A Survey of Preprocessing and Feature Extraction Techniques for Radiographic Images

Hall, Ernest L.; Kruger, R. P.; Dwyer, Samuel J.; Hall, David L.; McLaren, Robert W.; Lodwick, Gwilym S.

doi:10.1109/t-c.1971.223399

Cited by 180 publications

(42 citation statements)

References 9 publications

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“…Texture analysis was initially developed for the assessment of aerial photographs, 3,4 with the first reported applications to medical image interpretation appearing shortly thereafter. [5][6][7] Although initially slow to build clinical interest, there has been a sort of "texture renaissance" during the past decade, coincident with the steep increase in computational and digital storage capability, as well as a growing comfort with (and demand for) automatic or semiautomatic image analysis tools. 8 The emerging hypothesis is that by examining the nature of gray-level transitions in medical images, we can extract a subset of textural features, or what Tourassi 8 referred to as a "texture signature," that will best characterize the pathology or disease process of interest.…”

mentioning

confidence: 99%

Texture Analysis: A Review of Neurologic MR Imaging Applications

Kassner¹,

Thornhill²

2010

AJNR Am J Neuroradiol

352

265

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SUMMARY:Texture analysis describes a variety of image-analysis techniques that quantify the variation in surface intensity or patterns, including some that are imperceptible to the human visual system. Texture analysis may be particularly well-suited for lesion segmentation and characterization and for the longitudinal monitoring of disease or recovery. We begin this review by outlining the general procedure for performing texture analysis, identifying some potential pitfalls and strategies for avoiding them. We then provide an overview of some intriguing neuro-MR imaging applications of texture analysis, particularly in the characterization of brain tumors, prediction of seizures in epilepsy, and a host of applications to MS.ABBREVIATIONS: ABSV ϭ absolute gradient value; AIS ϭ acute ischemic stroke; ANN ϭ artificial neural network; CIS ϭ clinically isolated syndrome; d ϭ distance; DCE ϭ dynamic contrastenhanced; FCD ϭ focal cortical dysplasia; FLAIR ϭ fluid-attenuated inversion recovery; f x ϭ second-order gray-level co-occurrence feature number "x"; GLCM ϭ gray-level co-occurrence matrix; GM ϭ gray matter; HT ϭ hemorrhagic transformation; LDA ϭ linear discriminant analysis; MGL ϭ mean gray level; MGR ϭ mean gradient; MS ϭ multiple sclerosis; MTR ϭ magnetization transfer ratio; NAWM ϭ normal-appearing white matter; N g ϭ number of gray levels; PCA ϭ principal components analysis; PPMS ϭ primary-progressive MS; RLM ϭ run-length matrix; ROC ϭ receiver-operator characteristic; ROI ϭ region of interest; RRMS ϭ relapsing-remitting MS; rtPA ϭ recombinant tissue plasminogen activator; SPMS ϭ secondary-progressive MS; SVM ϭ support vector machine; ϭ direction; VGL ϭ variance of gray levels; VGR ϭ variance gradient; WM ϭ white matter

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mentioning

confidence: 99%

Texture Analysis: A Review of Neurologic MR Imaging Applications

Kassner¹,

Thornhill²

2010

AJNR Am J Neuroradiol

352

265

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“…A related technique is enhancement of high frequency details (sharpening). A variety of preprocessing techniques for chest radiographs based on local equalization, sharpening and combinations and modifications of these techniques have been proposed [98,41,176,180,218,109,21,230], sometimes not only through software but also by using hardware [23,78]. Nowadays almost all vendors sell digital chest units with a larger dynamic range than conventional units and most vendors automatically preprocess the images with proprietary algorithms.…”

Section: General Processing Enhancementmentioning

confidence: 99%

Computer aided diagnosis in chest radiography

Mouton¹,

Douglas

2003

Academic Radiology

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“…The Fourier trans form of equation 4 is easily found to be I(r, s) (5) where r, and s are the transform variables, P(*, *) is the transform of P(*, *), T(*, *) is the transform of the transmission funetion, I(*, *)…”

Section: Decorrelationmentioning

confidence: 99%

“…The general segmentation problem has been attaeked on a broad front [1,2], and thresholding, in partieular, is a popular method [1,[3][4][5][6]. Unfortunately, geometrie unsharpness destroys the erisp edges needed for unambiguous deeisions, and this diffieulty ean be eonsidered a problem in filtering in whieh the objeet is to devise a high-pass (sharpening) filter.…”

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confidence: 99%

Radiographic Image Enhancement by Wiener Decorrelation

Bell¹,

Godfrey²

1992

Review of Progress in Quantitative Nondestructive Evaluation

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The primary foeus of the applieation of image proeessing to radiography is the problem of segmentation. The general segmentation problem has been attaeked on a broad front [1,2], and thresholding, in partieular, is a popular method [1,[3][4][5][6]. Unfortunately, geometrie unsharpness destroys the erisp edges needed for unambiguous deeisions, and this diffieulty ean be eonsidered a problem in filtering in whieh the objeet is to devise a high-pass (sharpening) filter.This approach has been studied for more than 20 years [7][8][9][10][11][12][13].The inverse filter ean be devised either in the spatial domain or the frequeney domain.Both eases require either a model of the degradation proeess (geometrie unsharpness), or a set of "ideal" image/degraded image pairs from whieh an inverse filter ean be ealeulated by some estimation method (such as least squares).In the ease of radiography with an ineoherent source, a simple model of the imaging proeess leading to a frequeney domain solution ean be developed.The remainder of this paper deseribes a model of the radiographie proeess and the eonsequenees of eertain eonditions and simplifying assumptions. A partieular method of inverse filtering (Wiener filtering) is applied to some digitized radiographs and the results are diseussed. IMAGING PROCESSThe formation of radiographie images on a plane loeated at z be shown [14] to be a superposition integral of the formwhere u, v, and ware the direetion eosines with respeet to the X-, y-, and z-axes, P(x',y') is the intensity of an isotropie souree at z = 0, ft(x", y" z") is the attenuation eoeffieient along the ray from

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A Survey of Preprocessing and Feature Extraction Techniques for Radiographic Images

Cited by 180 publications

References 9 publications

Texture Analysis: A Review of Neurologic MR Imaging Applications

Texture Analysis: A Review of Neurologic MR Imaging Applications

Computer aided diagnosis in chest radiography

Radiographic Image Enhancement by Wiener Decorrelation

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