A statistical design of experiments approach for texture description

Ganesan, L.; Bhattacharyya, P.

doi:10.1016/0031-3203(94)00081-v

Cited by 17 publications

(5 citation statements)

References 4 publications

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“…Since the FFT and power spectral analysis employ complex mathematical calculations/transformations, they are generally more time consuming when applied for image analysis. 9 In contrast, the spectral moment analysis takes less time. 18,19 Therefore, it can be a more efficient means of indicating the collagen orientation for measuring the physiological condition of the AC.…”

Section: Discussionmentioning

confidence: 94%

Quantitative Characterization of Collagen Orientation in the Superficial Zone for Studying Early Degenerative Changes in Articular Cartilage

Kirk

Peng

et al. 2006

J. Musculoskelet. Res.

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Collagen fibres in the superficial zone are critical to the durability and stability of articular cartilage. Early osteoarthritis is often characterized by lesions on the surface of articular cartilage as a result of deterioration or degeneration of the collagen meshwork in the superficial zone. Therefore, traditional histology employing visual inspection of the microstructure of collagen fibres is one of the methods frequently used to evaluate the physiological state of articular cartilage in the medical field. However, traditional histology is often limited to 2D observations. It requires physical sectioning and dehydration of articular cartilage for stereological study. In addition, visual inspection is subjective and time consuming. Confocal microscopy provides a way to study the three-dimensional collagen structure in bulk hydrated articular cartilage. Utilizing fibre optical laser scanning confocal microscopy, the aim of the present study was to apply the spectral moment as a method for quantitatively describing the orientation of the collagen fibres in the superficial zone in relation to the physiological status of the cartilage, such as distinguishing normal cartilage from the early osteoarthritic cartilage.

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Section: Discussionmentioning

confidence: 94%

Quantitative Characterization of Collagen Orientation in the Superficial Zone for Studying Early Degenerative Changes in Articular Cartilage

Kirk

Peng

et al. 2006

J. Musculoskelet. Res.

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“…In this representation, the image region is considered to be a linear combination of un correlated (orthogonal) effects due to spatial variations. The uncorrelated effects due to the presence of textures have been separated successfully from those due to the presence of Gaussian noise (Ganesan and Bhattacharyya, 1995). The presence of texture in the image region under analysis is detected on the basis of the strength of the appropriate orthogonal effects.…”

Section: Texture Primitives and Representationmentioning

confidence: 99%

“…Textures are normally ranging from micro to macro. A small image region which is a function of two spatial co ordinates, is represented by a set of orthogonal polynomials (Ganesan and Bhattacharyya, 1995). In this representation, the image region is considered to be a linear combination of un correlated (orthogonal) effects due to spatial variations.…”

Section: Texture Primitives and Representationmentioning

confidence: 99%

“…The spatial variation g(x,y) can be approximated by using an appropriate set of orthogonal functions. In this regard, a set of orthogonal polynomials have been proposed and the discrete-discrete formulation has been used to determine a set of orthogonal effects, B ij , because of the texture and noise (Ganesan and Bhattacharyya, 1995). These effects and their corresponding mean squares Z ij are obtained as follows: texture, the set of orthogonal effects [B ij ] is divided in to two disjoint subsets, namely, the set {B 01 ,B 02 ,B 10 ,B 20 } of effects due to the presence of Gaussian noise and the set {B 11 ,B 12 ,B 21 ,B 22 } of interaction effects due to the presence of texture.…”

Section: Texture Primitives and Representationmentioning

confidence: 99%

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Analysis of skin images using texture descriptor by a combined statistical and structural approach

Umarani¹,

Ganesan²,

Sudhakar

2007

Int J Imaging Syst Tech

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In this article, an attempt has been made to analyze various skin (textured) images. They are caused because of hot water, chemical, electrical, thermal, cigarette, etc. These images are analyzed using our texture representation scheme. Our approach uses a set of 92 texture primitives. They are tested for the presence of texture by a statistical design of experiments based approach [Ganesan and Bhattacharyya, Pattern Recogn 28 (1995), 99-105]. These texture primitives are concluded as the local descriptor and their distribution over the entire image is the global representation called texture primitive spectrum. The set of texture primitives and the texture primitive spectrums are successful for a number of Bench mark images (Brodatz, Texture-A photographic album for artists and designers, Reinbold, New York, 1968; Vistex, Available at http://www.white.media. mit.edu\vismod\imgery\Vision Texture, MIT Media Lab, 1995). Using the texture primitive spectrum, several texture images have been categorized as micro, macro, fine, and coarse and a trend is obtained. Similarly, a set of skin images affected by severe burn with many causes are analyzed. The severity has been quantified and concluded based on simple and weighted mean computed for the texture primitive spectrums. The extent of burn and hence the curing duration can be approximated from the results. The outcome of our experimentation with ground truth and the opinion from the experts are closely matching.

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“…al. in [11] proposed texture representation using positional number system for small regions and has been further used for classification. Experiments were conducted on Brodatz textural album for grayscale natural textured categorical images.…”

Section: Introductionmentioning

confidence: 99%

Statistical Learning for Texture Characterization

Goswami

Agarwal

Rao

2014

Proceedings of the 2014 Indian Conference on Computer Vision Graphics and Image Processing

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In this paper, we propose a novel method for image texture characterization. Characterization is governed by simple perceptual variations in relative orientations in terms of either no variations present or variations present as row specific, column specific or diagonal specific. This generalization is obtained by modeling the input as a whole or image blocks depending on the broader or narrow coverage respectively. Most of the texture characterization is done either keeping a specific domain (synthetic or natural images specific to a category) or is application specific (segmentation on specific benchmark dataset or image retrieval). Contrary to this, our method is not biased towards any domain or application; rather it acts as a pre-processing step for guiding towards locating both non-textural and orientation specific textural image blocks. The proposed method quantifies the texture-tonal characterization of an image or image blocks using statistical ANOVA grading system. Once the grading for abstraction is assigned for both image as a whole and also for image blocks, the decision as to which higher-level algorithms need to be implemented on which block will become easier. The proposed method can be considered to be a three stage process -progressive sampling, image partitioning in blocks, ANOVA analysis and grading.

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A statistical design of experiments approach for texture description

Cited by 17 publications

References 4 publications

Quantitative Characterization of Collagen Orientation in the Superficial Zone for Studying Early Degenerative Changes in Articular Cartilage

Quantitative Characterization of Collagen Orientation in the Superficial Zone for Studying Early Degenerative Changes in Articular Cartilage

Analysis of skin images using texture descriptor by a combined statistical and structural approach

Statistical Learning for Texture Characterization

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