Global grouping is achieved using medium-sized superpixels through a sparse representation of superpixels' features by solving a ℓ0-minimization problem, and thereby enabling continuity or propagation of local smoothness over long-range connections. Small- and large-sized superpixels are then used to achieve local smoothness through an adjacent graph in a given feature space, and thus implementing perceptual laws, for example, similarity and proximity. Finally, a bipartite graph is also introduced to enable propagation of grouping cues between superpixels of different scales. Extensive experiments are carried out on the Berkeley segmentation database in comparison with several state-of-the-art graph constructions. The results show the effectiveness of the proposed approach, which outperforms state-of-the-art graphs using four different objective criteria, namely, the probabilistic rand index, the variation of information, the global consistency error, and the
Shifts in color discrimination during early pregnancy.
Orbán, Levente L; Dastur, Farhad N
2012-05-25
The present study explores two hypotheses: a) women during early pregnancy should experience increased color discrimination ability, and b) women during early pregnancy should experience shifts in subjective preference away from images of foods that appear either unripe or spoiled. Both of these hypotheses derive from an adaptive view of pregnancy sickness that proposes the function of pregnancy sickness is to decrease the likelihood of ingestion of foods with toxins or teratogens. Changes to color discrimination could be part of a network of perceptual and physiological defenses (e.g., changes to olfaction, nausea, vomiting) that support such a function. Participants included 13 pregnant women and 18 non-pregnant women. Pregnant women scored significantly higher than non-pregnant controls on the Farnsworth-Munsell (FM) 100 Hue Test, an objective test of color discrimination, although no difference was found between groups in preferences for food images at different stages of ripeness or spoilage. These results are the first indication that changes to color discrimination may occur during early pregnancy, and is consistent with the view that pregnancy sickness may function as an adaptive defense mechanism.
Image segmentation via foreground and background semantic descriptors
NASA Astrophysics Data System (ADS)
Yuan, Ding; Qiang, Jingjing; Yin, Jihao
2017-09-01
In the field of image processing, it has been a challenging task to obtain a complete foreground that is not uniform in color or texture. Unlike other methods, which segment the image by only using low-level features, we present a segmentation framework, in which high-level visual features, such as semantic information, are used. First, the initial semantic labels were obtained by using the nonparametric method. Then, a subset of the training images, with a similar foreground to the input image, was selected. Consequently, the semantic labels could be further refined according to the subset. Finally, the input image was segmented by integrating the object affinity and refined semantic labels. State-of-the-art performance was achieved in experiments with the challenging MSRC 21 dataset.
Held, Christian; Wenzel, Jens; Webel, Rike; Marschall, Manfred; Lang, Roland; Palmisano, Ralf; Wittenberg, Thomas
2011-01-01
In order to improve reproducibility and objectivity of fluorescence microscopy based experiments and to enable the evaluation of large datasets, flexible segmentation methods are required which are able to adapt to different stainings and cell types. This adaption is usually achieved by the manual adjustment of the segmentation methods parameters, which is time consuming and challenging for biologists with no knowledge on image processing. To avoid this, parameters of the presented methods automatically adapt to user generated ground truth to determine the best method and the optimal parameter setup. These settings can then be used for segmentation of the remaining images. As robust segmentation methods form the core of such a system, the currently used watershed transform based segmentation routine is replaced by a fast marching level set based segmentation routine which incorporates knowledge on the cell nuclei. Our evaluations reveal that incorporation of multimodal information improves segmentation quality for the presented fluorescent datasets.
NASA Astrophysics Data System (ADS)
Kopriva, Ivica; Popović Hadžija, Marijana; Hadžija, Mirko; Aralica, Gorana
2015-06-01
Low-contrast images, such as color microscopic images of unstained histological specimens, are composed of objects with highly correlated spectral profiles. Such images are very hard to segment. Here, we present a method that nonlinearly maps low-contrast color image into an image with an increased number of non-physical channels and a decreased correlation between spectral profiles. The method is a proof-of-concept validated on the unsupervised segmentation of color images of unstained specimens, in which case the tissue components appear colorless when viewed under the light microscope. Specimens of human hepatocellular carcinoma, human liver with metastasis from colon and gastric cancer and mouse fatty liver were used for validation. The average correlation between the spectral profiles of the tissue components was greater than 0.9985, and the worst case correlation was greater than 0.9997. The proposed method can potentially be applied to the segmentation of low-contrast multichannel images with high spatial resolution that arise in other imaging modalities.
Ochiai, Nobuhisa; Kondo, Hiroyuki
2017-01-01
The effects of color perception are utilized in visual displays for the purpose of safety in the workplace and in daily life. These effects, generally known as color functionality, are divided into four classifications: visibility, legibility, conspicuity and discriminability. This article focuses on the relationship between the color functionality of color schemes used in visual displays for occupational and environmental safety and color vision deficiency (particularly congenital red-green color deficiency), a critical issue in ophthalmology, and examines the effects of color functionality on the perception of the color red in individuals with protan defects. Due to abrupt system reforms, current Japanese clinical ophthalmology finds itself in a situation where it is insufficiently prepared to handle congenital red-green color deficiencies. Indeed, occupational problems caused by color vision deficiencies have been almost completely neglected, and are an occupational safety and health concern that will need to be solved in the future. This report will present the guidelines for the color vision testing established by the British Health and Safety Executive (HSE), a pioneering example of a model meant to solve these problems. Issues relating to the creation of guidelines adapted to Japanese clinical ophthalmology will also be examined, and we will discuss ways to utilize color functionality used in visual displays for occupational and environmental safety to help manage color vision deficiency.
Color enhancement of landsat agricultural imagery: JPL LACIE image processing support task
NASA Technical Reports Server (NTRS)
Madura, D. P.; Soha, J. M.; Green, W. B.; Wherry, D. B.; Lewis, S. D.
1978-01-01
Color enhancement techniques were applied to LACIE LANDSAT segments to determine if such enhancement can assist analysis in crop identification. The procedure involved increasing the color range by removing correlation between components. First, a principal component transformation was performed, followed by contrast enhancement to equalize component variances, followed by an inverse transformation to restore familiar color relationships. Filtering was applied to lower order components to reduce color speckle in the enhanced products. Use of single acquisition and multiple acquisition statistics to control the enhancement were compared, and the effects of normalization investigated. Evaluation is left to LACIE personnel.
Perry, George H; Martin, Robert D; Verrelli, Brian C
2007-09-01
While color vision perception is thought to be adaptively correlated with foraging efficiency for diurnal mammals, those that forage exclusively at night may not need color vision nor have the capacity for it. Indeed, although the basic condition for mammals is dichromacy, diverse nocturnal mammals have only monochromatic vision, resulting from functional loss of the short-wavelength sensitive opsin gene. However, many nocturnal primates maintain intact two opsin genes and thus have dichromatic capacity. The evolutionary significance of this surprising observation has not yet been elucidated. We used a molecular population genetics approach to test evolutionary hypotheses for the two intact opsin genes of the fully nocturnal aye-aye (Daubentonia madagascariensis), a highly unusual and endangered Madagascar primate. No evidence of gene degradation in either opsin gene was observed for any of 8 aye-aye individuals examined. Furthermore, levels of nucleotide diversity for opsin gene functional sites were lower than those for 15 neutrally evolving intergenic regions (>25 kb in total), which is consistent with a history of purifying selection on aye-aye opsin genes. The most likely explanation for these findings is that dichromacy is advantageous for aye-ayes despite their nocturnal activity pattern. We speculate that dichromatic nocturnal primates may be able to perceive color while foraging under moonlight conditions, and suggest that behavioral and ecological comparisons among dichromatic and monochromatic nocturnal primates will help to elucidate the specific activities for which color vision perception is advantageous.
Fast and robust segmentation of white blood cell images by self-supervised learning.
Zheng, Xin; Wang, Yong; Wang, Guoyou; Liu, Jianguo
2018-04-01
A fast and accurate white blood cell (WBC) segmentation remains a challenging task, as different WBCs vary significantly in color and shape due to cell type differences, staining technique variations and the adhesion between the WBC and red blood cells. In this paper, a self-supervised learning approach, consisting of unsupervised initial segmentation and supervised segmentation refinement, is presented. The first module extracts the overall foreground region from the cell image by K-means clustering, and then generates a coarse WBC region by touching-cell splitting based on concavity analysis. The second module further uses the coarse segmentation result of the first module as automatic labels to actively train a support vector machine (SVM) classifier. Then, the trained SVM classifier is further used to classify each pixel of the image and achieve a more accurate segmentation result. To improve its segmentation accuracy, median color features representing the topological structure and a new weak edge enhancement operator (WEEO) handling fuzzy boundary are introduced. To further reduce its time cost, an efficient cluster sampling strategy is also proposed. We tested the proposed approach with two blood cell image datasets obtained under various imaging and staining conditions. The experiment results show that our approach has a superior performance of accuracy and time cost on both datasets. Copyright © 2018 Elsevier Ltd. All rights reserved.
Automated retinal vessel type classification in color fundus images
NASA Astrophysics Data System (ADS)
Yu, H.; Barriga, S.; Agurto, C.; Nemeth, S.; Bauman, W.; Soliz, P.
2013-02-01
Automated retinal vessel type classification is an essential first step toward machine-based quantitative measurement of various vessel topological parameters and identifying vessel abnormalities and alternations in cardiovascular disease risk analysis. This paper presents a new and accurate automatic artery and vein classification method developed for arteriolar-to-venular width ratio (AVR) and artery and vein tortuosity measurements in regions of interest (ROI) of 1.5 and 2.5 optic disc diameters from the disc center, respectively. This method includes illumination normalization, automatic optic disc detection and retinal vessel segmentation, feature extraction, and a partial least squares (PLS) classification. Normalized multi-color information, color variation, and multi-scale morphological features are extracted on each vessel segment. We trained the algorithm on a set of 51 color fundus images using manually marked arteries and veins. We tested the proposed method in a previously unseen test data set consisting of 42 images. We obtained an area under the ROC curve (AUC) of 93.7% in the ROI of AVR measurement and 91.5% of AUC in the ROI of tortuosity measurement. The proposed AV classification method has the potential to assist automatic cardiovascular disease early detection and risk analysis.
Korzynska, Anna; Roszkowiak, Lukasz; Lopez, Carlos; Bosch, Ramon; Witkowski, Lukasz; Lejeune, Marylene
2013-03-25
The comparative study of the results of various segmentation methods for the digital images of the follicular lymphoma cancer tissue section is described in this paper. The sensitivity and specificity and some other parameters of the following adaptive threshold methods of segmentation: the Niblack method, the Sauvola method, the White method, the Bernsen method, the Yasuda method and the Palumbo method, are calculated. Methods are applied to three types of images constructed by extraction of the brown colour information from the artificial images synthesized based on counterpart experimentally captured images. This paper presents usefulness of the microscopic image synthesis method in evaluation as well as comparison of the image processing results. The results of thoughtful analysis of broad range of adaptive threshold methods applied to: (1) the blue channel of RGB, (2) the brown colour extracted by deconvolution and (3) the 'brown component' extracted from RGB allows to select some pairs: method and type of image for which this method is most efficient considering various criteria e.g. accuracy and precision in area detection or accuracy in number of objects detection and so on. The comparison shows that the White, the Bernsen and the Sauvola methods results are better than the results of the rest of the methods for all types of monochromatic images. All three methods segments the immunopositive nuclei with the mean accuracy of 0.9952, 0.9942 and 0.9944 respectively, when treated totally. However the best results are achieved for monochromatic image in which intensity shows brown colour map constructed by colour deconvolution algorithm. The specificity in the cases of the Bernsen and the White methods is 1 and sensitivities are: 0.74 for White and 0.91 for Bernsen methods while the Sauvola method achieves sensitivity value of 0.74 and the specificity value of 0.99. According to Bland-Altman plot the Sauvola method selected objects are segmented without
Object knowledge changes visual appearance: semantic effects on color afterimages.
Lupyan, Gary
2015-10-01
According to predictive coding models of perception, what we see is determined jointly by the current input and the priors established by previous experience, expectations, and other contextual factors. The same input can thus be perceived differently depending on the priors that are brought to bear during viewing. Here, I show that expected (diagnostic) colors are perceived more vividly than arbitrary or unexpected colors, particularly when color input is unreliable. Participants were tested on a version of the 'Spanish Castle Illusion' in which viewing a hue-inverted image renders a subsequently shown achromatic version of the image in vivid color. Adapting to objects with intrinsic colors (e.g., a pumpkin) led to stronger afterimages than adapting to arbitrarily colored objects (e.g., a pumpkin-colored car). Considerably stronger afterimages were also produced by scenes containing intrinsically colored elements (grass, sky) compared to scenes with arbitrarily colored objects (books). The differences between images with diagnostic and arbitrary colors disappeared when the association between the image and color priors was weakened by, e.g., presenting the image upside-down, consistent with the prediction that color appearance is being modulated by color knowledge. Visual inputs that conflict with prior knowledge appear to be phenomenologically discounted, but this discounting is moderated by input certainty, as shown by the final study which uses conventional images rather than afterimages. As input certainty is increased, unexpected colors can become easier to detect than expected ones, a result consistent with predictive-coding models. Copyright © 2015 Elsevier B.V. All rights reserved.
Janowczyk, Andrew; Doyle, Scott; Gilmore, Hannah; Madabhushi, Anant
2018-01-01
Deep learning (DL) has recently been successfully applied to a number of image analysis problems. However, DL approaches tend to be inefficient for segmentation on large image data, such as high-resolution digital pathology slide images. For example, typical breast biopsy images scanned at 40× magnification contain billions of pixels, of which usually only a small percentage belong to the class of interest. For a typical naïve deep learning scheme, parsing through and interrogating all the image pixels would represent hundreds if not thousands of hours of compute time using high performance computing environments. In this paper, we present a resolution adaptive deep hierarchical (RADHicaL) learning scheme wherein DL networks at lower resolutions are leveraged to determine if higher levels of magnification, and thus computation, are necessary to provide precise results. We evaluate our approach on a nuclear segmentation task with a cohort of 141 ER+ breast cancer images and show we can reduce computation time on average by about 85%. Expert annotations of 12,000 nuclei across these 141 images were employed for quantitative evaluation of RADHicaL. A head-to-head comparison with a naïve DL approach, operating solely at the highest magnification, yielded the following performance metrics: .9407 vs .9854 Detection Rate, .8218 vs .8489 F -score, .8061 vs .8364 true positive rate and .8822 vs 0.8932 positive predictive value. Our performance indices compare favourably with state of the art nuclear segmentation approaches for digital pathology images.
Correlation based efficient face recognition and color change detection
NASA Astrophysics Data System (ADS)
Elbouz, M.; Alfalou, A.; Brosseau, C.; Alam, M. S.; Qasmi, S.
2013-01-01
Identifying the human face via correlation is a topic attracting widespread interest. At the heart of this technique lies the comparison of an unknown target image to a known reference database of images. However, the color information in the target image remains notoriously difficult to interpret. In this paper, we report a new technique which: (i) is robust against illumination change, (ii) offers discrimination ability to detect color change between faces having similar shape, and (iii) is specifically designed to detect red colored stains (i.e. facial bleeding). We adopt the Vanderlugt correlator (VLC) architecture with a segmented phase filter and we decompose the color target image using normalized red, green, and blue (RGB), and hue, saturation, and value (HSV) scales. We propose a new strategy to effectively utilize color information in signatures for further increasing the discrimination ability. The proposed algorithm has been found to be very efficient for discriminating face subjects with different skin colors, and those having color stains in different areas of the facial image.
Vane segment support and alignment device
McLaurin, Leroy Dixon; Sizemore, John Derek
1999-01-01
A support and alignment assembly for supporting and aligning a vane segment is provided. The support and alignment assembly comprises a torque plate which defines an opening for receiving an eccentric pin and a locking end member for receiving a lock socket member. An eccentric pin adjustably supported by the torque plate opening for supporting and aligning a vane segment is provided. A lock socket member adapted to securely receive the eccentric pin and rotated therewith, and adjustably engage the torque plate locking end is provided. The lock socket member receives the eccentric pin, such that when the eccentric pin is adjusted to align the vane segment, the lock socket member engages the torque plate locking end to secure the vane segment in the desired position.
Yi, Chucai; Tian, Yingli
2012-09-01
In this paper, we propose a novel framework to extract text regions from scene images with complex backgrounds and multiple text appearances. This framework consists of three main steps: boundary clustering (BC), stroke segmentation, and string fragment classification. In BC, we propose a new bigram-color-uniformity-based method to model both text and attachment surface, and cluster edge pixels based on color pairs and spatial positions into boundary layers. Then, stroke segmentation is performed at each boundary layer by color assignment to extract character candidates. We propose two algorithms to combine the structural analysis of text stroke with color assignment and filter out background interferences. Further, we design a robust string fragment classification based on Gabor-based text features. The features are obtained from feature maps of gradient, stroke distribution, and stroke width. The proposed framework of text localization is evaluated on scene images, born-digital images, broadcast video images, and images of handheld objects captured by blind persons. Experimental results on respective datasets demonstrate that the framework outperforms state-of-the-art localization algorithms.
Demosaicking algorithm for the Kodak-RGBW color filter array
NASA Astrophysics Data System (ADS)
Rafinazari, M.; Dubois, E.
2015-01-01
Digital cameras capture images through different Color Filter Arrays and then reconstruct the full color image. Each CFA pixel only captures one primary color component; the other primary components will be estimated using information from neighboring pixels. During the demosaicking algorithm, the two unknown color components will be estimated at each pixel location. Most of the demosaicking algorithms use the RGB Bayer CFA pattern with Red, Green and Blue filters. The least-Squares Luma-Chroma demultiplexing method is a state of the art demosaicking method for the Bayer CFA. In this paper we develop a new demosaicking algorithm using the Kodak-RGBW CFA. This particular CFA reduces noise and improves the quality of the reconstructed images by adding white pixels. We have applied non-adaptive and adaptive demosaicking method using the Kodak-RGBW CFA on the standard Kodak image dataset and the results have been compared with previous work.
Video Segmentation Descriptors for Event Recognition
2014-12-08
Velastin, 3D Extended Histogram of Oriented Gradients (3DHOG) for Classification of Road Users in Urban Scenes , BMVC, 2009. [3] M.-Y. Chen and A. Hauptmann...computed on 3D volume outputted by the hierarchical segmentation . Each video is described as follows. Each supertube is temporally divided in n-frame...strength of these descriptors is their adaptability to the scene variations since they are grounded on a video segmentation . This makes them naturally robust
DOE Office of Scientific and Technical Information (OSTI.GOV)
Zhang, J; Ates, O; Li, X
Purpose: To develop a tool that can quickly and automatically assess contour quality generated from auto segmentation during online adaptive replanning. Methods: Due to the strict time requirement of online replanning and lack of ‘ground truth’ contours in daily images, our method starts with assessing image registration accuracy focusing on the surface of the organ in question. Several metrics tightly related to registration accuracy including Jacobian maps, contours shell deformation, and voxel-based root mean square (RMS) analysis were computed. To identify correct contours, additional metrics and an adaptive decision tree are introduced. To approve in principle, tests were performed withmore » CT sets, planned and daily CTs acquired using a CT-on-rails during routine CT-guided RT delivery for 20 prostate cancer patients. The contours generated on daily CTs using an auto-segmentation tool (ADMIRE, Elekta, MIM) based on deformable image registration of the planning CT and daily CT were tested. Results: The deformed contours of 20 patients with total of 60 structures were manually checked as baselines. The incorrect rate of total contours is 49%. To evaluate the quality of local deformation, the Jacobian determinant (1.047±0.045) on contours has been analyzed. In an analysis of rectum contour shell deformed, the higher rate (0.41) of error contours detection was obtained compared to 0.32 with manual check. All automated detections took less than 5 seconds. Conclusion: The proposed method can effectively detect contour errors in micro and macro scope by evaluating multiple deformable registration metrics in a parallel computing process. Future work will focus on improving practicability and optimizing calculation algorithms and metric selection.« less
Multiview road sign detection via self-adaptive color model and shape context matching
NASA Astrophysics Data System (ADS)
Liu, Chunsheng; Chang, Faliang; Liu, Chengyun
2016-09-01
The multiview appearance of road signs in uncontrolled environments has made the detection of road signs a challenging problem in computer vision. We propose a road sign detection method to detect multiview road signs. This method is based on several algorithms, including the classical cascaded detector, the self-adaptive weighted Gaussian color model (SW-Gaussian model), and a shape context matching method. The classical cascaded detector is used to detect the frontal road signs in video sequences and obtain the parameters for the SW-Gaussian model. The proposed SW-Gaussian model combines the two-dimensional Gaussian model and the normalized red channel together, which can largely enhance the contrast between the red signs and background. The proposed shape context matching method can match shapes with big noise, which is utilized to detect road signs in different directions. The experimental results show that compared with previous detection methods, the proposed multiview detection method can reach higher detection rate in detecting signs with different directions.
Eleven Colors That Are Almost Never Confused
NASA Astrophysics Data System (ADS)
Boynton, Robert M.
1989-08-01
1.1. Three functions of color vision. Setting aside the complex psychological effects of color, related to esthetics, fashion, and mood, three relatively basic functions of color vision, which can be examined scientifically, are discernable. (1) With the eye in a given state of adaptation, color vision allows the perception of signals that otherwise would be below threshold, and therefore lost to perception. Evidence for this comes from a variety of two-color threshold experiments. (2) Visible contours can be maintained by color differences alone, regardless of the relative radiances of the two parts of the field whose junction defines the border. For achromatic vision, contour disappears at the isoluminant point. (3) Color specifies what seems to be an absolute property of a surface, one that enhances its recognizability and allows a clearer separation and classification of non-contiguous elements in the visual field.
Possible influences on color constancy by motion of color targets and by attention-controlled gaze.
Wan, Lifang; Shinomori, Keizo
2018-04-01
We investigated the influence of motion on color constancy using a chromatic stimulus presented in various conditions (static, motion, and rotation). Attention to the stimulus and background was also controlled in different gaze modes, constant fixation of the stimulus, and random viewing of the stimulus. Color constancy was examined in six young observers using a haploscopic view of a computer monitor. The target and background were illuminated in simulation by red, green, blue, and yellow, shifted from daylight (D65) by specific color differences along L - M or S - (L + M) axes on the equiluminance plane. The standard pattern (under D65) and test pattern (under the color illuminant) of a 5-deg square were presented side by side, consisting of 1.2-deg square targets with one of 12 colors at each center, surrounded by 230 background ellipses consisting of eight other colors. The central color targets in both patterns flipped between top and bottom locations at the rate of 3 deg/s in the motion condition. The results indicated an average reduction of color constancy over the 12 test colors by motion. The random viewing parameter indicated better color constancy by more attention to the background, although the difference was not significant. Color constancy of the four color illuminations was better to worse in green, red, yellow, and blue, respectively. The reduction of color constancy by motion could be explained by less contribution of the illumination estimation effect on color constancy. In the motion with constant fixation condition, the retina strongly adapted to the mean chromaticity of the background. However, motion resulted in less attention to the color of the background, causing a weaker effect of the illumination estimation. Conversely, in the static state with a random viewing condition, more attention to the background colors caused a stronger illumination estimation effect, and color constancy was improved overall.
Retinal adaptation abnormalities in primary open-angle glaucoma.
Dul, Mitchell; Ennis, Robert; Radner, Shira; Lee, Barry; Zaidi, Qasim
2015-01-22
Dynamic color and brightness adaptation are crucial for visual functioning. The effects of glaucoma on retinal ganglion cells (RGCs) could compromise these functions. We have previously used slow dynamic changes of light at moderate intensities to measure the speed and magnitude of subtractive adaptation in RGCs. We used the same procedure to test if RGC abnormalities cause slower and weaker adaptation for patients with glaucoma when compared to age-similar controls. We assessed adaptation deficits in specific classes of RGCs by testing along the three cardinal color axes that isolate konio, parvo, and magno RGCs. For one eye each of 10 primary open-angle glaucoma patients and their age-similar controls, we measured the speed and magnitude of adapting to 1/32 Hz color modulations along the three cardinal axes, at central fixation and 8° superior, inferior, nasal, and temporal to fixation. In all 15 comparisons (5 locations × 3 color axes), average adaptation was slower and weaker for glaucoma patients than for controls. Adaptation developed slower at central targets than at 8° eccentricities for controls, but not for patients. Adaptation speed and magnitude differed between affected and control eyes even at retinal locations showing no visual field loss with clinical perimetry. Neural adaptation is weaker in glaucoma patients for all three classes of RGCs. Since adaptation abnormalities are manifested even at retinal locations not exhibiting a visual field loss, this novel form of assessment may offer a functional insight into glaucoma and an early diagnosis tool. Copyright 2015 The Association for Research in Vision and Ophthalmology, Inc.
Retinal Adaptation Abnormalities in Primary Open-Angle Glaucoma
Dul, Mitchell; Ennis, Robert; Radner, Shira; Lee, Barry; Zaidi, Qasim
2015-01-01
Purpose. Dynamic color and brightness adaptation are crucial for visual functioning. The effects of glaucoma on retinal ganglion cells (RGCs) could compromise these functions. We have previously used slow dynamic changes of light at moderate intensities to measure the speed and magnitude of subtractive adaptation in RGCs. We used the same procedure to test if RGC abnormalities cause slower and weaker adaptation for patients with glaucoma when compared to age-similar controls. We assessed adaptation deficits in specific classes of RGCs by testing along the three cardinal color axes that isolate konio, parvo, and magno RGCs. Methods. For one eye each of 10 primary open-angle glaucoma patients and their age-similar controls, we measured the speed and magnitude of adapting to 1/32 Hz color modulations along the three cardinal axes, at central fixation and 8° superior, inferior, nasal, and temporal to fixation. Results. In all 15 comparisons (5 locations × 3 color axes), average adaptation was slower and weaker for glaucoma patients than for controls. Adaptation developed slower at central targets than at 8° eccentricities for controls, but not for patients. Adaptation speed and magnitude differed between affected and control eyes even at retinal locations showing no visual field loss with clinical perimetry. Conclusions. Neural adaptation is weaker in glaucoma patients for all three classes of RGCs. Since adaptation abnormalities are manifested even at retinal locations not exhibiting a visual field loss, this novel form of assessment may offer a functional insight into glaucoma and an early diagnosis tool. PMID:25613950
[Research on developping the spectral dataset for Dunhuang typical colors based on color constancy].
Liu, Qiang; Wan, Xiao-Xia; Liu, Zhen; Li, Chan; Liang, Jin-Xing
2013-11-01
The present paper aims at developping a method to reasonably set up the typical spectral color dataset for different kinds of Chinese cultural heritage in color rendering process. The world famous wall paintings dating from more than 1700 years ago in Dunhuang Mogao Grottoes was taken as typical case in this research. In order to maintain the color constancy during the color rendering workflow of Dunhuang culture relics, a chromatic adaptation based method for developping the spectral dataset of typical colors for those wall paintings was proposed from the view point of human vision perception ability. Under the help and guidance of researchers in the art-research institution and protection-research institution of Dunhuang Academy and according to the existing research achievement of Dunhuang Research in the past years, 48 typical known Dunhuang pigments were chosen and 240 representative color samples were made with reflective spectral ranging from 360 to 750 nm was acquired by a spectrometer. In order to find the typical colors of the above mentioned color samples, the original dataset was devided into several subgroups by clustering analysis. The grouping number, together with the most typical samples for each subgroup which made up the firstly built typical color dataset, was determined by wilcoxon signed rank test according to the color inconstancy index comprehensively calculated under 6 typical illuminating conditions. Considering the completeness of gamut of Dunhuang wall paintings, 8 complementary colors was determined and finally the typical spectral color dataset was built up which contains 100 representative spectral colors. The analytical calculating results show that the median color inconstancy index of the built dataset in 99% confidence level by wilcoxon signed rank test was 3.28 and the 100 colors are distributing in the whole gamut uniformly, which ensures that this dataset can provide reasonable reference for choosing the color with highest
NASA Astrophysics Data System (ADS)
Wang, Dongsheng; Zou, Jizuo; Yang, Yunping; Dong, Jianhua; Zhang, Yuanxiang
1996-10-01
A high-speed automatic agricultural produce grading and sorting system using color CCD and new color identification algorithm has been developed. In a typical application, the system can sort almonds into tow output grades according to their color. Almonds ar rich in 18 kinds of amino acids and 13 kinds of micro minerals and vitamins and can be made into almond drink. In order to ensure the drink quality, almonds must be sorted carefully before being made into a drink. Using this system, almonds can be sorted into two grades: up to grade and below grade almonds or foreign materials. A color CCD inspects the almonds passing on a conveyor of rotating rollers, a color identification algorithm grades almonds and distinguishes foreign materials from almonds. Employing an elaborately designed mechanism, the below grade almonds and foreign materials can be removed effectively from the raw almonds. This system can be easily adapted for inspecting and sorting other kinds of agricultural produce such as peanuts, beans tomatoes and so on.
Vane segment support and alignment device
McLaurin, L.D.; Sizemore, J.D.
1999-07-13
A support and alignment assembly for supporting and aligning a vane segment is provided. The support and alignment assembly comprises a torque plate which defines an opening for receiving an eccentric pin and a locking end member for receiving a lock socket member. An eccentric pin adjustably supported by the torque plate opening for supporting and aligning a vane segment is provided. A lock socket member adapted to securely receive the eccentric pin and rotated therewith, and adjustably engage the torque plate locking end is provided. The lock socket member receives the eccentric pin, such that when the eccentric pin is adjusted to align the vane segment, the lock socket member engages the torque plate locking end to secure the vane segment in the desired position. 5 figs.
Vane segment support and alignment device
DOE Office of Scientific and Technical Information (OSTI.GOV)
McLaurin, L.D.; Sizemore, J.D.
1999-07-13
A support and alignment assembly for supporting and aligning a vane segment is provided. The support and alignment assembly comprises a torque plate which defines an opening for receiving an eccentric pin and a locking end member for receiving a lock socket member. An eccentric pin adjustably supported by the torque plate opening for supporting and aligning a vane segment is provided. A lock socket member adapted to securely receive the eccentric pin and rotated therewith, and adjustably engage the torque plate locking end is provided. The lock socket member receives the eccentric pin, such that when the eccentric pinmore » is adjusted to align the vane segment, the lock socket member engages the torque plate locking end to secure the vane segment in the desired position. 5 figs.« less
Mercurio, Meagan D; Dambergs, Robert G; Herderich, Markus J; Smith, Paul A
2007-06-13
The methyl cellulose precipitable (MCP) tannin assay and a modified version of the Somers and Evans color assay were adapted to high-throughput (HTP) analysis. To improve efficiency of the MCP tannin assay, a miniaturized 1 mL format and a HTP format using 96 well plates were developed. The Somers color assay was modified to allow the standardization of pH and ethanol concentrations of wine samples in a simple one-step dilution with a buffer solution, thus removing inconsistencies between wine matrices prior to analysis and allowing for its adaptation to a HTP format. Validation studies showed that all new formats were efficient, and results were reproducible and analogous to the original formats.
Alertness function of thalamus in conflict adaptation.
Wang, Xiangpeng; Zhao, Xiaoyue; Xue, Gui; Chen, Antao
2016-05-15
Conflict adaptation reflects the ability to improve current conflict resolution based on previously experienced conflict, which is crucial for our goal-directed behaviors. In recent years, the roles of alertness are attracting increasing attention when discussing the generation of conflict adaptation. However, due to the difficulty of manipulating alertness, very limited progress has been made in this line. Inspired by that color may affect alertness, we manipulated background color of experimental task and found that conflict adaptation significantly presented in gray and red backgrounds but did not in blue background. Furthermore, behavioral and functional magnetic resonance imaging results revealed that the modulation of color on conflict adaptation was implemented through changing alertness level. In particular, blue background eliminated conflict adaptation by damping the alertness regulating function of thalamus and the functional connectivity between thalamus and inferior frontal gyrus (IFG). In contrast, in gray and red backgrounds where alertness levels are typically high, the thalamus and the right IFG functioned normally and conflict adaptations were significant. Therefore, the alertness function of thalamus is determinant to conflict adaptation, and thalamus and right IFG are crucial nodes of the neural circuit subserving this ability. Present findings provide new insights into the neural mechanisms of conflict adaptation. Copyright © 2016 Elsevier Inc. All rights reserved.
An ecological valence theory of human color preference
Palmer, Stephen E.; Schloss, Karen B.
2010-01-01
Color preference is an important aspect of visual experience, but little is known about why people in general like some colors more than others. Previous research suggested explanations based on biological adaptations [Hurlbert AC, Ling YL (2007) Curr Biol 17:623–625] and color-emotions [Ou L-C, Luo MR, Woodcock A, Wright A (2004) Color Res Appl 29:381–389]. In this article we articulate an ecological valence theory in which color preferences arise from people’s average affective responses to color-associated objects. An empirical test provides strong support for this theory: People like colors strongly associated with objects they like (e.g., blues with clear skies and clean water) and dislike colors strongly associated with objects they dislike (e.g., browns with feces and rotten food). Relative to alternative theories, the ecological valence theory both fits the data better (even with fewer free parameters) and provides a more plausible, comprehensive causal explanation of color preferences. PMID:20421475
An ecological valence theory of human color preference.
Palmer, Stephen E; Schloss, Karen B
2010-05-11
Color preference is an important aspect of visual experience, but little is known about why people in general like some colors more than others. Previous research suggested explanations based on biological adaptations [Hurlbert AC, Ling YL (2007) Curr Biol 17:623-625] and color-emotions [Ou L-C, Luo MR, Woodcock A, Wright A (2004) Color Res Appl 29:381-389]. In this article we articulate an ecological valence theory in which color preferences arise from people's average affective responses to color-associated objects. An empirical test provides strong support for this theory: People like colors strongly associated with objects they like (e.g., blues with clear skies and clean water) and dislike colors strongly associated with objects they dislike (e.g., browns with feces and rotten food). Relative to alternative theories, the ecological valence theory both fits the data better (even with fewer free parameters) and provides a more plausible, comprehensive causal explanation of color preferences.
Adaptive typography for dynamic mapping environments
NASA Astrophysics Data System (ADS)
Bardon, Didier
1991-08-01
When typography moves across a map, it passes over areas of different colors, densities, and textures. In such a dynamic environment, the aspect of typography must be constantly adapted to provide disernibility for every new background. Adaptive typography undergoes two adaptive operations: background control and contrast control. The background control prevents the features of the map (edges, lines, abrupt changes of densities) from destroying the integrity of the letterform. This is achieved by smoothing the features of the map in the area where a text label is displayed. The modified area is limited to the space covered by the characters of the label. Dispositions are taken to insure that the smoothing operation does not introduce any new visual noise. The contrast control assures that there are sufficient lightness differences between the typography and its ever-changing background. For every new situation, background color and foreground color are compared and the foreground color lightness is adjusted according to a chosen contrast value. Criteria and methods of choosing the appropriate contrast value are presented as well as the experiments that led to them.
