17,711 research outputs found
Multi texture analysis of colorectal cancer continuum using multispectral imagery
Purpose
This paper proposes to characterize the continuum of colorectal cancer (CRC) using multiple texture features extracted from multispectral optical microscopy images. Three types of pathological tissues (PT) are considered: benign hyperplasia, intraepithelial neoplasia and carcinoma.
Materials and Methods
In the proposed approach, the region of interest containing PT is first extracted from multispectral
images using active contour segmentation. This region is then encoded using texture features based on the Laplacian-of-Gaussian (LoG) filter, discrete wavelets (DW) and gray level co-occurrence matrices (GLCM). To assess the significance of textural differences between PT types, a statistical analysis based on the Kruskal-Wallis test is performed. The usefulness of texture features is then evaluated quantitatively in terms of their ability to predict PT types using various classifier models.
Results
Preliminary results show significant texture differences between PT types, for all texture features (p-value < 0.01). Individually, GLCM texture features outperform LoG and DW features in terms of PT type prediction. However, a higher performance can be achieved by combining all texture features, resulting in a mean classification accuracy of 98.92%, sensitivity of 98.12%, and specificity of 99.67%.
Conclusions
These results demonstrate the efficiency and effectiveness of combining multiple texture features for characterizing the continuum of CRC and discriminating between pathological tissues in multispectral images
Texture Features Extraction of Human Leather Ports Based on Histogram
Skin problems general are distinguished on healthy and unhealthy skin. Based on the pores, unhealthy skin: dry, moist or oily skin. Skin problems are identified from the image capture results. Skin image is processed using histogram method which aim to get skin type pattern. The study used 7 images classified by skin type, determined histogram, then extracted with features of average intensity, contrast, slope, energy, entropy and subtlety. Specified skin type reference as a skin test comparator. The histogram-based skin feature feature aims to determine the pattern of pore classification of human skin. The results of the 1, 2, 3 leaf image testing were lean to normal skin (43%), 4, 5, tends to dry skin (29%), 6.7 tend to oily skin (29%). Percentage of feature-based extraction of histogram in image processing reaches 90-95%
2D Texture Features
ProtoĹľe je textura objektu velice cenná informace pro poÄŤĂtaÄŤovĂ© vidÄ›nĂ, je dĹŻleĹľitĂ© ji nÄ›jakĂ˝m zpĹŻsobem popsat. K tomu sloužà texturnĂ pĹ™Ăznaky. OptimálnĂ vĂ˝bÄ›r pĹ™ĂznakĹŻ je dĹŻleĹľitĂ˝ pro rozpoznávánĂ textur. V tĂ©to práci byla pro zĂskánĂ pĹ™ĂznakĹŻ vybrána metoda lokálnĂch binárnĂch vzorĹŻ (LBP). TexturnĂm pĹ™Ăznakem u tĂ©to metody nenĂ jejĂ hodnota, ale histogram ÄŤetnosti hodnot v celĂ© textuĹ™e. Pro porovnánĂ tÄ›chto histogramĹŻ se zde uĹľĂvá Euklidovská vzdálenost, Bhattacharyyova vzdálenost nebo Mahalanobisova vzdálenost. HlavnĂm účelem tĂ©to práce je vzájemnĂ© porovnánĂ klasifikacĂ textur nÄ›kolika variantami metody LBP a vyhodnocenĂ jejich vĂ˝sledkĹŻ Euklidovskou, Bhattacharyyovou nebo Mahalanobisovou vzdálenostĂ.Because texture of object is very valuable information in computer vision, it is important to describe it somehow. And for this serve texture features. Optimal selection of features is very important for recognizing texture. In this bachelor thesis were used local binary patterns (LBP) as a method of gaining texture feature. In this method is not its value the texture feature, but histogram of percent occurrence values in the entire texture. To compare histograms there is used Euclidean distance, Bhattacharyya distance or Mahalanobis distance. Main purpose of this thesis is mutually comparing of texture clasification by several variants of LBP and evaluation of their outcomes by Euclidean distance, Bhattacharyya distance or Mahalanobis distance.
Perceptual-based textures for scene labeling: a bottom-up and a top-down approach
Due to the semantic gap, the automatic interpretation of digital images is a very challenging task. Both the segmentation and classification are intricate because of the high variation of the data. Therefore, the application of appropriate features is of utter importance. This paper presents biologically inspired texture features for material classification and interpreting outdoor scenery images. Experiments show that the presented texture features obtain the best classification results for material recognition compared to other well-known texture features, with an average classification rate of 93.0%. For scene analysis, both a bottom-up and top-down strategy are employed to bridge the semantic gap. At first, images are segmented into regions based on the perceptual texture and next, a semantic label is calculated for these regions. Since this emerging interpretation is still error prone, domain knowledge is ingested to achieve a more accurate description of the depicted scene. By applying both strategies, 91.9% of the pixels from outdoor scenery images obtained a correct label
Plant image retrieval using color, shape and texture features
We present a content-based image retrieval system for plant image retrieval, intended especially for the house plant identification problem. A plant image consists of a collection of overlapping leaves and possibly flowers, which makes the problem challenging.We studied the suitability of various well-known color, shape and texture features for this problem, as well as introducing some new texture matching techniques and shape features. Feature extraction is applied after segmenting the plant region from the background using the max-flow min-cut technique. Results on a database of 380 plant images belonging to 78 different types of plants show promise of the proposed new techniques
and the overall system: in 55% of the queries, the correct plant image is retrieved among the top-15 results. Furthermore, the accuracy goes up to 73% when a 132-image subset of well-segmented plant images are considered
Signature Verification Approach using Fusion of Hybrid Texture Features
In this paper, a writer-dependent signature verification method is proposed.
Two different types of texture features, namely Wavelet and Local Quantized
Patterns (LQP) features, are employed to extract two kinds of transform and
statistical based information from signature images. For each writer two
separate one-class support vector machines (SVMs) corresponding to each set of
LQP and Wavelet features are trained to obtain two different authenticity
scores for a given signature. Finally, a score level classifier fusion method
is used to integrate the scores obtained from the two one-class SVMs to achieve
the verification score. In the proposed method only genuine signatures are used
to train the one-class SVMs. The proposed signature verification method has
been tested using four different publicly available datasets and the results
demonstrate the generality of the proposed method. The proposed system
outperforms other existing systems in the literature.Comment: Neural Computing and Applicatio
Quantitative Ultrasound and B-mode Image Texture Features Correlate with Collagen and Myelin Content in Human Ulnar Nerve Fascicles
We investigate the usefulness of quantitative ultrasound (QUS) and B-mode
texture features for characterization of ulnar nerve fascicles. Ultrasound data
were acquired from cadaveric specimens using a nominal 30 MHz probe. Next, the
nerves were extracted to prepare histology sections. 85 fascicles were matched
between the B-mode images and the histology sections. For each fascicle image,
we selected an intra-fascicular region of interest. We used histology sections
to determine features related to the concentration of collagen and myelin, and
ultrasound data to calculate backscatter coefficient (-24.89 dB 8.31),
attenuation coefficient (0.92 db/cm-MHz 0.04), Nakagami parameter (1.01
0.18) and entropy (6.92 0.83), as well as B-mode texture features
obtained via the gray level co-occurrence matrix algorithm. Significant
Spearman's rank correlations between the combined collagen and myelin
concentrations were obtained for the backscatter coefficient (R=-0.68), entropy
(R=-0.51), and for several texture features. Our study demonstrates that QUS
may potentially provide information on structural components of nerve
fascicles
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