Multiresolution statistical analysis of high-resolution digital mammograms

Heine, John J.; Deans, Stanley R.; Cullers, D. K.; Stauduhar, Richard P.; Clarke, Laurence P.

doi:10.1109/42.640740

Cited by 59 publications

(37 citation statements)

References 34 publications

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“…Recently, much research has been devoted to developing reliable computer aided diagnosis (CAD) methods (see Doi et al, 1993, for a general review). Many of these methods are based on multiresolution analysis, global and local thresholding, difference image techniques, stastistical approaches, neural networks, fuzzy logic, and the wavelet transform (WT) and related techniques (Heine et al, 1997;Netsch et al, 1999;Qian et al, 2000). Currently most of these methods are often combined to detect and classify clusters of microcalcifications (MC) which is an important mammographic sign of early (in situ) breast cancer despite the fact that several benign diseases show MC as well.…”

Section: Introductionmentioning

confidence: 99%

Wavelet-Based Multifractal Formalism to Assist in Diagnosis in Digitized Mammograms

Kestener

Lina²,

Saint-Jean

et al. 2011

Image Anal Stereol

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We apply the 2D wavelet transform (WTMM) method to perform a multifractal analysis of digitized mammograms. We show that normal regions display monofractal scaling properties as characterized by the socalled Hurst exponent H 0¡ 3 ¢ 0¡ 1 in fatty areas which look like antipersistent self-similar random surfaces, while H 0¡ 65 ¢ 0¡ 1 in dense areas which exibit long-range correlations and possibly multifractal scaling properties. We further demonstrate that the 2D WTMM method provides a very efficient way to detect tumors as well as microcalcifications (MC) which correspond to much stronger singularities than those involved in the background tissue roughness fluctuations. These preliminary results indicate that the texture discriminatory power of the 2D WTMM method may lead to significant improvement in computer-assisted diagnosis in digitized mammograms.

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Section: Introductionmentioning

confidence: 99%

Wavelet-Based Multifractal Formalism to Assist in Diagnosis in Digitized Mammograms

Kestener

Lina²,

Saint-Jean

et al. 2011

Image Anal Stereol

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“…Evidence indicates that mammograms, regardless of resolution, obey an inverse power law with respect to their power spectral density [51][52][53]. Specifically, the power spectrum of a particular image drops off a 1/ f γ , with γ on the order of three.…”

Section: The "Donut" Filtermentioning

confidence: 97%

“…important. This translates into about 8-16 pixels in an image generated with a 35 m/pixel digital resolution; our original work was performed at this high resolution and this experience will be discussed here [46]. In pilot studies, the Rather than impose a detection or decision rule on the process, we decided early on to see whether a parametric approach to decision making could be followed.…”

Section: Symmlet Wavelet Approachmentioning

confidence: 99%

“…It may be safe to say that, if the resolution is doubled or halved, we could pick different expansion images and our technique would still be "the best" within this hypothetical situation; that is, if the images were scaled to down 70 m (lower resolution or halved), the d 1 and d 2 images would most likely be the most relevant choices. However, if the scaling was not applied by a factor of two (or half) we might end up with a less then optimal representation for detection purposes.With this in mind and the outcome of the resolution studies mentioned earlier, we developed another band pass filter that we will refer to as the "donut" filter [51]. This filter has three infinitely adjustable parameters that control: (1) the central band location, (2) the band pass width, and (3) the sharpness of the cut-off.…”

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Computer-Aided Diagnosis of Mammographic Calcification Clusters: Impact of Segmentation