Bilayer segmentation of webcam videos using tree-based classifiers.
Yin, Pei; Criminisi, Antonio; Winn, John; Essa, Irfan
2011-01-01
This paper presents an automatic segmentation algorithm for video frames captured by a (monocular) webcam that closely approximates depth segmentation from a stereo camera. The frames are segmented into foreground and background layers that comprise a subject (participant) and other objects and individuals. The algorithm produces correct segmentations even in the presence of large background motion with a nearly stationary foreground. This research makes three key contributions: First, we introduce a novel motion representation, referred to as "motons," inspired by research in object recognition. Second, we propose estimating the segmentation likelihood from the spatial context of motion. The estimation is efficiently learned by random forests. Third, we introduce a general taxonomy of tree-based classifiers that facilitates both theoretical and experimental comparisons of several known classification algorithms and generates new ones. In our bilayer segmentation algorithm, diverse visual cues such as motion, motion context, color, contrast, and spatial priors are fused by means of a conditional random field (CRF) model. Segmentation is then achieved by binary min-cut. Experiments on many sequences of our videochat application demonstrate that our algorithm, which requires no initialization, is effective in a variety of scenes, and the segmentation results are comparable to those obtained by stereo systems.
3D geometric split-merge segmentation of brain MRI datasets.
Marras, Ioannis; Nikolaidis, Nikolaos; Pitas, Ioannis
2014-05-01
In this paper, a novel method for MRI volume segmentation based on region adaptive splitting and merging is proposed. The method, called Adaptive Geometric Split Merge (AGSM) segmentation, aims at finding complex geometrical shapes that consist of homogeneous geometrical 3D regions. In each volume splitting step, several splitting strategies are examined and the most appropriate is activated. A way to find the maximal homogeneity axis of the volume is also introduced. Along this axis, the volume splitting technique divides the entire volume in a number of large homogeneous 3D regions, while at the same time, it defines more clearly small homogeneous regions within the volume in such a way that they have greater probabilities of survival at the subsequent merging step. Region merging criteria are proposed to this end. The presented segmentation method has been applied to brain MRI medical datasets to provide segmentation results when each voxel is composed of one tissue type (hard segmentation). The volume splitting procedure does not require training data, while it demonstrates improved segmentation performance in noisy brain MRI datasets, when compared to the state of the art methods. Copyright © 2014 Elsevier Ltd. All rights reserved.
Segmentation of nuclear images in automated cervical cancer screening
NASA Astrophysics Data System (ADS)
Dadeshidze, Vladimir; Olsson, Lars J.; Domanik, Richard A.
1995-08-01
This paper describes an efficient method of segmenting cell nuclei from complex scenes based upon the use of adaptive region growing in conjuction with nucleus-specific filters. Results of segmenting potentially abnormal (cancer or neoplastic) cell nuclei in Papanicolaou smears from 0.8 square micrometers resolution images are also presented.
Biomimetics, color, and the arts
NASA Astrophysics Data System (ADS)
Schenk, Franziska
2015-03-01
Color as dramatic, dynamic and dazzling as the iridescent hues on the wings of certain butterflies has never been encountered in the art world. Unlike and unmatched by the chemical pigments of the artists' palette, this changeable color is created by transparent, colorless nanostructures that, as with prisms, diffract and reflect light to render spectral color visible. Until now, iridescent colors, by their very nature, have defied artists' best efforts to fully capture these rainbow hues. Now, for the first time, the artist and researcher Franziska Schenk employs latest nature-inspired color-shift technology to actually simulate the iridescence of butterflies and beetles on canvas. Crucially, studying the ingenious ways in which a range of such displays are created by insects has provided the artist with vital clues on how to adapt and adopt these challenging optical nano-materials for painting. And indeed, after years of meticulous and painstaking research both in the lab and studio, the desired effect is achieved. The resulting paintings, like an iridescent insect, do in fact fluctuate in perceived color - depending on the light and viewing angle. In tracing the artist's respective biomimetic approach, the paper not only provides an insight into the new color technology's evolution and innovative artistic possibilities, but also suggests what artists can learn from nature.
Seymour, K J; Williams, M A; Rich, A N
2016-05-01
Many theories of visual object perception assume the visual system initially extracts borders between objects and their background and then "fills in" color to the resulting object surfaces. We investigated the transformation of chromatic signals across the human ventral visual stream, with particular interest in distinguishing representations of object surface color from representations of chromatic signals reflecting the retinal input. We used fMRI to measure brain activity while participants viewed figure-ground stimuli that differed either in the position or in the color contrast polarity of the foreground object (the figure). Multivariate pattern analysis revealed that classifiers were able to decode information about which color was presented at a particular retinal location from early visual areas, whereas regions further along the ventral stream exhibited biases for representing color as part of an object's surface, irrespective of its position on the retina. Additional analyses showed that although activity in V2 contained strong chromatic contrast information to support the early parsing of objects within a visual scene, activity in this area also signaled information about object surface color. These findings are consistent with the view that mechanisms underlying scene segmentation and the binding of color to object surfaces converge in V2. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
2013-01-01
The comparative study of the results of various segmentation methods for the digital images of the follicular lymphoma cancer tissue section is described in this paper. The sensitivity and specificity and some other parameters of the following adaptive threshold methods of segmentation: the Niblack method, the Sauvola method, the White method, the Bernsen method, the Yasuda method and the Palumbo method, are calculated. Methods are applied to three types of images constructed by extraction of the brown colour information from the artificial images synthesized based on counterpart experimentally captured images. This paper presents usefulness of the microscopic image synthesis method in evaluation as well as comparison of the image processing results. The results of thoughtful analysis of broad range of adaptive threshold methods applied to: (1) the blue channel of RGB, (2) the brown colour extracted by deconvolution and (3) the ’brown component’ extracted from RGB allows to select some pairs: method and type of image for which this method is most efficient considering various criteria e.g. accuracy and precision in area detection or accuracy in number of objects detection and so on. The comparison shows that the White, the Bernsen and the Sauvola methods results are better than the results of the rest of the methods for all types of monochromatic images. All three methods segments the immunopositive nuclei with the mean accuracy of 0.9952, 0.9942 and 0.9944 respectively, when treated totally. However the best results are achieved for monochromatic image in which intensity shows brown colour map constructed by colour deconvolution algorithm. The specificity in the cases of the Bernsen and the White methods is 1 and sensitivities are: 0.74 for White and 0.91 for Bernsen methods while the Sauvola method achieves sensitivity value of 0.74 and the specificity value of 0.99. According to Bland-Altman plot the Sauvola method selected objects are segmented without
Li, Heng; Su, Xiaofan; Wang, Jing; Kan, Han; Han, Tingting; Zeng, Yajie; Chai, Xinyu
2018-01-01
Current retinal prostheses can only generate low-resolution visual percepts constituted of limited phosphenes which are elicited by an electrode array and with uncontrollable color and restricted grayscale. Under this visual perception, prosthetic recipients can just complete some simple visual tasks, but more complex tasks like face identification/object recognition are extremely difficult. Therefore, it is necessary to investigate and apply image processing strategies for optimizing the visual perception of the recipients. This study focuses on recognition of the object of interest employing simulated prosthetic vision. We used a saliency segmentation method based on a biologically plausible graph-based visual saliency model and a grabCut-based self-adaptive-iterative optimization framework to automatically extract foreground objects. Based on this, two image processing strategies, Addition of Separate Pixelization and Background Pixel Shrink, were further utilized to enhance the extracted foreground objects. i) The results showed by verification of psychophysical experiments that under simulated prosthetic vision, both strategies had marked advantages over Direct Pixelization in terms of recognition accuracy and efficiency. ii) We also found that recognition performance under two strategies was tied to the segmentation results and was affected positively by the paired-interrelated objects in the scene. The use of the saliency segmentation method and image processing strategies can automatically extract and enhance foreground objects, and significantly improve object recognition performance towards recipients implanted a high-density implant. Copyright © 2017 Elsevier B.V. All rights reserved.
Spatially adapted augmentation of age-specific atlas-based segmentation using patch-based priors
NASA Astrophysics Data System (ADS)
Liu, Mengyuan; Seshamani, Sharmishtaa; Harrylock, Lisa; Kitsch, Averi; Miller, Steven; Chau, Van; Poskitt, Kenneth; Rousseau, Francois; Studholme, Colin
2014-03-01
One of the most common approaches to MRI brain tissue segmentation is to employ an atlas prior to initialize an Expectation- Maximization (EM) image labeling scheme using a statistical model of MRI intensities. This prior is commonly derived from a set of manually segmented training data from the population of interest. However, in cases where subject anatomy varies significantly from the prior anatomical average model (for example in the case where extreme developmental abnormalities or brain injuries occur), the prior tissue map does not provide adequate information about the observed MRI intensities to ensure the EM algorithm converges to an anatomically accurate labeling of the MRI. In this paper, we present a novel approach for automatic segmentation of such cases. This approach augments the atlas-based EM segmentation by exploring methods to build a hybrid tissue segmentation scheme that seeks to learn where an atlas prior fails (due to inadequate representation of anatomical variation in the statistical atlas) and utilize an alternative prior derived from a patch driven search of the atlas data. We describe a framework for incorporating this patch-based augmentation of EM (PBAEM) into a 4D age-specific atlas-based segmentation of developing brain anatomy. The proposed approach was evaluated on a set of MRI brain scans of premature neonates with ages ranging from 27.29 to 46.43 gestational weeks (GWs). Results indicated superior performance compared to the conventional atlas-based segmentation method, providing improved segmentation accuracy for gray matter, white matter, ventricles and sulcal CSF regions.
Li, Junfeng; Wan, Xiaoxia
2018-01-15
To enrich the contents of digital archive and to guide the copy and restoration of colored relics, non-invasive methods for extraction of painting boundary and identification of pigment composition are proposed in this study based on the visible spectral images of colored relics. Superpixel concept is applied for the first time to the field of oversegmentation of visible spectral images and implemented on the visible spectral images of colored relics to extract their painting boundary. Since different pigments are characterized by their own spectrum and the same kind of pigment has the similar geometric profile in spectrum, an automatic identification method is established by comparing the proximity between the geometric profiles of the unknown spectrum from each superpixel and the pre-known spectrum from a deliberately prepared database. The methods are validated using the visible spectral images of the ancient wall paintings in Mogao Grottoes. By the way, the visible spectral images are captured by a multispectral imaging system consisting of two broadband filters and a RGB camera with high spatial resolution. Copyright © 2017 Elsevier B.V. All rights reserved.
Distance-based over-segmentation for single-frame RGB-D images
NASA Astrophysics Data System (ADS)
Fang, Zhuoqun; Wu, Chengdong; Chen, Dongyue; Jia, Tong; Yu, Xiaosheng; Zhang, Shihong; Qi, Erzhao
2017-11-01
Over-segmentation, known as super-pixels, is a widely used preprocessing step in segmentation algorithms. Oversegmentation algorithm segments an image into regions of perceptually similar pixels, but performs badly based on only color image in the indoor environments. Fortunately, RGB-D images can improve the performances on the images of indoor scene. In order to segment RGB-D images into super-pixels effectively, we propose a novel algorithm, DBOS (Distance-Based Over-Segmentation), which realizes full coverage of super-pixels on the image. DBOS fills the holes in depth images to fully utilize the depth information, and applies SLIC-like frameworks for fast running. Additionally, depth features such as plane projection distance are extracted to compute distance which is the core of SLIC-like frameworks. Experiments on RGB-D images of NYU Depth V2 dataset demonstrate that DBOS outperforms state-ofthe-art methods in quality while maintaining speeds comparable to them.
Wang, Chen; Ji, Na
2012-06-01
The intrinsic aberrations of high-NA gradient refractive index (GRIN) lenses limit their image quality as well as field of view. Here we used a pupil-segmentation-based adaptive optical approach to correct the inherent aberrations in a two-photon fluorescence endoscope utilizing a 0.8 NA GRIN lens. By correcting the field-dependent aberrations, we recovered diffraction-limited performance across a large imaging field. The consequent improvements in imaging signal and resolution allowed us to detect fine structures that were otherwise invisible inside mouse brain slices.
NASA Technical Reports Server (NTRS)
Tilton, James C.; Ramapriyan, H. K.
1989-01-01
A case study is presented where an image segmentation based compression technique is applied to LANDSAT Thematic Mapper (TM) and Nimbus-7 Coastal Zone Color Scanner (CZCS) data. The compression technique, called Spatially Constrained Clustering (SCC), can be regarded as an adaptive vector quantization approach. The SCC can be applied to either single or multiple spectral bands of image data. The segmented image resulting from SCC is encoded in small rectangular blocks, with the codebook varying from block to block. Lossless compression potential (LDP) of sample TM and CZCS images are evaluated. For the TM test image, the LCP is 2.79. For the CZCS test image the LCP is 1.89, even though when only a cloud-free section of the image is considered the LCP increases to 3.48. Examples of compressed images are shown at several compression ratios ranging from 4 to 15. In the case of TM data, the compressed data are classified using the Bayes' classifier. The results show an improvement in the similarity between the classification results and ground truth when compressed data are used, thus showing that compression is, in fact, a useful first step in the analysis.
Graph-based segmentation for RGB-D data using 3-D geometry enhanced superpixels.
Yang, Jingyu; Gan, Ziqiao; Li, Kun; Hou, Chunping
2015-05-01
With the advances of depth sensing technologies, color image plus depth information (referred to as RGB-D data hereafter) is more and more popular for comprehensive description of 3-D scenes. This paper proposes a two-stage segmentation method for RGB-D data: 1) oversegmentation by 3-D geometry enhanced superpixels and 2) graph-based merging with label cost from superpixels. In the oversegmentation stage, 3-D geometrical information is reconstructed from the depth map. Then, a K-means-like clustering method is applied to the RGB-D data for oversegmentation using an 8-D distance metric constructed from both color and 3-D geometrical information. In the merging stage, treating each superpixel as a node, a graph-based model is set up to relabel the superpixels into semantically-coherent segments. In the graph-based model, RGB-D proximity, texture similarity, and boundary continuity are incorporated into the smoothness term to exploit the correlations of neighboring superpixels. To obtain a compact labeling, the label term is designed to penalize labels linking to similar superpixels that likely belong to the same object. Both the proposed 3-D geometry enhanced superpixel clustering method and the graph-based merging method from superpixels are evaluated by qualitative and quantitative results. By the fusion of color and depth information, the proposed method achieves superior segmentation performance over several state-of-the-art algorithms.
Wolff, A P; Groen, G J; Crul, B J
2001-01-01
Selective spinal nerve infiltration blocks are used diagnostically in patients with chronic low back pain radiating into the leg. Generally, a segmental nerve block is considered successful if the pain is reduced substantially. Hypesthesia and elicited paresthesias coinciding with the presumed segmental level are used as controls. The interpretation depends on a standard dermatomal map. However, it is not clear if this interpretation is reliable enough, because standard dermatomal maps do not show the overlap of neighboring dermatomes. The goal of the present study is to establish if dissimilarities exist between areas of hypesthesia, spontaneous pain reported by the patient, pain reduction by local anesthetics, and paresthesias elicited by sensory electrostimulation. A secondary goal is to determine to what extent the interpretation is improved when the overlaps of neighboring dermatomes are taken into account. Patients suffering from chronic low back pain with pain radiating into the leg underwent lumbosacral segmental nerve root blocks at subsequent levels on separate days. Lidocaine (2%, 0.5 mL) mixed with radiopaque fluid (0.25 mL) was injected after verifying the target location using sensory and motor electrostimulation. Sensory changes (pinprick method), paresthesias (reported by the patient), and pain reduction (Numeric Rating Scale) were reported. Hypesthesia and paresthesias were registered in a standard dermatomal map and in an adapted map which included overlap of neighboring dermatomes. The relationships between spinal level of injection, extent of hypesthesia, location of paresthesias, and corresponding dermatome were assessed quantitatively. Comparison of the results between both dermatomal maps was done by paired t-tests. After inclusion, data were processed for 40 segmental nerve blocks (L2-S1) performed in 29 patients. Pain reduction was achieved in 43%. Hypesthetic areas showed a large variability in size and location, and also in comparison to
Study of chromatic adaptation via neutral white matches on different viewing media.
Zhai, Qiyan; Luo, Ming R
2018-03-19
Two experiments were carried out to study the neutral white and the chromatic adaptation in human vision and color science. After matching neutral whites under different illuminants using both surface and self-luminous colors, the result were used to verify the previous study about the chromatic adaptation. Not all the white illuminants were found neutral even the adaptation time is long. The baseline illuminant of the two-step chromatic adaptation transform was found as the illuminant with the same chromaticity of the neutral white under it and depended on viewing medium in the present study. The results were also used as corresponding colors to derive models of the effective degree of chromatic adaptation, which were found highly associated with the chromaticity of the adapting illuminant.
A new region-edge based level set model with applications to image segmentation
NASA Astrophysics Data System (ADS)
Zhi, Xuhao; Shen, Hong-Bin
2018-04-01
Level set model has advantages in handling complex shapes and topological changes, and is widely used in image processing tasks. The image segmentation oriented level set models can be grouped into region-based models and edge-based models, both of which have merits and drawbacks. Region-based level set model relies on fitting to color intensity of separated regions, but is not sensitive to edge information. Edge-based level set model evolves by fitting to local gradient information, but can get easily affected by noise. We propose a region-edge based level set model, which considers saliency information into energy function and fuses color intensity with local gradient information. The evolution of the proposed model is implemented by a hierarchical two-stage protocol, and the experimental results show flexible initialization, robust evolution and precise segmentation.
Xiao, X; Bai, B; Xu, N; Wu, K
2015-04-01
Oversegmentation is a major drawback of the morphological watershed algorithm. Here, we study and reveal that the oversegmentation is not only because of the irregular shapes of the particle images, which people are familiar with, but also because of some particles, such as ellipses, with more than one centre. A new parameter, the striping level, is introduced and the criterion for striping parameter is built to help find the right markers prior to segmentation. An adaptive striping watershed algorithm is established by applying a procedure, called the marker searching algorithm, to find the markers, which can effectively suppress the oversegmentation. The effectiveness of the proposed method is validated by analysing some typical particle images including the images of gold nanorod ensembles. © 2014 The Authors Journal of Microscopy © 2014 Royal Microscopical Society.
Statistics of natural scenes and cortical color processing.
Cecchi, Guillermo A; Rao, A Ravishankar; Xiao, Youping; Kaplan, Ehud
2010-09-01
We investigate the spatial correlations of orientation and color information in natural images. We find that the correlation of orientation information falls off rapidly with increasing distance, while color information is more highly correlated over longer distances. We show that orientation and color information are statistically independent in natural images and that the spatial correlation of jointly encoded orientation and color information decays faster than that of color alone. Our findings suggest that: (a) orientation and color information should be processed in separate channels and (b) the organization of cortical color and orientation selectivity at low spatial frequencies is a reflection of the cortical adaptation to the statistical structure of the visual world. These findings are in agreement with biological observations, as form and color are thought to be represented by different classes of neurons in the primary visual cortex, and the receptive fields of color-selective neurons are larger than those of orientation-selective neurons. The agreement between our findings and biological observations supports the ecological theory of perception.
Peripheral visual response time to colored stimuli imaged on the horizontal meridian
NASA Technical Reports Server (NTRS)
Haines, R. F.; Gross, M. M.; Nylen, D.; Dawson, L. M.
1974-01-01
Two male observers were administered a binocular visual response time task to small (45 min arc), flashed, photopic stimuli at four dominant wavelengths (632 nm red; 583 nm yellow; 526 nm green; 464 nm blue) imaged across the horizontal retinal meridian. The stimuli were imaged at 10 deg arc intervals from 80 deg left to 90 deg right of fixation. Testing followed either prior light adaptation or prior dark adaptation. Results indicated that mean response time (RT) varies with stimulus color. RT is faster to yellow than to blue and green and slowest to red. In general, mean RT was found to increase from fovea to periphery for all four colors, with the curve for red stimuli exhibiting the most rapid positive acceleration with increasing angular eccentricity from the fovea. The shape of the RT distribution across the retina was also found to depend upon the state of light or dark adaptation. The findings are related to previous RT research and are discussed in terms of optimizing the color and position of colored displays on instrument panels.
Finding a good segmentation strategy for tree crown transparency estimation
Neil A. Clark; Sang-Mook Lee; Philip A. Araman
2003-01-01
Image segmentation is a general term for delineating image areas into informational categories. A wide variety of general techniques exist depending on application and the image data specifications. Specialized algorithms, utilizing components of several techniques, usually are needed to meet the rigors for a specific application. This paper considers automated color...
Perceptual significance of colorimetric data for colors of plumes and haze
NASA Astrophysics Data System (ADS)
MacAdam, David L.
Colorimetric reduction of spectroradiometric and spectral absorption and scattering data, by use of C.I.E. (Commission Internationale de l'Eclairage) data is appropriate for assessment of the color appearances of plumes and haze and of vistas seen through haze. Chromatic adaptation needs to be taken into account, however, because a wide variety of chromaticities (e.g., color temperatures from 4000 K to at least 7000 K) can be perceived as white under various circumstances. The perceptions of all other colors shift correspondingly. Natural clouds or snow appear white: they have the chromaticity relative to which the perceived hues of all other objects in the same scene (including plumes and haze layers) are perceived. Those hues can be determined by drawing the straight line from that white point through the point representing the plume or haze. The wavelength at which that line intersects the spectrum locus is the dominant wavelength of the plume or haze, or other feature in the vista, for the state of chromatic adaptation of the observer. The dominant wavelength identifies the hue. The percentage of the distance from that white point to the spectrum locus is the purity of the plume, haze, or haze-veiled color. The perceived amount of coloration (saturation) can be evaluated as a multiple of the just-noticeable difference from the adaptation white.
Villanueva Campos, A M; Tardáguila de la Fuente, G; Utrera Pérez, E; Jurado Basildo, C; Mera Fernández, D; Martínez Rodríguez, C
To analyze whether there are significant differences in the objective quantitative parameters obtained in the postprocessing of dual-energy CT enterography studies between bowel segments with radiologic signs of Crohn's disease and radiologically normal segments. This retrospective study analyzed 33 patients (16 men and 17 women; mean age 54 years) with known Crohn's disease who underwent CT enterography on a dual-energy scanner with oral sorbitol and intravenous contrast material in the portal phase. Images obtained with dual energy were postprocessed to obtain color maps (iodine maps). For each patient, regions of interest were traced on these color maps and the density of iodine (mg/ml) and the fat fraction (%) were calculated for the wall of a pathologic bowel segment with radiologic signs of Crohn's disease and for the wall of a healthy bowel segment; the differences in these parameters between the two segments were analyzed. The density of iodine was lower in the radiologically normal segments than in the pathologic segments [1.8 ± 0.4mg/ml vs. 3.7 ± 0.9mg/ml; p<0.05]. The fat fraction was higher in the radiologically normal segments than in the pathologic segments [32.42% ± 6.5 vs. 22.23% ± 9.4; p<0.05]. There are significant differences in the iodine density and fat fraction between bowel segments with radiologic signs of Crohn's disease and radiologically normal segments. Copyright © 2018 SERAM. Publicado por Elsevier España, S.L.U. All rights reserved.
Color rendering indices in global illumination methods
NASA Astrophysics Data System (ADS)
Geisler-Moroder, David; Dür, Arne
2009-02-01
Human perception of material colors depends heavily on the nature of the light sources used for illumination. One and the same object can cause highly different color impressions when lit by a vapor lamp or by daylight, respectively. Based on state-of-the-art colorimetric methods we present a modern approach for calculating color rendering indices (CRI), which were defined by the International Commission on Illumination (CIE) to characterize color reproduction properties of illuminants. We update the standard CIE method in three main points: firstly, we use the CIELAB color space, secondly, we apply a Bradford transformation for chromatic adaptation, and finally, we evaluate color differences using the CIEDE2000 total color difference formula. Moreover, within a real-world scene, light incident on a measurement surface is composed of a direct and an indirect part. Neumann and Schanda1 have shown for the cube model that interreflections can influence the CRI of an illuminant. We analyze how color rendering indices vary in a real-world scene with mixed direct and indirect illumination and recommend the usage of a spectral rendering engine instead of an RGB based renderer for reasons of accuracy of CRI calculations.
Boundary segmentation for fluorescence microscopy using steerable filters
NASA Astrophysics Data System (ADS)
Ho, David Joon; Salama, Paul; Dunn, Kenneth W.; Delp, Edward J.
2017-02-01
Fluorescence microscopy is used to image multiple subcellular structures in living cells which are not readily observed using conventional optical microscopy. Moreover, two-photon microscopy is widely used to image structures deeper in tissue. Recent advancement in fluorescence microscopy has enabled the generation of large data sets of images at different depths, times, and spectral channels. Thus, automatic object segmentation is necessary since manual segmentation would be inefficient and biased. However, automatic segmentation is still a challenging problem as regions of interest may not have well defined boundaries as well as non-uniform pixel intensities. This paper describes a method for segmenting tubular structures in fluorescence microscopy images of rat kidney and liver samples using adaptive histogram equalization, foreground/background segmentation, steerable filters to capture directional tendencies, and connected-component analysis. The results from several data sets demonstrate that our method can segment tubular boundaries successfully. Moreover, our method has better performance when compared to other popular image segmentation methods when using ground truth data obtained via manual segmentation.
Satellite classification and segmentation using non-additive entropy
NASA Astrophysics Data System (ADS)
Assirati, Lucas; Souto Martinez, Alexandre; Martinez Bruno, Odemir
2014-03-01
Here we compare the Boltzmann-Gibbs-Shannon (standard) with the Tsallis entropy on the pattern recognition and segmentation of colored images obtained by satellites, via "Google Earth". By segmentation we mean particionate an image to locate regions of interest. Here, we discriminate and define an image partition classes according to a training basis. This training basis consists of three pattern classes: aquatic, urban and vegetation regions. Our numerical experiments demonstrate that the Tsallis entropy, used as a feature vector composed of distinct entropic indexes q outperforms the standard entropy. There are several applications of our proposed methodology, once satellite images can be used to monitor migration form rural to urban regions, agricultural activities, oil spreading on the ocean etc.
Categorical encoding of color in the brain.
Bird, Chris M; Berens, Samuel C; Horner, Aidan J; Franklin, Anna
2014-03-25
The areas of the brain that encode color categorically have not yet been reliably identified. Here, we used functional MRI adaptation to identify neuronal populations that represent color categories irrespective of metric differences in color. Two colors were successively presented within a block of trials. The two colors were either from the same or different categories (e.g., "blue 1 and blue 2" or "blue 1 and green 1"), and the size of the hue difference was varied. Participants performed a target detection task unrelated to the difference in color. In the middle frontal gyrus of both hemispheres and to a lesser extent, the cerebellum, blood-oxygen level-dependent response was greater for colors from different categories relative to colors from the same category. Importantly, activation in these regions was not modulated by the size of the hue difference, suggesting that neurons in these regions represent color categorically, regardless of metric color difference. Representational similarity analyses, which investigated the similarity of the pattern of activity across local groups of voxels, identified other regions of the brain (including the visual cortex), which responded to metric but not categorical color differences. Therefore, categorical and metric hue differences appear to be coded in qualitatively different ways and in different brain regions. These findings have implications for the long-standing debate on the origin and nature of color categories, and also further our understanding of how color is processed by the brain.
Categorical encoding of color in the brain
Bird, Chris M.; Berens, Samuel C.; Horner, Aidan J.; Franklin, Anna
2014-01-01
The areas of the brain that encode color categorically have not yet been reliably identified. Here, we used functional MRI adaptation to identify neuronal populations that represent color categories irrespective of metric differences in color. Two colors were successively presented within a block of trials. The two colors were either from the same or different categories (e.g., “blue 1 and blue 2” or “blue 1 and green 1”), and the size of the hue difference was varied. Participants performed a target detection task unrelated to the difference in color. In the middle frontal gyrus of both hemispheres and to a lesser extent, the cerebellum, blood-oxygen level-dependent response was greater for colors from different categories relative to colors from the same category. Importantly, activation in these regions was not modulated by the size of the hue difference, suggesting that neurons in these regions represent color categorically, regardless of metric color difference. Representational similarity analyses, which investigated the similarity of the pattern of activity across local groups of voxels, identified other regions of the brain (including the visual cortex), which responded to metric but not categorical color differences. Therefore, categorical and metric hue differences appear to be coded in qualitatively different ways and in different brain regions. These findings have implications for the long-standing debate on the origin and nature of color categories, and also further our understanding of how color is processed by the brain. PMID:24591602
Kumar, Rajesh; Srivastava, Subodh; Srivastava, Rajeev
2017-07-01
For cancer detection from microscopic biopsy images, image segmentation step used for segmentation of cells and nuclei play an important role. Accuracy of segmentation approach dominate the final results. Also the microscopic biopsy images have intrinsic Poisson noise and if it is present in the image the segmentation results may not be accurate. The objective is to propose an efficient fuzzy c-means based segmentation approach which can also handle the noise present in the image during the segmentation process itself i.e. noise removal and segmentation is combined in one step. To address the above issues, in this paper a fourth order partial differential equation (FPDE) based nonlinear filter adapted to Poisson noise with fuzzy c-means segmentation method is proposed. This approach is capable of effectively handling the segmentation problem of blocky artifacts while achieving good tradeoff between Poisson noise removals and edge preservation of the microscopic biopsy images during segmentation process for cancer detection from cells. The proposed approach is tested on breast cancer microscopic biopsy data set with region of interest (ROI) segmented ground truth images. The microscopic biopsy data set contains 31 benign and 27 malignant images of size 896 × 768. The region of interest selected ground truth of all 58 images are also available for this data set. Finally, the result obtained from proposed approach is compared with the results of popular segmentation algorithms; fuzzy c-means, color k-means, texture based segmentation, and total variation fuzzy c-means approaches. The experimental results shows that proposed approach is providing better results in terms of various performance measures such as Jaccard coefficient, dice index, Tanimoto coefficient, area under curve, accuracy, true positive rate, true negative rate, false positive rate, false negative rate, random index, global consistency error, and variance of information as compared to other
What predicts the strength of simultaneous color contrast?
Ratnasingam, Sivalogeswaran; Anderson, Barton L.
2017-01-01
The perceived color of a uniform image patch depends not only on the spectral content of the light that reaches the eye but also on its context. One of the most extensively studied forms of context dependence is a simultaneous contrast display: a center-surround display containing a homogeneous target embedded in a homogenous surround. A number of models have been proposed to account for the chromatic transformations of targets induced by such surrounds, but they were typically derived in the restricted context of experiments using achromatic targets with surrounds that varied along the cardinal axes of color space. There is currently no theoretical consensus that predicts the target color that produces the largest perceived color difference for two arbitrarily chosen surround colors, or what surround would give the largest color induction for an arbitrarily chosen target. Here, we present a method for assessing simultaneous contrast that avoids some of the methodological issues that arise with nulling and matching experiments and diminishes the contribution of temporal adaption. Observers were presented with pairs of center-surround patterns and ordered them from largest to smallest in perceived dissimilarity. We find that the perceived difference for two arbitrarily chosen surrounds is largest when the target falls on the line connecting the two surrounds in color space. We also find that the magnitude of induction is larger for larger differences between chromatic targets and surrounds of the same hue. Our results are consistent with the direction law (Ekroll & Faul, 2012b), and with a generalization of Kirschmann's fourth law, even for viewing conditions that do not favor temporal adaptation. PMID:28245494
Effect of color coding and subtraction on the accuracy of contrast echocardiography
NASA Technical Reports Server (NTRS)
Pasquet, A.; Greenberg, N.; Brunken, R.; Thomas, J. D.; Marwick, T. H.
1999-01-01
BACKGROUND: Contrast echocardiography may be used to assess myocardial perfusion. However, gray scale assessment of myocardial contrast echocardiography (MCE) is difficult because of variations in regional backscatter intensity, difficulties in distinguishing varying shades of gray, and artifacts or attenuation. We sought to determine whether the assessment of rest myocardial perfusion by MCE could be improved with subtraction and color coding. METHODS AND RESULTS: MCE was performed in 31 patients with previous myocardial infarction with a 2nd generation agent (NC100100, Nycomed AS), using harmonic triggered or continuous imaging and gain settings were kept constant throughout the study. Digitized images were post processed by subtraction of baseline from contrast data and colorized to reflect the intensity of myocardial contrast. Gray scale MCE alone, MCE images combined with baseline and subtracted colorized images were scored independently using a 16 segment model. The presence and severity of myocardial contrast abnormalities were compared with perfusion defined by rest MIBI-SPECT. Segments that were not visualized by continuous (17%) or triggered imaging (14%) after color processing were excluded from further analysis. The specificity of gray scale MCE alone (56%) or MCE combined with baseline 2D (47%) was significantly enhanced by subtraction and color coding (76%, p<0.001) of triggered images. The accuracy of the gray scale approaches (respectively 52% and 47%) was increased to 70% (p<0.001). Similarly, for continuous images, the specificity of gray scale MCE with and without baseline comparison was 23% and 42% respectively, compared with 60% after post processing (p<0.001). The accuracy of colorized images (59%) was also significantly greater than gray scale MCE (43% and 29%, p<0.001). The sensitivity of MCE for both acquisitions was not altered by subtraction. CONCLUSION: Post-processing with subtraction and color coding significantly improves the accuracy
Regional Principal Color Based Saliency Detection
Lou, Jing; Ren, Mingwu; Wang, Huan
2014-01-01
Saliency detection is widely used in many visual applications like image segmentation, object recognition and classification. In this paper, we will introduce a new method to detect salient objects in natural images. The approach is based on a regional principal color contrast modal, which incorporates low-level and medium-level visual cues. The method allows a simple computation of color features and two categories of spatial relationships to a saliency map, achieving higher F-measure rates. At the same time, we present an interpolation approach to evaluate resulting curves, and analyze parameters selection. Our method enables the effective computation of arbitrary resolution images. Experimental results on a saliency database show that our approach produces high quality saliency maps and performs favorably against ten saliency detection algorithms. PMID:25379960
SuBSENSE: a universal change detection method with local adaptive sensitivity.
