The use of texture analysis to delineate suspicious masses in mammography

Abstract

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.

Automatic Detection of Pectoral Muscle Using Average Gradient and Shape Based Feature

“…In the band division, algorithm tolerance δ 1 and δ 2 are introduced. Any small positive integer in the range [3][4][5][6][7][8][9][10][11][12] and [1][2][3][4][5] may be used for them. Small variations of these parameter values do not affect the performance.…”

“…Normally, in medio-lateral oblique (MLO) view of mammogram, pectoral muscle appears as a triangular, high-density region at the posterior corner of the image. The presence of pectoral muscle can affect the automatic detection of suspicious regions such as mass [3,4], or automatic identification of breast tissue density [5,6]; as the pectoral muscle approximately have the same density, so is the dense tissues of interest in the image.…”

K-means Clustering for Classifying Unlabelled MRI Data

“…Texture analysis for computer-aided diagnosis (CAD) in medical images has been studied in many disciplines including the diagnosis of breast cancer in mammograms [1][2][3][4][5], lung nodules in chest radiographs [6][7][8], osteoporosis in bone x-ray images [9][10][11], and abnormalities in kidney and liver [12][13][14]. Analysis is typically based on regions-of-interest (ROIs).…”

Automatic Pectoral Muscle Segmentation on Mediolateral Oblique View Mammograms

“…The work of Gupta and Undrill [5] indicates that mammographic parenchyma and the pectoral region may have similar texture characteristics, causing a high number of false positives when detecting suspicious masses. In other words, the pectoral muscle could interfere with automated detection of cancers.…”