Directional features for automatic tumor classification of mammogram images

Buciu, Ioan; Gacsádi, A.

doi:10.1016/j.bspc.2010.10.003

Cited by 88 publications

(37 citation statements)

References 17 publications

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“…The methodology proposed by [7,8] suffers from the limitation that the usage of phase portraits to represent the patterns of subtle signs does not tend to create a well-defined converging pattern. Buciu and Gacsadi [9] proposed an automatic approach to retrieve the directional features in mammograms from MIAS database, which were further filtered by Gabor wavelets. The size of ROIs used for feature extraction was kept at 140×140 pixels.…”

Section: Introductionmentioning

confidence: 99%

Characterization of Architectural Distortion in Mammograms Based on Texture Analysis Using Support Vector Machine Classifier with Clinical Evaluation

et al. 2015

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Architecture distortion (AD) is an important and early sign of breast cancer, but due to its subtlety, it is often missed on the screening mammograms. The objective of this study is to create a quantitative approach for texture classification of AD based on various texture models, using support vector machine (SVM) classifier. The texture analysis has been done on the region of interest (ROI) selected from the original mammogram. A comprehensive analysis has been done on samples from three databases; out of which, two data sets are from the public domain, and the third data set is for clinical evaluation. The public domain databases are IRMA version of digital database for screening mammogram (DDSM) and Mammographic Image Analysis Society (MIAS). For clinical evaluation, the actual patient's database has been obtained from ACE Healthways, Diagnostic Centre Ludhiana, India. The significant finding of proposed study lies in appropriate selection of the size of ROIs. The experiments have been done on fixed size of ROIs as well as on the ground truth (variable size) ROIs. Best results pertain to an accuracy of 92.94 % obtained in case of DDSM database for fixed-size ROIs. In case of MIAS database, an accuracy of 95.34 % is achieved in AD versus non-AD (normal) cases for ground truth ROIs. Clinically, an accuracy of 88 % was achieved for ACE dataset. The results obtained in the present study are encouraging, as optimal result has been achieved for the proposed study in comparison with other related work in the same area.

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Section: Introductionmentioning

confidence: 99%

Characterization of Architectural Distortion in Mammograms Based on Texture Analysis Using Support Vector Machine Classifier with Clinical Evaluation

et al. 2015

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“…One valuable detection tools to identify breast cancer at early stage is through visual inspection on radiographic (X-ray) images (known as mammograms) [3]. However, by using this technique, the classification of normal, benign, and malignant become a complex job due to variations of tissue characteristics, such as shape, grey level, size, intensities, and location [4].…”

Section: Introductionmentioning

confidence: 99%

Mammograms Classification Using Gray-level Co-occurrence Matrix and Radial Basis Function Neural Network

Pratiwi

Alexander

Harefa

et al. 2015

Procedia Computer Science

122

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Computer Aided Diagnosis (CAD) is used to assist radiologist in classifying various type of breast cancers. It already proved its success not only in reducing human error in reading the mammograms but also shows better and reliable classification into benign and malignant abnormalities. This paper will report and attempt on using Radial Basis Function Neural Network (RBFNN) for mammograms classification based on Gray-level Co-occurrence Matrix (GLCM) texture based features. In this study, normal and abnormal breast image used as the standard input are taken from Mammographic Image Analysis Society (MIAS) digital mammogram database. The computational experiments show that RBFNN is better than Back-propagation Neural Network (BPNN) in performing breast cancer classification. For normal and abnormal classification, the result shows that RBFNN's accuracy is 93.98%, which is 14% higher than BPNN, while the accuracy of benign and malignant classification is 94.29% which is 2% higher than BPNN.

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“…Other alternative for optimal feature selection is by using multi objective Genetic algorithm [30]. Principal Component Analysis can be one alternative to reduce the dimensionality of filtered and unfiltered high dimensional data [22].…”

Section: Feature Extraction and Selection For Mammographic Imagesmentioning

confidence: 99%

“…The mammogram images can be filtered using Gabour wavelets and directional features are extracted at different orientation and frequencies. Principal Component analysis can be used for reducing the dimensions of filtered and unfiltered high dimensional data [22]. Contourlet coefficients can be employed as a feature extractor to obtain the contourlet coefficients.…”

Section: Feature Extraction and Selection For Mammographic Imagesmentioning

confidence: 99%

Content-Based Image Retrieval Techniques for Mammographic Images Using Soft Computing Techniques

Chopra¹,

Banga²

2017

ijarcs

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Content-based image Retrieval has been one of the lively research in the field of the computer science over the last decade or so. Many programs and tools have been used to formulate and execute queries based on the visual or audio content and to help browsing large multimedia repositories. But still a lot of work needs to be done in case of mammographic images. Breast cancer has been one of the causes of the increasing death of the European women.

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Directional features for automatic tumor classification of mammogram images

Cited by 88 publications

References 17 publications

Characterization of Architectural Distortion in Mammograms Based on Texture Analysis Using Support Vector Machine Classifier with Clinical Evaluation

Characterization of Architectural Distortion in Mammograms Based on Texture Analysis Using Support Vector Machine Classifier with Clinical Evaluation

Mammograms Classification Using Gray-level Co-occurrence Matrix and Radial Basis Function Neural Network

Content-Based Image Retrieval Techniques for Mammographic Images Using Soft Computing Techniques

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