St-Charles, Pierre-Luc; Bilodeau, Guillaume-Alexandre; Bergevin, Robert
2015-01-01
Foreground/background segmentation via change detection in video sequences is often used as a stepping stone in high-level analytics and applications. Despite the wide variety of methods that have been proposed for this problem, none has been able to fully address the complex nature of dynamic scenes in real surveillance tasks. In this paper, we present a universal pixel-level segmentation method that relies on spatiotemporal binary features as well as color information to detect changes. This allows camouflaged foreground objects to be detected more easily while most illumination variations are ignored. Besides, instead of using manually set, frame-wide constants to dictate model sensitivity and adaptation speed, we use pixel-level feedback loops to dynamically adjust our method's internal parameters without user intervention. These adjustments are based on the continuous monitoring of model fidelity and local segmentation noise levels. This new approach enables us to outperform all 32 previously tested state-of-the-art methods on the 2012 and 2014 versions of the ChangeDetection.net dataset in terms of overall F-Measure. The use of local binary image descriptors for pixel-level modeling also facilitates high-speed parallel implementations: our own version, which used no low-level or architecture-specific instruction, reached real-time processing speed on a midlevel desktop CPU. A complete C++ implementation based on OpenCV is available online.
Simonis, Priscilla; Bay, Annick; Welch, Victoria L; Colomer, Jean-François; Vigneron, Jean Pol
2013-03-25
The large male tarantula Pamphobeteus antinous is easily recognized at the presence of blue-violet iridescent bristles on some of the segments of its legs and pedipalps. The optical properties of these colored appendages have been measured and the internal geometrical structure of the bristles have been investigated. The coloration is shown to be caused by a curved coaxial multilayer which acts as a "cylindrical Bragg mirror".
Imaging tristimulus colorimeter for the evaluation of color in printed textiles
NASA Astrophysics Data System (ADS)
Hunt, Martin A.; Goddard, James S., Jr.; Hylton, Kathy W.; Karnowski, Thomas P.; Richards, Roger K.; Simpson, Marc L.; Tobin, Kenneth W., Jr.; Treece, Dale A.
1999-03-01
The high-speed production of textiles with complicated printed patterns presents a difficult problem for a colorimetric measurement system. Accurate assessment of product quality requires a repeatable measurement using a standard color space, such as CIELAB, and the use of a perceptually based color difference formula, e.g. (Delta) ECMC color difference formula. Image based color sensors used for on-line measurement are not colorimetric by nature and require a non-linear transformation of the component colors based on the spectral properties of the incident illumination, imaging sensor, and the actual textile color. This research and development effort describes a benchtop, proof-of-principle system that implements a projection onto convex sets (POCS) algorithm for mapping component color measurements to standard tristimulus values and incorporates structural and color based segmentation for improved precision and accuracy. The POCS algorithm consists of determining the closed convex sets that describe the constraints on the reconstruction of the true tristimulus values based on the measured imperfect values. We show that using a simulated D65 standard illuminant, commercial filters and a CCD camera, accurate (under perceptibility limits) per-region based (Delta) ECMC values can be measured on real textile samples.
Vessel network detection using contour evolution and color components
DOE Office of Scientific and Technical Information (OSTI.GOV)
Ushizima, Daniela; Medeiros, Fatima; Cuadros, Jorge
2011-06-22
Automated retinal screening relies on vasculature segmentation before the identification of other anatomical structures of the retina. Vasculature extraction can also be input to image quality ranking, neovascularization detection and image registration, among other applications. There is an extensive literature related to this problem, often excluding the inherent heterogeneity of ophthalmic clinical images. The contribution of this paper relies on an algorithm using front propagation to segment the vessel network. The algorithm includes a penalty in the wait queue on the fast marching heap to minimize leakage of the evolving interface. The method requires no manual labeling, a minimum numbermore » of parameters and it is capable of segmenting color ocular fundus images in real scenarios, where multi-ethnicity and brightness variations are parts of the problem.« less
A Raven in a Coal Scuttle: Theodore Roosevelt & the Animal Coloration Controversy.
ERIC Educational Resources Information Center
Hendrick, Robert
1995-01-01
Recounts a debate between Theodore Roosevelt and Abbott Thayer in 1909-12 over whether animal coloration was an adaptation resulting from natural selection or whether the animal's environment acted directly on it to form its color patterns. (ZWH)
Hybrid mode-scattering/sound-absorbing segmented liner system and method
NASA Technical Reports Server (NTRS)
Walker, Bruce E. (Inventor); Hersh, Alan S. (Inventor); Rice, Edward J. (Inventor)
1999-01-01
A hybrid mode-scattering/sound-absorbing segmented liner system and method in which an initial sound field within a duct is steered or scattered into higher-order modes in a first mode-scattering segment such that it is more readily and effectively absorbed in a second sound-absorbing segment. The mode-scattering segment is preferably a series of active control components positioned along the annulus of the duct, each of which includes a controller and a resonator into which a piezoelectric transducer generates the steering noise. The sound-absorbing segment is positioned acoustically downstream of the mode-scattering segment, and preferably comprises a honeycomb-backed passive acoustic liner. The invention is particularly adapted for use in turbofan engines, both in the inlet and exhaust.
Low voltage solid-state lateral coloration electrochromic device
Tracy, C.E.; Benson, D.K.; Ruth, M.R.
1984-12-21
A solid-state transition metal oxide device comprising a plurality of layers having a predisposed orientation including an electrochromic oxide layer. Conductive material including anode and cathode contacts is secured to the device. Coloration is actuated within the electrochromic oxide layer after the application of a predetermined potential between the contacts. The coloration action is adapted to sweep or dynamically extend across the length of the electrochromic oxide layer.
Low voltage solid-state lateral coloration electrochromic device
Tracy, C. Edwin; Benson, David K.; Ruth, Marta R.
1987-01-01
A solid-state transition metal oxide device comprising a plurality of lay having a predisposed orientation including an electrochromic oxide layer. Conductive material including anode and cathode contacts is secured to the device. Coloration is actuated within the electrochromic oxide layer after the application of a predetermined potential between the contacts. The coloration action is adapted to sweep or dynamically extend across the length of the electrochromic oxide layer.
Using deep learning in image hyper spectral segmentation, classification, and detection
NASA Astrophysics Data System (ADS)
Zhao, Xiuying; Su, Zhenyu
2018-02-01
Recent years have shown that deep learning neural networks are a valuable tool in the field of computer vision. Deep learning method can be used in applications like remote sensing such as Land cover Classification, Detection of Vehicle in Satellite Images, Hyper spectral Image classification. This paper addresses the use of the deep learning artificial neural network in Satellite image segmentation. Image segmentation plays an important role in image processing. The hue of the remote sensing image often has a large hue difference, which will result in the poor display of the images in the VR environment. Image segmentation is a pre processing technique applied to the original images and splits the image into many parts which have different hue to unify the color. Several computational models based on supervised, unsupervised, parametric, probabilistic region based image segmentation techniques have been proposed. Recently, one of the machine learning technique known as, deep learning with convolution neural network has been widely used for development of efficient and automatic image segmentation models. In this paper, we focus on study of deep neural convolution network and its variants for automatic image segmentation rather than traditional image segmentation strategies.
Interactive-cut: Real-time feedback segmentation for translational research.
Egger, Jan; Lüddemann, Tobias; Schwarzenberg, Robert; Freisleben, Bernd; Nimsky, Christopher
2014-06-01
In this contribution, a scale-invariant image segmentation algorithm is introduced that "wraps" the algorithm's parameters for the user by its interactive behavior, avoiding the definition of "arbitrary" numbers that the user cannot really understand. Therefore, we designed a specific graph-based segmentation method that only requires a single seed-point inside the target-structure from the user and is thus particularly suitable for immediate processing and interactive, real-time adjustments by the user. In addition, color or gray value information that is needed for the approach can be automatically extracted around the user-defined seed point. Furthermore, the graph is constructed in such a way, so that a polynomial-time mincut computation can provide the segmentation result within a second on an up-to-date computer. The algorithm presented here has been evaluated with fixed seed points on 2D and 3D medical image data, such as brain tumors, cerebral aneurysms and vertebral bodies. Direct comparison of the obtained automatic segmentation results with costlier, manual slice-by-slice segmentations performed by trained physicians, suggest a strong medical relevance of this interactive approach. Copyright © 2014 Elsevier Ltd. All rights reserved.
Purmann, Sascha; Pollmann, Stefan
2015-01-01
To process information selectively and to continuously fine-tune selectivity of information processing are important abilities for successful goal-directed behavior. One phenomenon thought to represent this fine-tuning are conflict adaptation effects in interference tasks, i.e., reduction of interference after an incompatible trial and when incompatible trials are frequent. The neurocognitive mechanisms of these effects are currently only partly understood and results from brainimaging studies so far are mixed. In our study we validate and extend recent findings by examining adaption to recent conflict in the classical Stroop task using functional magnetic resonance imaging. Consistent with previous research we found increased activity in a fronto-parietal network comprising the medial prefrontal cortex, ventro-lateral prefrontal cortex, and posterior parietal cortex when contrasting incompatible with compatible trials. These areas have been associated with attentional processes and might reflect increased cognitive conflict and resolution thereof during incompatible trials. While carefully controlling for non-attentional sequential effects we found smaller Stroop interference after an incompatible trial (conflict adaptation effect). These behavioral conflict adaptation effects were accompanied by changes in activity in visual color-selective areas (V4, V4α), while there was no modulation by previous trial compatibility in a visual word-selective area (VWFA). Our results provide further evidence for the notion, that adaptation to recent conflict seems to be based mainly on enhancement of processing of the task-relevant information.
DOE Office of Scientific and Technical Information (OSTI.GOV)
Schoot, A. J. A. J. van de, E-mail: a.j.schootvande@amc.uva.nl; Schooneveldt, G.; Wognum, S.
Purpose: The aim of this study is to develop and validate a generic method for automatic bladder segmentation on cone beam computed tomography (CBCT), independent of gender and treatment position (prone or supine), using only pretreatment imaging data. Methods: Data of 20 patients, treated for tumors in the pelvic region with the entire bladder visible on CT and CBCT, were divided into four equally sized groups based on gender and treatment position. The full and empty bladder contour, that can be acquired with pretreatment CT imaging, were used to generate a patient-specific bladder shape model. This model was used tomore » guide the segmentation process on CBCT. To obtain the bladder segmentation, the reference bladder contour was deformed iteratively by maximizing the cross-correlation between directional grey value gradients over the reference and CBCT bladder edge. To overcome incorrect segmentations caused by CBCT image artifacts, automatic adaptations were implemented. Moreover, locally incorrect segmentations could be adapted manually. After each adapted segmentation, the bladder shape model was expanded and new shape patterns were calculated for following segmentations. All available CBCTs were used to validate the segmentation algorithm. The bladder segmentations were validated by comparison with the manual delineations and the segmentation performance was quantified using the Dice similarity coefficient (DSC), surface distance error (SDE) and SD of contour-to-contour distances. Also, bladder volumes obtained by manual delineations and segmentations were compared using a Bland-Altman error analysis. Results: The mean DSC, mean SDE, and mean SD of contour-to-contour distances between segmentations and manual delineations were 0.87, 0.27 cm and 0.22 cm (female, prone), 0.85, 0.28 cm and 0.22 cm (female, supine), 0.89, 0.21 cm and 0.17 cm (male, supine) and 0.88, 0.23 cm and 0.17 cm (male, prone), respectively. Manual local adaptations improved the
Two-stage color palettization for error diffusion
NASA Astrophysics Data System (ADS)
Mitra, Niloy J.; Gupta, Maya R.
2002-06-01
Image-adaptive color palettization chooses a decreased number of colors to represent an image. Palettization is one way to decrease storage and memory requirements for low-end displays. Palettization is generally approached as a clustering problem, where one attempts to find the k palette colors that minimize the average distortion for all the colors in an image. This would be the optimal approach if the image was to be displayed with each pixel quantized to the closest palette color. However, to improve the image quality the palettization may be followed by error diffusion. In this work, we propose a two-stage palettization where the first stage finds some m << k clusters, and the second stage chooses palette points that cover the spread of each of the M clusters. After error diffusion, this method leads to better image quality at less computational cost and with faster display speed than full k-means palettization.
Vessel segmentation in 3D spectral OCT scans of the retina
NASA Astrophysics Data System (ADS)
Niemeijer, Meindert; Garvin, Mona K.; van Ginneken, Bram; Sonka, Milan; Abràmoff, Michael D.
2008-03-01
The latest generation of spectral optical coherence tomography (OCT) scanners is able to image 3D cross-sectional volumes of the retina at a high resolution and high speed. These scans offer a detailed view of the structure of the retina. Automated segmentation of the vessels in these volumes may lead to more objective diagnosis of retinal vascular disease including hypertensive retinopathy, retinopathy of prematurity. Additionally, vessel segmentation can allow color fundus images to be registered to these 3D volumes, possibly leading to a better understanding of the structure and localization of retinal structures and lesions. In this paper we present a method for automatically segmenting the vessels in a 3D OCT volume. First, the retina is automatically segmented into multiple layers, using simultaneous segmentation of their boundary surfaces in 3D. Next, a 2D projection of the vessels is produced by only using information from certain segmented layers. Finally, a supervised, pixel classification based vessel segmentation approach is applied to the projection image. We compared the influence of two methods for the projection on the performance of the vessel segmentation on 10 optic nerve head centered 3D OCT scans. The method was trained on 5 independent scans. Using ROC analysis, our proposed vessel segmentation system obtains an area under the curve of 0.970 when compared with the segmentation of a human observer.
Face detection in color images using skin color, Laplacian of Gaussian, and Euler number
NASA Astrophysics Data System (ADS)
Saligrama Sundara Raman, Shylaja; Kannanedhi Narasimha Sastry, Balasubramanya Murthy; Subramanyam, Natarajan; Senkutuvan, Ramya; Srikanth, Radhika; John, Nikita; Rao, Prateek
2010-02-01
In this a paper, a feature based approach to face detection has been proposed using an ensemble of algorithms. The method uses chrominance values and edge features to classify the image as skin and nonskin regions. The edge detector used for this purpose is Laplacian of Gaussian (LoG) which is found to be appropriate when images having multiple faces with noise in them. Eight connectivity analysis of these regions will segregate them as probable face or nonface. The procedure is made more robust by identifying local features within these skin regions which include number of holes, percentage of skin and the golden ratio. The method proposed has been tested on color face images of various races obtained from different sources and its performance is found to be encouraging as the color segmentation cleans up almost all the complex facial features. The result obtained has a calculated accuracy of 86.5% on a test set of 230 images.
Robust Machine Learning-Based Correction on Automatic Segmentation of the Cerebellum and Brainstem.
Wang, Jun Yi; Ngo, Michael M; Hessl, David; Hagerman, Randi J; Rivera, Susan M
2016-01-01
Automated segmentation is a useful method for studying large brain structures such as the cerebellum and brainstem. However, automated segmentation may lead to inaccuracy and/or undesirable boundary. The goal of the present study was to investigate whether SegAdapter, a machine learning-based method, is useful for automatically correcting large segmentation errors and disagreement in anatomical definition. We further assessed the robustness of the method in handling size of training set, differences in head coil usage, and amount of brain atrophy. High resolution T1-weighted images were acquired from 30 healthy controls scanned with either an 8-channel or 32-channel head coil. Ten patients, who suffered from brain atrophy because of fragile X-associated tremor/ataxia syndrome, were scanned using the 32-channel head coil. The initial segmentations of the cerebellum and brainstem were generated automatically using Freesurfer. Subsequently, Freesurfer's segmentations were both manually corrected to serve as the gold standard and automatically corrected by SegAdapter. Using only 5 scans in the training set, spatial overlap with manual segmentation in Dice coefficient improved significantly from 0.956 (for Freesurfer segmentation) to 0.978 (for SegAdapter-corrected segmentation) for the cerebellum and from 0.821 to 0.954 for the brainstem. Reducing the training set size to 2 scans only decreased the Dice coefficient ≤0.002 for the cerebellum and ≤ 0.005 for the brainstem compared to the use of training set size of 5 scans in corrective learning. The method was also robust in handling differences between the training set and the test set in head coil usage and the amount of brain atrophy, which reduced spatial overlap only by <0.01. These results suggest that the combination of automated segmentation and corrective learning provides a valuable method for accurate and efficient segmentation of the cerebellum and brainstem, particularly in large-scale neuroimaging
Robust Machine Learning-Based Correction on Automatic Segmentation of the Cerebellum and Brainstem
Wang, Jun Yi; Ngo, Michael M.; Hessl, David; Hagerman, Randi J.; Rivera, Susan M.
2016-01-01
Automated segmentation is a useful method for studying large brain structures such as the cerebellum and brainstem. However, automated segmentation may lead to inaccuracy and/or undesirable boundary. The goal of the present study was to investigate whether SegAdapter, a machine learning-based method, is useful for automatically correcting large segmentation errors and disagreement in anatomical definition. We further assessed the robustness of the method in handling size of training set, differences in head coil usage, and amount of brain atrophy. High resolution T1-weighted images were acquired from 30 healthy controls scanned with either an 8-channel or 32-channel head coil. Ten patients, who suffered from brain atrophy because of fragile X-associated tremor/ataxia syndrome, were scanned using the 32-channel head coil. The initial segmentations of the cerebellum and brainstem were generated automatically using Freesurfer. Subsequently, Freesurfer’s segmentations were both manually corrected to serve as the gold standard and automatically corrected by SegAdapter. Using only 5 scans in the training set, spatial overlap with manual segmentation in Dice coefficient improved significantly from 0.956 (for Freesurfer segmentation) to 0.978 (for SegAdapter-corrected segmentation) for the cerebellum and from 0.821 to 0.954 for the brainstem. Reducing the training set size to 2 scans only decreased the Dice coefficient ≤0.002 for the cerebellum and ≤ 0.005 for the brainstem compared to the use of training set size of 5 scans in corrective learning. The method was also robust in handling differences between the training set and the test set in head coil usage and the amount of brain atrophy, which reduced spatial overlap only by <0.01. These results suggest that the combination of automated segmentation and corrective learning provides a valuable method for accurate and efficient segmentation of the cerebellum and brainstem, particularly in large-scale neuroimaging
Maximov, Vadim; Maximova, Elena; Damjanović, Ilija; Maximov, Paul
2014-09-01
Responses of direction-selective and orientation-selective motion detectors were recorded extracellularly from the axon terminals of ganglion cells in the superficial layers of the tectum opticum of immobilized goldfish, Carassius gibelio (Bloch, 1782). Color stripes or edges moving on some color background (presented on the CRT monitor with known emission spectra of its phosphors) served as stimuli. It was shown that stimuli of any color can be more or less matched with the background by varying their intensities what is indicative of color blindness of the motion detectors. Sets of stimuli which matched the background proved to represent planes in the three-dimensional color space of the goldfish. A relative contribution of different types of cones to the spectral sensitivity was estimated according to orientation of the plane of color matches. The spectral sensitivity of any motion detector was shown to be determined mainly by long-wave cones with a weak negative (opponent) contributions of middle-wave and/or short-wave ones. This resulted in reduced sensitivity in the blue-green end of the spectrum, what may be considered as an adaptation to the aquatic environment where, because of the substantial light scattering of a blue-green light, acute vision is possible only in a red region of the spectrum.
Fast and fully automatic phalanx segmentation using a grayscale-histogram morphology algorithm
NASA Astrophysics Data System (ADS)
Hsieh, Chi-Wen; Liu, Tzu-Chiang; Jong, Tai-Lang; Chen, Chih-Yen; Tiu, Chui-Mei; Chan, Din-Yuen
2011-08-01
Bone age assessment is a common radiological examination used in pediatrics to diagnose the discrepancy between the skeletal and chronological age of a child; therefore, it is beneficial to develop a computer-based bone age assessment to help junior pediatricians estimate bone age easily. Unfortunately, the phalanx on radiograms is not easily separated from the background and soft tissue. Therefore, we proposed a new method, called the grayscale-histogram morphology algorithm, to segment the phalanges fast and precisely. The algorithm includes three parts: a tri-stage sieve algorithm used to eliminate the background of hand radiograms, a centroid-edge dual scanning algorithm to frame the phalanx region, and finally a segmentation algorithm based on disk traverse-subtraction filter to segment the phalanx. Moreover, two more segmentation methods: adaptive two-mean and adaptive two-mean clustering were performed, and their results were compared with the segmentation algorithm based on disk traverse-subtraction filter using five indices comprising misclassification error, relative foreground area error, modified Hausdorff distances, edge mismatch, and region nonuniformity. In addition, the CPU time of the three segmentation methods was discussed. The result showed that our method had a better performance than the other two methods. Furthermore, satisfactory segmentation results were obtained with a low standard error.
Putting Martian 'Tribulation' Behind Enhanced Color
2017-05-15
NASA's Mars Exploration Rover Opportunity worked for 30 months on a raised segment of Endeavour Crater's rim called "Cape Tribulation" until departing that segment in mid-April 2017, southbound toward a new destination. This view looks back at the southern end of Cape Tribulation from about two football fields' distance away. In this version of the scene the landscape is presented in enhanced color to make differences in surface materials more easily visible. The component images were taken by the rover's Panoramic Camera (Pancam) on April 21, during the 4,707th Martian day, or sol, of Opportunity's mission on Mars. Wheel tracks can be traced back to see the rover's route as it descended and departed Cape Tribulation. For scale, the distance between the two parallel tracks is about 3.3 feet (1 meter). The rover drove from the foot of Cape Tribulation to the head of "Perseverance Valley" in seven drives totaling about one-fifth of a mile (one-third of a kilometer). The elevation difference between the highest point visible in this scene and the rover's location when the images were taken is about 180 feet (55 meters). This view looks northward. It merges exposures taken through three of the Pancam's color filters, centered on wavelengths of 753 nanometers (near-infrared), 535 nanometers (green) and 432 nanometers (violet). https://photojournal.jpl.nasa.gov/catalog/PIA21498
Shrestha, Ravi; Mohammed, Shahed K; Hasan, Md Mehedi; Zhang, Xuechao; Wahid, Khan A
2016-08-01
Wireless capsule endoscopy (WCE) plays an important role in the diagnosis of gastrointestinal (GI) diseases by capturing images of human small intestine. Accurate diagnosis of endoscopic images depends heavily on the quality of captured images. Along with image and frame rate, brightness of the image is an important parameter that influences the image quality which leads to the design of an efficient illumination system. Such design involves the choice and placement of proper light source and its ability to illuminate GI surface with proper brightness. Light emitting diodes (LEDs) are normally used as sources where modulated pulses are used to control LED's brightness. In practice, instances like under- and over-illumination are very common in WCE, where the former provides dark images and the later provides bright images with high power consumption. In this paper, we propose a low-power and efficient illumination system that is based on an automated brightness algorithm. The scheme is adaptive in nature, i.e., the brightness level is controlled automatically in real-time while the images are being captured. The captured images are segmented into four equal regions and the brightness level of each region is calculated. Then an adaptive sigmoid function is used to find the optimized brightness level and accordingly a new value of duty cycle of the modulated pulse is generated to capture future images. The algorithm is fully implemented in a capsule prototype and tested with endoscopic images. Commercial capsules like Pillcam and Mirocam were also used in the experiment. The results show that the proposed algorithm works well in controlling the brightness level accordingly to the environmental condition, and as a result, good quality images are captured with an average of 40% brightness level that saves power consumption of the capsule.
Color-rendering indices in global illumination methods
NASA Astrophysics Data System (ADS)
Geisler-Moroder, David; Dür, Arne
2009-10-01
Human perception of material colors depends heavily on the nature of the light sources that are used for illumination. One and the same object can cause highly different color impressions when lit by a vapor lamp or by daylight, respectively. On the basis of state-of-the-art colorimetric methods, we present a modern approach for the calculation of color-rendering indices (CRI), which were defined by the International Commission on Illumination (CIE) to characterize color reproduction properties of illuminants. We update the standard CIE method in three main points: first, we use the CIELAB color space; second, we apply a linearized Bradford transformation for chromatic adaptation; and finally, we evaluate color differences using the CIEDE2000 total color difference formula. Moreover, within a real-world scene, light incident on a measurement surface is composed of a direct and an indirect part. Neumann and Schanda [Proc. CGIV'06 Conf., Leeds, UK, pp. 283-286 (2006)] have shown for the cube model that diffuse interreflections can influence the CRI of a light source. We analyze how color-rendering indices vary in a real-world scene with mixed direct and indirect illumination and recommend the usage of a spectral rendering engine instead of an RGB-based renderer for reasons of accuracy of CRI calculations.
DOE Office of Scientific and Technical Information (OSTI.GOV)
Liu, Q; Yan, D
2014-06-01
Purpose: Evaluate the accuracy of atlas-based auto segmentation of organs at risk (OARs) on both helical CT (HCT) and cone beam CT (CBCT) images in head and neck (HN) cancer adaptive radiotherapy (ART). Methods: Six HN patients treated in the ART process were included in this study. For each patient, three images were selected: pretreatment planning CT (PreTx-HCT), in treatment CT for replanning (InTx-HCT) and a CBCT acquired in the same day of the InTx-HCT. Three clinical procedures of auto segmentation and deformable registration performed in the ART process were evaluated: a) auto segmentation on PreTx-HCT using multi-subject atlases, b)more » intra-patient propagation of OARs from PreTx-HCT to InTx-HCT using deformable HCT-to-HCT image registration, and c) intra-patient propagation of OARs from PreTx-HCT to CBCT using deformable CBCT-to-HCT image registration. Seven OARs (brainstem, cord, L/R parotid, L/R submandibular gland and mandible) were manually contoured on PreTx-HCT and InTx-HCT for comparison. In addition, manual contours on InTx-CT were copied on the same day CBCT, and a local region rigid body registration was performed accordingly for each individual OAR. For procedures a) and b), auto contours were compared to manual contours, and for c) auto contours were compared to those rigidly transferred contours on CBCT. Dice similarity coefficients (DSC) and mean surface distances of agreement (MSDA) were calculated for evaluation. Results: For procedure a), the mean DSC/MSDA of most OARs are >80%/±2mm. For intra-patient HCT-to-HCT propagation, the Resultimproved to >85%/±1.5mm. Compared to HCT-to-HCT, the mean DSC for HCT-to-CBCT propagation drops ∼2–3% and MSDA increases ∼0.2mm. This Resultindicates that the inferior imaging quality of CBCT seems only degrade auto propagation performance slightly. Conclusion: Auto segmentation and deformable propagation can generate OAR structures on HCT and CBCT images with clinically acceptable accuracy
Improvement of submerged culture conditions to produce colorants by Penicillium purpurogenum
Santos-Ebinuma, Valéria Carvalho; Roberto, Inês Conceição; Teixeira, Maria Francisca Simas; Pessoa, Adalberto
2014-01-01
Safety issues related to the employment of synthetic colorants in different industrial segments have increased the interest in the production of colorants from natural sources, such as microorganisms. Improved cultivation technologies have allowed the use of microorganisms as an alternative source of natural colorants. The objective of this work was to evaluate the influence of some factors on natural colorants production by a recently isolated from Amazon Forest, Penicillium purpurogenum DPUA 1275 employing statistical tools. To this purpose the following variables: orbital stirring speed, pH, temperature, sucrose and yeast extract concentrations and incubation time were studied through two fractional factorial, one full factorial and a central composite factorial designs. The regression analysis pointed out that sucrose and yeast extract concentrations were the variables that influenced more in colorants production. Under the best conditions (yeast extract concentration around 10 g/L and sucrose concentration of 50 g/L) an increase of 10, 33 and 23% respectively to yellow, orange and red colorants absorbance was achieved. These results show that P. purpurogenum is an alternative colorants producer and the production of these biocompounds can be improved employing statistical tool. PMID:25242965
Surgical wound segmentation based on adaptive threshold edge detection and genetic algorithm
NASA Astrophysics Data System (ADS)
Shih, Hsueh-Fu; Ho, Te-Wei; Hsu, Jui-Tse; Chang, Chun-Che; Lai, Feipei; Wu, Jin-Ming
2017-02-01
Postsurgical wound care has a great impact on patients' prognosis. It often takes few days, even few weeks, for the wound to stabilize, which incurs a great cost of health care and nursing resources. To assess the wound condition and diagnosis, it is important to segment out the wound region for further analysis. However, the scenario of this strategy often consists of complicated background and noise. In this study, we propose a wound segmentation algorithm based on Canny edge detector and genetic algorithm with an unsupervised evaluation function. The results were evaluated by the 112 clinical images, and 94.3% of images were correctly segmented. The judgment was based on the evaluation of experimented medical doctors. This capability to extract complete wound regions, makes it possible to conduct further image analysis such as intelligent recovery evaluation and automatic infection requirements.
Efficient terrestrial laser scan segmentation exploiting data structure
NASA Astrophysics Data System (ADS)
Mahmoudabadi, Hamid; Olsen, Michael J.; Todorovic, Sinisa
2016-09-01
New technologies such as lidar enable the rapid collection of massive datasets to model a 3D scene as a point cloud. However, while hardware technology continues to advance, processing 3D point clouds into informative models remains complex and time consuming. A common approach to increase processing efficiently is to segment the point cloud into smaller sections. This paper proposes a novel approach for point cloud segmentation using computer vision algorithms to analyze panoramic representations of individual laser scans. These panoramas can be quickly created using an inherent neighborhood structure that is established during the scanning process, which scans at fixed angular increments in a cylindrical or spherical coordinate system. In the proposed approach, a selected image segmentation algorithm is applied on several input layers exploiting this angular structure including laser intensity, range, normal vectors, and color information. These segments are then mapped back to the 3D point cloud so that modeling can be completed more efficiently. This approach does not depend on pre-defined mathematical models and consequently setting parameters for them. Unlike common geometrical point cloud segmentation methods, the proposed method employs the colorimetric and intensity data as another source of information. The proposed algorithm is demonstrated on several datasets encompassing variety of scenes and objects. Results show a very high perceptual (visual) level of segmentation and thereby the feasibility of the proposed algorithm. The proposed method is also more efficient compared to Random Sample Consensus (RANSAC), which is a common approach for point cloud segmentation.
Outer Space Research Helps Color Habitability in Earth Interiors
NASA Technical Reports Server (NTRS)
Haines, Richard F.
1977-01-01
Color is one of the most important elements in making an environment habitable. Both color and light level combine to create comfortable and efficient work areas and satisfying leisure time environments. Indeed, without light, color cannot even be experienced. It is vitally important for the designer to understand the subject of habitability and to know how to make a positive impact upon the habitability of spaces through the application of proven principles of color-design developed by the scientific community. Consider some of these possibilities and pitfalls: A color chosen in broad daylight will not appear the same under dim lighting conditions. If a designer were creating a dimly lit cocktail lounge, for example, there is little sense in using dark colors, which also tend to be more expensive. When the eyes have dark-adapted for even five minutes, any color reflecting 20 percent or less will appear black, and the color experience will be lost. Therefore, surface reflectances should be kept at least above 20 to 25 percent to maintain color where illumination is at low levels. In effect, for lower reflectance surfaces, higher levels of illumination are required to produce the most accurate color discriminability.
Facial skin color measurement based on camera colorimetric characterization
NASA Astrophysics Data System (ADS)
Yang, Boquan; Zhou, Changhe; Wang, Shaoqing; Fan, Xin; Li, Chao
2016-10-01
The objective measurement of facial skin color and its variance is of great significance as much information can be obtained from it. In this paper, we developed a new skin color measurement procedure which includes following parts: first, a new skin tone color checker made of pantone Skin Tone Color Checker was designed for camera colorimetric characterization; second, the chromaticity of light source was estimated via a new scene illumination estimation method considering several previous algorithms; third, chromatic adaption was used to convert the input facial image into output facial image which appears taken under canonical light; finally the validity and accuracy of our method was verified by comparing the results obtained by our procedure with these by spectrophotometer.
Color Improves Speed of Processing But Not Perception in a Motion Illusion
Perry, Carolyn J.; Fallah, Mazyar
2012-01-01
When two superimposed surfaces of dots move in different directions, the perceived directions are shifted away from each other. This perceptual illusion has been termed direction repulsion and is thought to be due to mutual inhibition between the representations of the two directions. It has further been shown that a speed difference between the two surfaces attenuates direction repulsion. As speed and direction are both necessary components of representing motion, the reduction in direction repulsion can be attributed to the additional motion information strengthening the representations of the two directions and thus reducing the mutual inhibition. We tested whether bottom-up attention and top-down task demands, in the form of color differences between the two surfaces, would also enhance motion processing, reducing direction repulsion. We found that the addition of color differences did not improve direction discrimination and reduce direction repulsion. However, we did find that adding a color difference improved performance on the task. We hypothesized that the performance differences were due to the limited presentation time of the stimuli. We tested this in a follow-up experiment where we varied the time of presentation to determine the duration needed to successfully perform the task with and without the color difference. As we expected, color segmentation reduced the amount of time needed to process and encode both directions of motion. Thus we find a dissociation between the effects of attention on the speed of processing and conscious perception of direction. We propose four potential mechanisms wherein color speeds figure-ground segmentation of an object, attentional switching between objects, direction discrimination and/or the accumulation of motion information for decision-making, without affecting conscious perception of the direction. Potential neural bases are also explored. PMID:22479255
Color improves speed of processing but not perception in a motion illusion.
Perry, Carolyn J; Fallah, Mazyar
2012-01-01
When two superimposed surfaces of dots move in different directions, the perceived directions are shifted away from each other. This perceptual illusion has been termed direction repulsion and is thought to be due to mutual inhibition between the representations of the two directions. It has further been shown that a speed difference between the two surfaces attenuates direction repulsion. As speed and direction are both necessary components of representing motion, the reduction in direction repulsion can be attributed to the additional motion information strengthening the representations of the two directions and thus reducing the mutual inhibition. We tested whether bottom-up attention and top-down task demands, in the form of color differences between the two surfaces, would also enhance motion processing, reducing direction repulsion. We found that the addition of color differences did not improve direction discrimination and reduce direction repulsion. However, we did find that adding a color difference improved performance on the task. We hypothesized that the performance differences were due to the limited presentation time of the stimuli. We tested this in a follow-up experiment where we varied the time of presentation to determine the duration needed to successfully perform the task with and without the color difference. As we expected, color segmentation reduced the amount of time needed to process and encode both directions of motion. Thus we find a dissociation between the effects of attention on the speed of processing and conscious perception of direction. We propose four potential mechanisms wherein color speeds figure-ground segmentation of an object, attentional switching between objects, direction discrimination and/or the accumulation of motion information for decision-making, without affecting conscious perception of the direction. Potential neural bases are also explored.
