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Journal ArticleDOI

Texture analysis in digitally-acquired echocardiographic images: the effect of JPEG compression and video storage.

TLDR
The analysis of texture in video-stored echocardiographic images is an established method to characterize myocardial pathologies and it is concluded that texture of video-Stored images is not comparable to that of digitally-storied images and that JPEG compression changes important second-order texture parameters.
Abstract
The analysis of texture in video-stored echocardiographic images is an established method to characterize myocardial pathologies. We investigated whether or not texture parameters calculated from video-stored images and those derived from the joint photographic expert group (JPEG) format compressed data are equivalent to those calculated from uncompressed digital images. Texture parameters were calculated using uncompressed digital data, images stored on videotape, and three forms of compressed digital data (baseline JPEG, JPEG 2000 and lossless JPEG 2000). Video storage heavily affected most texture parameters. Although first-order texture parameters derived from JPEG-compressed images were generally equivalent to those derived from the uncompressed data, several second-order parameters differed significantly. We conclude that texture of video-stored images is not comparable to that of digitally-stored images and that JPEG compression changes important second-order texture parameters. This observation should be taken into account when analyzing texture of modern image data (uncompressed or compressed) and comparing the results with earlier studies utilizing video-stored data.

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Citations
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Journal ArticleDOI

Noise- and compression-robust biological features for texture classification

TL;DR: This paper proposes the use of features based on the human visual system for texture classification using a semisupervised, hierarchical approach and shows that the classification rate of the textures using the presented biologically inspired features is hardly affected by image compression techniques.
Journal ArticleDOI

Udder Ultrasonography of Dairy Cows: Investigating the Relationship between Echotexture, Blood Flow, Somatic Cell Count and Milk Yield during Dry Period and Lactation

TL;DR: In this article , the relationship between ultrasonographic features and indicators of udder health and productivity was investigated using B-mode ultrasonography on twenty-one Holstein cows during a crucial period starting from the end of the late lactation stage, continuing throughout the dry period and ending in the early stage of consecutive lactation period.
References
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Journal ArticleDOI

Textural Features for Image Classification

TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
Journal ArticleDOI

Texture analysis using gray level run lengths

TL;DR: In this paper, a set of texture features based on gray level run lengths is described, and good classification results are obtained with these features on a sets of samples representing nine terrain types.

Texture analysis using grey level run lengths

TL;DR: A set of texture features based on gray level run lengths is described, and good classification results are obtained with these features on a set of samples representing nine terrain types.
Journal ArticleDOI

Use of gray value distribution of run lengths for texture analysis

TL;DR: The gray value distribution of the runs is proposed to be used to define two new features, viz., low gray level run emphasis ( LGRE) and high gray levelrun emphasis ( HGRE).
Journal ArticleDOI

Markovian analysis of cervical cell images.

TL;DR: Results using Markov texture parameters show that the selection of a Markov step size strongly affects classification error rates and the number of parameters required to achieve the maximum correct classification rates.
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