Detection of Masses in Digital Mammograms using K-Means and Support Vector Machine

Martins, Leonardo de Oliveira; Bráz, Geraldo; Silva, Aristófanes Correâ; Paiva, Anselmo Cardoso de; Gattass, Marcelo

doi:10.5565/rev/elcvia.216

Cited by 77 publications

(23 citation statements)

References 21 publications

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“…The researchers are still trying to optimize the performance of the CADe system on using the mammogram image shown in the recent literature. In general, the CADe system developed by the researchers is divided into two classes with some kinds of variations of the class type, including: normal and abnormal [5]; mass and non-mass [6] and the finding of microcalcification and not [4] [7].…”

Section: Introductionmentioning

confidence: 99%

“…Generally, previous researches, for the CADe system, use the mammography that is developed based on the three features on the mammogram image, those are the features of color, texture and shape. [8] use the color feature, while [9] use the shape feature and [6] combine the shape and texture on their research. Among both features, the last one is most widely used for mammography in previous researches [10] [11][12] [13] and [14].…”

Section: Introductionmentioning

confidence: 99%

“…The method of feature extraction widely used for the classification of RoI in statistic way is efficient and optimal and may describe the texture of the mammogram itself. Some previous researches that have developed the RoI classification system into two classes RoI (mass and a nonmass) mostly conduct the feature extraction statistically [6] and [12]. However, the determination of the part of RoI is still done manually; the system can only determine the class of RoI.…”

Section: Introductionmentioning

confidence: 99%

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Improvement of Sample Selection: A Cascade-Based Approach for Lesion Automatic Detection

Uyun¹,

Wahyudi²,

Choridah³

2016

ijacsa

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Abstract-Computer-Aided Detection (CADe) system has a significant role as a preventative effort in the early detection of breast cancer. There are some phases in developing the pattern recognition on the CADe system, including the availability of a large number of data, feature extraction, selection and use of features, and the selection of the appropriate classification method. Haar cascade classifier has been successfully developed to detect the faces in the multimedia image automatically and quickly. The success of the face detection system must not be separated from the availability of the training data in the large numbers. However, it is not easy to implement on a medical image because of some reasons, including its low quality, the very little gray-value differences, and the limited number of the patches for the examples of the positive data. Therefore, this research proposes an algorithm to overcome the limitation of the number of patches on the region of interest to detect whether the lesion exists or not on the mammogram images based on the Haar cascade classifier. This research uses the mammogram and ultrasonography images from the breast imaging of 60 probands and patients in the Clinic of Oncology, Yogyakarta. The testing of the CADe system is done by comparing the reading result of that system with the mammography reading result validated with the reading of the ultrasonography image by the Radiologist. The testing result of the k-fold cross validation demonstrates that the use of the algorithm for the multiplication of intersection rectangle may improve the system performance with accuracy, sensitivity, and specificity of 76%, 89%, and 63%, respectively.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improvement of Sample Selection: A Cascade-Based Approach for Lesion Automatic Detection

Uyun¹,

Wahyudi²,

Choridah³

2016

ijacsa

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“…As explained in Chapter 3.2.3, SVM classifier is based on the idea of minimizing the generalization error when the classifier is applied to test samples that do not exactly match any training sample used to train (42).…”

Section: Svmmentioning

confidence: 99%

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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“…We propose and compare three clustering methods to segment soil images (K-Means, Fuzzy c-Means and Self Organising Maps). These clustering methods have been used to segment natural images (Jian and Zhou, 2004;Lázaro et al, 2006;Ye, 2009), satellite images (Chuang et al, 2006;Arias et al, 2009) and mammograms images (Vega-Corona et al, 2003;De Oliveira et al, 2009;.…”

Section: Image Segmentation Using Clustering Techniquesmentioning

confidence: 99%