Progressive low-bitrate digital color/monochrome image coding by neuro-fuzzy clustering
NASA Astrophysics Data System (ADS)
Mitra, Sunanda; Meadows, Steven
1997-10-01
Color image coding at low bit rates is an area of research that is just being addressed in recent literature since the problems of storage and transmission of color images are becoming more prominent in many applications. Current trends in image coding exploit the advantage of subband/wavelet decompositions in reducing the complexity in optimal scalar/vector quantizer (SQ/VQ) design. Compression ratios (CRs) of the order of 10:1 to 20:1 with high visual quality have been achieved by using vector quantization of subband decomposed color images in perceptually weighted color spaces. We report the performance of a recently developed adaptive vector quantizer, namely, AFLC-VQ for effective reduction in bit rates while maintaining high visual quality of reconstructed color as well as monochrome images. For 24 bit color images, excellent visual quality is maintained upto a bit rate reduction to approximately 0.48 bpp (for each color plane or monochrome 0.16 bpp, CR 50:1) by using the RGB color space. Further tuning of the AFLC-VQ, and addition of an entropy coder module after the VQ stage results in extremely low bit rates (CR 80:1) for good quality, reconstructed images. Our recent study also reveals that for similar visual quality, RGB color space requires less bits/pixel than either the YIQ, or HIS color space for storing the same information when entropy coding is applied. AFLC-VQ outperforms other standard VQ and adaptive SQ techniques in retaining visual fidelity at similar bit rate reduction.
Yokoyama, Shozo; Takenaka, Naomi
2005-04-01
Red-green color vision is strongly suspected to enhance the survival of its possessors. Despite being red-green color blind, however, many species have successfully competed in nature, which brings into question the evolutionary advantage of achieving red-green color vision. Here, we propose a new method of identifying positive selection at individual amino acid sites with the premise that if positive Darwinian selection has driven the evolution of the protein under consideration, then it should be found mostly at the branches in the phylogenetic tree where its function had changed. The statistical and molecular methods have been applied to 29 visual pigments with the wavelengths of maximal absorption at approximately 510-540 nm (green- or middle wavelength-sensitive [MWS] pigments) and at approximately 560 nm (red- or long wavelength-sensitive [LWS] pigments), which are sampled from a diverse range of vertebrate species. The results show that the MWS pigments are positively selected through amino acid replacements S180A, Y277F, and T285A and that the LWS pigments have been subjected to strong evolutionary conservation. The fact that these positively selected M/LWS pigments are found not only in animals with red-green color vision but also in those with red-green color blindness strongly suggests that both red-green color vision and color blindness have undergone adaptive evolution independently in different species.
Segmentation of the heart and major vascular structures in cardiovascular CT images
NASA Astrophysics Data System (ADS)
Peters, J.; Ecabert, O.; Lorenz, C.; von Berg, J.; Walker, M. J.; Ivanc, T. B.; Vembar, M.; Olszewski, M. E.; Weese, J.
2008-03-01
Segmentation of organs in medical images can be successfully performed with shape-constrained deformable models. A surface mesh is attracted to detected image boundaries by an external energy, while an internal energy keeps the mesh similar to expected shapes. Complex organs like the heart with its four chambers can be automatically segmented using a suitable shape variablility model based on piecewise affine degrees of freedom. In this paper, we extend the approach to also segment highly variable vascular structures. We introduce a dedicated framework to adapt an extended mesh model to freely bending vessels. This is achieved by subdividing each vessel into (short) tube-shaped segments ("tubelets"). These are assigned to individual similarity transformations for local orientation and scaling. Proper adaptation is achieved by progressively adapting distal vessel parts to the image only after proximal neighbor tubelets have already converged. In addition, each newly activated tubelet inherits the local orientation and scale of the preceeding one. To arrive at a joint segmentation of chambers and vasculature, we extended a previous model comprising endocardial surfaces of the four chambers, the left ventricular epicardium, and a pulmonary artery trunk. Newly added are the aorta (ascending and descending plus arch), superior and inferior vena cava, coronary sinus, and four pulmonary veins. These vessels are organized as stacks of triangulated rings. This mesh configuration is most suitable to define tubelet segments. On 36 CT data sets reconstructed at several cardiac phases from 17 patients, segmentation accuracies of 0.61-0.80mm are obtained for the cardiac chambers. For the visible parts of the newly added great vessels, surface accuracies of 0.47-1.17mm are obtained (larger errors are asscociated with faintly contrasted venous structures).
Holcomb, Valerie B; von Lindern, Marieke; Jong, Willeke M. C; Zeeuw, Chris I. De; Suh, Yousin; Hasty, Paul; Hoeijmakers, Jan H. J; van der Horst, Gijsbertus T. J; Mitchell, James R
2006-01-01
How congenital defects causing genome instability can result in the pleiotropic symptoms reminiscent of aging but in a segmental and accelerated fashion remains largely unknown. Most segmental progerias are associated with accelerated fibroblast senescence, suggesting that cellular senescence is a likely contributing mechanism. Contrary to expectations, neither accelerated senescence nor acute oxidative stress hypersensitivity was detected in primary fibroblast or erythroblast cultures from multiple progeroid mouse models for defects in the nucleotide excision DNA repair pathway, which share premature aging features including postnatal growth retardation, cerebellar ataxia, and death before weaning. Instead, we report a prominent phenotypic overlap with long-lived dwarfism and calorie restriction during postnatal development (2 wk of age), including reduced size, reduced body temperature, hypoglycemia, and perturbation of the growth hormone/insulin-like growth factor 1 neuroendocrine axis. These symptoms were also present at 2 wk of age in a novel progeroid nucleotide excision repair-deficient mouse model (XPDG602D/R722W/XPA−/−) that survived weaning with high penetrance. However, despite persistent cachectic dwarfism, blood glucose and serum insulin-like growth factor 1 levels returned to normal by 10 wk, with hypoglycemia reappearing near premature death at 5 mo of age. These data strongly suggest changes in energy metabolism as part of an adaptive response during the stressful period of postnatal growth. Interestingly, a similar perturbation of the postnatal growth axis was not detected in another progeroid mouse model, the double-strand DNA break repair deficient Ku80 −/− mouse. Specific (but not all) types of genome instability may thus engage a conserved response to stress that evolved to cope with environmental pressures such as food shortage. PMID:17173483
van de Ven, Marieke; Andressoo, Jaan-Olle; Holcomb, Valerie B; von Lindern, Marieke; Jong, Willeke M C; De Zeeuw, Chris I; Suh, Yousin; Hasty, Paul; Hoeijmakers, Jan H J; van der Horst, Gijsbertus T J; Mitchell, James R
2006-12-15
How congenital defects causing genome instability can result in the pleiotropic symptoms reminiscent of aging but in a segmental and accelerated fashion remains largely unknown. Most segmental progerias are associated with accelerated fibroblast senescence, suggesting that cellular senescence is a likely contributing mechanism. Contrary to expectations, neither accelerated senescence nor acute oxidative stress hypersensitivity was detected in primary fibroblast or erythroblast cultures from multiple progeroid mouse models for defects in the nucleotide excision DNA repair pathway, which share premature aging features including postnatal growth retardation, cerebellar ataxia, and death before weaning. Instead, we report a prominent phenotypic overlap with long-lived dwarfism and calorie restriction during postnatal development (2 wk of age), including reduced size, reduced body temperature, hypoglycemia, and perturbation of the growth hormone/insulin-like growth factor 1 neuroendocrine axis. These symptoms were also present at 2 wk of age in a novel progeroid nucleotide excision repair-deficient mouse model (XPD(G602D/R722W)/XPA(-/-)) that survived weaning with high penetrance. However, despite persistent cachectic dwarfism, blood glucose and serum insulin-like growth factor 1 levels returned to normal by 10 wk, with hypoglycemia reappearing near premature death at 5 mo of age. These data strongly suggest changes in energy metabolism as part of an adaptive response during the stressful period of postnatal growth. Interestingly, a similar perturbation of the postnatal growth axis was not detected in another progeroid mouse model, the double-strand DNA break repair deficient Ku80(-/-) mouse. Specific (but not all) types of genome instability may thus engage a conserved response to stress that evolved to cope with environmental pressures such as food shortage.
Sjöberg, C; Ahnesjö, A
2013-06-01
Label fusion multi-atlas approaches for image segmentation can give better segmentation results than single atlas methods. We present a multi-atlas label fusion strategy based on probabilistic weighting of distance maps. Relationships between image similarities and segmentation similarities are estimated in a learning phase and used to derive fusion weights that are proportional to the probability for each atlas to improve the segmentation result. The method was tested using a leave-one-out strategy on a database of 21 pre-segmented prostate patients for different image registrations combined with different image similarity scorings. The probabilistic weighting yields results that are equal or better compared to both fusion with equal weights and results using the STAPLE algorithm. Results from the experiments demonstrate that label fusion by weighted distance maps is feasible, and that probabilistic weighted fusion improves segmentation quality more the stronger the individual atlas segmentation quality depends on the corresponding registered image similarity. The regions used for evaluation of the image similarity measures were found to be more important than the choice of similarity measure. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.
True Color Image Analysis For Determination Of Bone Growth In Fluorochromic Biopsies
NASA Astrophysics Data System (ADS)
Madachy, Raymond J.; Chotivichit, Lee; Huang, H. K.; Johnson, Eric E.
1989-05-01
A true color imaging technique has been developed for analysis of microscopic fluorochromic bone biopsy images to quantify new bone growth. The technique searches for specified colors in a medical image for quantification of areas of interest. Based on a user supplied training set, a multispectral classification of pixel values is performed and used for segmenting the image. Good results were obtained when compared to manual tracings of new bone growth performed by an orthopedic surgeon. At a 95% confidence level, the hypothesis that there is no difference between the two methods can be accepted. Work is in progress to test bone biopsies with different colored stains and further optimize the analysis process using three-dimensional spectral ordering techniques.
Kynard, B.; Zhuang, P.; Zhang, L.; Zhang, T.; Zhang, Z.
2002-01-01
We conducted laboratory experiments with Volga River Russian sturgeon, Acipenser gueldenstaedtii, to develop a conceptual model of early behavior. We daily observed fish from day-0 (embryos, first life interval after hatching) to day-29 feeding larvae for preference of bright habitat and cover, swimming distance above the bottom, up- and downstream movement, and diel activity. Hatchling embryos initiated a downstream migration, which suggests that predation risk of embryos at spawning sites is high. Migration peaked on days 0-5 and ceased on day 7 (8-day migration). Migrants preferred bright, open habitat and early migrants swam-up far above the bottom (maximum daily median, 140 cm) in a vertical swim tube. Post-migrant embryos did not prefer bright illumination but continued to prefer white substrate, increased use of cover habitat, and swam on the bottom. Larvae initiated feeding on day 10 after 170.6 cumulative temperature degree-days. Larvae did not migrate, weakly preferred bright illumination, preferred white substrate and open habitat, and swam near the bottom (daily median 5-78 cm). The lack of a strong preference by larvae for bright illumination suggests foraging relies more on olfaction than vision for locating prey. A short migration by embryos would disperse wild sturgeon from a spawning area, but larvae did not migrate, so a second later migration by juveniles disperses young sturgeon to the sea (2-step migration). Embryo and larva body color was light tan and tail color was black. The migration, behavior, and light body color of Russian sturgeon embryos was similar to species of Acipenser and Scaphirhynchus in North America and to Acipenser in Asia that migrate after hatching as embryos. The similarity in migration style and body color among species with diverse phylogenies likely reflects convergence for common adaptations across biogeographic regions. ?? 2002 Kluwer Academic Publishers.
Pretorius, M L; Van Huyssteen, C W; Brown, L R
2017-10-13
A relationship between soil organic carbon and soil color is acknowledged-albeit not a direct one. Since heightened carbon contents can be an indicator of wetlands, a quantifiable relationship between color and carbon might assist in determining wetland boundaries by rapid, field-based appraisal. The overarching aim of this initial study was to determine the potential of top soil color to indicate soil organic carbon, and by extension wetland boundaries, on a sandy coastal plain in South Africa. Data were collected from four wetland types in northern KwaZulu-Natal in South Africa. Soil samples were taken to a depth of 300 mm in three transects in each wetland type and analyzed for soil organic carbon. The matrix color was described using a Munsell soil color chart. Various color indices were correlated with soil organic carbon. The relationship between color and carbon were further elucidated using segmented quantile regression. This showed that potentially maximal carbon contents will occur at values of low color indices, and predictably minimal carbon contents will occur at values of low or high color indices. Threshold values can thus be used to make deductions such as "when the sum of dry and wet Value and Chroma values is 9 or more, carbon content will be 4.79% and less." These threshold values can then be used to differentiate between wetland and non-wetland sites with a 70 to 100% certainty. This study successfully developed a quantifiable correlation between color and carbon and showed that wetland boundaries can be determined based thereon.
Mera, David; Cotos, José M; Varela-Pet, José; Garcia-Pineda, Oscar
2012-10-01
Satellite Synthetic Aperture Radar (SAR) has been established as a useful tool for detecting hydrocarbon spillage on the ocean's surface. Several surveillance applications have been developed based on this technology. Environmental variables such as wind speed should be taken into account for better SAR image segmentation. This paper presents an adaptive thresholding algorithm for detecting oil spills based on SAR data and a wind field estimation as well as its implementation as a part of a functional prototype. The algorithm was adapted to an important shipping route off the Galician coast (northwest Iberian Peninsula) and was developed on the basis of confirmed oil spills. Image testing revealed 99.93% pixel labelling accuracy. By taking advantage of multi-core processor architecture, the prototype was optimized to get a nearly 30% improvement in processing time. Copyright © 2012 Elsevier Ltd. All rights reserved.
Color machine vision in industrial process control: case limestone mine
NASA Astrophysics Data System (ADS)
Paernaenen, Pekka H. T.; Lemstrom, Guy F.; Koskinen, Seppo
1994-11-01
An optical sorter technology has been developed to improve profitability of a mine by using color line scan machine vision technology. The new technology adapted longers the expected life time of the limestone mine and improves its efficiency. Also the project has proved that color line scan technology of today can successfully be applied to industrial use in harsh environments.
Potential for La Crosse virus segment reassortment in nature
Reese, Sara M; Blitvich, Bradley J; Blair, Carol D; Geske, Dave; Beaty, Barry J; Black, William C
2008-01-01
The evolutionary success of La Crosse virus (LACV, family Bunyaviridae) is due to its ability to adapt to changing conditions through intramolecular genetic changes and segment reassortment. Vertical transmission of LACV in mosquitoes increases the potential for segment reassortment. Studies were conducted to determine if segment reassortment was occurring in naturally infected Aedes triseriatus from Wisconsin and Minnesota in 2000, 2004, 2006 and 2007. Mosquito eggs were collected from various sites in Wisconsin and Minnesota. They were reared in the laboratory and adults were tested for LACV antigen by immunofluorescence assay. RNA was isolated from the abdomen of infected mosquitoes and portions of the small (S), medium (M) and large (L) viral genome segments were amplified by RT-PCR and sequenced. Overall, the viral sequences from 40 infected mosquitoes and 5 virus isolates were analyzed. Phylogenetic and linkage disequilibrium analyses revealed that approximately 25% of infected mosquitoes and viruses contained reassorted genome segments, suggesting that LACV segment reassortment is frequent in nature. PMID:19114023
Hu, Yu-Chi J; Grossberg, Michael D; Mageras, Gikas S
2008-01-01
Planning radiotherapy and surgical procedures usually require onerous manual segmentation of anatomical structures from medical images. In this paper we present a semi-automatic and accurate segmentation method to dramatically reduce the time and effort required of expert users. This is accomplished by giving a user an intuitive graphical interface to indicate samples of target and non-target tissue by loosely drawing a few brush strokes on the image. We use these brush strokes to provide the statistical input for a Conditional Random Field (CRF) based segmentation. Since we extract purely statistical information from the user input, we eliminate the need of assumptions on boundary contrast previously used by many other methods, A new feature of our method is that the statistics on one image can be reused on related images without registration. To demonstrate this, we show that boundary statistics provided on a few 2D slices of volumetric medical data, can be propagated through the entire 3D stack of images without using the geometric correspondence between images. In addition, the image segmentation from the CRF can be formulated as a minimum s-t graph cut problem which has a solution that is both globally optimal and fast. The combination of a fast segmentation and minimal user input that is reusable, make this a powerful technique for the segmentation of medical images.
Harnessing color vision for visual oximetry in central cyanosis.
Changizi, Mark; Rio, Kevin
2010-01-01
Central cyanosis refers to a bluish discoloration of the skin, lips, tongue, nails, and mucous membranes, and is due to poor arterial oxygenation. Although skin color is one of its characteristic properties, it has long been realized that by the time skin color signs become visible, oxygen saturation is dangerously low. Here we investigate the visibility of cyanosis in light of recent discoveries on what color vision evolved for in primates. We elucidate why low arterial oxygenation is visible at all, why it is perceived as blue, and why it can be so difficult to perceive. With a better understanding of the relationship between color vision and blood physiology, we suggest two simple techniques for greatly enhancing the clinician's ability to detect cyanosis and other clinical color changes. The first is called "skin-tone adaptation", wherein sheets, gowns, walls and other materials near a patient have a color close to that of the patient's skin, thereby optimizing a color-normal viewer's ability to sense skin color modulations. The second technique is called "biosensor color tabs", wherein adhesive tabs with a color matching the patient's skin tone are placed in several spots on the skin, and subsequent skin color changes have the effect of making the initially-invisible tabs change color, their hue and saturation indicating the direction and magnitude of the skin color shift.
NASA Astrophysics Data System (ADS)
Wang, Zhihua; Yang, Xiaomei; Lu, Chen; Yang, Fengshuo
2018-07-01
Automatic updating of land use/cover change (LUCC) databases using high spatial resolution images (HSRI) is important for environmental monitoring and policy making, especially for coastal areas that connect the land and coast and that tend to change frequently. Many object-based change detection methods are proposed, especially those combining historical LUCC with HSRI. However, the scale parameter(s) segmenting the serial temporal images, which directly determines the average object size, is hard to choose without experts' intervention. And the samples transferred from historical LUCC also need experts' intervention to avoid insufficient or wrong samples. With respect to the scale parameter(s) choosing, a Scale Self-Adapting Segmentation (SSAS) approach based on the exponential sampling of a scale parameter and location of the local maximum of a weighted local variance was proposed to determine the scale selection problem when segmenting images constrained by LUCC for detecting changes. With respect to the samples transferring, Knowledge Transfer (KT), a classifier trained on historical images with LUCC and applied in the classification of updated images, was also proposed. Comparison experiments were conducted in a coastal area of Zhujiang, China, using SPOT 5 images acquired in 2005 and 2010. The results reveal that (1) SSAS can segment images more effectively without intervention of experts. (2) KT can also reach the maximum accuracy of samples transfer without experts' intervention. Strategy SSAS + KT would be a good choice if the temporal historical image and LUCC match, and the historical image and updated image are obtained from the same resource.
Segment phasing experiments on the High Order Test bench
NASA Astrophysics Data System (ADS)
Aller-Carpentier, E.; Kasper, M.; Martinez, P.
The segmented primary mirror of the E-ELT imposes particular requirements on an Extreme Adaptive Optics (XAO) system. At present, there are already several AO systems working on segmented telescopes but the achieved performances are too low to draw conclusions for XAO systems aiming at some 90% Strehl ratio in the NIR. On other hand, several analytical studies and simulations were done, but laboratory studies are required to confirm the corrections expected. The goal of the present study is to determina the capability of XAO systems to deal with segmentation piston errors. In particular, the effects on the AO performance and the ability of the AO system to correct the segmentation piston errors were studied. The experiments were carried out on the High Order Test Bench at ESO (Munich) using a Shack-Hartmann wave front sensor and under most realistic conditions with phase screens simulating atmospheric turbulence and segmentation piston errors. Segment geometry was chosen such that about 6 actuators of the XAO DM cover one segment representing the design of EPICS at the EELT.
Foreground-background segmentation and attention: a change blindness study.
Mazza, Veronica; Turatto, Massimo; Umiltà, Carlo
2005-01-01
One of the most debated questions in visual attention research is what factors affect the deployment of attention in the visual scene? Segmentation processes are influential factors, providing candidate objects for further attentional selection, and the relevant literature has concentrated on how figure-ground segmentation mechanisms influence visual attention. However, another crucial process, namely foreground-background segmentation, seems to have been neglected. By using a change blindness paradigm, we explored whether attention is preferentially allocated to the foreground elements or to the background ones. The results indicated that unless attention was voluntarily deployed to the background, large changes in the color of its elements remained unnoticed. In contrast, minor changes in the foreground elements were promptly reported. Differences in change blindness between the two regions of the display indicate that attention is, by default, biased toward the foreground elements. This also supports the phenomenal observations made by Gestaltists, who demonstrated the greater salience of the foreground than the background.
Wang, Hongzhi; Das, Sandhitsu R.; Suh, Jung Wook; Altinay, Murat; Pluta, John; Craige, Caryne; Avants, Brian; Yushkevich, Paul A.
2011-01-01
We propose a simple but generally applicable approach to improving the accuracy of automatic image segmentation algorithms relative to manual segmentations. The approach is based on the hypothesis that a large fraction of the errors produced by automatic segmentation are systematic, i.e., occur consistently from subject to subject, and serves as a wrapper method around a given host segmentation method. The wrapper method attempts to learn the intensity, spatial and contextual patterns associated with systematic segmentation errors produced by the host method on training data for which manual segmentations are available. The method then attempts to correct such errors in segmentations produced by the host method on new images. One practical use of the proposed wrapper method is to adapt existing segmentation tools, without explicit modification, to imaging data and segmentation protocols that are different from those on which the tools were trained and tuned. An open-source implementation of the proposed wrapper method is provided, and can be applied to a wide range of image segmentation problems. The wrapper method is evaluated with four host brain MRI segmentation methods: hippocampus segmentation using FreeSurfer (Fischl et al., 2002); hippocampus segmentation using multi-atlas label fusion (Artaechevarria et al., 2009); brain extraction using BET (Smith, 2002); and brain tissue segmentation using FAST (Zhang et al., 2001). The wrapper method generates 72%, 14%, 29% and 21% fewer erroneously segmented voxels than the respective host segmentation methods. In the hippocampus segmentation experiment with multi-atlas label fusion as the host method, the average Dice overlap between reference segmentations and segmentations produced by the wrapper method is 0.908 for normal controls and 0.893 for patients with mild cognitive impairment. Average Dice overlaps of 0.964, 0.905 and 0.951 are obtained for brain extraction, white matter segmentation and gray matter
VIIRS On-Orbit Calibration for Ocean Color Data Processing
NASA Technical Reports Server (NTRS)
Eplee, Robert E., Jr.; Turpie, Kevin R.; Fireman, Gwyn F.; Meister, Gerhard; Stone, Thomas C.; Patt, Frederick S.; Franz, Bryan; Bailey, Sean W.; Robinson, Wayne D.; McClain, Charles R.
2012-01-01
The NASA VIIRS Ocean Science Team (VOST) has the task of evaluating Suomi NPP VIIRS ocean color data for the continuity of the NASA ocean color climate data records. The generation of science quality ocean color data products requires an instrument calibration that is stable over time. Since the VIIRS NIR Degradation Anomaly directly impacts the bands used for atmospheric correction of the ocean color data (Bands M6 and M7), the VOST has adapted the VIIRS on-orbit calibration approach to meet the ocean science requirements. The solar diffuser calibration time series and the solar diffuser stability monitor time series have been used to derive changes in the instrument response and diffuser reflectance over time for bands M1-M11.
NASA Astrophysics Data System (ADS)
Sakamoto, Takashi
2015-01-01
This study describes a color enhancement method that uses a color palette especially designed for protan and deutan defects, commonly known as red-green color blindness. The proposed color reduction method is based on a simple color mapping. Complicated computation and image processing are not required by using the proposed method, and the method can replace protan and deutan confusion (p/d-confusion) colors with protan and deutan safe (p/d-safe) colors. Color palettes for protan and deutan defects proposed by previous studies are composed of few p/d-safe colors. Thus, the colors contained in these palettes are insufficient for replacing colors in photographs. Recently, Ito et al. proposed a p/dsafe color palette composed of 20 particular colors. The author demonstrated that their p/d-safe color palette could be applied to image color reduction in photographs as a means to replace p/d-confusion colors. This study describes the results of the proposed color reduction in photographs that include typical p/d-confusion colors, which can be replaced. After the reduction process is completed, color-defective observers can distinguish these confusion colors.
NASA Technical Reports Server (NTRS)
Tarabalka, Y.; Tilton, J. C.; Benediktsson, J. A.; Chanussot, J.
2012-01-01
The Hierarchical SEGmentation (HSEG) algorithm, which combines region object finding with region object clustering, has given good performances for multi- and hyperspectral image analysis. This technique produces at its output a hierarchical set of image segmentations. The automated selection of a single segmentation level is often necessary. We propose and investigate the use of automatically selected markers for this purpose. In this paper, a novel Marker-based HSEG (M-HSEG) method for spectral-spatial classification of hyperspectral images is proposed. Two classification-based approaches for automatic marker selection are adapted and compared for this purpose. Then, a novel constrained marker-based HSEG algorithm is applied, resulting in a spectral-spatial classification map. Three different implementations of the M-HSEG method are proposed and their performances in terms of classification accuracies are compared. The experimental results, presented for three hyperspectral airborne images, demonstrate that the proposed approach yields accurate segmentation and classification maps, and thus is attractive for remote sensing image analysis.
Automatic patient-adaptive bleeding detection in a capsule endoscopy
NASA Astrophysics Data System (ADS)
Jung, Yun Sub; Kim, Yong Ho; Lee, Dong Ha; Lee, Sang Ho; Song, Jeong Joo; Kim, Jong Hyo
2009-02-01
We present a method for patient-adaptive detection of bleeding region for a Capsule Endoscopy (CE) images. The CE system has 320x320 resolution and transmits 3 images per second to receiver during around 10-hour. We have developed a technique to detect the bleeding automatically utilizing color spectrum transformation (CST) method. However, because of irregular conditions like organ difference, patient difference and illumination condition, detection performance is not uniform. To solve this problem, the detection method in this paper include parameter compensation step which compensate irregular image condition using color balance index (CBI). We have investigated color balance through sequential 2 millions images. Based on this pre-experimental result, we defined ΔCBI to represent deviate of color balance compared with standard small bowel color balance. The ΔCBI feature value is extracted from each image and used in CST method as parameter compensation constant. After candidate pixels were detected using CST method, they were labeled and examined with a bleeding character. We tested our method with 4,800 images in 12 patient data set (9 abnormal, 3 normal). Our experimental results show the proposed method achieves (before patient adaptive method : 80.87% and 74.25%, after patient adaptive method : 94.87% and 96.12%) of sensitivity and specificity.
NASA Astrophysics Data System (ADS)
Erdt, Marius; Sakas, Georgios
2010-03-01
This work presents a novel approach for model based segmentation of the kidney in images acquired by Computed Tomography (CT). The developed computer aided segmentation system is expected to support computer aided diagnosis and operation planning. We have developed a deformable model based approach based on local shape constraints that prevents the model from deforming into neighboring structures while allowing the global shape to adapt freely to the data. Those local constraints are derived from the anatomical structure of the kidney and the presence and appearance of neighboring organs. The adaptation process is guided by a rule-based deformation logic in order to improve the robustness of the segmentation in areas of diffuse organ boundaries. Our work flow consists of two steps: 1.) a user guided positioning and 2.) an automatic model adaptation using affine and free form deformation in order to robustly extract the kidney. In cases which show pronounced pathologies, the system also offers real time mesh editing tools for a quick refinement of the segmentation result. Evaluation results based on 30 clinical cases using CT data sets show an average dice correlation coefficient of 93% compared to the ground truth. The results are therefore in most cases comparable to manual delineation. Computation times of the automatic adaptation step are lower than 6 seconds which makes the proposed system suitable for an application in clinical practice.
Zhao, Jieliang; Wu, Jianing; Yan, Shaoze
2015-01-01
Honeybees (Apis mellifera) curl their abdomens for daily rhythmic activities. Prior to determining this fact, people have concluded that honeybees could curl their abdomen casually. However, an intriguing but less studied feature is the possible unidirectional abdominal deformation in free-flying honeybees. A high-speed video camera was used to capture the curling and to analyze the changes in the arc length of the honeybee abdomen not only in free-flying mode but also in the fixed sample. Frozen sections and environment scanning electron microscope were used to investigate the microstructure and motion principle of honeybee abdomen and to explore the physical structure restricting its curling. An adaptive segmented structure, especially the folded intersegmental membrane (FIM), plays a dominant role in the flexion and extension of the abdomen. The structural features of FIM were utilized to mimic and exhibit movement restriction on honeybee abdomen. Combining experimental analysis and theoretical demonstration, a unidirectional bending mechanism of honeybee abdomen was revealed. Through this finding, a new perspective for aerospace vehicle design can be imitated. PMID:26223946
Narayanaswamy, Arunachalam; Dwarakapuram, Saritha; Bjornsson, Christopher S; Cutler, Barbara M; Shain, William; Roysam, Badrinath
2010-03-01
This paper presents robust 3-D algorithms to segment vasculature that is imaged by labeling laminae, rather than the lumenal volume. The signal is weak, sparse, noisy, nonuniform, low-contrast, and exhibits gaps and spectral artifacts, so adaptive thresholding and Hessian filtering based methods are not effective. The structure deviates from a tubular geometry, so tracing algorithms are not effective. We propose a four step approach. The first step detects candidate voxels using a robust hypothesis test based on a model that assumes Poisson noise and locally planar geometry. The second step performs an adaptive region growth to extract weakly labeled and fine vessels while rejecting spectral artifacts. To enable interactive visualization and estimation of features such as statistical confidence, local curvature, local thickness, and local normal, we perform the third step. In the third step, we construct an accurate mesh representation using marching tetrahedra, volume-preserving smoothing, and adaptive decimation algorithms. To enable topological analysis and efficient validation, we describe a method to estimate vessel centerlines using a ray casting and vote accumulation algorithm which forms the final step of our algorithm. Our algorithm lends itself to parallel processing, and yielded an 8 x speedup on a graphics processor (GPU). On synthetic data, our meshes had average error per face (EPF) values of (0.1-1.6) voxels per mesh face for peak signal-to-noise ratios from (110-28 dB). Separately, the error from decimating the mesh to less than 1% of its original size, the EPF was less than 1 voxel/face. When validated on real datasets, the average recall and precision values were found to be 94.66% and 94.84%, respectively.
Karnowski, Thomas P; Govindasamy, V; Tobin, Kenneth W; Chaum, Edward; Abramoff, M D
2008-01-01
In this work we report on a method for lesion segmentation based on the morphological reconstruction methods of Sbeh et. al. We adapt the method to include segmentation of dark lesions with a given vasculature segmentation. The segmentation is performed at a variety of scales determined using ground-truth data. Since the method tends to over-segment imagery, ground-truth data was used to create post-processing filters to separate nuisance blobs from true lesions. A sensitivity and specificity of 90% of classification of blobs into nuisance and actual lesion was achieved on two data sets of 86 images and 1296 images.
Singaravelan, Natarajan; Raz, Shmuel; Tzur, Shay; Belifante, Shirli; Pavlicek, Tomas; Beiles, Avigdor; Ito, Shosuke; Wakamatsu, Kazumasa; Nevo, Eviatar
2013-01-01
Concealing coloration in rodents is well established. However, only a few studies examined how soil color, pelage color, hair-melanin content, and genetics (i.e., the causal chain) synergize to configure it. This study investigates the causal chain of dorsal coloration in Israeli subterranean blind mole rats, Spalax ehrenbergi. We examined pelage coloration of 128 adult animals from 11 populations belonging to four species of Spalax ehrenbergi superspecies (Spalax galili, Spalax golani, Spalax carmeli, and Spalax judaei) and the corresponding coloration of soil samples from the collection sites using a digital colorimeter. Additionally, we quantified hair-melanin contents of 67 animals using HPLC and sequenced the MC1R gene in 68 individuals from all four mole rat species. Due to high variability of soil colors, the correlation between soil and pelage color coordinates was weak and significant only between soil hue and pelage lightness. Multiple stepwise forward regression revealed that soil lightness was significantly associated with all pelage color variables. Pelage color lightness among the four species increased with the higher southward aridity in accordance to Gloger's rule (darker in humid habitats and lighter in arid habitats). Darker and lighter pelage colors are associated with darker basalt and terra rossa, and lighter rendzina soils, respectively. Despite soil lightness varying significantly, pelage lightness and eumelanin converged among populations living in similar soil types. Partial sequencing of the MC1R gene identified three allelic variants, two of which were predominant in northern species (S. galili and S. golani), and the third was exclusive to southern species (S. carmeli and S. judaei), which might have caused the differences found in pheomelanin/eumelanin ratio. Darker dorsal pelage in darker basalt and terra rossa soils in the north and lighter pelage in rendzina and loess soils in the south reflect the combined results of crypsis and
Perceptual approach for unsupervised digital color restoration of cinematographic archives
NASA Astrophysics Data System (ADS)
Chambah, Majed; Rizzi, Alessandro; Gatta, Carlo; Besserer, Bernard; Marini, Daniele
2003-01-01
The cinematographic archives represent an important part of our collective memory. We present in this paper some advances in automating the color fading restoration process, especially with regard to the automatic color correction technique. The proposed color correction method is based on the ACE model, an unsupervised color equalization algorithm based on a perceptual approach and inspired by some adaptation mechanisms of the human visual system, in particular lightness constancy and color constancy. There are some advantages in a perceptual approach: mainly its robustness and its local filtering properties, that lead to more effective results. The resulting technique, is not just an application of ACE on movie images, but an enhancement of ACE principles to meet the requirements in the digital film restoration field. The presented preliminary results are satisfying and promising.
Achuthan, Anusha; Rajeswari, Mandava; Ramachandram, Dhanesh; Aziz, Mohd Ezane; Shuaib, Ibrahim Lutfi
2010-07-01
This paper introduces an approach to perform segmentation of regions in computed tomography (CT) images that exhibit intra-region intensity variations and at the same time have similar intensity distributions with surrounding/adjacent regions. In this work, we adapt a feature computed from wavelet transform called wavelet energy to represent the region information. The wavelet energy is embedded into a level set model to formulate the segmentation model called wavelet energy-guided level set-based active contour (WELSAC). The WELSAC model is evaluated using several synthetic and CT images focusing on tumour cases, which contain regions demonstrating the characteristics of intra-region intensity variations and having high similarity in intensity distributions with the adjacent regions. The obtained results show that the proposed WELSAC model is able to segment regions of interest in close correspondence with the manual delineation provided by the medical experts and to provide a solution for tumour detection. Copyright 2010 Elsevier Ltd. All rights reserved.
