Usefulness of Texture Analysis for Computerized Classification of Breast Lesions on Mammograms

Pereira, Roberto; Azevedo-Marques, Paulo Mazzoncini de; Honda, Marcelo Ossamu; Kinoshita, Sérgio Koodi; Engelmann, Roger; Muramatsu, Chisako; Doi, Kunio

doi:10.1007/s10278-006-9945-8

Cited by 36 publications

(25 citation statements)

References 13 publications

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“…The issue of feature selection has been a general problem in image analysis methods, more specifically in pattern recognition related applications in order to minimize the classification error (Peng 2005, Pereira et al 2007, Laliberte et al 2012, Silva et al 2012. Besides aiming at higher classification accuracies, the application of feature selection methods has been related to the reduction of redundant information in order to speed up the classification approach by an optimal decrease in the evaluated features (Mahmoud et al 2011, Bindel et al 2011 …”

Section: Statistical Feature Selection Methodsmentioning

confidence: 99%

Aerial Image Classification for the Mapping of Riparian Vegetation Habitats

Kollár¹,

Vekerdy²,

Márkus³

2013

Acta Silvatica Et Lignaria Hungarica

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-In the current study, aerial image analysis has been applied to map vegetation communities in a riparian wetland ecosystem, Szigetköz (Hungary). Remote sensing offers an objective and timeeffective method for the detection of detailed vegetation habitats with the use of high resolution aerial photos combined with ancillary botanical and silvicultural data. Three images of the same test site, acquired in three different years have been analysed by sample-based semi-automated image classification technique. Due to the heterogeneous nature of the target vegetation classes, besides using spectral features (e.g. vegetation indices) textural descriptors were also involved in the classification procedure. The most appropriate parameters have been chosen using a statistical feature selection method based on the Jeffries-Matusita distance. The accuracy assessment proved for each scene that the combined use of spectral and textural features gave the best classification results in comparison to the exclusive use of spectral or textural measures. The here-applied feature set can be applied for the analysis of similar riparian sites. remote sensing / high resolution imagery / riparian wetland / texture analysis Kivonat -Légifelvételek osztályozása vizes élőhelyek térképezése céljából. A tanulmány célja légi-felvételek elemzésére szolgáló módszer kidolgozása vizes élőhelyek vegetációtérképezéséhez, melyet a szigetközi folyómenti mintaterületen vizsgáltunk. A hagyományos terepi felméréssel szemben a távérzékelés lehetővé teszi vizes élőhelyek megközelítően objektív és gyors térképezését nagy felbontású légifelvételek és kiegészítő botanikai és erdészeti adatok felhasználásával. A mintavételen alapuló fél-automatikus képosztályozás eredményesnek bizonyult a kiválasztott három képre alkalmazva (adott tesztterület három időpontra). A vegetációs célosztályok heterogén természetéből adódik, hogy a spektrális jellemzők (vegetációs index) vizsgálata mellett texturális jellemzők bevonására is szükség van az osztályozási algoritmusok kialakításához. A legjelentősebb paramétereket a Jeffries-Matusita statisztikai kiválasztó módszer segítségével határoztuk meg. Megbízhatósági elemzés alapján a spektrális és texturális jellemzők együttes alkalmazása adta a legjobb osztályozási eredményeket a kizárólag spektrális vagy texturális paraméterek felhasználásával szemben. Hasonló ártéri területek növényzeti térképezéséhez a kiválasztott jellemzők alapértelmezett alkalmazása javasolt. távérzékelés / nagy felbontású felvétel / folyómenti vizes élőhely / texturális elemzés

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Section: Statistical Feature Selection Methodsmentioning

confidence: 99%

Aerial Image Classification for the Mapping of Riparian Vegetation Habitats

Kollár¹,

Vekerdy²,

Márkus³

2013

Acta Silvatica Et Lignaria Hungarica

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“…The author reported a detection rate of 82% with 2.8 FPs/I under 3762 mammograms. Pereira et al [24] used sixteen texture features to represent a ROI. Then nonparametric KNN classifier was trained to discriminant normal ROIs from abnormal ROIs.…”

Section: Mass Detectionmentioning

confidence: 99%

A Survey of Computer-aided Detection of Breast Cancer with Mammography

Yanfeng¹,

Chen²,

Chen³

et al. 2016

J Health Med Inform

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Computer-aided detection (CAD) systems can be served as a second view for radiologist. An overview of recent development in CAD methods are presented in this paper. Abnormalities detection, abnormalities classification and content-based image retrieval (CBIR) are briefly reviewed. For the abnormalities detection, micro-calcification detection, mass detection and multi-view based detection are introduced. For the abnormalities classification, micro-calcification classification and mass classification are given.

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“…The comparison of images from one exam with others that already have confirmed reports and diagnoses, when applied in several areas where the automated evaluation of the image content is relevant [14,15], provides broad support for the medical diagnosis. The automation of this process results in Computer Aided Diagnosis CAD [16,17]. Some CADs are important resources for diagnosis support, as in the case of mammographies, approved as a diagnostic resource in breast cancer by the FDA (USA Food and Drug Administration).…”

Section: Stage 4 -Conceptual Modelingmentioning

confidence: 99%

Computational Environment for Radiology and Image-Diagnosis Teaching: A Proposal Using Systems Methodology

Pinheiro

Figueiredo

Azevedo-Marques

et al. 2009

Int. J. Emerg. Technol. Learn.

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Abstract-Defining a computational environment for radiology and image-diagnosis education requires the development of a model that covers the resident doctor's training necessities and creates the mechanisms necessary for the construction of this environment, especially integrating the various systems in use or in development, in a hospital school. This work presents a proposal to define strategies for the creation of a computational environment for radiology and image-diagnosis teaching, specifically aimed towards the education of the resident physician in this area. The Soft Systems Methodology (SSM) was used to elaborate a comprehensive model under a systemic focus and bring up actions for its implementation. Initially, the current radiology teaching structure will be contextualized, and information systems that could be applied to support this type of activity will be discussed. The next step will be the revision of the SSM and the application of each step proposed by this methodology within the medical residency context of a large Brazilian hospital school. This residencyeducation activity survey was performed through interviews, questionnaires and the review of the norms, criteria and minimum requirements for the accreditation of medical residency programs in Brazil. Considering the complexity of the doctor-training environment and the diversity of computer systems under development to support it, the systemic approach helps the understanding and can guide the necessary integration. An integratedenvironment frame, and its action plan, is proposed for radiology and image-diagnosis teaching. Finally, the strategies identified for its implementation are discussed.

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Usefulness of Texture Analysis for Computerized Classification of Breast Lesions on Mammograms

Cited by 36 publications

References 13 publications

Aerial Image Classification for the Mapping of Riparian Vegetation Habitats

Aerial Image Classification for the Mapping of Riparian Vegetation Habitats

A Survey of Computer-aided Detection of Breast Cancer with Mammography

Computational Environment for Radiology and Image-Diagnosis Teaching: A Proposal Using Systems Methodology

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