False Positive Reduction in Mammographic Mass Detection Using Local Binary Patterns

Oliver, Arnau; Lladó, Xavier; Freixenet, Jordi; Martı́, Joan

doi:10.1007/978-3-540-75757-3_35

Cited by 81 publications

(57 citation statements)

References 15 publications

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“…It is believed that the texture plays an important role in the visual system for recognition and interpretation of data. Local binary pattern [4], [7], [9], [10] and Gabor filter is used in proposed system to extract the texture features from the processed image.…”

Section: Feature Extractionmentioning

confidence: 99%

Classification of Breast cancer Using Local Binary Pattern and Gabor Filter

Sansare¹

2017

IJRASET

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Section: Feature Extractionmentioning

confidence: 99%

Classification of Breast cancer Using Local Binary Pattern and Gabor Filter

Sansare¹

2017

IJRASET

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“…The performance is measured using ROC, which is greater than 0.8. Arnau Oliver et al [10] propose an approach focusing on reduction of false positive mass detection using Local Binary Pattern. A group of 1792 uncommon region of interest was obtained from DDSM database.…”

Section: Ugc Approved Journalmentioning

confidence: 99%

“…Feature extraction can be described as extracting significant fine information from given input while rejecting all other data. LBP texture feature [4], [7], [9], [10], texture [2], [5], intensity value [11], eigenfaces approach [8], shape [6] feature were extracted from digital mammograms.…”

Section: Cmentioning

confidence: 99%

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A Survey of Breast Cancer Detection and Classification Based on Texture Feature

Kinge¹,

Rani²,

Sutaone³

et al. 2017

International Journal of Innovative Research in Electrical, Ele

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Breast cancer is second most dangerous disease in the world after the lung cancer among women. Because of this reason, breast cancer detection is most focused area by many researchers. Digital mammographic images and advanced techniques are required by many researchers for developing Computer aided detection (CAD) for breast cancer detection. This study describes the recent advances in image processing and machine learning techniques for breast cancer detection. The study shows that Local Binary Pattern method used for feature extraction and Support vector machine for classification as foremost technique used for breast cancer detection. The comparative study of literature work summarizes the effectiveness of different approach used by researchers for breast cancer detection.

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“…Regarding the application of LBP or its variations to solve medical imaging problems, researchers are getting very good results, often improving other existing approaches (Iakovidis et al, 2008;Oliver et al, 2007;Sørensen et al, 2008).…”

Section: Review Of Existing Cad Systemsmentioning

confidence: 99%

Detection of Normality/Pathology on Chest Radiographs Using LBP

Gea

Garcı́a-Mateos

2010

Proceedings of the First International Conference on Bioinformatics

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Abstract:Since the discovery of X-rays and their applications, medical imaging has been a great help for radiologists in the diagnosis of diseases. In recent years, there has been a great effort in the computer vision community in the research of systems for the analysis and extraction of useful information from medical images. In this scenario, we have designed, implemented and validated a novel method to detect normality/pathology in chest radiographs, which constitutes the core of a computer-aided detection (CADe) system. Although the problem addressed is very complex and little explored, our approach is completely automatic, starting from the location of areas of interest using template matching techniques. The main novelty of our contribution is the application of a transformation known as local binary patterns (LBP) to these areas. LBP histograms are then used as input features for a classification system, which is ultimately responsible for the decision of normality/pathology. The results of our preliminary experiments are quite promising. With success rates in the best cases close to 90%, we believe that increased performance could be obtained with bigger training sets and more advanced classification systems, which will make these systems to be fully viable in the near future.

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False Positive Reduction in Mammographic Mass Detection Using Local Binary Patterns

Cited by 81 publications

References 15 publications

Classification of Breast cancer Using Local Binary Pattern and Gabor Filter

Classification of Breast cancer Using Local Binary Pattern and Gabor Filter

A Survey of Breast Cancer Detection and Classification Based on Texture Feature

Detection of Normality/Pathology on Chest Radiographs Using LBP

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