Image segmentation algorithm based on improved PCNN
NASA Astrophysics Data System (ADS)
Chen, Hong; Wu, Chengdong; Yu, Xiaosheng; Wu, Jiahui
2017-11-01
A modified simplified Pulse Coupled Neural Network (PCNN) model is proposed in this article based on simplified PCNN. Some work have done to enrich this model, such as imposing restrictions items of the inputs, improving linking inputs and internal activity of PCNN. A self-adaptive parameter setting method of linking coefficient and threshold value decay time constant is proposed here, too. At last, we realized image segmentation algorithm for five pictures based on this proposed simplified PCNN model and PSO. Experimental results demonstrate that this image segmentation algorithm is much better than method of SPCNN and OTSU.
Evaluation of the effectiveness of color attributes for video indexing
NASA Astrophysics Data System (ADS)
Chupeau, Bertrand; Forest, Ronan
2001-10-01
Color features are reviewed and their effectiveness assessed in the application framework of key-frame clustering for abstracting unconstrained video. Existing color spaces and associated quantization schemes are first studied. Description of global color distribution by means of histograms is then detailed. In our work, 12 combinations of color space and quantization were selected, together with 12 histogram metrics. Their respective effectiveness with respect to picture similarity measurement was evaluated through a query-by-example scenario. For that purpose, a set of still-picture databases was built by extracting key frames from several video clips, including news, documentaries, sports and cartoons. Classical retrieval performance evaluation criteria were adapted to the specificity of our testing methodology.
Evaluation of the effectiveness of color attributes for video indexing
NASA Astrophysics Data System (ADS)
Chupeau, Bertrand; Forest, Ronan
2001-01-01
Color features are reviewed and their effectiveness assessed in the application framework of key-frame clustering for abstracting unconstrained video. Existing color spaces and associated quantization schemes are first studied. Description of global color distribution by means of histograms is then detailed. In our work, twelve combinations of color space and quantization were selected, together with twelve histogram metrics. Their respective effectiveness with respect to picture similarity measurement was evaluated through a query-be-example scenario. For that purpose, a set of still-picture databases was built by extracting key-frames from several video clips, including news, documentaries, sports and cartoons. Classical retrieval performance evaluation criteria were adapted to the specificity of our testing methodology.
Evaluation of the effectiveness of color attributes for video indexing
NASA Astrophysics Data System (ADS)
Chupeau, Bertrand; Forest, Ronan
2000-12-01
Color features are reviewed and their effectiveness assessed in the application framework of key-frame clustering for abstracting unconstrained video. Existing color spaces and associated quantization schemes are first studied. Description of global color distribution by means of histograms is then detailed. In our work, twelve combinations of color space and quantization were selected, together with twelve histogram metrics. Their respective effectiveness with respect to picture similarity measurement was evaluated through a query-be-example scenario. For that purpose, a set of still-picture databases was built by extracting key-frames from several video clips, including news, documentaries, sports and cartoons. Classical retrieval performance evaluation criteria were adapted to the specificity of our testing methodology.
Hyperspectral image segmentation using a cooperative nonparametric approach
NASA Astrophysics Data System (ADS)
Taher, Akar; Chehdi, Kacem; Cariou, Claude
2013-10-01
In this paper a new unsupervised nonparametric cooperative and adaptive hyperspectral image segmentation approach is presented. The hyperspectral images are partitioned band by band in parallel and intermediate classification results are evaluated and fused, to get the final segmentation result. Two unsupervised nonparametric segmentation methods are used in parallel cooperation, namely the Fuzzy C-means (FCM) method, and the Linde-Buzo-Gray (LBG) algorithm, to segment each band of the image. The originality of the approach relies firstly on its local adaptation to the type of regions in an image (textured, non-textured), and secondly on the introduction of several levels of evaluation and validation of intermediate segmentation results before obtaining the final partitioning of the image. For the management of similar or conflicting results issued from the two classification methods, we gradually introduced various assessment steps that exploit the information of each spectral band and its adjacent bands, and finally the information of all the spectral bands. In our approach, the detected textured and non-textured regions are treated separately from feature extraction step, up to the final classification results. This approach was first evaluated on a large number of monocomponent images constructed from the Brodatz album. Then it was evaluated on two real applications using a respectively multispectral image for Cedar trees detection in the region of Baabdat (Lebanon) and a hyperspectral image for identification of invasive and non invasive vegetation in the region of Cieza (Spain). A correct classification rate (CCR) for the first application is over 97% and for the second application the average correct classification rate (ACCR) is over 99%.
A novel multiphoton microscopy images segmentation method based on superpixel and watershed.
Wu, Weilin; Lin, Jinyong; Wang, Shu; Li, Yan; Liu, Mingyu; Liu, Gaoqiang; Cai, Jianyong; Chen, Guannan; Chen, Rong
2017-04-01
Multiphoton microscopy (MPM) imaging technique based on two-photon excited fluorescence (TPEF) and second harmonic generation (SHG) shows fantastic performance for biological imaging. The automatic segmentation of cellular architectural properties for biomedical diagnosis based on MPM images is still a challenging issue. A novel multiphoton microscopy images segmentation method based on superpixels and watershed (MSW) is presented here to provide good segmentation results for MPM images. The proposed method uses SLIC superpixels instead of pixels to analyze MPM images for the first time. The superpixels segmentation based on a new distance metric combined with spatial, CIE Lab color space and phase congruency features, divides the images into patches which keep the details of the cell boundaries. Then the superpixels are used to reconstruct new images by defining an average value of superpixels as image pixels intensity level. Finally, the marker-controlled watershed is utilized to segment the cell boundaries from the reconstructed images. Experimental results show that cellular boundaries can be extracted from MPM images by MSW with higher accuracy and robustness. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.
Rule-based fuzzy vector median filters for 3D phase contrast MRI segmentation
NASA Astrophysics Data System (ADS)
Sundareswaran, Kartik S.; Frakes, David H.; Yoganathan, Ajit P.
2008-02-01
Recent technological advances have contributed to the advent of phase contrast magnetic resonance imaging (PCMRI) as standard practice in clinical environments. In particular, decreased scan times have made using the modality more feasible. PCMRI is now a common tool for flow quantification, and for more complex vector field analyses that target the early detection of problematic flow conditions. Segmentation is one component of this type of application that can impact the accuracy of the final product dramatically. Vascular segmentation, in general, is a long-standing problem that has received significant attention. Segmentation in the context of PCMRI data, however, has been explored less and can benefit from object-based image processing techniques that incorporate fluids specific information. Here we present a fuzzy rule-based adaptive vector median filtering (FAVMF) algorithm that in combination with active contour modeling facilitates high-quality PCMRI segmentation while mitigating the effects of noise. The FAVMF technique was tested on 111 synthetically generated PC MRI slices and on 15 patients with congenital heart disease. The results were compared to other multi-dimensional filters namely the adaptive vector median filter, the adaptive vector directional filter, and the scalar low pass filter commonly used in PC MRI applications. FAVMF significantly outperformed the standard filtering methods (p < 0.0001). Two conclusions can be drawn from these results: a) Filtering should be performed after vessel segmentation of PC MRI; b) Vector based filtering methods should be used instead of scalar techniques.
A novel pipeline for adrenal tumour segmentation.
Koyuncu, Hasan; Ceylan, Rahime; Erdogan, Hasan; Sivri, Mesut
2018-06-01
Adrenal tumours occur on adrenal glands surrounded by organs and osteoid. These tumours can be categorized as either functional, non-functional, malign, or benign. Depending on their appearance in the abdomen, adrenal tumours can arise from one adrenal gland (unilateral) or from both adrenal glands (bilateral) and can connect with other organs, including the liver, spleen, pancreas, etc. This connection phenomenon constitutes the most important handicap against adrenal tumour segmentation. Size change, variety of shape, diverse location, and low contrast (similar grey values between the various tissues) are other disadvantages compounding segmentation difficulty. Few studies have considered adrenal tumour segmentation, and no significant improvement has been achieved for unilateral, bilateral, adherent, or noncohesive tumour segmentation. There is also no recognised segmentation pipeline or method for adrenal tumours including different shape, size, or location information. This study proposes an adrenal tumour segmentation (ATUS) pipeline designed to eliminate the above disadvantages for adrenal tumour segmentation. ATUS incorporates a number of image methods, including contrast limited adaptive histogram equalization, split and merge based on quadtree decomposition, mean shift segmentation, large grey level eliminator, and region growing. Performance assessment of ATUS was realised on 32 arterial and portal phase computed tomography images using six metrics: dice, jaccard, sensitivity, specificity, accuracy, and structural similarity index. ATUS achieved remarkable segmentation performance, and was not affected by the discussed handicaps, on particularly adherence to other organs, with success rates of 83.06%, 71.44%, 86.44%, 99.66%, 99.43%, and 98.51% for the metrics, respectively, for images including sufficient contrast uptake. The proposed ATUS system realises detailed adrenal tumour segmentation, and avoids known disadvantages preventing accurate
Boundary fitting based segmentation of fluorescence microscopy images
NASA Astrophysics Data System (ADS)
Lee, Soonam; Salama, Paul; Dunn, Kenneth W.; Delp, Edward J.
2015-03-01
Segmentation is a fundamental step in quantifying characteristics, such as volume, shape, and orientation of cells and/or tissue. However, quantification of these characteristics still poses a challenge due to the unique properties of microscopy volumes. This paper proposes a 2D segmentation method that utilizes a combination of adaptive and global thresholding, potentials, z direction refinement, branch pruning, end point matching, and boundary fitting methods to delineate tubular objects in microscopy volumes. Experimental results demonstrate that the proposed method achieves better performance than an active contours based scheme.
In Situ 3D Segmentation of Individual Plant Leaves Using a RGB-D Camera for Agricultural Automation.
Xia, Chunlei; Wang, Longtan; Chung, Bu-Keun; Lee, Jang-Myung
2015-08-19
In this paper, we present a challenging task of 3D segmentation of individual plant leaves from occlusions in the complicated natural scene. Depth data of plant leaves is introduced to improve the robustness of plant leaf segmentation. The low cost RGB-D camera is utilized to capture depth and color image in fields. Mean shift clustering is applied to segment plant leaves in depth image. Plant leaves are extracted from the natural background by examining vegetation of the candidate segments produced by mean shift. Subsequently, individual leaves are segmented from occlusions by active contour models. Automatic initialization of the active contour models is implemented by calculating the center of divergence from the gradient vector field of depth image. The proposed segmentation scheme is tested through experiments under greenhouse conditions. The overall segmentation rate is 87.97% while segmentation rates for single and occluded leaves are 92.10% and 86.67%, respectively. Approximately half of the experimental results show segmentation rates of individual leaves higher than 90%. Nevertheless, the proposed method is able to segment individual leaves from heavy occlusions.
In Situ 3D Segmentation of Individual Plant Leaves Using a RGB-D Camera for Agricultural Automation
Xia, Chunlei; Wang, Longtan; Chung, Bu-Keun; Lee, Jang-Myung
2015-01-01
In this paper, we present a challenging task of 3D segmentation of individual plant leaves from occlusions in the complicated natural scene. Depth data of plant leaves is introduced to improve the robustness of plant leaf segmentation. The low cost RGB-D camera is utilized to capture depth and color image in fields. Mean shift clustering is applied to segment plant leaves in depth image. Plant leaves are extracted from the natural background by examining vegetation of the candidate segments produced by mean shift. Subsequently, individual leaves are segmented from occlusions by active contour models. Automatic initialization of the active contour models is implemented by calculating the center of divergence from the gradient vector field of depth image. The proposed segmentation scheme is tested through experiments under greenhouse conditions. The overall segmentation rate is 87.97% while segmentation rates for single and occluded leaves are 92.10% and 86.67%, respectively. Approximately half of the experimental results show segmentation rates of individual leaves higher than 90%. Nevertheless, the proposed method is able to segment individual leaves from heavy occlusions. PMID:26295395
Fast Appearance Modeling for Automatic Primary Video Object Segmentation.
Yang, Jiong; Price, Brian; Shen, Xiaohui; Lin, Zhe; Yuan, Junsong
2016-02-01
Automatic segmentation of the primary object in a video clip is a challenging problem as there is no prior knowledge of the primary object. Most existing techniques thus adapt an iterative approach for foreground and background appearance modeling, i.e., fix the appearance model while optimizing the segmentation and fix the segmentation while optimizing the appearance model. However, these approaches may rely on good initialization and can be easily trapped in local optimal. In addition, they are usually time consuming for analyzing videos. To address these limitations, we propose a novel and efficient appearance modeling technique for automatic primary video object segmentation in the Markov random field (MRF) framework. It embeds the appearance constraint as auxiliary nodes and edges in the MRF structure, and can optimize both the segmentation and appearance model parameters simultaneously in one graph cut. The extensive experimental evaluations validate the superiority of the proposed approach over the state-of-the-art methods, in both efficiency and effectiveness.
Adaptive neuro-heuristic hybrid model for fruit peel defects detection.
Woźniak, Marcin; Połap, Dawid
2018-02-01
Fusion of machine learning methods benefits in decision support systems. A composition of approaches gives a possibility to use the most efficient features composed into one solution. In this article we would like to present an approach to the development of adaptive method based on fusion of proposed novel neural architecture and heuristic search into one co-working solution. We propose a developed neural network architecture that adapts to processed input co-working with heuristic method used to precisely detect areas of interest. Input images are first decomposed into segments. This is to make processing easier, since in smaller images (decomposed segments) developed Adaptive Artificial Neural Network (AANN) processes less information what makes numerical calculations more precise. For each segment a descriptor vector is composed to be presented to the proposed AANN architecture. Evaluation is run adaptively, where the developed AANN adapts to inputs and their features by composed architecture. After evaluation, selected segments are forwarded to heuristic search, which detects areas of interest. As a result the system returns the image with pixels located over peel damages. Presented experimental research results on the developed solution are discussed and compared with other commonly used methods to validate the efficacy and the impact of the proposed fusion in the system structure and training process on classification results. Copyright © 2017 Elsevier Ltd. All rights reserved.
Considering the Influence of Nonadaptive Evolution on Primate Color Vision.
Jacobs, Rachel L; Bradley, Brenda J
2016-01-01
Color vision in primates is variable across species, and it represents a rare trait in which the genetic mechanisms underlying phenotypic variation are fairly well-understood. Research on primate color vision has largely focused on adaptive explanations for observed variation, but it remains unclear why some species have trichromatic or polymorphic color vision while others are red-green color blind. Lemurs, in particular, are highly variable. While some species are polymorphic, many closely-related species are strictly dichromatic. We provide the first characterization of color vision in a wild population of red-bellied lemurs (Eulemur rubriventer, Ranomafana National Park, Madagascar) with a sample size (87 individuals; NX chromosomes = 134) large enough to detect even rare variants (0.95 probability of detection at ≥ 3% frequency). By sequencing exon 5 of the X-linked opsin gene we identified opsin spectral sensitivity based on known diagnostic sites and found this population to be dichromatic and monomorphic for a long wavelength allele. Apparent fixation of this long allele is in contrast to previously published accounts of Eulemur species, which exhibit either polymorphic color vision or only the medium wavelength opsin. This unexpected result may represent loss of color vision variation, which could occur through selective processes and/or genetic drift (e.g., genetic bottleneck). To indirectly assess the latter scenario, we genotyped 55 adult red-bellied lemurs at seven variable microsatellite loci and used heterozygosity excess and M-ratio tests to assess if this population may have experienced a recent genetic bottleneck. Results of heterozygosity excess but not M-ratio tests suggest a bottleneck might have occurred in this red-bellied lemur population. Therefore, while selection may also play a role, the unique color vision observed in this population might have been influenced by a recent genetic bottleneck. These results emphasize the need to
Considering the Influence of Nonadaptive Evolution on Primate Color Vision
Jacobs, Rachel L.; Bradley, Brenda J.
2016-01-01
Color vision in primates is variable across species, and it represents a rare trait in which the genetic mechanisms underlying phenotypic variation are fairly well-understood. Research on primate color vision has largely focused on adaptive explanations for observed variation, but it remains unclear why some species have trichromatic or polymorphic color vision while others are red-green color blind. Lemurs, in particular, are highly variable. While some species are polymorphic, many closely-related species are strictly dichromatic. We provide the first characterization of color vision in a wild population of red-bellied lemurs (Eulemur rubriventer, Ranomafana National Park, Madagascar) with a sample size (87 individuals; NX chromosomes = 134) large enough to detect even rare variants (0.95 probability of detection at ≥ 3% frequency). By sequencing exon 5 of the X-linked opsin gene we identified opsin spectral sensitivity based on known diagnostic sites and found this population to be dichromatic and monomorphic for a long wavelength allele. Apparent fixation of this long allele is in contrast to previously published accounts of Eulemur species, which exhibit either polymorphic color vision or only the medium wavelength opsin. This unexpected result may represent loss of color vision variation, which could occur through selective processes and/or genetic drift (e.g., genetic bottleneck). To indirectly assess the latter scenario, we genotyped 55 adult red-bellied lemurs at seven variable microsatellite loci and used heterozygosity excess and M-ratio tests to assess if this population may have experienced a recent genetic bottleneck. Results of heterozygosity excess but not M-ratio tests suggest a bottleneck might have occurred in this red-bellied lemur population. Therefore, while selection may also play a role, the unique color vision observed in this population might have been influenced by a recent genetic bottleneck. These results emphasize the need to
Active mask segmentation of fluorescence microscope images.
Srinivasa, Gowri; Fickus, Matthew C; Guo, Yusong; Linstedt, Adam D; Kovacević, Jelena
2009-08-01
We propose a new active mask algorithm for the segmentation of fluorescence microscope images of punctate patterns. It combines the (a) flexibility offered by active-contour methods, (b) speed offered by multiresolution methods, (c) smoothing offered by multiscale methods, and (d) statistical modeling offered by region-growing methods into a fast and accurate segmentation tool. The framework moves from the idea of the "contour" to that of "inside and outside," or masks, allowing for easy multidimensional segmentation. It adapts to the topology of the image through the use of multiple masks. The algorithm is almost invariant under initialization, allowing for random initialization, and uses a few easily tunable parameters. Experiments show that the active mask algorithm matches the ground truth well and outperforms the algorithm widely used in fluorescence microscopy, seeded watershed, both qualitatively, as well as quantitatively.
SWT voting-based color reduction for text detection in natural scene images
NASA Astrophysics Data System (ADS)
Ikica, Andrej; Peer, Peter
2013-12-01
In this article, we propose a novel stroke width transform (SWT) voting-based color reduction method for detecting text in natural scene images. Unlike other text detection approaches that mostly rely on either text structure or color, the proposed method combines both by supervising text-oriented color reduction process with additional SWT information. SWT pixels mapped to color space vote in favor of the color they correspond to. Colors receiving high SWT vote most likely belong to text areas and are blocked from being mean-shifted away. Literature does not explicitly address SWT search direction issue; thus, we propose an adaptive sub-block method for determining correct SWT direction. Both SWT voting-based color reduction and SWT direction determination methods are evaluated on binary (text/non-text) images obtained from a challenging Computer Vision Lab optical character recognition database. SWT voting-based color reduction method outperforms the state-of-the-art text-oriented color reduction approach.
Tongue's substance and coating recognition analysis using HSV color threshold in tongue diagnosis
NASA Astrophysics Data System (ADS)
Kamarudin, Nur Diyana; Ooi, Chia Yee; Kawanabe, Tadaaki; Mi, Xiaoyu
2016-07-01
In ISO TC249 conference, tongue diagnosis has been one of the most active research and their objectifications has become significant with the help of numerous statistical and machine learning algorithm. Color information of substance or tongue body has kept valuable information regarding the state of disease and its correlation with the internal organs. In order to produce high reproducibility of color measurement analysis, tongue images have to undergo several procedures such as color correction, segmentation and tongue's substance-coating separation. This paper presents a novel method to recognize substance and coating from tongue images and eliminate the tongue coating for accurate substance color measurement for diagnosis. By utilizing Hue, Saturation, Value (HSV) color space, new color-brightness threshold parameters have been devised to improve the efficiency of tongue's substance and coating separation procedures and eliminate shadows. The algorithm offers fast processing time around 0.98 seconds for 60,000 pixels tongue image. The successful tongue's substance and coating separation rate reported is 90% compared to the labelled data verified by the practitioners. Using 300 tongue images, the substance Lab color measurement with small standard deviation had revealed the effectiveness of this proposed method in computerized tongue diagnosis system.
Cognitive control predicted by color vision, and vice versa.
Colzato, Lorenza S; Sellaro, Roberta; Hulka, Lea M; Quednow, Boris B; Hommel, Bernhard
2014-09-01
One of the most important functions of cognitive control is to continuously adapt cognitive processes to changing and often conflicting demands of the environment. Dopamine (DA) has been suggested to play a key role in the signaling and resolution of such response conflict. Given that DA is found in high concentration in the retina, color vision discrimination has been suggested as an index of DA functioning and in particular blue-yellow color vision impairment (CVI) has been used to indicate a central hypodopaminergic state. We used color discrimination (indexed by the total color distance score; TCDS) to predict individual differences in the cognitive control of response conflict, as reflected by conflict-resolution efficiency in an auditory Simon task. As expected, participants showing better color discrimination were more efficient in resolving response conflict. Interestingly, participants showing a blue-yellow CVI were associated with less efficiency in handling response conflict. Our findings indicate that color vision discrimination might represent a promising predictor of cognitive controlability in healthy individuals. Copyright © 2014 Elsevier Ltd. All rights reserved.
NASA Astrophysics Data System (ADS)
Chen, Chao; Gao, Nan; Wang, Xiangjun; Zhang, Zonghua
2018-05-01
Three-dimensional (3D) shape measurement based on fringe pattern projection techniques has been commonly used in various fields. One of the remaining challenges in fringe pattern projection is that camera sensor saturation may occur if there is a large range of reflectivity variation across the surface that causes measurement errors. To overcome this problem, a novel fringe pattern projection method is proposed to avoid image saturation and maintain high-intensity modulation for measuring shiny surfaces by adaptively adjusting the pixel-to-pixel projection intensity according to the surface reflectivity. First, three sets of orthogonal color fringe patterns and a sequence of uniform gray-level patterns with different gray levels are projected onto a measured surface by a projector. The patterns are deformed with respect to the object surface and captured by a camera from a different viewpoint. Subsequently, the optimal projection intensity at each pixel is determined by fusing different gray levels and transforming the camera pixel coordinate system into the projector pixel coordinate system. Finally, the adapted fringe patterns are created and used for 3D shape measurement. Experimental results on a flat checkerboard and shiny objects demonstrate that the proposed method can measure shiny surfaces with high accuracy.
Commercial printing and electronic color printing
NASA Astrophysics Data System (ADS)
Webb, Joseph W.
1995-04-01
Technologies such as Xeikon, Indigo, and the Heidelberg/Presstek GTO-DI can change both the way print buyers may purchase printed material and the way printers and trade services respond to changing demands. Our recent study surveys the graphic arts industry for their current views of these new products and provides forecasts of installations and usage with breakdowns by market segment and size of firm. The acceptance of desktop publishing and electronic prepress have not only paved the way for a totally electronic printing process, but it has broadened the base of people who develop color originals for reproduction. Electronic printing adds the ability to customize jobs on the fly. How print providers will respond to the impact of electronic color printing depends on how each firm perceives the 'threat.' Most printing companies are run by entrepreneurial individuals who have, as their highest priority, their own economic survival. Service bureaus are already looking at electronic color printing as yet another way to differentiate their businesses. The study was based on a mail survey with 682 responses from graphic arts firms, interviews with printers, suppliers, associations and industry executives, and detailed secondary research. Results of a new survey in progress in January 1995 is also presented.
NASA Astrophysics Data System (ADS)
Keller, Brad M.; Gastounioti, Aimilia; Batiste, Rebecca C.; Kontos, Despina; Feldman, Michael D.
2016-03-01
Visual characterization of histologic specimens is known to suffer from intra- and inter-observer variability. To help address this, we developed an automated framework for characterizing digitized histology specimens based on a novel application of color histogram and color texture analysis. We perform a preliminary evaluation of this framework using a set of 73 trichrome-stained, digitized slides of normal breast tissue which were visually assessed by an expert pathologist in terms of the percentage of collagenous stroma, stromal collagen density, duct-lobular unit density and the presence of elastosis. For each slide, our algorithm automatically segments the tissue region based on the lightness channel in CIELAB colorspace. Within each tissue region, a color histogram feature vector is extracted using a common color palette for trichrome images generated with a previously described method. Then, using a whole-slide, lattice-based methodology, color texture maps are generated using a set of color co-occurrence matrix statistics: contrast, correlation, energy and homogeneity. The extracted features sets are compared to the visually assessed tissue characteristics. Overall, the extracted texture features have high correlations to both the percentage of collagenous stroma (r=0.95, p<0.001) and duct-lobular unit density (r=0.71, p<0.001) seen in the tissue samples, and several individual features were associated with either collagen density and/or the presence of elastosis (p<=0.05). This suggests that the proposed framework has promise as a means to quantitatively extract descriptors reflecting tissue-level characteristics and thus could be useful in detecting and characterizing histological processes in digitized histology specimens.
Banisadr, Seyedali; Chen, Jian
2017-12-13
Cephalopods, such as cuttlefish, demonstrate remarkable adaptability to the coloration and texture of their surroundings by modulating their skin color and surface morphology simultaneously, for the purpose of adaptive camouflage and signal communication. Inspired by this unique feature of cuttlefish skins, we present a general approach to remote-controlled, smart films that undergo simultaneous changes of surface color and morphology upon infrared (IR) actuation. The smart film has a reconfigurable laminated structure that comprises an IR-responsive nanocomposite actuator layer and a mechanochromic elastomeric photonic crystal layer. Upon global or localized IR irradiation, the actuator layer exhibits fast, large, and reversible strain in the irradiated region, which causes a synergistically coupled change in the shape of the laminated film and color of the mechanochromic elastomeric photonic crystal layer in the same region. Bending and twisting deformations can be created under IR irradiation, through modulating the strain direction in the actuator layer of the laminated film. Furthermore, the laminated film has been used in a remote-controlled inchworm walker that can directly couple a color-changing skin with the robotic movements. Such remote-controlled, smart films may open up new application possibilities in soft robotics and wearable devices.
Color naming: color scientists do it between Munsell sheets of color
NASA Astrophysics Data System (ADS)
Beretta, Giordano B.; Moroney, Nathan M.
2010-01-01
With the advent of high dynamic range imaging and wide gamut color spaces, gamut mapping algorithms have to nudge image colors much more drastically to constrain them within a rendering device's gamut. Classical colorimetry is concerned with color matching and the developed color difference metrics are for small distances. For larger distances, categorization becomes a more useful concept. In the gamut mapping case, lexical distance induced by color names is a more useful metric, which translates to the condition that a nudged color may not cross a name boundary. The new problem is to find these color name boundaries. We compare the experimental procedures used for color naming by linguists, ethnologists, and color scientists and propose a methodology that leads to robust repeatable experiments.
Processing of Color Words Activates Color Representations
ERIC Educational Resources Information Center
Richter, Tobias; Zwaan, Rolf A.
2009-01-01
Two experiments were conducted to investigate whether color representations are routinely activated when color words are processed. Congruency effects of colors and color words were observed in both directions. Lexical decisions on color words were faster when preceding colors matched the color named by the word. Color-discrimination responses…
Color discrimination, color naming and color preferences in 80-year olds.
Wijk, H; Berg, S; Sivik, L; Steen, B
1999-06-01
The aim of the present study was to investigate color discrimination, color naming and color preference in a random sample of 80-year-old men and women. Knowledge of color perception in old age can be of value when using color contrast, cues and codes in the environment to promote orientation and function. The color naming test indicated that the colors white, black, yellow, red, blue and green promoted recognition to the highest degree among all subjects. A gender-related difference, in favor of women, occurred in naming five of the mixed colors. Women also used more varied color names than men. Color discrimination was easier in the red and yellow area than in the blue and green area. This result correlates positively with visual function on far sight, and negatively with diagnosis of a cataract. The preference order for seven colors put blue, green and red at the top, and brown at the bottom, hence agreeing with earlier studies, and indicating that the preference order for colors remains relatively stable also in old age. This result should be considered when designing environments for old people.
Basic characteristics of simultaneous color contrast revisited.
Ekroll, Vebjørn; Faul, Franz
2012-10-01
In this article, we present evidence supporting the hypothesis that the local mechanism of simultaneous color contrast is the same as the mechanism responsible for the crispening effect and the gamut expansion effect. A theoretically important corollary of this hypothesis is that the basic characteristics of simultaneous contrast are at odds with traditional laws. First, this hypothesis implies that the direction of the simultaneous contrast effect in color space is given by the vector from surround to target and not--as traditionally assumed--by the hue complementary to that of the surround. Second, it implies that the size of the simultaneous contrast effect depends on the difference between the target and surround colors in a way that challenges Kirschmann's fourth law. The widespread belief in the traditional laws, we argue, is due to the confounding influence of temporal adaptation.
Color categories and color appearance
Webster, Michael A.; Kay, Paul
2011-01-01
We examined categorical effects in color appearance in two tasks, which in part differed in the extent to which color naming was explicitly required for the response. In one, we measured the effects of color differences on perceptual grouping for hues that spanned the blue–green boundary, to test whether chromatic differences across the boundary were perceptually exaggerated. This task did not require overt judgments of the perceived colors, and the tendency to group showed only a weak and inconsistent categorical bias. In a second case, we analyzed results from two prior studies of hue scaling of chromatic stimuli (De Valois, De Valois, Switkes, & Mahon, 1997; Malkoc, Kay, & Webster, 2005), to test whether color appearance changed more rapidly around the blue–green boundary. In this task observers directly judge the perceived color of the stimuli and these judgments tended to show much stronger categorical effects. The differences between these tasks could arise either because different signals mediate color grouping and color appearance, or because linguistic categories might differentially intrude on the response to color and/or on the perception of color. Our results suggest that the interaction between language and color processing may be highly dependent on the specific task and cognitive demands and strategies of the observer, and also highlight pronounced individual differences in the tendency to exhibit categorical responses. PMID:22176751
ERIC Educational Resources Information Center
Kovach, Amy L.
2003-01-01
Presents an activity on natural selection and how the peppered moth's adaptive values for their colors changed during the Industrial Revolution in Manchester, England, influencing their survival and ultimately affecting the survival of their offspring. Includes activity objectives. (Author/KHR)
Color universal design: analysis of color category dependency on color vision type (3)
NASA Astrophysics Data System (ADS)
Kojima, Natsuki; Ichihara, Yasuyo G.; Ikeda, Tomohiro; Kamachi, Miyuki G.; Ito, Kei
2012-01-01
We report on the results of a study investigating the color perception characteristics of people with red-green color confusion. We believe that this is an important step towards achieving Color Universal Design. In Japan, approximately 5% of men and 0.2% of women have red-green confusion. The percentage for men is higher in Europe and the United States; up to 8% in some countries. Red-green confusion involves a perception of colors different from normal color vision. Colors are used as a means of disseminating clear information to people; however, it may be difficult to convey the correct information to people who have red-green confusion. Consequently, colors should be chosen that minimize accidents and that promote more effective communication. In a previous survey, we investigated color categories common to each color vision type, trichromat (C-type color vision), protan (P-type color vision) and deuteran (D-type color vision). In the present study, first, we conducted experiments in order to verify a previous survey of C-type color vision and P-type color vision. Next, we investigated color difference levels within "CIE 1976 L*a*b*" (the CIELAB uniform color space), where neither C-type nor P-type color vision causes accidents under certain conditions (rain maps/contour line levels and graph color legend levels). As a result, we propose a common chromaticity of colors that the two color vision types are able to categorize by means of color names common to C-type color vision. We also offer a proposal to explain perception characteristics of color differences with normal color vision and red-green confusion using the CIELAB uniform color space. This report is a follow-up to SPIE-IS & T / Vol. 7528 7528051-8 and SPIE-IS & T /vol. 7866 78660J-1-8.
NASA Astrophysics Data System (ADS)
Ezhova, Kseniia; Fedorenko, Dmitriy; Chuhlamov, Anton
2016-04-01
The article deals with the methods of image segmentation based on color space conversion, and allow the most efficient way to carry out the detection of a single color in a complex background and lighting, as well as detection of objects on a homogeneous background. The results of the analysis of segmentation algorithms of this type, the possibility of their implementation for creating software. The implemented algorithm is very time-consuming counting, making it a limited application for the analysis of the video, however, it allows us to solve the problem of analysis of objects in the image if there is no dictionary of images and knowledge bases, as well as the problem of choosing the optimal parameters of the frame quantization for video analysis.
Color in Reference Production: The Role of Color Similarity and Color Codability.
Viethen, Jette; van Vessem, Thomas; Goudbeek, Martijn; Krahmer, Emiel
2017-05-01
It has often been observed that color is a highly preferred attribute for use in distinguishing descriptions, that is, referring expressions produced with the purpose of identifying an object within a visual scene. However, most of these observations were based on visual displays containing only colors that were maximally different in hue and for which the language of experimentation possessed basic color terms. The experiments described in this paper investigate whether speakers' preference for color is reduced if the color of the target referent is similar to that of the distractors. Because colors that look similar are often also harder to distinguish linguistically, we also examine the impact of the codability of color values. As a third factor, we investigate the salience of available alternative attributes and its impact on the use of color. The results of our experiments show that, while speakers are indeed less likely to use color when the colors in a display are similar, this effect is mostly due to the difficulty in naming similar colors. Color use for color with a basic color term is affected only when the colors of target and distractors are very similar (yet still distinguishable). The salience of our alternative attribute size, manipulated by varying the difference in size between target and distractors, had no impact on the use of color. Copyright © 2016 Cognitive Science Society, Inc.
Nucleus segmentation in histology images with hierarchical multilevel thresholding
NASA Astrophysics Data System (ADS)
Ahmady Phoulady, Hady; Goldgof, Dmitry B.; Hall, Lawrence O.; Mouton, Peter R.
2016-03-01
Automatic segmentation of histological images is an important step for increasing throughput while maintaining high accuracy, avoiding variation from subjective bias, and reducing the costs for diagnosing human illnesses such as cancer and Alzheimer's disease. In this paper, we present a novel method for unsupervised segmentation of cell nuclei in stained histology tissue. Following an initial preprocessing step involving color deconvolution and image reconstruction, the segmentation step consists of multilevel thresholding and a series of morphological operations. The only parameter required for the method is the minimum region size, which is set according to the resolution of the image. Hence, the proposed method requires no training sets or parameter learning. Because the algorithm requires no assumptions or a priori information with regard to cell morphology, the automatic approach is generalizable across a wide range of tissues. Evaluation across a dataset consisting of diverse tissues, including breast, liver, gastric mucosa and bone marrow, shows superior performance over four other recent methods on the same dataset in terms of F-measure with precision and recall of 0.929 and 0.886, respectively.