Kallergi

Heine

Tembey

Topics in Biomedical Engineering International Book Series

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Medical image analysis is an area that has always attracted the interest of engineers and basic scientists. Research in the field has been intensified in the last 15-20 years. Significant work has been done and reported for breast cancer imaging with particular emphasis on mammography. The reasons for the impressive volume of work in this field include (a) increased awareness and education of women on the issues of early breast cancer detection and mammography, (b) the potential for significant improvements both in the fields of imaging and management, and (c) the multidisciplinary aspects of the problems and the challenge presented to both engineers and basic scientists.The importance of mammography and computer applications in mammography has been and continues to be the topic of numerous workshops, conferences, and publications [1,2]. There seems to be sufficient evidence that mammogra- 708Kallergi, Heine, and Tembey arguments that support an opposite view [3]. It should be noted that breast cancer was the second major cause of death for women in 2003 and mammography has been responsibly for a mortality reduction of 20-40% [4,5]. Despite its success, mammography still has a false negative rate of 10-30% and great variability [6].Calcifications are one of the main and earliest indicators of cancer in mammograms. They are present in 50-80% of all mammographically detected cancers but pathologic examinations reveal an even greater percentage [7]. Most of the minimal cancers and in-situ carcinomas are detected by the presence of calcifications [7]. A review of the literature on missed breast cancers indicates that calcifications are not commonly found among the missed lesions [8]. Although perception errors are not excluded, particularly in the case of microcalcifications (size < 1 mm), the technique of screen/film mammography (SFM) has been significantly improved over the years offering high-contrast and high-resolution mammograms that make calcification perception relatively easy. A greater and continuing problem for radiologists, with a major impact on the specificity of the diagnosis, is the mammographic differentiation between benign and malignant clustered calcifications. Almost all cases with calcifications are recommended for biopsy but only about 15-34% of these prove to be malignant [9]. The biopsies necessary to make the determination between benign and malignant disease represent the largest category of induced costs of mammography screening and a major source of concern for radiologists, surgeons, and patients. The advent of full field direct digital mammography will probably amplify this problem by providing more details and revealing breast abnormalities at very early stages [10].In the last 20 years, researchers have developed various computer schemes for analyzing mammograms with calcifications, masses, and other breast abnormalities in an effort to improve mammography and breast cancer detection and diagnosis [11]. Computer algorithms can be divided in three groups depending on their final goal:...

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“…El tercer caso de a tiene niveles de 0.42 y 0.58; y as sucesivamente hasta el ultimo caso de b con niveles de 0.47 y 0. 53. En la gura 3.6 est an representadas las funciones gaussianas utilizadas.…”

Section: Estudio Comparativounclassified

Contribución a la detección y análisis de microcalcificaciones en mamografías mediante tratamiento digital de imagen.

García¹,

Manuel²

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A Maria Dolors i Claudia \... l'esperan ca es mentida si no hi ha cada dia un esfor c pel nou dem a" Llu s Llach AgradecimientosQuiero mostrar mi agradecimiento a mi director, por toda la con anza que ha depositado en mi y por toda la ayuda que me ha prestado. Gracias, Antonio.A Maria Dolors, mi mujer, por su apoyo durante todo este tiempo.A Pepa, mi hermana, por su fe en mi y su disposici on a ayudarme.Quiero mostrar mi agradecimiento tambi en, a todas aquellas personas del Departamento de Comunicaciones con las que he compartido estos años de trabajo y que de manera directa o indirecta me han prestado su ayuda, entre ellos, Juan Morales, Ram on Miralles, Esther Hidalgo, Inma Mora, Alberto, Pablo, Joaqu n y especialmente mi compañera de asignatura, Valery.A Ferr an Marqu es y Luis Torres por su cordial visita a Valencia y la invitaci on al curso que impart an, que nos abri o las puertas a la Morfolog a Matem atica. Finalmente a Lu s Vergara, Director del Departamento de Comunicaciones, y al Doctor Jos e Mar a P erez Clavijo, jefe del Servicio de Radiolog a del Hospital de Sagunto, quienes hicieron posible que comenzara a trabajar en mamograf a.

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Multiresolution statistical analysis of high-resolution digital mammograms

Cited by 59 publications

References 34 publications

Wavelet-Based Multifractal Formalism to Assist in Diagnosis in Digitized Mammograms

Wavelet-Based Multifractal Formalism to Assist in Diagnosis in Digitized Mammograms

Computer-Aided Diagnosis of Mammographic Calcification Clusters: Impact of Segmentation

Contribución a la detección y análisis de microcalcificaciones en mamografías mediante tratamiento digital de imagen.

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