Texture-based features for classification of mammograms using decision tree

Mohanty, A.R.; Senapati, Manas Ranjan; Beberta, Swapnasikta; Lenka, Saroj Kumar

doi:10.1007/s00521-012-1025-z

Cited by 48 publications

(35 citation statements)

References 27 publications

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“…There are seven features of GLRLM which used in this research as follows short run emphasis (SRE), long run emphasis (LRE), gray level non-uniformity (GLN), run length non-uniformity (RLN), run percentage (RP), low gray level run emphasis (LGRE), and high gray level run emphasis (HGRE). These features can be calculated by the following equations [20].…”

Section: Gray Level Run Length Matrixmentioning

confidence: 99%

Computer Aided Diagnosis using Margin and Posterior Acoustic Featuresfor Breast Ultrasound Images

et al. 2017

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Section: Gray Level Run Length Matrixmentioning

confidence: 99%

Computer Aided Diagnosis using Margin and Posterior Acoustic Featuresfor Breast Ultrasound Images

et al. 2017

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“…Mammograms are medical images frequently difficult to interpret because the preprocessing is a necessary step to improve image quality and to ensure greater accuracy of result (23,45,65).…”

Section: Initial Data Preprocessingmentioning

confidence: 99%

“…Using the described software, and existing functions (fspecial, imtophat, imadjust) the images were smoothed by applying a Gaussian filter to eliminate noise, and the discrete wavelet transform was then implemented, both with proven efficacy (45,63,67). Wavelet transform is a sparse and efficient way to represent an image by improving the image quality multiresolution representation.…”

Section: Initial Data Preprocessingmentioning

confidence: 99%

“…Texture is an important image characteristic that has been widely used in medical image analysis especially in their automatic classification (45,69).…”

Section: Features Extractionmentioning

confidence: 99%

“…In the same section some studies using C4.5 DT as the classifier were also presented. Both C4.5 and the improved versions use information gained as a measure of purity, which is based on the notion of entropy (45). as an open source Java ® implementation of the C4.5 decision tree algorithm.…”

Section: Decision Treementioning

confidence: 99%

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Using Data Mining Techniques to Support Breast Cancer Diagnosis

Diz

Marreiros

2015

New Contributions in Information Systems and Technologies

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More than ever, there is an increase of the number of decision support methods and computer aided diagnostic systems applied to various areas of medicine. In breast cancer research, many works have been done in order to reduce false-positives when used as a double reading method.In this study, we aimed to present a set of data mining techniques that were applied to approach a decision support system in the area of breast cancer diagnosis. This method is geared to assist clinical practice in identifying mammographic findings such as microcalcifications, masses and even normal tissues, in order to avoid misdiagnosis.In this work a reliable database was used, with 410 images from about 115 patients, containing previous reviews performed by radiologists as microcalcifications, masses and also normal tissue findings.Throughout this work, two feature extraction techniques were used: the gray level co-occurrence matrix and the gray level run length matrix.For classification purposes, we considered various scenarios according to different distinct patterns of injuries and several classifiers in order to distinguish the best performance in each case described. The many classifiers used were Naïve Bayes, Support Vector Machines, k-nearest Neighbors and Decision Trees (J48 and Random Forests).The results in distinguishing mammographic findings revealed great percentages of PPV and very good accuracy values. Furthermore, it also presented other related results of classification of breast density and BI-RADS ® scale.The best predictive method found for all tested groups was the Random Forest classifier, and the best performance has been achieved through the distinction of microcalcifications.The conclusions based on the several tested scenarios represent a new perspective in breast cancer diagnosis using data mining techniques. VIII IX ResumoCada vez mais assistimos a um aumento global do número de métodos de apoio a decisão e diagnóstico assistido por computador, aplicados a diversas áreas da medicina.Na área de investigação do cancro da mama muitos são os trabalhos que têm sido desenvolvidos como segunda leitura de modo a reduzir o número de falsos positivos no diagnóstico.Neste estudo é apresentado um conjunto de técnicas de data mining que poderão ser aplicadas a um sistema de apoio à decisão na área do diagnóstico de cancro da mama.Esta abordagem tem por objetivo ajudar os clínicos na identificação de achados mamográficos como microcalcificações, massas e mesmo tecidos normais, de forma a evitar diagnósticos errados.Para isso, neste trabalho é usada uma base de dados fidedigna, de 410 imagens correspondentes a 115 pacientes, contendo análises prévias, realizadas por radiologistas, de microcalcificações, massas e tecidos considerados normais.Ao longo deste trabalho são utilizadas duas técnicas de extração de características, a matriz de coocorrência de níveis de cinza e a matriz de comprimento da linha de níveis de cinza.

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A review of mammographic region of interest classification

Tasdemir

Taşdemir

Aydın

2020

WIREs Data Min & Knowl

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Early detection of breast cancer is important and highly valuable in clinical practice. X-ray mammography is broadly used for prescreening the breast and is also attractive due to its noninvasive nature. However, experts can misdiagnose a significant proportion of the cases, which may either cause redundant examinations or cancer. In order to reduce false positive and negative rates of mammography screening, computer-aided breast cancer detection has been studied for more than 30 years and many methods have been proposed by the researchers. In this review, region of interest (ROI) classification methods, which operate on a predefined or segmented ROIs with a focus on mass classification are surveyed.A total of 72 high quality journal and conference papers are selected from the Web of Science (WOS) database that meet several inclusion criteria. A comparative analysis is provided based on ROI extraction methods, data sets and machine learning techniques employed, the prediction accuracies, and usage frequency statistics. Based on the performances obtained on publicly available data sets, the ROI classification problem from mammogram images can be considered as approaching to be solved. Nonetheless, it can still be used as complementary information in breast cancer detection from the whole mammograms, which has room for improvement.

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Texture-based features for classification of mammograms using decision tree

Cited by 48 publications

References 27 publications

Computer Aided Diagnosis using Margin and Posterior Acoustic Featuresfor Breast Ultrasound Images

Computer Aided Diagnosis using Margin and Posterior Acoustic Featuresfor Breast Ultrasound Images

Using Data Mining Techniques to Support Breast Cancer Diagnosis

A review of mammographic region of interest classification

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