Preferred skin color enhancement for photographic color reproduction
NASA Astrophysics Data System (ADS)
Zeng, Huanzhao; Luo, Ronnier
2011-01-01
Skin tones are the most important colors among the memory color category. Reproducing skin colors pleasingly is an important factor in photographic color reproduction. Moving skin colors toward their preferred skin color center improves the color preference of skin color reproduction. Several methods to morph skin colors to a smaller preferred skin color region has been reported in the past. In this paper, a new approach is proposed to further improve the result of skin color enhancement. An ellipsoid skin color model is applied to compute skin color probabilities for skin color detection and to determine a weight for skin color adjustment. Preferred skin color centers determined through psychophysical experiments were applied for color adjustment. Preferred skin color centers for dark, medium, and light skin colors are applied to adjust skin colors differently. Skin colors are morphed toward their preferred color centers. A special processing is applied to avoid contrast loss in highlight. A 3-D interpolation method is applied to fix a potential contouring problem and to improve color processing efficiency. An psychophysical experiment validates that the method of preferred skin color enhancement effectively identifies skin colors, improves the skin color preference, and does not objectionably affect preferred skin colors in original images.
Misimi, E; Mathiassen, J R; Erikson, U
2007-01-01
Computer vision method was used to evaluate the color of Atlantic salmon (Salmo salar) fillets. Computer vision-based sorting of fillets according to their color was studied on 2 separate groups of salmon fillets. The images of fillets were captured using a digital camera of high resolution. Images of salmon fillets were then segmented in the regions of interest and analyzed in red, green, and blue (RGB) and CIE Lightness, redness, and yellowness (Lab) color spaces, and classified according to the Roche color card industrial standard. Comparisons of fillet color between visual evaluations were made by a panel of human inspectors, according to the Roche SalmoFan lineal standard, and the color scores generated from computer vision algorithm showed that there were no significant differences between the methods. Overall, computer vision can be used as a powerful tool to sort fillets by color in a fast and nondestructive manner. The low cost of implementing computer vision solutions creates the potential to replace manual labor in fish processing plants with automation.
Adaptive intercolor error prediction coder for lossless color (rgb) picutre compression
NASA Astrophysics Data System (ADS)
Mann, Y.; Peretz, Y.; Mitchell, Harvey B.
2001-09-01
Most of the current lossless compression algorithms, including the new international baseline JPEG-LS algorithm, do not exploit the interspectral correlations that exist between the color planes in an input color picture. To improve the compression performance (i.e., lower the bit rate) it is necessary to exploit these correlations. A major concern is to find efficient methods for exploiting the correlations that, at the same time, are compatible with and can be incorporated into the JPEG-LS algorithm. One such algorithm is the method of intercolor error prediction (IEP), which when used with the JPEG-LS algorithm, results on average in a reduction of 8% in the overall bit rate. We show how the IEP algorithm can be simply modified and that it nearly doubles the size of the reduction in bit rate to 15%.
Global-constrained hidden Markov model applied on wireless capsule endoscopy video segmentation
NASA Astrophysics Data System (ADS)
Wan, Yiwen; Duraisamy, Prakash; Alam, Mohammad S.; Buckles, Bill
2012-06-01
Accurate analysis of wireless capsule endoscopy (WCE) videos is vital but tedious. Automatic image analysis can expedite this task. Video segmentation of WCE into the four parts of the gastrointestinal tract is one way to assist a physician. The segmentation approach described in this paper integrates pattern recognition with statiscal analysis. Iniatially, a support vector machine is applied to classify video frames into four classes using a combination of multiple color and texture features as the feature vector. A Poisson cumulative distribution, for which the parameter depends on the length of segments, models a prior knowledge. A priori knowledge together with inter-frame difference serves as the global constraints driven by the underlying observation of each WCE video, which is fitted by Gaussian distribution to constrain the transition probability of hidden Markov model.Experimental results demonstrated effectiveness of the approach.
Thermally Tunable Hydrogels Displaying Angle-Independent Structural Colors.
Ohtsuka, Yumiko; Seki, Takahiro; Takeoka, Yukikazu
2015-12-14
We report the preparation of thermally tunable hydrogels displaying angle-independent structural colors. The porous structures were formed with short-range order using colloidal amorphous array templates and a small amount of carbon black (CB). The resultant porous hydrogels prepared using colloidal amorphous arrays without CB appeared white, whereas the hydrogels with CB revealed bright structural colors. The brightly colored hydrogels rapidly changed hues in a reversible manner, and the hues varied widely depending on the water temperature. Moreover, the structural colors were angle-independent under diffusive lighting because of the isotropic nanostructure generated from the colloidal amorphous arrays. © 2015 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA. This is an open access article under the terms of the Creative Commons Attribution Non-Commercial NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Correction tool for Active Shape Model based lumbar muscle segmentation.
Valenzuela, Waldo; Ferguson, Stephen J; Ignasiak, Dominika; Diserens, Gaelle; Vermathen, Peter; Boesch, Chris; Reyes, Mauricio
2015-08-01
In the clinical environment, accuracy and speed of the image segmentation process plays a key role in the analysis of pathological regions. Despite advances in anatomic image segmentation, time-effective correction tools are commonly needed to improve segmentation results. Therefore, these tools must provide faster corrections with a low number of interactions, and a user-independent solution. In this work we present a new interactive correction method for correcting the image segmentation. Given an initial segmentation and the original image, our tool provides a 2D/3D environment, that enables 3D shape correction through simple 2D interactions. Our scheme is based on direct manipulation of free form deformation adapted to a 2D environment. This approach enables an intuitive and natural correction of 3D segmentation results. The developed method has been implemented into a software tool and has been evaluated for the task of lumbar muscle segmentation from Magnetic Resonance Images. Experimental results show that full segmentation correction could be performed within an average correction time of 6±4 minutes and an average of 68±37 number of interactions, while maintaining the quality of the final segmentation result within an average Dice coefficient of 0.92±0.03.
Segment scheduling method for reducing 360° video streaming latency
NASA Astrophysics Data System (ADS)
Gudumasu, Srinivas; Asbun, Eduardo; He, Yong; Ye, Yan
2017-09-01
360° video is an emerging new format in the media industry enabled by the growing availability of virtual reality devices. It provides the viewer a new sense of presence and immersion. Compared to conventional rectilinear video (2D or 3D), 360° video poses a new and difficult set of engineering challenges on video processing and delivery. Enabling comfortable and immersive user experience requires very high video quality and very low latency, while the large video file size poses a challenge to delivering 360° video in a quality manner at scale. Conventionally, 360° video represented in equirectangular or other projection formats can be encoded as a single standards-compliant bitstream using existing video codecs such as H.264/AVC or H.265/HEVC. Such method usually needs very high bandwidth to provide an immersive user experience. While at the client side, much of such high bandwidth and the computational power used to decode the video are wasted because the user only watches a small portion (i.e., viewport) of the entire picture. Viewport dependent 360°video processing and delivery approaches spend more bandwidth on the viewport than on non-viewports and are therefore able to reduce the overall transmission bandwidth. This paper proposes a dual buffer segment scheduling algorithm for viewport adaptive streaming methods to reduce latency when switching between high quality viewports in 360° video streaming. The approach decouples the scheduling of viewport segments and non-viewport segments to ensure the viewport segment requested matches the latest user head orientation. A base layer buffer stores all lower quality segments, and a viewport buffer stores high quality viewport segments corresponding to the most recent viewer's head orientation. The scheduling scheme determines viewport requesting time based on the buffer status and the head orientation. This paper also discusses how to deploy the proposed scheduling design for various viewport adaptive video
NASA Astrophysics Data System (ADS)
Bolkas, Dimitrios; Martinez, Aaron
2018-01-01
Point-cloud coordinate information derived from terrestrial Light Detection And Ranging (LiDAR) is important for several applications in surveying and civil engineering. Plane fitting and segmentation of target-surfaces is an important step in several applications such as in the monitoring of structures. Reliable parametric modeling and segmentation relies on the underlying quality of the point-cloud. Therefore, understanding how point-cloud errors affect fitting of planes and segmentation is important. Point-cloud intensity, which accompanies the point-cloud data, often goes hand-in-hand with point-cloud noise. This study uses industrial particle boards painted with eight different colors (black, white, grey, red, green, blue, brown, and yellow) and two different sheens (flat and semi-gloss) to explore how noise and plane residuals vary with scanning geometry (i.e., distance and incidence angle) and target-color. Results show that darker colors, such as black and brown, can produce point clouds that are several times noisier than bright targets, such as white. In addition, semi-gloss targets manage to reduce noise in dark targets by about 2-3 times. The study of plane residuals with scanning geometry reveals that, in many of the cases tested, residuals decrease with increasing incidence angles, which can assist in understanding the distribution of plane residuals in a dataset. Finally, a scheme is developed to derive survey guidelines based on the data collected in this experiment. Three examples demonstrate that users should consider instrument specification, required precision of plane residuals, required point-spacing, target-color, and target-sheen, when selecting scanning locations. Outcomes of this study can aid users to select appropriate instrumentation and improve planning of terrestrial LiDAR data-acquisition.
Automatic tissue segmentation of breast biopsies imaged by QPI
NASA Astrophysics Data System (ADS)
Majeed, Hassaan; Nguyen, Tan; Kandel, Mikhail; Marcias, Virgilia; Do, Minh; Tangella, Krishnarao; Balla, Andre; Popescu, Gabriel
2016-03-01
The current tissue evaluation method for breast cancer would greatly benefit from higher throughput and less inter-observer variation. Since quantitative phase imaging (QPI) measures physical parameters of tissue, it can be used to find quantitative markers, eliminating observer subjectivity. Furthermore, since the pixel values in QPI remain the same regardless of the instrument used, classifiers can be built to segment various tissue components without need for color calibration. In this work we use a texton-based approach to segment QPI images of breast tissue into various tissue components (epithelium, stroma or lumen). A tissue microarray comprising of 900 unstained cores from 400 different patients was imaged using Spatial Light Interference Microscopy. The training data were generated by manually segmenting the images for 36 cores and labelling each pixel (epithelium, stroma or lumen.). For each pixel in the data, a response vector was generated by the Leung-Malik (LM) filter bank and these responses were clustered using the k-means algorithm to find the centers (called textons). A random forest classifier was then trained to find the relationship between a pixel's label and the histogram of these textons in that pixel's neighborhood. The segmentation was carried out on the validation set by calculating the texton histogram in a pixel's neighborhood and generating a label based on the model learnt during training. Segmentation of the tissue into various components is an important step toward efficiently computing parameters that are markers of disease. Automated segmentation, followed by diagnosis, can improve the accuracy and speed of analysis leading to better health outcomes.
GLISTR: Glioma Image Segmentation and Registration
Pohl, Kilian M.; Bilello, Michel; Cirillo, Luigi; Biros, George; Melhem, Elias R.; Davatzikos, Christos
2015-01-01
We present a generative approach for simultaneously registering a probabilistic atlas of a healthy population to brain magnetic resonance (MR) scans showing glioma and segmenting the scans into tumor as well as healthy tissue labels. The proposed method is based on the expectation maximization (EM) algorithm that incorporates a glioma growth model for atlas seeding, a process which modifies the original atlas into one with tumor and edema adapted to best match a given set of patient’s images. The modified atlas is registered into the patient space and utilized for estimating the posterior probabilities of various tissue labels. EM iteratively refines the estimates of the posterior probabilities of tissue labels, the deformation field and the tumor growth model parameters. Hence, in addition to segmentation, the proposed method results in atlas registration and a low-dimensional description of the patient scans through estimation of tumor model parameters. We validate the method by automatically segmenting 10 MR scans and comparing the results to those produced by clinical experts and two state-of-the-art methods. The resulting segmentations of tumor and edema outperform the results of the reference methods, and achieve a similar accuracy from a second human rater. We additionally apply the method to 122 patients scans and report the estimated tumor model parameters and their relations with segmentation and registration results. Based on the results from this patient population, we construct a statistical atlas of the glioma by inverting the estimated deformation fields to warp the tumor segmentations of patients scans into a common space. PMID:22907965
Color naming across languages reflects color use
Gibson, Edward; Futrell, Richard; Mahowald, Kyle; Bergen, Leon; Ratnasingam, Sivalogeswaran; Gibson, Mitchell; Piantadosi, Steven T.; Conway, Bevil R.
2017-01-01
What determines how languages categorize colors? We analyzed results of the World Color Survey (WCS) of 110 languages to show that despite gross differences across languages, communication of chromatic chips is always better for warm colors (yellows/reds) than cool colors (blues/greens). We present an analysis of color statistics in a large databank of natural images curated by human observers for salient objects and show that objects tend to have warm rather than cool colors. These results suggest that the cross-linguistic similarity in color-naming efficiency reflects colors of universal usefulness and provide an account of a principle (color use) that governs how color categories come about. We show that potential methodological issues with the WCS do not corrupt information-theoretic analyses, by collecting original data using two extreme versions of the color-naming task, in three groups: the Tsimane', a remote Amazonian hunter-gatherer isolate; Bolivian-Spanish speakers; and English speakers. These data also enabled us to test another prediction of the color-usefulness hypothesis: that differences in color categorization between languages are caused by differences in overall usefulness of color to a culture. In support, we found that color naming among Tsimane' had relatively low communicative efficiency, and the Tsimane' were less likely to use color terms when describing familiar objects. Color-naming among Tsimane' was boosted when naming artificially colored objects compared with natural objects, suggesting that industrialization promotes color usefulness. PMID:28923921
Highway pavement performance test for colored thin anti-skidding layers
NASA Astrophysics Data System (ADS)
Gao, Wei; Cui, Wei; Xu, Ming
2018-03-01
Based on the actual service condition of highway pavement colored thin anti-skidding layers, with materials of color quartz sand and two-component acrylic resin as basis, we designed such tests as the bond strength, shearing strength, tear strength, fatigue performance and aggregate polished value, and included the freeze-thaw cycle and de-icing salt and other factors in the experiment, connecting with the climate characteristics of circumpolar latitude and low altitude in Heilongjiang province. Through the pavement performance test, it is confirmed that the colored thin anti-skidding layers can adapt to cold and humid climate conditions, and its physical mechanical properties are good.
Multivariate statistical model for 3D image segmentation with application to medical images.
John, Nigel M; Kabuka, Mansur R; Ibrahim, Mohamed O
2003-12-01
In this article we describe a statistical model that was developed to segment brain magnetic resonance images. The statistical segmentation algorithm was applied after a pre-processing stage involving the use of a 3D anisotropic filter along with histogram equalization techniques. The segmentation algorithm makes use of prior knowledge and a probability-based multivariate model designed to semi-automate the process of segmentation. The algorithm was applied to images obtained from the Center for Morphometric Analysis at Massachusetts General Hospital as part of the Internet Brain Segmentation Repository (IBSR). The developed algorithm showed improved accuracy over the k-means, adaptive Maximum Apriori Probability (MAP), biased MAP, and other algorithms. Experimental results showing the segmentation and the results of comparisons with other algorithms are provided. Results are based on an overlap criterion against expertly segmented images from the IBSR. The algorithm produced average results of approximately 80% overlap with the expertly segmented images (compared with 85% for manual segmentation and 55% for other algorithms).
NASA Astrophysics Data System (ADS)
Lecoeur, Jérémy; Ferré, Jean-Christophe; Collins, D. Louis; Morrisey, Sean P.; Barillot, Christian
2009-02-01
A new segmentation framework is presented taking advantage of multimodal image signature of the different brain tissues (healthy and/or pathological). This is achieved by merging three different modalities of gray-level MRI sequences into a single RGB-like MRI, hence creating a unique 3-dimensional signature for each tissue by utilising the complementary information of each MRI sequence. Using the scale-space spectral gradient operator, we can obtain a spatial gradient robust to intensity inhomogeneity. Even though it is based on psycho-visual color theory, it can be very efficiently applied to the RGB colored images. More over, it is not influenced by the channel assigment of each MRI. Its optimisation by the graph cuts paradigm provides a powerful and accurate tool to segment either healthy or pathological tissues in a short time (average time about ninety seconds for a brain-tissues classification). As it is a semi-automatic method, we run experiments to quantify the amount of seeds needed to perform a correct segmentation (dice similarity score above 0.85). Depending on the different sets of MRI sequences used, this amount of seeds (expressed as a relative number in pourcentage of the number of voxels of the ground truth) is between 6 to 16%. We tested this algorithm on brainweb for validation purpose (healthy tissue classification and MS lesions segmentation) and also on clinical data for tumours and MS lesions dectection and tissues classification.
Color universal design: analysis of color category dependency on color vision type (4)
NASA Astrophysics Data System (ADS)
Ikeda, Tomohiro; Ichihara, Yasuyo G.; Kojima, Natsuki; Tanaka, Hisaya; Ito, Kei
2013-02-01
This report is af ollow-up to SPIE-IS+T / Vol. 7528 7528051-8, SPIE-IS+T / Vol. 7866 78660J-1-8 and SPIE-IS+T / Vol. 8292 829206-1-8. Colors are used to communicate information in various situations, not just for design and apparel. However, visual information given only by color may be perceived differently by individuals with different color vision types. Human color vision is non-uniform and the variation in most cases is genetically linked to L-cones and M-cones. Therefore, color appearance is not the same for all color vision types. Color Universal Design is an easy-to-understand system that was created to convey color-coded information accurately to most people, taking color vision types into consideration. In the present research, we studied trichromat (C-type), prolan (P-type), and deutan (D-type) forms of color vision. We here report the result of two experiments. The first was the validation of the confusion colors using the color chart on CIELAB uniform color space. We made an experimental color chart (total of color cells is 622, the color difference between color cells is 2.5) for fhis experiment, and subjects have P-type or D-type color vision. From the data we were able to determine "the limits with high probability of confusion" and "the limits with possible confusion" around various basing points. The direction of the former matched with the theoretical confusion locus, but the range did not extend across the entire a* range. The latter formed a belt-like zone above and below the theoretical confusion locus. This way we re-analyzed a part of the theoretical confusion locus suggested by Pitt-Judd. The second was an experiment in color classification of the subjects with C-type, P-type, or D-type color vision. The color caps of fhe 100 Hue Test were classified into seven categories for each color vision type. The common and different points of color sensation were compared for each color vision type, and we were able to find a group of color caps
Lung lobe modeling and segmentation with individualized surface meshes
NASA Astrophysics Data System (ADS)
Blaffert, Thomas; Barschdorf, Hans; von Berg, Jens; Dries, Sebastian; Franz, Astrid; Klinder, Tobias; Lorenz, Cristian; Renisch, Steffen; Wiemker, Rafael
2008-03-01
An automated segmentation of lung lobes in thoracic CT images is of interest for various diagnostic purposes like the quantification of emphysema or the localization of tumors within the lung. Although the separating lung fissures are visible in modern multi-slice CT-scanners, their contrast in the CT-image often does not separate the lobes completely. This makes it impossible to build a reliable segmentation algorithm without additional information. Our approach uses general anatomical knowledge represented in a geometrical mesh model to construct a robust lobe segmentation, which even gives reasonable estimates of lobe volumes if fissures are not visible at all. The paper describes the generation of the lung model mesh including lobes by an average volume model, its adaptation to individual patient data using a special fissure feature image, and a performance evaluation over a test data set showing an average segmentation accuracy of 1 to 3 mm.
NASA Technical Reports Server (NTRS)
Hall, Lawrence O.; Bensaid, Amine M.; Clarke, Laurence P.; Velthuizen, Robert P.; Silbiger, Martin S.; Bezdek, James C.
1992-01-01
Magnetic resonance (MR) brain section images are segmented and then synthetically colored to give visual representations of the original data with three approaches: the literal and approximate fuzzy c-means unsupervised clustering algorithms and a supervised computational neural network, a dynamic multilayered perception trained with the cascade correlation learning algorithm. Initial clinical results are presented on both normal volunteers and selected patients with brain tumors surrounded by edema. Supervised and unsupervised segmentation techniques provide broadly similar results. Unsupervised fuzzy algorithms were visually observed to show better segmentation when compared with raw image data for volunteer studies. However, for a more complex segmentation problem with tumor/edema or cerebrospinal fluid boundary, where the tissues have similar MR relaxation behavior, inconsistency in rating among experts was observed.
DOE Office of Scientific and Technical Information (OSTI.GOV)
Willingham, David G.; Naes, Benjamin E.; Heasler, Patrick G.
A novel approach to particle identification and particle isotope ratio determination has been developed for nuclear safeguard applications. This particle search approach combines an adaptive thresholding algorithm and marker-controlled watershed segmentation (MCWS) transform, which improves the secondary ion mass spectrometry (SIMS) isotopic analysis of uranium containing particle populations for nuclear safeguards applications. The Niblack assisted MCWS approach (a.k.a. SEEKER) developed for this work has improved the identification of isotopically unique uranium particles under conditions that have historically presented significant challenges for SIMS image data processing techniques. Particles obtained from five NIST uranium certified reference materials (CRM U129A, U015, U150, U500more » and U850) were successfully identified in regions of SIMS image data 1) where a high variability in image intensity existed, 2) where particles were touching or were in close proximity to one another and/or 3) where the magnitude of ion signal for a given region was count limited. Analysis of the isotopic distributions of uranium containing particles identified by SEEKER showed four distinct, accurately identified 235U enrichment distributions, corresponding to the NIST certified 235U/238U isotope ratios for CRM U129A/U015 (not statistically differentiated), U150, U500 and U850. Additionally, comparison of the minor uranium isotope (234U, 235U and 236U) atom percent values verified that, even in the absence of high precision isotope ratio measurements, SEEKER could be used to segment isotopically unique uranium particles from SIMS image data. Although demonstrated specifically for SIMS analysis of uranium containing particles for nuclear safeguards, SEEKER has application in addressing a broad set of image processing challenges.« less
Online Denoising Based on the Second-Order Adaptive Statistics Model.
Yi, Sheng-Lun; Jin, Xue-Bo; Su, Ting-Li; Tang, Zhen-Yun; Wang, Fa-Fa; Xiang, Na; Kong, Jian-Lei
2017-07-20
Online denoising is motivated by real-time applications in the industrial process, where the data must be utilizable soon after it is collected. Since the noise in practical process is usually colored, it is quite a challenge for denoising techniques. In this paper, a novel online denoising method was proposed to achieve the processing of the practical measurement data with colored noise, and the characteristics of the colored noise were considered in the dynamic model via an adaptive parameter. The proposed method consists of two parts within a closed loop: the first one is to estimate the system state based on the second-order adaptive statistics model and the other is to update the adaptive parameter in the model using the Yule-Walker algorithm. Specifically, the state estimation process was implemented via the Kalman filter in a recursive way, and the online purpose was therefore attained. Experimental data in a reinforced concrete structure test was used to verify the effectiveness of the proposed method. Results show the proposed method not only dealt with the signals with colored noise, but also achieved a tradeoff between efficiency and accuracy.
Hanson, Erik A; Lundervold, Arvid
2013-11-01
Multispectral, multichannel, or time series image segmentation is important for image analysis in a wide range of applications. Regularization of the segmentation is commonly performed using local image information causing the segmented image to be locally smooth or piecewise constant. A new spatial regularization method, incorporating non-local information, was developed and tested. Our spatial regularization method applies to feature space classification in multichannel images such as color images and MR image sequences. The spatial regularization involves local edge properties, region boundary minimization, as well as non-local similarities. The method is implemented in a discrete graph-cut setting allowing fast computations. The method was tested on multidimensional MRI recordings from human kidney and brain in addition to simulated MRI volumes. The proposed method successfully segment regions with both smooth and complex non-smooth shapes with a minimum of user interaction.
Color correction with blind image restoration based on multiple images using a low-rank model
NASA Astrophysics Data System (ADS)
Li, Dong; Xie, Xudong; Lam, Kin-Man
2014-03-01
We present a method that can handle the color correction of multiple photographs with blind image restoration simultaneously and automatically. We prove that the local colors of a set of images of the same scene exhibit the low-rank property locally both before and after a color-correction operation. This property allows us to correct all kinds of errors in an image under a low-rank matrix model without particular priors or assumptions. The possible errors may be caused by changes of viewpoint, large illumination variations, gross pixel corruptions, partial occlusions, etc. Furthermore, a new iterative soft-segmentation method is proposed for local color transfer using color influence maps. Due to the fact that the correct color information and the spatial information of images can be recovered using the low-rank model, more precise color correction and many other image-restoration tasks-including image denoising, image deblurring, and gray-scale image colorizing-can be performed simultaneously. Experiments have verified that our method can achieve consistent and promising results on uncontrolled real photographs acquired from the Internet and that it outperforms current state-of-the-art methods.
Do focal colors look particularly "colorful"?
Witzel, Christoph; Franklin, Anna
2014-04-01
If the most typical red, yellow, green, and blue were particularly colorful (i.e., saturated), they would "jump out to the eye." This would explain why even fundamentally different languages have distinct color terms for these focal colors, and why unique hues play a prominent role in subjective color appearance. In this study, the subjective saturation of 10 colors around each of these focal colors was measured through a pairwise matching task. Results show that subjective saturation changes systematically across hues in a way that is strongly correlated to the visual gamut, and exponentially related to sensitivity but not to focal colors.
Wavefront Reconstruction and Mirror Surface Optimizationfor Adaptive Optics
2014-06-01
TERMS Wavefront reconstruction, Adaptive optics , Wavelets, Atmospheric turbulence , Branch points, Mirror surface optimization, Space telescope, Segmented...contribution adapts the proposed algorithm to work when branch points are present from significant atmospheric turbulence . An analysis of vector spaces...estimate the distortion of the collected light caused by the atmosphere and corrected by adaptive optics . A generalized orthogonal wavelet wavefront
Segmentation-based wavelet transform for still-image compression
NASA Astrophysics Data System (ADS)
Mozelle, Gerard; Seghier, Abdellatif; Preteux, Francoise J.
1996-10-01
In order to address simultaneously the two functionalities, content-based scalability required by MPEG-4, we introduce a segmentation-based wavelet transform (SBWT). SBWT takes into account both the mathematical properties of multiresolution analysis and the flexibility of region-based approaches for image compression. The associated methodology has two stages: 1) image segmentation into convex and polygonal regions; 2) 2D-wavelet transform of the signal corresponding to each region. In this paper, we have mathematically studied a method for constructing a multiresolution analysis (VjOmega)j (epsilon) N adapted to a polygonal region which provides an adaptive region-based filtering. The explicit construction of scaling functions, pre-wavelets and orthonormal wavelets bases defined on a polygon is carried out by using scaling functions is established by using the theory of Toeplitz operators. The corresponding expression can be interpreted as a location property which allow defining interior and boundary scaling functions. Concerning orthonormal wavelets and pre-wavelets, a similar expansion is obtained by taking advantage of the properties of the orthogonal projector P(V(j(Omega )) perpendicular from the space Vj(Omega ) + 1 onto the space (Vj(Omega )) perpendicular. Finally the mathematical results provide a simple and fast algorithm adapted to polygonal regions.
Individual and age-related variation in chromatic contrast adaptation
Elliott, Sarah L.; Werner, John S.; Webster, Michael A.
2012-01-01
Precortical color channels are tuned primarily to the LvsM (stimulation of L and M cones varied, but S cone stimulation held constant) or SvsLM (stimulation of S cones varied, but L and M cone stimulation held constant) cone-opponent (cardinal) axes, but appear elaborated in the cortex to form higher-order mechanisms tuned to both cardinal and intermediate directions. One source of evidence for these higher-order mechanisms has been the selectivity of color contrast adaptation for noncardinal directions, yet the degree of this selectivity has varied widely across the small sample of observers tested in previous studies. This study explored the possible bases for this variation, and in particular tested whether it reflected age-related changes in the distribution or tuning of color mechanisms. Observers included 15 younger (18–22 years of age) and 15 older individuals (66–82), who adapted to temporal modulations along one of four chromatic axes (two cardinal and two intermediate axes) and then matched the hue and contrast of test stimuli lying along eight different directions in the equiluminant plane. All observers exhibited aftereffects that were selective for both the cardinal and intermediate directions, although selectivity was weaker for the intermediate axes. The degree of selectivity increased with the magnitude of adaptation for all axes, and thus adaptation strength alone may account for much of the variance in selectivity among observers. Older observers showed a stronger magnitude of adaptation thus, surprisingly, more conspicuous evidence for higher-order mechanisms. For both age groups the aftereffects were well predicted by response changes in chromatic channels with linear spectral sensitivities, and there was no evidence for weakened channel tuning with aging. The results suggest that higher-order mechanisms may become more exposed in observers or conditions in which the strength of adaptation is greater, and that both chromatic contrast
Using color management in color document processing
NASA Astrophysics Data System (ADS)
Nehab, Smadar
1995-04-01
Color Management Systems have been used for several years in Desktop Publishing (DTP) environments. While this development hasn't matured yet, we are already experiencing the next generation of the color imaging revolution-Device Independent Color for the small office/home office (SOHO) environment. Though there are still open technical issues with device independent color matching, they are not the focal point of this paper. This paper discusses two new and crucial aspects in using color management in color document processing: the management of color objects and their associated color rendering methods; a proposal for a precedence order and handshaking protocol among the various software components involved in color document processing. As color peripherals become affordable to the SOHO market, color management also becomes a prerequisite for common document authoring applications such as word processors. The first color management solutions were oriented towards DTP environments whose requirements were largely different. For example, DTP documents are image-centric, as opposed to SOHO documents that are text and charts centric. To achieve optimal reproduction on low-cost SOHO peripherals, it is critical that different color rendering methods are used for the different document object types. The first challenge in using color management of color document processing is the association of rendering methods with object types. As a result of an evolutionary process, color matching solutions are now available as application software, as driver embedded software and as operating system extensions. Consequently, document processing faces a new challenge, the correct selection of the color matching solution while avoiding duplicate color corrections.
AUTOMATED CELL SEGMENTATION WITH 3D FLUORESCENCE MICROSCOPY IMAGES.
Kong, Jun; Wang, Fusheng; Teodoro, George; Liang, Yanhui; Zhu, Yangyang; Tucker-Burden, Carol; Brat, Daniel J
2015-04-01
A large number of cell-oriented cancer investigations require an effective and reliable cell segmentation method on three dimensional (3D) fluorescence microscopic images for quantitative analysis of cell biological properties. In this paper, we present a fully automated cell segmentation method that can detect cells from 3D fluorescence microscopic images. Enlightened by fluorescence imaging techniques, we regulated the image gradient field by gradient vector flow (GVF) with interpolated and smoothed data volume, and grouped voxels based on gradient modes identified by tracking GVF field. Adaptive thresholding was then applied to voxels associated with the same gradient mode where voxel intensities were enhanced by a multiscale cell filter. We applied the method to a large volume of 3D fluorescence imaging data of human brain tumor cells with (1) small cell false detection and missing rates for individual cells; and (2) trivial over and under segmentation incidences for clustered cells. Additionally, the concordance of cell morphometry structure between automated and manual segmentation was encouraging. These results suggest a promising 3D cell segmentation method applicable to cancer studies.
Layered motion segmentation and depth ordering by tracking edges.
Smith, Paul; Drummond, Tom; Cipolla, Roberto
2004-04-01
This paper presents a new Bayesian framework for motion segmentation--dividing a frame from an image sequence into layers representing different moving objects--by tracking edges between frames. Edges are found using the Canny edge detector, and the Expectation-Maximization algorithm is then used to fit motion models to these edges and also to calculate the probabilities of the edges obeying each motion model. The edges are also used to segment the image into regions of similar color. The most likely labeling for these regions is then calculated by using the edge probabilities, in association with a Markov Random Field-style prior. The identification of the relative depth ordering of the different motion layers is also determined, as an integral part of the process. An efficient implementation of this framework is presented for segmenting two motions (foreground and background) using two frames. It is then demonstrated how, by tracking the edges into further frames, the probabilities may be accumulated to provide an even more accurate and robust estimate, and segment an entire sequence. Further extensions are then presented to address the segmentation of more than two motions. Here, a hierarchical method of initializing the Expectation-Maximization algorithm is described, and it is demonstrated that the Minimum Description Length principle may be used to automatically select the best number of motion layers. The results from over 30 sequences (demonstrating both two and three motions) are presented and discussed.
Color-coded depth information in volume-rendered magnetic resonance angiography
NASA Astrophysics Data System (ADS)
Smedby, Orjan; Edsborg, Karin; Henriksson, John
2004-05-01
Magnetic Resonance Angiography (MRA) and Computed Tomography Angiography (CTA) data are usually presented using Maximum Intensity Projection (MIP) or Volume Rendering Technique (VRT), but these often fail to demonstrate a stenosis if the projection angle is not suitably chosen. In order to make vascular stenoses visible in projection images independent of the choice of viewing angle, a method is proposed to supplement these images with colors representing the local caliber of the vessel. After preprocessing the volume image with a median filter, segmentation is performed by thresholding, and a Euclidean distance transform is applied. The distance to the background from each voxel in the vessel is mapped to a color. These colors can either be rendered directly using MIP or be presented together with opacity information based on the original image using VRT. The method was tested in a synthetic dataset containing a cylindrical vessel with stenoses in varying angles. The results suggest that the visibility of stenoses is enhanced by the color information. In clinical feasibility experiments, the technique was applied to clinical MRA data. The results are encouraging and indicate that the technique can be used with clinical images.
NASA Astrophysics Data System (ADS)
Kesiman, Made Windu Antara; Valy, Dona; Burie, Jean-Christophe; Paulus, Erick; Sunarya, I. Made Gede; Hadi, Setiawan; Sok, Kim Heng; Ogier, Jean-Marc
2017-01-01
Due to their specific characteristics, palm leaf manuscripts provide new challenges for text line segmentation tasks in document analysis. We investigated the performance of six text line segmentation methods by conducting comparative experimental studies for the collection of palm leaf manuscript images. The image corpus used in this study comes from the sample images of palm leaf manuscripts of three different Southeast Asian scripts: Balinese script from Bali and Sundanese script from West Java, both from Indonesia, and Khmer script from Cambodia. For the experiments, four text line segmentation methods that work on binary images are tested: the adaptive partial projection line segmentation approach, the A* path planning approach, the shredding method, and our proposed energy function for shredding method. Two other methods that can be directly applied on grayscale images are also investigated: the adaptive local connectivity map method and the seam carving-based method. The evaluation criteria and tool provided by ICDAR2013 Handwriting Segmentation Contest were used in this experiment.
