The use of texture analysis to delineate suspicious masses in mammography.
In mammography, national breast screening programmes have lead to a large increase in the number of mammograms needing to be studied by radiologists. Lesion indicators can be pointlike as in microcalcifications or extended as in stellate (spiculate) lesions or regular masses. Texture analysis has been proposed as a promising method for studying radiographic images in relation to the quantitation of extended objects. Filters have been designed, which may be used to segment or classify an image using textural features, and these have been reported as being of value in automatic mammographic glandular tissue classification. The work reported here suggests the incorporation of additional steps of image processing in an attempt to improve the performance of these filters in the quantitation of lesions. By deriving approximate outlines, which are used to identify suspicious regions, the investigation illustrates the properties of one of the filters. After applying the method to a small prediagnosed database of stellate lesions and regular masses, the results show that the filter is able to outline the malignant masses in all cases presented. The erroneous areas extracted are small for the initial part of the work, which deals with 256 x 256 pixel image extracts, though slightly larger in some cases when the whole mammogram is considered. Simple methods for the removal of these artefacts are proposed. For each non-suspicious case studied, the sum of any false positive areas is statistically insignificant when compared with the regions correctly outlined in the prediagnosed instances.
Gupta
R
R
Department of Bio-Medical Physics and BioEngineering, University of Aberdeen, Foresterhill, UK.
Undrill
P E
PE
D001699:Biometry; D055592:Biophysical Phenomena; D001703:Biophysics; D001943:Breast Neoplasms / Q000000981:diagnostic imaging / Q000517:prevention & control; D016208:Databases, Factual; D005069:Evaluation Studies as Topic; D005189:False Positive Reactions; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*