Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture feature space
نویسندگان
چکیده
منابع مشابه
Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture feature space.
We studied the effectiveness of using texture features derived from spatial grey level dependence (SGLD) matrices for classification of masses and normal breast tissue on mammograms. One hundred and sixty-eight regions of interest (ROIS) containing biopsy-proven masses and 504 ROIS containing normal breast tissue were extracted from digitized mammograms for this study. Eight features were calcu...
متن کاملTexture Classification Using Hierarchical Linear Discriminant Space
As a representative of the linear discriminant analysis, the Fisher method is most widely used in practice and it is very effective in twoclass classification. However, when it is expanded to a multi-class classification problem, the precision of its discrimination may become worse. A main reason is an occurrence of overlapped distributions on the discriminant space built by Fisher criterion. I...
متن کاملComputer-aided diagnosis in mammography: classification of mass and normal tissue by texture analysis.
Computer-aided diagnosis schemes are being developed to assist radiologists in mammographic interpretation. In this study, we investigated whether texture features could be used to distinguish between mass and non-mass regions in clinical mammograms. Forty-five regions of interest (ROIs) containing true masses with various degrees of visibility and 135 ROIs containing normal breast parenchyma w...
متن کاملComputer-aided diagnosis of mammographic masses
We propose a statistical method for nding masses on mammograms. The technique is based on tting broken line regressions to local intensity plots of the images. The method is illustrated on a small database of mammograms that have been read by a radiologist and connrmed by operative data.
متن کاملFeature Extraction and Classification of Mammographic Masses
The aim of this project is to classify the mammographic masses as benign or malignant using texture and shape features. A set of 73 mammograms is used for the analysis, out of which 41 are benign and 32 are malignant. Manually segmented masses are obtained from the DDSM, USF database [2]. Texture and shape features are extracted from the manually segmented masses. Stepwise linear discriminant a...
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ژورنال
عنوان ژورنال: Physics in Medicine and Biology
سال: 1995
ISSN: 0031-9155,1361-6560
DOI: 10.1088/0031-9155/40/5/010