Song, Xiaoying; Huang, Qijun; Chang, Sheng; He, Jin; Wang, Hao
2018-06-01
To improve the compression rates for lossless compression of medical images, an efficient algorithm, based on irregular segmentation and region-based prediction, is proposed in this paper. Considering that the first step of a region-based compression algorithm is segmentation, this paper proposes a hybrid method by combining geometry-adaptive partitioning and quadtree partitioning to achieve adaptive irregular segmentation for medical images. Then, least square (LS)-based predictors are adaptively designed for each region (regular subblock or irregular subregion). The proposed adaptive algorithm not only exploits spatial correlation between pixels but it utilizes local structure similarity, resulting in efficient compression performance. Experimental results show that the average compression performance of the proposed algorithm is 10.48, 4.86, 3.58, and 0.10% better than that of JPEG 2000, CALIC, EDP, and JPEG-LS, respectively. Graphical abstract ᅟ.
Hiding Information Using different lighting Color images
NASA Astrophysics Data System (ADS)
Majead, Ahlam; Awad, Rash; Salman, Salema S.
2018-05-01
The host medium for the secret message is one of the important principles for the designers of steganography method. In this study, the best color image was studied to carrying any secret image.The steganography approach based Lifting Wavelet Transform (LWT) and Least Significant Bits (LSBs) substitution. The proposed method offers lossless and unnoticeable changes in the contrast carrier color image and imperceptible by human visual system (HVS), especially the host images which was captured in dark lighting conditions. The aim of the study was to study the process of masking the data in colored images with different light intensities. The effect of the masking process was examined on the images that are classified by a minimum distance and the amount of noise and distortion in the image. The histogram and statistical characteristics of the cover image the results showed the efficient use of images taken with different light intensities in hiding data using the least important bit substitution method. This method succeeded in concealing textual data without distorting the original image (low light) Lire developments due to the concealment process.The digital image segmentation technique was used to distinguish small areas with masking. The result is that smooth homogeneous areas are less affected as a result of hiding comparing with high light areas. It is possible to use dark color images to send any secret message between two persons for the purpose of secret communication with good security.
Spatial Statistics for Segmenting Histological Structures in H&E Stained Tissue Images.
Nguyen, Luong; Tosun, Akif Burak; Fine, Jeffrey L; Lee, Adrian V; Taylor, D Lansing; Chennubhotla, S Chakra
2017-07-01
Segmenting a broad class of histological structures in transmitted light and/or fluorescence-based images is a prerequisite for determining the pathological basis of cancer, elucidating spatial interactions between histological structures in tumor microenvironments (e.g., tumor infiltrating lymphocytes), facilitating precision medicine studies with deep molecular profiling, and providing an exploratory tool for pathologists. This paper focuses on segmenting histological structures in hematoxylin- and eosin-stained images of breast tissues, e.g., invasive carcinoma, carcinoma in situ, atypical and normal ducts, adipose tissue, and lymphocytes. We propose two graph-theoretic segmentation methods based on local spatial color and nuclei neighborhood statistics. For benchmarking, we curated a data set of 232 high-power field breast tissue images together with expertly annotated ground truth. To accurately model the preference for histological structures (ducts, vessels, tumor nets, adipose, etc.) over the remaining connective tissue and non-tissue areas in ground truth annotations, we propose a new region-based score for evaluating segmentation algorithms. We demonstrate the improvement of our proposed methods over the state-of-the-art algorithms in both region- and boundary-based performance measures.
Automatic color preference correction for color reproduction
NASA Astrophysics Data System (ADS)
Tsukada, Masato; Funayama, Chisato; Tajima, Johji
2000-12-01
The reproduction of natural objects in color images has attracted a great deal of attention. Reproduction more pleasing colors of natural objects is one of the methods available to improve image quality. We developed an automatic color correction method to maintain preferred color reproduction for three significant categories: facial skin color, green grass and blue sky. In this method, a representative color in an object area to be corrected is automatically extracted from an input image, and a set of color correction parameters is selected depending on the representative color. The improvement in image quality for reproductions of natural image was more than 93 percent in subjective experiments. These results show the usefulness of our automatic color correction method for the reproduction of preferred colors.
Driving color management into the office
NASA Astrophysics Data System (ADS)
Newman, Todd
2007-01-01
In much the same way that the automobile industry develops new technologies in racing cars and then brings them to a broader market for commercial and consumer vehicles, CIE Division 8 is trying to spread color management from the graphic arts market into the broader office and home markets. In both areas, the professional environment is characterized by highly motivated, highly trained practitioners who see their activity as an end in itself and have access to expensive technology, state of the art measurement and calibration equipment, and an environment that, if not as sedate as a research laboratory, is controlled and well-understood. In contrast, the broader market features users who have relatively little training at the imaging tasks and see them as a means to an end, which is where their real attention is focused. These users have mass-market equipment and little or no equipment for measurement and calibration. They use their tools (cars or imaging equipment) in a variety of environments under highly unpredictable conditions. The challenge to the automobile and imaging engineering communities is to design practical solutions to work in these real world environments that are less demanding in terms of strict performance, but more demanding in terms of flexibility and robustness. In the graphic arts, we have standards that tell us how to perform comparisons between printed images (hardcopy) and images displayed on a screen (softcopy). The users are told to use sequential binocular comparisons using memory matching, where they first adapt completely to one viewing condition, study one image, and then adapt to the other viewing condition and compare the second image against their memory of the first. This provides a nicely controlled environment where the observer's state of adaptation is easy to calculate. Unfortunately, in the office and home markets, users insist on comparing the softcopy and hardcopy side by side, and rapidly switching their gaze between
Albert H. Munsell: A sense of color at the interface of art and science
Landa, E.R.
2004-01-01
The color theory conceived and commercialized by Albert H. Munsell (1858-1918) has become a universal part of the lexicon of soil science. An American painter noted for his seascapes and portraits, he had a long-standing interest in the description of color. Munsell began studies aimed at standardizing color description, using hue, value, and chroma scales, around 1898. His landmark treatise, "A Color Notation," was published in 1905. Munsell died about 30 years before his color charts came into wide-spread use in soil survey programs in the United States. Dorothy Nickerson, who began her career as secretary and laboratory assistant to Munsell's son, and later spent 37 years at USDA as a color-science specialist, did much to adapt the Munsell Color System to soil-color usage. The legacy of color research pioneered by A.H. Munsell is honored today by the Munsell Color Science Laboratory established in 1983 at the Rochester Institute of Technology.
Characterization of flotation color by machine vision
NASA Astrophysics Data System (ADS)
Siren, Ari
1999-09-01
Flotation is the most common industrial method by which valuable minerals are separated from waste rock after crushing and grinding the ore. For process control, flotation plants and devices are equipped with conventional and specialized sensors. However, certain variables are left to the visual observation of the operator, such as the color of the froth and the size of the bubbles in the froth. The ChaCo-Project (EU-Project 24931) was launched in November 1997. In this project a measuring station was built at the Pyhasalmi flotation plant. The system includes an RGB camera and a spectral color measuring instrument for the color inspection of the flotation. The RGB camera or visible spectral range is also measured to compare the operators' comments on the color of the froth relating to the sphalerite concentration and the process balance. Different dried mineral (sphalerite) ratios were studied with iron pyrite to find out about the minerals' typical spectral features. The correlation between sphalerite spectral reflectance and sphalerite concentration over various wavelengths are used to select the proper camera system with filters or to compare the results with the color information from the RGB camera. Various machine vision candidate techniques are discussed for this application and the preprocessed information of the dried mineral colors is used and adapted to the online measuring station. Moving froth bubbles produce total reflections, disturbing the color information. Polarization filters are used and the results are reported. Also the reflectance outside the visible light is studied and reported.
Interactive vs. automatic ultrasound image segmentation methods for staging hepatic lipidosis.
Weijers, Gert; Starke, Alexander; Haudum, Alois; Thijssen, Johan M; Rehage, Jürgen; De Korte, Chris L
2010-07-01
The aim of this study was to test the hypothesis that automatic segmentation of vessels in ultrasound (US) images can produce similar or better results in grading fatty livers than interactive segmentation. A study was performed in postpartum dairy cows (N=151), as an animal model of human fatty liver disease, to test this hypothesis. Five transcutaneous and five intraoperative US liver images were acquired in each animal and a liverbiopsy was taken. In liver tissue samples, triacylglycerol (TAG) was measured by biochemical analysis and hepatic diseases other than hepatic lipidosis were excluded by histopathologic examination. Ultrasonic tissue characterization (UTC) parameters--Mean echo level, standard deviation (SD) of echo level, signal-to-noise ratio (SNR), residual attenuation coefficient (ResAtt) and axial and lateral speckle size--were derived using a computer-aided US (CAUS) protocol and software package. First, the liver tissue was interactively segmented by two observers. With increasing fat content, fewer hepatic vessels were visible in the ultrasound images and, therefore, a smaller proportion of the liver needed to be excluded from these images. Automatic-segmentation algorithms were implemented and it was investigated whether better results could be achieved than with the subjective and time-consuming interactive-segmentation procedure. The automatic-segmentation algorithms were based on both fixed and adaptive thresholding techniques in combination with a 'speckle'-shaped moving-window exclusion technique. All data were analyzed with and without postprocessing as contained in CAUS and with different automated-segmentation techniques. This enabled us to study the effect of the applied postprocessing steps on single and multiple linear regressions ofthe various UTC parameters with TAG. Improved correlations for all US parameters were found by using automatic-segmentation techniques. Stepwise multiple linear-regression formulas where derived and used
Fast hierarchical knowledge-based approach for human face detection in color images
NASA Astrophysics Data System (ADS)
Jiang, Jun; Gong, Jie; Zhang, Guilin; Hu, Ruolan
2001-09-01
This paper presents a fast hierarchical knowledge-based approach for automatically detecting multi-scale upright faces in still color images. The approach consists of three levels. At the highest level, skin-like regions are determinated by skin model, which is based on the color attributes hue and saturation in HSV color space, as well color attributes red and green in normalized color space. In level 2, a new eye model is devised to select human face candidates in segmented skin-like regions. An important feature of the eye model is that it is independent of the scale of human face. So it is possible for finding human faces in different scale with scanning image only once, and it leads to reduction the computation time of face detection greatly. In level 3, a human face mosaic image model, which is consistent with physical structure features of human face well, is applied to judge whether there are face detects in human face candidate regions. This model includes edge and gray rules. Experiment results show that the approach has high robustness and fast speed. It has wide application perspective at human-computer interactions and visual telephone etc.
Segmenting human from photo images based on a coarse-to-fine scheme.
Lu, Huchuan; Fang, Guoliang; Shao, Xinqing; Li, Xuelong
2012-06-01
Human segmentation in photo images is a challenging and important problem that finds numerous applications ranging from album making and photo classification to image retrieval. Previous works on human segmentation usually demand a time-consuming training phase for complex shape-matching processes. In this paper, we propose a straightforward framework to automatically recover human bodies from color photos. Employing a coarse-to-fine strategy, we first detect a coarse torso (CT) using the multicue CT detection algorithm and then extract the accurate region of the upper body. Then, an iterative multiple oblique histogram algorithm is presented to accurately recover the lower body based on human kinematics. The performance of our algorithm is evaluated on our own data set (contains 197 images with human body region ground truth data), VOC 2006, and the 2010 data set. Experimental results demonstrate the merits of the proposed method in segmenting a person with various poses.
NASA Astrophysics Data System (ADS)
Zhang, Chao; Zhang, Qian; Zheng, Chi; Qiu, Guoping
2018-04-01
Video foreground segmentation is one of the key problems in video processing. In this paper, we proposed a novel and fully unsupervised approach for foreground object co-localization and segmentation of unconstrained videos. We firstly compute both the actual edges and motion boundaries of the video frames, and then align them by their HOG feature maps. Then, by filling the occlusions generated by the aligned edges, we obtained more precise masks about the foreground object. Such motion-based masks could be derived as the motion-based likelihood. Moreover, the color-base likelihood is adopted for the segmentation process. Experimental Results show that our approach outperforms most of the State-of-the-art algorithms.
Clarifying color category border according to color vision
NASA Astrophysics Data System (ADS)
Ichihara, Takumi; Ichihara, Yasuyo G.
2015-01-01
We usually recognize color by two kinds of processes. In the first, the color is recognized continually and a small difference in color is recognized. In the second, the color is recognized discretely. This process recognizes a similar color of a certain range as being in the same color category. The small difference in color is ignored. Recognition by using the color category is important for communication using color. It is known that a color vision defect confuses colors on the confusion locus of color. However, the color category of a color vision defect has not been thoroughly researched. If the color category of the color vision defect is clarified, it will become an important key for color universal design. In this research, we classified color stimuli into four categories to check the shape and the border of the color categories of varied color vision. The experimental result was as follows. The border of protanopia is the following three on the CIE 1931 (x, y) chromaticity diagram: y = -0.3068x + 0.4795, y = -0.1906x + 0.4021, y = -0.2624x + 0.3896. The border of deuteranopia is the following three on the CIE 1931 (x, y) chromaticity diagram: y = -0.7931x + 0.7036, y = -0.718x + 0.5966, y = -0.6667x + 0.5061.
Lafer-Sousa, Rosa; Liu, Yang O; Lafer-Sousa, Luis; Wiest, Michael C; Conway, Bevil R
2012-05-01
Colors defined by the two intermediate directions in color space, "orange-cyan" and "lime-magenta," elicit the same spatiotemporal average response from the two cardinal chromatic channels in the lateral geniculate nucleus (LGN). While we found LGN functional magnetic resonance imaging (fMRI) responses to these pairs of colors were statistically indistinguishable, primary visual cortex (V1) fMRI responses were stronger to orange-cyan. Moreover, linear combinations of single-cell responses to cone-isolating stimuli of V1 cone-opponent cells also yielded stronger predicted responses to orange-cyan over lime-magenta, suggesting these neurons underlie the fMRI result. These observations are consistent with the hypothesis that V1 recombines LGN signals into "higher-order" mechanisms tuned to noncardinal color directions. In light of work showing that natural images and daylight samples are biased toward orange-cyan, our findings further suggest that V1 is adapted to daylight. V1, especially double-opponent cells, may function to extract spatial information from color boundaries correlated with scene-structure cues, such as shadows lit by ambient blue sky juxtaposed with surfaces reflecting sunshine. © 2012 Optical Society of America
Temperature and color management of silicon solar cells for building integrated photovoltaic
NASA Astrophysics Data System (ADS)
Amara, Mohamed; Mandorlo, Fabien; Couderc, Romain; Gerenton, Félix; Lemiti, Mustapha
2018-01-01
Color management of integrated photovoltaics must meet two criteria of performance: provide maximum conversion efficiency and allow getting the chosen colors with an appropriate brightness, more particularly when using side by side solar cells of different colors. As the cooling conditions are not necessarily optimal, we need to take into account the influence of the heat transfer and temperature. In this article, we focus on the color space and brightness achieved by varying the antireflective properties of flat silicon solar cells. We demonstrate that taking into account the thermal effects allows freely choosing the color and adapting the brightness with a small impact on the conversion efficiency, except for dark blue solar cells. This behavior is especially true when heat exchange by convection is low. Our optical simulations show that the perceived color, for single layer ARC, is not varying with the position of the observer, whatever the chosen color. The use of a double layer ARC adds flexibility to tune the wanted color since the color space is greatly increased in the green and yellow directions. Last, choosing the accurate material allows both bright colors and high conversion efficiency at the same time.
Microfluidic device and method for focusing, segmenting, and dispensing of a fluid stream
Jacobson, Stephen C [Knoxville, TN; Ramsey, J Michael [Knoxville, TN
2008-09-09
A microfluidic device and method for forming and dispensing minute volume segments of a material are described. In accordance with the present invention, a microfluidic device and method are provided for spatially confining the material in a focusing element. The device is also adapted for segmenting the confined material into minute volume segments, and dispensing a volume segment to a waste or collection channel. The device further includes means for driving the respective streams of sample and focusing fluids through respective channels into a chamber, such that the focusing fluid streams spatially confine the sample material. The device may also include additional means for driving a minute volume segment of the spatially confined sample material into a collection channel in fluid communication with the waste reservoir.
Automated segmentation of three-dimensional MR brain images
NASA Astrophysics Data System (ADS)
Park, Jonggeun; Baek, Byungjun; Ahn, Choong-Il; Ku, Kyo Bum; Jeong, Dong Kyun; Lee, Chulhee
2006-03-01
Brain segmentation is a challenging problem due to the complexity of the brain. In this paper, we propose an automated brain segmentation method for 3D magnetic resonance (MR) brain images which are represented as a sequence of 2D brain images. The proposed method consists of three steps: pre-processing, removal of non-brain regions (e.g., the skull, meninges, other organs, etc), and spinal cord restoration. In pre-processing, we perform adaptive thresholding which takes into account variable intensities of MR brain images corresponding to various image acquisition conditions. In segmentation process, we iteratively apply 2D morphological operations and masking for the sequences of 2D sagittal, coronal, and axial planes in order to remove non-brain tissues. Next, final 3D brain regions are obtained by applying OR operation for segmentation results of three planes. Finally we reconstruct the spinal cord truncated during the previous processes. Experiments are performed with fifteen 3D MR brain image sets with 8-bit gray-scale. Experiment results show the proposed algorithm is fast, and provides robust and satisfactory results.
- and Graph-Based Point Cloud Segmentation of 3d Scenes Using Perceptual Grouping Laws
NASA Astrophysics Data System (ADS)
Xu, Y.; Hoegner, L.; Tuttas, S.; Stilla, U.
2017-05-01
Segmentation is the fundamental step for recognizing and extracting objects from point clouds of 3D scene. In this paper, we present a strategy for point cloud segmentation using voxel structure and graph-based clustering with perceptual grouping laws, which allows a learning-free and completely automatic but parametric solution for segmenting 3D point cloud. To speak precisely, two segmentation methods utilizing voxel and supervoxel structures are reported and tested. The voxel-based data structure can increase efficiency and robustness of the segmentation process, suppressing the negative effect of noise, outliers, and uneven points densities. The clustering of voxels and supervoxel is carried out using graph theory on the basis of the local contextual information, which commonly conducted utilizing merely pairwise information in conventional clustering algorithms. By the use of perceptual laws, our method conducts the segmentation in a pure geometric way avoiding the use of RGB color and intensity information, so that it can be applied to more general applications. Experiments using different datasets have demonstrated that our proposed methods can achieve good results, especially for complex scenes and nonplanar surfaces of objects. Quantitative comparisons between our methods and other representative segmentation methods also confirms the effectiveness and efficiency of our proposals.
NASA Astrophysics Data System (ADS)
Gholami, Peyman; Roy, Priyanka; Kuppuswamy Parthasarathy, Mohana; Ommani, Abbas; Zelek, John; Lakshminarayanan, Vasudevan
2018-02-01
Retinal layer shape and thickness are one of the main indicators in the diagnosis of ocular diseases. We present an active contour approach to localize intra-retinal boundaries of eight retinal layers from OCT images. The initial locations of the active contour curves are determined using a Viterbi dynamic programming method. The main energy function is a Chan-Vese active contour model without edges. A boundary term is added to the energy function using an adaptive weighting method to help curves converge to the retinal layer edges more precisely, after evolving of curves towards boundaries, in final iterations. A wavelet-based denoising method is used to remove speckle from OCT images while preserving important details and edges. The performance of the proposed method was tested on a set of healthy and diseased eye SD-OCT images. The experimental results, compared between the proposed method and the manual segmentation, which was determined by an optometrist, indicate that our method has obtained an average of 95.29%, 92.78%, 95.86%, 87.93%, 82.67%, and 90.25% respectively, for accuracy, sensitivity, specificity, precision, Jaccard Index, and Dice Similarity Coefficient over all segmented layers. These results justify the robustness of the proposed method in determining the location of different retinal layers.
Sensory Drive, Color, and Color Vision.
Price, Trevor D
2017-08-01
Colors often appear to differ in arbitrary ways among related species. However, a fraction of color diversity may be explained because some signals are more easily perceived in one environment rather than another. Models show that not only signals but also the perception of signals should regularly evolve in response to different environments, whether these primarily involve detection of conspecifics or detection of predators and prey. Thus, a deeper understanding of how perception of color correlates with environmental attributes should help generate more predictive models of color divergence. Here, I briefly review our understanding of color vision in vertebrates. Then I focus on opsin spectral tuning and opsin expression, two traits involved in color perception that have become amenable to study. I ask how opsin tuning is correlated with ecological differences, notably the light environment, and how this potentially affects perception of conspecific colors. Although opsin tuning appears to evolve slowly, opsin expression levels are more evolutionarily labile but have been difficult to connect to color perception. The challenge going forward will be to identify how physiological differences involved in color vision, such as opsin expression levels, translate into perceptual differences, the selection pressures that have driven those differences, and ultimately how this may drive evolution of conspecific colors.
NASA Astrophysics Data System (ADS)
Farahi, Maria; Rabbani, Hossein; Talebi, Ardeshir; Sarrafzadeh, Omid; Ensafi, Shahab
2015-12-01
Visceral Leishmaniasis is a parasitic disease that affects liver, spleen and bone marrow. According to World Health Organization report, definitive diagnosis is possible just by direct observation of the Leishman body in the microscopic image taken from bone marrow samples. We utilize morphological and CV level set method to segment Leishman bodies in digital color microscopic images captured from bone marrow samples. Linear contrast stretching method is used for image enhancement and morphological method is applied to determine the parasite regions and wipe up unwanted objects. Modified global and local CV level set methods are proposed for segmentation and a shape based stopping factor is used to hasten the algorithm. Manual segmentation is considered as ground truth to evaluate the proposed method. This method is tested on 28 samples and achieved 10.90% mean of segmentation error for global model and 9.76% for local model.
Color planner for designers based on color emotions
NASA Astrophysics Data System (ADS)
Cheng, Ka-Man; Xin, John H.; Taylor, Gail
2002-06-01
During the color perception process, an associated feeling or emotion is induced in our brains, and this kind of emotion is termed as 'color emotion.' The researchers in the field of color emotions have put many efforts in quantifying color emotions with the standard color specifications and evaluating the influence of hue, lightness and chroma to the color emotions of human beings. In this study, a color planner was derived according to these findings so that the correlation of color emotions and standard color specifications was clearly indicated. Since people of different nationalities usually have different color emotions as different cultural and traditional backgrounds, the subjects in this study were all native Hong Kong Chinese and the color emotion words were all written in Chinese language in the visual assessments. Through the color planner, the designers from different areas, no matter fashion, graphic, interior or web site etc., can select suitable colors for inducing target color emotions to the customers or product-users since different colors convey different meanings to them. In addition, the designers can enhance the functionality and increase the attractiveness of their designed products by selecting suitable colors.
Singaravelan, Natarajan; Pavlicek, Tomas; Beharav, Alex; Wakamatsu, Kazumasa; Ito, Shosuke; Nevo, Eviatar
2010-01-01
Background Coat coloration in mammals is an explicit adaptation through natural selection. Camouflaging with the environment is the foremost evolutionary drive in explaining overall coloration. Decades of enquiries on this topic have been limited to repetitive coat color measurements to correlate the morphs with background/habitat blending. This led to an overwhelming endorsement of concealing coloration as a local phenotypic adaptation in animals, primarily rodents to evade predators. However, most such studies overlooked how rodents actually achieve such cryptic coloration. Cryptic coloration could be attained only through optimization between the yellow- to brown-colored “pheomelanin” and gray to black-colored “eumelanin” in the hairs. However, no study has explored this conjecture yet. “Evolution Canyon” (EC) in Israel is a natural microscale laboratory where the relationship between organism and environment can be explored. EC is comprised of an “African” slope (AS), which exhibits a yellow-brownish background habitat, and a “European” slope (ES), exhibiting a dark grayish habitat; both slopes harbor spiny mice (Acomys cahirinus). Here, we examine how hair melanin content of spiny mice living in the opposing slopes of EC evolves toward blending with their respective background habitat. Methodology/Principal Findings We measured hair-melanin (both eumelanin and pheomelanin) contents of 30 spiny mice from the EC using high-performance liquid chromatography (HPLC) that detects specific degradation products of eumelanin and pheomelanin. The melanin pattern of A. cahirinus approximates the background color of the slope on which they dwell. Pheomelanin is slightly (insignificantly) higher in individuals found on the AS to match the brownish background, whereas individuals of the ES had significantly greater eumelanin content to mimic the dark grayish background. This is further substantiated by a significantly higher eumelanin and pheomelanin
NASA Astrophysics Data System (ADS)
Alshehhi, Rasha; Marpu, Prashanth Reddy
2017-04-01
Extraction of road networks in urban areas from remotely sensed imagery plays an important role in many urban applications (e.g. road navigation, geometric correction of urban remote sensing images, updating geographic information systems, etc.). It is normally difficult to accurately differentiate road from its background due to the complex geometry of the buildings and the acquisition geometry of the sensor. In this paper, we present a new method for extracting roads from high-resolution imagery based on hierarchical graph-based image segmentation. The proposed method consists of: 1. Extracting features (e.g., using Gabor and morphological filtering) to enhance the contrast between road and non-road pixels, 2. Graph-based segmentation consisting of (i) Constructing a graph representation of the image based on initial segmentation and (ii) Hierarchical merging and splitting of image segments based on color and shape features, and 3. Post-processing to remove irregularities in the extracted road segments. Experiments are conducted on three challenging datasets of high-resolution images to demonstrate the proposed method and compare with other similar approaches. The results demonstrate the validity and superior performance of the proposed method for road extraction in urban areas.
Automatic 2D and 3D segmentation of liver from Computerised Tomography
NASA Astrophysics Data System (ADS)
Evans, Alun
As part of the diagnosis of liver disease, a Computerised Tomography (CT) scan is taken of the patient, which the clinician then uses for assistance in determining the presence and extent of the disease. This thesis presents the background, methodology, results and future work of a project that employs automated methods to segment liver tissue. The clinical motivation behind this work is the desire to facilitate the diagnosis of liver disease such as cirrhosis or cancer, assist in volume determination for liver transplantation, and possibly assist in measuring the effect of any treatment given to the liver. Previous attempts at automatic segmentation of liver tissue have relied on 2D, low-level segmentation techniques, such as thresholding and mathematical morphology, to obtain the basic liver structure. The derived boundary can then be smoothed or refined using more advanced methods. The 2D results presented in this thesis improve greatly on this previous work by using a topology adaptive active contour model to accurately segment liver tissue from CT images. The use of conventional snakes for liver segmentation is difficult due to the presence of other organs closely surrounding the liver this new technique avoids this problem by adding an inflationary force to the basic snake equation, and initialising the snake inside the liver. The concepts underlying the 2D technique are extended to 3D, and results of full 3D segmentation of the liver are presented. The 3D technique makes use of an inflationary active surface model which is adaptively reparameterised, according to its size and local curvature, in order that it may more accurately segment the organ. Statistical analysis of the accuracy of the segmentation is presented for 18 healthy liver datasets, and results of the segmentation of unhealthy livers are also shown. The novel work developed during the course of this project has possibilities for use in other areas of medical imaging research, for example the
An adaptive DPCM algorithm for predicting contours in NTSC composite video signals
NASA Astrophysics Data System (ADS)
Cox, N. R.
An adaptive DPCM algorithm is proposed for encoding digitized National Television Systems Committee (NTSC) color video signals. This algorithm essentially predicts picture contours in the composite signal without resorting to component separation. The contour parameters (slope thresholds) are optimized using four 'typical' television frames that have been sampled at three times the color subcarrier frequency. Three variations of the basic predictor are simulated and compared quantitatively with three non-adaptive predictors of similar complexity. By incorporating a dual-word-length coder and buffer memory, high quality color pictures can be encoded at 4.0 bits/pel or 42.95 Mbit/s. The effect of channel error propagation is also investigated.
Psoriasis skin biopsy image segmentation using Deep Convolutional Neural Network.
Pal, Anabik; Garain, Utpal; Chandra, Aditi; Chatterjee, Raghunath; Senapati, Swapan
2018-06-01
Development of machine assisted tools for automatic analysis of psoriasis skin biopsy image plays an important role in clinical assistance. Development of automatic approach for accurate segmentation of psoriasis skin biopsy image is the initial prerequisite for developing such system. However, the complex cellular structure, presence of imaging artifacts, uneven staining variation make the task challenging. This paper presents a pioneering attempt for automatic segmentation of psoriasis skin biopsy images. Several deep neural architectures are tried for segmenting psoriasis skin biopsy images. Deep models are used for classifying the super-pixels generated by Simple Linear Iterative Clustering (SLIC) and the segmentation performance of these architectures is compared with the traditional hand-crafted feature based classifiers built on popularly used classifiers like K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Random Forest (RF). A U-shaped Fully Convolutional Neural Network (FCN) is also used in an end to end learning fashion where input is the original color image and the output is the segmentation class map for the skin layers. An annotated real psoriasis skin biopsy image data set of ninety (90) images is developed and used for this research. The segmentation performance is evaluated with two metrics namely, Jaccard's Coefficient (JC) and the Ratio of Correct Pixel Classification (RCPC) accuracy. The experimental results show that the CNN based approaches outperform the traditional hand-crafted feature based classification approaches. The present research shows that practical system can be developed for machine assisted analysis of psoriasis disease. Copyright © 2018 Elsevier B.V. All rights reserved.
Neural representation of form-contingent color filling-in in the early visual cortex.
Hong, Sang Wook; Tong, Frank
2017-11-01
Perceptual filling-in exemplifies the constructive nature of visual processing. Color, a prominent surface property of visual objects, can appear to spread to neighboring areas that lack any color. We investigated cortical responses to a color filling-in illusion that effectively dissociates perceived color from the retinal input (van Lier, Vergeer, & Anstis, 2009). Observers adapted to a star-shaped stimulus with alternating red- and cyan-colored points to elicit a complementary afterimage. By presenting an achromatic outline that enclosed one of the two afterimage colors, perceptual filling-in of that color was induced in the unadapted central region. Visual cortical activity was monitored with fMRI, and analyzed using multivariate pattern analysis. Activity patterns in early visual areas (V1-V4) reliably distinguished between the two color-induced filled-in conditions, but only higher extrastriate visual areas showed the predicted correspondence with color perception. Activity patterns allowed for reliable generalization between filled-in colors and physical presentations of perceptually matched colors in areas V3 and V4, but not in earlier visual areas. These findings suggest that the perception of filled-in surface color likely requires more extensive processing by extrastriate visual areas, in order for the neural representation of surface color to become aligned with perceptually matched real colors.
Segmentation of human face using gradient-based approach
NASA Astrophysics Data System (ADS)
Baskan, Selin; Bulut, M. Mete; Atalay, Volkan
2001-04-01
This paper describes a method for automatic segmentation of facial features such as eyebrows, eyes, nose, mouth and ears in color images. This work is an initial step for wide range of applications based on feature-based approaches, such as face recognition, lip-reading, gender estimation, facial expression analysis, etc. Human face can be characterized by its skin color and nearly elliptical shape. For this purpose, face detection is performed using color and shape information. Uniform illumination is assumed. No restrictions on glasses, make-up, beard, etc. are imposed. Facial features are extracted using the vertically and horizontally oriented gradient projections. The gradient of a minimum with respect to its neighbor maxima gives the boundaries of a facial feature. Each facial feature has a different horizontal characteristic. These characteristics are derived by extensive experimentation with many face images. Using fuzzy set theory, the similarity between the candidate and the feature characteristic under consideration is calculated. Gradient-based method is accompanied by the anthropometrical information, for robustness. Ear detection is performed using contour-based shape descriptors. This method detects the facial features and circumscribes each facial feature with the smallest rectangle possible. AR database is used for testing. The developed method is also suitable for real-time systems.
Brown, Angela M; Lindsey, Delwin T; Guckes, Kevin M
2011-01-01
The relation between colors and their names is a classic case-study for investigating the Sapir-Whorf hypothesis that categorical perception is imposed on perception by language. Here, we investigate the Sapir-Whorf prediction that visual search for a green target presented among blue distractors (or vice versa) should be faster than search for a green target presented among distractors of a different color of green (or for a blue target among different blue distractors). Gilbert, Regier, Kay & Ivry (2006) reported that this Sapir-Whorf effect is restricted to the right visual field (RVF), because the major brain language centers are in the left cerebral hemisphere. We found no categorical effect at the Green|Blue color boundary, and no categorical effect restricted to the RVF. Scaling of perceived color differences by Maximum Likelihood Difference Scaling (MLDS) also showed no categorical effect, including no effect specific to the RVF. Two models fit the data: a color difference model based on MLDS and a standard opponent-colors model of color discrimination based on the spectral sensitivities of the cones. Neither of these models, nor any of our data, suggested categorical perception of colors at the Green|Blue boundary, in either visual field. PMID:21980188
Prostate segmentation in MR images using discriminant boundary features.
Yang, Meijuan; Li, Xuelong; Turkbey, Baris; Choyke, Peter L; Yan, Pingkun
2013-02-01
Segmentation of the prostate in magnetic resonance image has become more in need for its assistance to diagnosis and surgical planning of prostate carcinoma. Due to the natural variability of anatomical structures, statistical shape model has been widely applied in medical image segmentation. Robust and distinctive local features are critical for statistical shape model to achieve accurate segmentation results. The scale invariant feature transformation (SIFT) has been employed to capture the information of the local patch surrounding the boundary. However, when SIFT feature being used for segmentation, the scale and variance are not specified with the location of the point of interest. To deal with it, the discriminant analysis in machine learning is introduced to measure the distinctiveness of the learned SIFT features for each landmark directly and to make the scale and variance adaptive to the locations. As the gray values and gradients vary significantly over the boundary of the prostate, separate appearance descriptors are built for each landmark and then optimized. After that, a two stage coarse-to-fine segmentation approach is carried out by incorporating the local shape variations. Finally, the experiments on prostate segmentation from MR image are conducted to verify the efficiency of the proposed algorithms.
Segmentation of fluorescence microscopy cell images using unsupervised mining.
Du, Xian; Dua, Sumeet
2010-05-28
The accurate measurement of cell and nuclei contours are critical for the sensitive and specific detection of changes in normal cells in several medical informatics disciplines. Within microscopy, this task is facilitated using fluorescence cell stains, and segmentation is often the first step in such approaches. Due to the complex nature of cell issues and problems inherent to microscopy, unsupervised mining approaches of clustering can be incorporated in the segmentation of cells. In this study, we have developed and evaluated the performance of multiple unsupervised data mining techniques in cell image segmentation. We adapt four distinctive, yet complementary, methods for unsupervised learning, including those based on k-means clustering, EM, Otsu's threshold, and GMAC. Validation measures are defined, and the performance of the techniques is evaluated both quantitatively and qualitatively using synthetic and recently published real data. Experimental results demonstrate that k-means, Otsu's threshold, and GMAC perform similarly, and have more precise segmentation results than EM. We report that EM has higher recall values and lower precision results from under-segmentation due to its Gaussian model assumption. We also demonstrate that these methods need spatial information to segment complex real cell images with a high degree of efficacy, as expected in many medical informatics applications.
NASA Astrophysics Data System (ADS)
Win, Khin Yadanar; Choomchuay, Somsak; Hamamoto, Kazuhiko
2017-06-01
The automated segmentation of cell nuclei is an essential stage in the quantitative image analysis of cell nuclei extracted from smear cytology images of pleural fluid. Cell nuclei can indicate cancer as the characteristics of cell nuclei are associated with cells proliferation and malignancy in term of size, shape and the stained color. Nevertheless, automatic nuclei segmentation has remained challenging due to the artifacts caused by slide preparation, nuclei heterogeneity such as the poor contrast, inconsistent stained color, the cells variation, and cells overlapping. In this paper, we proposed a watershed-based method that is capable to segment the nuclei of the variety of cells from cytology pleural fluid smear images. Firstly, the original image is preprocessed by converting into the grayscale image and enhancing by adjusting and equalizing the intensity using histogram equalization. Next, the cell nuclei are segmented using OTSU thresholding as the binary image. The undesirable artifacts are eliminated using morphological operations. Finally, the distance transform based watershed method is applied to isolate the touching and overlapping cell nuclei. The proposed method is tested with 25 Papanicolaou (Pap) stained pleural fluid images. The accuracy of our proposed method is 92%. The method is relatively simple, and the results are very promising.
SCOUT: simultaneous time segmentation and community detection in dynamic networks
Hulovatyy, Yuriy; Milenković, Tijana
2016-01-01
Many evolving complex real-world systems can be modeled via dynamic networks. An important problem in dynamic network research is community detection, which finds groups of topologically related nodes. Typically, this problem is approached by assuming either that each time point has a distinct community organization or that all time points share a single community organization. The reality likely lies between these two extremes. To find the compromise, we consider community detection in the context of the problem of segment detection, which identifies contiguous time periods with consistent network structure. Consequently, we formulate a combined problem of segment community detection (SCD), which simultaneously partitions the network into contiguous time segments with consistent community organization and finds this community organization for each segment. To solve SCD, we introduce SCOUT, an optimization framework that explicitly considers both segmentation quality and partition quality. SCOUT addresses limitations of existing methods that can be adapted to solve SCD, which consider only one of segmentation quality or partition quality. In a thorough evaluation, SCOUT outperforms the existing methods in terms of both accuracy and computational complexity. We apply SCOUT to biological network data to study human aging. PMID:27881879
Mammogram segmentation using maximal cell strength updation in cellular automata.
Anitha, J; Peter, J Dinesh
2015-08-01
Breast cancer is the most frequently diagnosed type of cancer among women. Mammogram is one of the most effective tools for early detection of the breast cancer. Various computer-aided systems have been introduced to detect the breast cancer from mammogram images. In a computer-aided diagnosis system, detection and segmentation of breast masses from the background tissues is an important issue. In this paper, an automatic segmentation method is proposed to identify and segment the suspicious mass regions of mammogram using a modified transition rule named maximal cell strength updation in cellular automata (CA). In coarse-level segmentation, the proposed method performs an adaptive global thresholding based on the histogram peak analysis to obtain the rough region of interest. An automatic seed point selection is proposed using gray-level co-occurrence matrix-based sum average feature in the coarse segmented image. Finally, the method utilizes CA with the identified initial seed point and the modified transition rule to segment the mass region. The proposed approach is evaluated over the dataset of 70 mammograms with mass from mini-MIAS database. Experimental results show that the proposed approach yields promising results to segment the mass region in the mammograms with the sensitivity of 92.25% and accuracy of 93.48%.
Automatic layer segmentation of H&E microscopic images of mice skin
NASA Astrophysics Data System (ADS)
Hussein, Saif; Selway, Joanne; Jassim, Sabah; Al-Assam, Hisham
2016-05-01
Mammalian skin is a complex organ composed of a variety of cells and tissue types. The automatic detection and quantification of changes in skin structures has a wide range of applications for biological research. To accurately segment and quantify nuclei, sebaceous gland, hair follicles, and other skin structures, there is a need for a reliable segmentation of different skin layers. This paper presents an efficient segmentation algorithm to segment the three main layers of mice skin, namely epidermis, dermis, and subcutaneous layers. It also segments the epidermis layer into two sub layers, basal and cornified layers. The proposed algorithm uses adaptive colour deconvolution technique on H&E stain images to separate different tissue structures, inter-modes and Otsu thresholding techniques were effectively combined to segment the layers. It then uses a set of morphological and logical operations on each layer to removing unwanted objects. A dataset of 7000 H&E microscopic images of mutant and wild type mice were used to evaluate the effectiveness of the algorithm. Experimental results examined by domain experts have confirmed the viability of the proposed algorithms.
A contrast enhancement method for improving the segmentation of breast lesions on ultrasonography.
Flores, Wilfrido Gómez; Pereira, Wagner Coelho de Albuquerque
2017-01-01
This paper presents an adaptive contrast enhancement method based on sigmoidal mapping function (SACE) used for improving the computerized segmentation of breast lesions on ultrasound. First, from the original ultrasound image an intensity variation map is obtained, which is used to generate local sigmoidal mapping functions related to distinct contextual regions. Then, a bilinear interpolation scheme is used to transform every original pixel to a new gray level value. Also, four contrast enhancement techniques widely used in breast ultrasound enhancement are implemented: histogram equalization (HEQ), contrast limited adaptive histogram equalization (CLAHE), fuzzy enhancement (FEN), and sigmoid based enhancement (SEN). In addition, these contrast enhancement techniques are considered in a computerized lesion segmentation scheme based on watershed transformation. The performance comparison among techniques is assessed in terms of both the quality of contrast enhancement and the segmentation accuracy. The former is quantified by the measure, where the greater the value, the better the contrast enhancement, whereas the latter is calculated by the Jaccard index, which should tend towards unity to indicate adequate segmentation. The experiments consider a data set with 500 breast ultrasound images. The results show that SACE outperforms its counterparts, where the median values for the measure are: SACE: 139.4, SEN: 68.2, HEQ: 64.1, CLAHE: 62.8, and FEN: 7.9. Considering the segmentation performance results, the SACE method presents the largest accuracy, where the median values for the Jaccard index are: SACE: 0.81, FEN: 0.80, CLAHE: 0.79, HEQ: 77, and SEN: 0.63. The SACE method performs well due to the combination of three elements: (1) the intensity variation map reduces intensity variations that could distort the real response of the mapping function, (2) the sigmoidal mapping function enhances the gray level range where the transition between lesion and background
Simultaneous macula detection and optic disc boundary segmentation in retinal fundus images
NASA Astrophysics Data System (ADS)
Girard, Fantin; Kavalec, Conrad; Grenier, Sébastien; Ben Tahar, Houssem; Cheriet, Farida
2016-03-01
The optic disc (OD) and the macula are important structures in automatic diagnosis of most retinal diseases inducing vision defects such as glaucoma, diabetic or hypertensive retinopathy and age-related macular degeneration. We propose a new method to detect simultaneously the macula and the OD boundary. First, the color fundus images are processed to compute several maps highlighting the different anatomical structures such as vessels, the macula and the OD. Then, macula candidates and OD candidates are found simultaneously and independently using seed detectors identified on the corresponding maps. After selecting a set of macula/OD pairs, the top candidates are sent to the OD segmentation method. The segmentation method is based on local K-means applied to color coordinates in polar space followed by a polynomial fitting regularization step. Pair scores are updated, resulting in the final best macula/OD pair. The method was evaluated on two public image databases: ONHSD and MESSIDOR. The results show an overlapping area of 0.84 on ONHSD and 0.90 on MESSIDOR, which is better than recent state of the art methods. Our segmentation method is robust to contrast and illumination problems and outputs the exact boundary of the OD, not just a circular or elliptical model. The macula detection has an accuracy of 94%, which again outperforms other macula detection methods. This shows that combining the OD and macula detections improves the overall accuracy. The computation time for the whole process is 6.4 seconds, which is faster than other methods in the literature.
Multi-scale hippocampal parcellation improves atlas-based segmentation accuracy
NASA Astrophysics Data System (ADS)
Plassard, Andrew J.; McHugo, Maureen; Heckers, Stephan; Landman, Bennett A.
2017-02-01
Known for its distinct role in memory, the hippocampus is one of the most studied regions of the brain. Recent advances in magnetic resonance imaging have allowed for high-contrast, reproducible imaging of the hippocampus. Typically, a trained rater takes 45 minutes to manually trace the hippocampus and delineate the anterior from the posterior segment at millimeter resolution. As a result, there has been a significant desire for automated and robust segmentation of the hippocampus. In this work we use a population of 195 atlases based on T1-weighted MR images with the left and right hippocampus delineated into the head and body. We initialize the multi-atlas segmentation to a region directly around each lateralized hippocampus to both speed up and improve the accuracy of registration. This initialization allows for incorporation of nearly 200 atlases, an accomplishment which would typically involve hundreds of hours of computation per target image. The proposed segmentation results in a Dice similiarity coefficient over 0.9 for the full hippocampus. This result outperforms a multi-atlas segmentation using the BrainCOLOR atlases (Dice 0.85) and FreeSurfer (Dice 0.75). Furthermore, the head and body delineation resulted in a Dice coefficient over 0.87 for both structures. The head and body volume measurements also show high reproducibility on the Kirby 21 reproducibility population (R2 greater than 0.95, p < 0.05 for all structures). This work signifies the first result in an ongoing work to develop a robust tool for measurement of the hippocampus and other temporal lobe structures.
A unified EM approach to bladder wall segmentation with coupled level-set constraints
Han, Hao; Li, Lihong; Duan, Chaijie; Zhang, Hao; Zhao, Yang; Liang, Zhengrong
2013-01-01
Magnetic resonance (MR) imaging-based virtual cystoscopy (VCys), as a non-invasive, safe and cost-effective technique, has shown its promising virtue for early diagnosis and recurrence management of bladder carcinoma. One primary goal of VCys is to identify bladder lesions with abnormal bladder wall thickness, and consequently a precise segmentation of the inner and outer borders of the wall is required. In this paper, we propose a unified expectation-maximization (EM) approach to the maximum-a-posteriori (MAP) solution of bladder wall segmentation, by integrating a novel adaptive Markov random field (AMRF) model and the coupled level-set (CLS) information into the prior term. The proposed approach is applied to the segmentation of T1-weighted MR images, where the wall is enhanced while the urine and surrounding soft tissues are suppressed. By introducing scale-adaptive neighborhoods as well as adaptive weights into the conventional MRF model, the AMRF model takes into account the local information more accurately. In order to mitigate the influence of image artifacts adjacent to the bladder wall and to preserve the continuity of the wall surface, we apply geometrical constraints on the wall using our previously developed CLS method. This paper not only evaluates the robustness of the presented approach against the known ground truth of simulated digital phantoms, but further compares its performance with our previous CLS approach via both volunteer and patient studies. Statistical analysis on experts’ scores of the segmented borders from both approaches demonstrates that our new scheme is more effective in extracting the bladder wall. Based on the wall thickness calibrated from the segmented single-layer borders, a three-dimensional virtual bladder model can be constructed and the wall thickness can be mapped on to the model, where the bladder lesions will be eventually detected via experts’ visualization and/or computer-aided detection. PMID:24001932
3D exemplar-based random walks for tooth segmentation from cone-beam computed tomography images.
Pei, Yuru; Ai, Xingsheng; Zha, Hongbin; Xu, Tianmin; Ma, Gengyu
2016-09-01
Tooth segmentation is an essential step in acquiring patient-specific dental geometries from cone-beam computed tomography (CBCT) images. Tooth segmentation from CBCT images is still a challenging task considering the comparatively low image quality caused by the limited radiation dose, as well as structural ambiguities from intercuspation and nearby alveolar bones. The goal of this paper is to present and discuss the latest accomplishments in semisupervised tooth segmentation with adaptive 3D shape constraints. The authors propose a 3D exemplar-based random walk method of tooth segmentation from CBCT images. The proposed method integrates semisupervised label propagation and regularization by 3D exemplar registration. To begin with, the pure random walk method is to get an initial segmentation of the teeth, which tends to be erroneous because of the structural ambiguity of CBCT images. And then, as an iterative refinement, the authors conduct a regularization by using 3D exemplar registration, as well as label propagation by random walks with soft constraints, to improve the tooth segmentation. In the first stage of the iteration, 3D exemplars with well-defined topologies are adapted to fit the tooth contours, which are obtained from the random walks based segmentation. The soft constraints on voxel labeling are defined by shape-based foreground dentine probability acquired by the exemplar registration, as well as the appearance-based probability from a support vector machine (SVM) classifier. In the second stage, the labels of the volume-of-interest (VOI) are updated by the random walks with soft constraints. The two stages are optimized iteratively. Instead of the one-shot label propagation in the VOI, an iterative refinement process can achieve a reliable tooth segmentation by virtue of exemplar-based random walks with adaptive soft constraints. The proposed method was applied for tooth segmentation of twenty clinically captured CBCT images. Three metrics
Color and luminance in the perception of 1- and 2-dimensional motion.
Farell, B
1999-08-01
An isoluminant color grating usually appears to move more slowly than a luminance grating that has the same physical speed. Yet a grating defined by both color and luminance is seen as perceptually unified and moving at a single intermediate speed. In experiments measuring perceived speed and direction, it was found that color- and luminance-based motion signals are combined differently in the perception of 1-D motion than they are in the perception of 2-D motion. Adding color to a moving 1-D luminance pattern, a grating, slows its perceived speed. Adding color to a moving 2-D luminance pattern, a plaid made of orthogonal gratings, leaves its perceived speed unchanged. Analogous results occur for the perception of the direction of 2-D motion. The visual system appears to discount color when analyzing the motion of luminance-bearing 2-D patterns. This strategy has adaptive advantages, making the sensing of object motion more veridical without sacrificing the ability to see motion at isoluminance.
A smartphone application for psoriasis segmentation and classification (Conference Presentation)
NASA Astrophysics Data System (ADS)
Vasefi, Fartash; MacKinnon, Nicholas B.; Horita, Timothy; Shi, Kevin; Khan Munia, Tamanna Tabassum; Tavakolian, Kouhyar; Alhashim, Minhal; Fazel-Rezai, Reza
2017-02-01
Psoriasis is a chronic skin disease affecting approximately 125 million people worldwide. Currently, dermatologists monitor changes of psoriasis by clinical evaluation or by measuring psoriasis severity scores over time which lead to Subjective management of this condition. The goal of this paper is to develop a reliable assessment system to quantitatively assess the changes of erythema and intensity of scaling of psoriatic lesions. A smartphone deployable mobile application is presented that uses the smartphone camera and cloud-based image processing to analyze physiological characteristics of psoriasis lesions, identify the type and stage of the scaling and erythema. The application targets to automatically evaluate Psoriasis Area Severity Index (PASI) by measuring the severity and extent of psoriasis. The mobile application performs the following core functions: 1) it captures text information from user input to create a profile in a HIPAA compliant database. 2) It captures an image of the skin with psoriasis as well as image-related information entered by the user. 3) The application color correct the image based on environmental lighting condition using calibration process including calibration procedure by capturing Macbeth ColorChecker image. 4) The color-corrected image will be transmitted to a cloud-based engine for image processing. In cloud, first, the algorithm removes the non-skin background to ensure the psoriasis segmentation is only applied to the skin regions. Then, the psoriasis segmentation algorithm estimates the erythema and scaling boundary regions of lesion. We analyzed 10 images of psoriasis images captured by cellphone, determined PASI score for each subject during our pilot study, and correlated it with changes in severity scores given by dermatologists. The success of this work allows smartphone application for psoriasis severity assessment in a long-term treatment.
Gaussian mixtures on tensor fields for segmentation: applications to medical imaging.
de Luis-García, Rodrigo; Westin, Carl-Fredrik; Alberola-López, Carlos
2011-01-01
In this paper, we introduce a new approach for tensor field segmentation based on the definition of mixtures of Gaussians on tensors as a statistical model. Working over the well-known Geodesic Active Regions segmentation framework, this scheme presents several interesting advantages. First, it yields a more flexible model than the use of a single Gaussian distribution, which enables the method to better adapt to the complexity of the data. Second, it can work directly on tensor-valued images or, through a parallel scheme that processes independently the intensity and the local structure tensor, on scalar textured images. Two different applications have been considered to show the suitability of the proposed method for medical imaging segmentation. First, we address DT-MRI segmentation on a dataset of 32 volumes, showing a successful segmentation of the corpus callosum and favourable comparisons with related approaches in the literature. Second, the segmentation of bones from hand radiographs is studied, and a complete automatic-semiautomatic approach has been developed that makes use of anatomical prior knowledge to produce accurate segmentation results. Copyright © 2010 Elsevier Ltd. All rights reserved.
Model-Based Learning of Local Image Features for Unsupervised Texture Segmentation
NASA Astrophysics Data System (ADS)
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.
Cochlea segmentation using iterated random walks with shape prior
NASA Astrophysics Data System (ADS)
Ruiz Pujadas, Esmeralda; Kjer, Hans Martin; Vera, Sergio; Ceresa, Mario; González Ballester, Miguel Ángel
2016-03-01
Cochlear implants can restore hearing to deaf or partially deaf patients. In order to plan the intervention, a model from high resolution µCT images is to be built from accurate cochlea segmentations and then, adapted to a patient-specific model. Thus, a precise segmentation is required to build such a model. We propose a new framework for segmentation of µCT cochlear images using random walks where a region term is combined with a distance shape prior weighted by a confidence map to adjust its influence according to the strength of the image contour. Then, the region term can take advantage of the high contrast between the background and foreground and the distance prior guides the segmentation to the exterior of the cochlea as well as to less contrasted regions inside the cochlea. Finally, a refinement is performed preserving the topology using a topological method and an error control map to prevent boundary leakage. We tested the proposed approach with 10 datasets and compared it with the latest techniques with random walks and priors. The experiments suggest that this method gives promising results for cochlea segmentation.
Computer Based Melanocytic and Nevus Image Enhancement and Segmentation.
Jamil, Uzma; Akram, M Usman; Khalid, Shehzad; Abbas, Sarmad; Saleem, Kashif
2016-01-01
Digital dermoscopy aids dermatologists in monitoring potentially cancerous skin lesions. Melanoma is the 5th common form of skin cancer that is rare but the most dangerous. Melanoma is curable if it is detected at an early stage. Automated segmentation of cancerous lesion from normal skin is the most critical yet tricky part in computerized lesion detection and classification. The effectiveness and accuracy of lesion classification are critically dependent on the quality of lesion segmentation. In this paper, we have proposed a novel approach that can automatically preprocess the image and then segment the lesion. The system filters unwanted artifacts including hairs, gel, bubbles, and specular reflection. A novel approach is presented using the concept of wavelets for detection and inpainting the hairs present in the cancer images. The contrast of lesion with the skin is enhanced using adaptive sigmoidal function that takes care of the localized intensity distribution within a given lesion's images. We then present a segmentation approach to precisely segment the lesion from the background. The proposed approach is tested on the European database of dermoscopic images. Results are compared with the competitors to demonstrate the superiority of the suggested approach.
Oh, Paul; Lee, Sukho; Kang, Moon Gi
2017-01-01
Recently, several RGB-White (RGBW) color filter arrays (CFAs) have been proposed, which have extra white (W) pixels in the filter array that are highly sensitive. Due to the high sensitivity, the W pixels have better SNR (Signal to Noise Ratio) characteristics than other color pixels in the filter array, especially, in low light conditions. However, most of the RGBW CFAs are designed so that the acquired RGBW pattern image can be converted into the conventional Bayer pattern image, which is then again converted into the final color image by using conventional demosaicing methods, i.e., color interpolation techniques. In this paper, we propose a new RGBW color filter array based on a totally different color interpolation technique, the colorization algorithm. The colorization algorithm was initially proposed for colorizing a gray image into a color image using a small number of color seeds. Here, we adopt this algorithm as a color interpolation technique, so that the RGBW color filter array can be designed with a very large number of W pixels to make the most of the highly sensitive characteristics of the W channel. The resulting RGBW color filter array has a pattern with a large proportion of W pixels, while the small-numbered RGB pixels are randomly distributed over the array. The colorization algorithm makes it possible to reconstruct the colors from such a small number of RGB values. Due to the large proportion of W pixels, the reconstructed color image has a high SNR value, especially higher than those of conventional CFAs in low light condition. Experimental results show that many important information which are not perceived in color images reconstructed with conventional CFAs are perceived in the images reconstructed with the proposed method. PMID:28657602
Oh, Paul; Lee, Sukho; Kang, Moon Gi
2017-06-28
Recently, several RGB-White (RGBW) color filter arrays (CFAs) have been proposed, which have extra white (W) pixels in the filter array that are highly sensitive. Due to the high sensitivity, the W pixels have better SNR (Signal to Noise Ratio) characteristics than other color pixels in the filter array, especially, in low light conditions. However, most of the RGBW CFAs are designed so that the acquired RGBW pattern image can be converted into the conventional Bayer pattern image, which is then again converted into the final color image by using conventional demosaicing methods, i.e., color interpolation techniques. In this paper, we propose a new RGBW color filter array based on a totally different color interpolation technique, the colorization algorithm. The colorization algorithm was initially proposed for colorizing a gray image into a color image using a small number of color seeds. Here, we adopt this algorithm as a color interpolation technique, so that the RGBW color filter array can be designed with a very large number of W pixels to make the most of the highly sensitive characteristics of the W channel. The resulting RGBW color filter array has a pattern with a large proportion of W pixels, while the small-numbered RGB pixels are randomly distributed over the array. The colorization algorithm makes it possible to reconstruct the colors from such a small number of RGB values. Due to the large proportion of W pixels, the reconstructed color image has a high SNR value, especially higher than those of conventional CFAs in low light condition. Experimental results show that many important information which are not perceived in color images reconstructed with conventional CFAs are perceived in the images reconstructed with the proposed method.
Segmentation and intensity estimation of microarray images using a gamma-t mixture model.
Baek, Jangsun; Son, Young Sook; McLachlan, Geoffrey J
2007-02-15
We present a new approach to the analysis of images for complementary DNA microarray experiments. The image segmentation and intensity estimation are performed simultaneously by adopting a two-component mixture model. One component of this mixture corresponds to the distribution of the background intensity, while the other corresponds to the distribution of the foreground intensity. The intensity measurement is a bivariate vector consisting of red and green intensities. The background intensity component is modeled by the bivariate gamma distribution, whose marginal densities for the red and green intensities are independent three-parameter gamma distributions with different parameters. The foreground intensity component is taken to be the bivariate t distribution, with the constraint that the mean of the foreground is greater than that of the background for each of the two colors. The degrees of freedom of this t distribution are inferred from the data but they could be specified in advance to reduce the computation time. Also, the covariance matrix is not restricted to being diagonal and so it allows for nonzero correlation between R and G foreground intensities. This gamma-t mixture model is fitted by maximum likelihood via the EM algorithm. A final step is executed whereby nonparametric (kernel) smoothing is undertaken of the posterior probabilities of component membership. The main advantages of this approach are: (1) it enjoys the well-known strengths of a mixture model, namely flexibility and adaptability to the data; (2) it considers the segmentation and intensity simultaneously and not separately as in commonly used existing software, and it also works with the red and green intensities in a bivariate framework as opposed to their separate estimation via univariate methods; (3) the use of the three-parameter gamma distribution for the background red and green intensities provides a much better fit than the normal (log normal) or t distributions; (4) the
Of colored numbers and numbered colors: interactive processes in grapheme-color synesthesia.
Gebuis, Titia; Nijboer, Tanja C W; van der Smagt, Maarten J
2009-01-01
Grapheme-color synesthetes experience a specific color when they see a grapheme but they do not report to perceive a grapheme when a color is presented. In this study, we investigate whether color can still evoke number-processes even when a vivid number experience is absent. We used color-number and number-color priming, both revealing faster responses in congruent compared to incongruent conditions. Interestingly, the congruency effect was of similar magnitude for both conditions, and a numerical distance effect was present only in the color-number priming task. In addition, a priming task in which synesthetes had to judge the parity of a colored number revealed faster responses in parity congruent than in parity incongruent trials. These combined results demonstrate that synesthesia is indeed bi-directional and of similar strength in both directions. Furthermore, they illustrate the precise nature of these interactions and show that the direction of these interactions is determined by task demands, not by the more vividly experienced aspect of the stimulus.
Modulation-frequency encoded multi-color fluorescent DNA analysis in an optofluidic chip.
Dongre, Chaitanya; van Weerd, Jasper; Besselink, Geert A J; Vazquez, Rebeca Martinez; Osellame, Roberto; Cerullo, Giulio; van Weeghel, Rob; van den Vlekkert, Hans H; Hoekstra, Hugo J W M; Pollnau, Markus
2011-02-21
We introduce a principle of parallel optical processing to an optofluidic lab-on-a-chip. During electrophoretic separation, the ultra-low limit of detection achieved with our set-up allows us to record fluorescence from covalently end-labeled DNA molecules. Different sets of exclusively color-labeled DNA fragments-otherwise rendered indistinguishable by spatio-temporal coincidence-are traced back to their origin by modulation-frequency-encoded multi-wavelength laser excitation, fluorescence detection with a single ultrasensitive, albeit color-blind photomultiplier, and Fourier analysis decoding. As a proof of principle, fragments obtained by multiplex ligation-dependent probe amplification from independent human genomic segments, associated with genetic predispositions to breast cancer and anemia, are simultaneously analyzed.
Defect Detection of Steel Surfaces with Global Adaptive Percentile Thresholding of Gradient Image
NASA Astrophysics Data System (ADS)
Neogi, Nirbhar; Mohanta, Dusmanta K.; Dutta, Pranab K.
2017-12-01
Steel strips are used extensively for white goods, auto bodies and other purposes where surface defects are not acceptable. On-line surface inspection systems can effectively detect and classify defects and help in taking corrective actions. For detection of defects use of gradients is very popular in highlighting and subsequently segmenting areas of interest in a surface inspection system. Most of the time, segmentation by a fixed value threshold leads to unsatisfactory results. As defects can be both very small and large in size, segmentation of a gradient image based on percentile thresholding can lead to inadequate or excessive segmentation of defective regions. A global adaptive percentile thresholding of gradient image has been formulated for blister defect and water-deposit (a pseudo defect) in steel strips. The developed method adaptively changes the percentile value used for thresholding depending on the number of pixels above some specific values of gray level of the gradient image. The method is able to segment defective regions selectively preserving the characteristics of defects irrespective of the size of the defects. The developed method performs better than Otsu method of thresholding and an adaptive thresholding method based on local properties.
Just Noticeable Distortion Model and Its Application in Color Image Watermarking
NASA Astrophysics Data System (ADS)
Liu, Kuo-Cheng
In this paper, a perceptually adaptive watermarking scheme for color images is proposed in order to achieve robustness and transparency. A new just noticeable distortion (JND) estimator for color images is first designed in the wavelet domain. The key issue of the JND model is to effectively integrate visual masking effects. The estimator is an extension to the perceptual model that is used in image coding for grayscale images. Except for the visual masking effects given coefficient by coefficient by taking into account the luminance content and the texture of grayscale images, the crossed masking effect given by the interaction between luminance and chrominance components and the effect given by the variance within the local region of the target coefficient are investigated such that the visibility threshold for the human visual system (HVS) can be evaluated. In a locally adaptive fashion based on the wavelet decomposition, the estimator applies to all subbands of luminance and chrominance components of color images and is used to measure the visibility of wavelet quantization errors. The subband JND profiles are then incorporated into the proposed color image watermarking scheme. Performance in terms of robustness and transparency of the watermarking scheme is obtained by means of the proposed approach to embed the maximum strength watermark while maintaining the perceptually lossless quality of the watermarked color image. Simulation results show that the proposed scheme with inserting watermarks into luminance and chrominance components is more robust than the existing scheme while retaining the watermark transparency.
Li, Xiang; Sun, Ming-Zhu; Li, Xu; Zhang, Shu-Hui; Dai, Liang-Ti; Liu, Xing-Yu; Zhao, Xin; Chen, Dong-Yan; Feng, Xi-Zeng
2017-11-01
The extensive usage of xenobiotic endocrine disrupting chemicals (XEDCs), such as Bisphenol A (BPA), has created obvious threat to aquatic ecosystems worldwide. Although a comprehensive understanding of the adverse effect of BPA on behaviors and physiology have been proven, the potential impact of low-dose BPA on altering the basic ability of aquatic organism in adapting to the surrounded complex environment still remains elusive. In this research, we report that treatment of adult male zebrafish with chronic (7 weeks) low-dose (0.22 nM-2.2 nM) BPA, altered the ability in adapting the complex environment by disturbing the natural color preference patterns. In addition, chronic 50 ng/L (0.22 nM) BPA exposure alleviated the anxiety behavior of male zebrafish confronted with the novel environment by enhancing the preference towards light in the light/dark preference test. This phenotype was associated with less expression of serotonin (5-TH) in the hypothalamus and the down-regulation of tyrosine hydroxylase (TH) in brain tissues. As such, our results show that low-dose BPA remnant in surface waters altered zebrafish behavior that are known to have ecological and evolutionary consequences. Here we reported that the impact of chronic low-dose BPA exposure on the basic capability of zebrafish to adapt to the environmental complexity. Specifically, BPA at low concentration, under the environmental safety level and 3000-fold lower than the accepted human daily exposure, interfered with the ability to discriminate color and alleviate anxiety induced by the novel environment, which finally altered the capability of male zebrafish to adapt to the environmental complexity. These findings revealed the ecological effect of low-dose BPA and regular BPA concentration standard are not necessarily safe. The result also provided the consideration of retuning the hazard concentration level of BPA. Copyright © 2017 Elsevier Ltd. All rights reserved.
Segmentation of Dilated Hemorrhoidal Veins in Hemorrhoidal Disease.
Díaz-Flores, Lucio; Gutiérrez, Ricardo; González-Gómez, Miriam; García, Pino; Sáez, Francisco J; Díaz-Flores, Lucio; Carrasco, José Luis; Madrid, Juan F
2018-06-18
Vein segmentation is a vascular remodeling process mainly studied in experimental conditions and linked to hemodynamic factors, with clinical implications. The aim of this work is to assess the morphologic characteristics, associated findings, and mechanisms that participate in vein segmentation in humans. To this end, we examined 156 surgically obtained cases of hemorrhoidal disease. Segmentation occurred in 65 and was most prominent in 15, which were selected for serial sections, immunohistochemistry, and immunofluorescence procedures. The dilated veins showed differently sized spaces, separated by thin septa. Findings associated with vein segmentation were: (a) vascular channels formed from the vein intima endothelial cells (ECs) and located in the vein wall and/or intraluminal fibrin, (b) vascular loops formed by interconnected vascular channels (venous-venous connections), which encircled vein wall components or fibrin and formed folds/pillars/papillae (FPPs; the encircling ECs formed the FPP cover and the encircled components formed the core), and (c) FPP splitting, remodeling, alignment, and fusion, originating septa. Thrombosis was observed in some nonsegmented veins, while the segmented veins only occasionally contained thrombi. Dense microvasculature was also present in the interstitium and around veins. In conclusion, the findings suggest that hemorrhoidal vein segmentation is an adaptive process in which a piecemeal angiogenic mechanism participates, predominantly by intussusception, giving rise to intravascular FPPs, followed by linear rearrangement, remodeling and fusion of FPPs, and septa formation. Identification of other markers, as well as the molecular bases, hemodynamic relevance, and possible therapeutic implications of vein segmentation in dilated hemorrhoidal veins require further studies. © 2018 S. Karger AG, Basel.
Iglesias, Juan Eugenio; Augustinack, Jean C; Nguyen, Khoa; Player, Christopher M; Player, Allison; Wright, Michelle; Roy, Nicole; Frosch, Matthew P; McKee, Ann C; Wald, Lawrence L; Fischl, Bruce; Van Leemput, Koen
2015-07-15
Automated analysis of MRI data of the subregions of the hippocampus requires computational atlases built at a higher resolution than those that are typically used in current neuroimaging studies. Here we describe the construction of a statistical atlas of the hippocampal formation at the subregion level using ultra-high resolution, ex vivo MRI. Fifteen autopsy samples were scanned at 0.13 mm isotropic resolution (on average) using customized hardware. The images were manually segmented into 13 different hippocampal substructures using a protocol specifically designed for this study; precise delineations were made possible by the extraordinary resolution of the scans. In addition to the subregions, manual annotations for neighboring structures (e.g., amygdala, cortex) were obtained from a separate dataset of in vivo, T1-weighted MRI scans of the whole brain (1mm resolution). The manual labels from the in vivo and ex vivo data were combined into a single computational atlas of the hippocampal formation with a novel atlas building algorithm based on Bayesian inference. The resulting atlas can be used to automatically segment the hippocampal subregions in structural MRI images, using an algorithm that can analyze multimodal data and adapt to variations in MRI contrast due to differences in acquisition hardware or pulse sequences. The applicability of the atlas, which we are releasing as part of FreeSurfer (version 6.0), is demonstrated with experiments on three different publicly available datasets with different types of MRI contrast. The results show that the atlas and companion segmentation method: 1) can segment T1 and T2 images, as well as their combination, 2) replicate findings on mild cognitive impairment based on high-resolution T2 data, and 3) can discriminate between Alzheimer's disease subjects and elderly controls with 88% accuracy in standard resolution (1mm) T1 data, significantly outperforming the atlas in FreeSurfer version 5.3 (86% accuracy) and
Evolution of the circuitry for conscious color vision in primates
Neitz, J; Neitz, M
2017-01-01
There are many ganglion cell types and subtypes in our retina that carry color information. These have appeared at different times over the history of the evolution of the vertebrate visual system. They project to several different places in the brain and serve a variety of purposes allowing wavelength information to contribute to diverse visual functions. These include circadian photoentrainment, regulation of sleep and mood, guidance of orienting movements, detection and segmentation of objects. Predecessors to some of the circuits serving these purposes presumably arose before mammals evolved and different functions are represented by distinct ganglion cell types. However, while other animals use color information to elicit motor movements and regulate activity rhythms, as do humans, using phylogenetically ancient circuitry, the ability to appreciate color appearance may have been refined in ancestors to primates, mediated by a special set of ganglion cells that serve only that purpose. Understanding the circuitry for color vision has implications for the possibility of treating color blindness using gene therapy by recapitulating evolution. In addition, understanding how color is encoded, including how chromatic and achromatic percepts are separated is a step toward developing a complete picture of the diversity of ganglion cell types and their functions. Such knowledge could be useful in developing therapeutic strategies for blinding eye disorders that rely on stimulating elements in the retina, where more than 50 different neuron types are organized into circuits that transform signals from photoreceptors into specialized detectors many of which are not directly involved in conscious vision. PMID:27935605