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Proceedings Paper

Neural networks in segmentation of mammographic microcalcifications
Author(s): Farzin Aghdasi; Rabab K. Ward; Branko Palcic
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Paper Abstract

Automatic detection and segmentation of microcalcifications may be achieved by application of algorithmic techniques or by use of artificial neural networks. We selected two neural network architectures and implemented object detection techniques on them. Further we have developed two algorithmic approaches to segment microcalcifications. In the first algorithm, thresholding of local image gray level histogram is used for object segmentation. In the first pass each object is labeled and object boundaries are marked but they are not segmented from the background. In the second pass the discontinuities due to region boundaries are corrected for, by allocating a unique threshold value for each object commensurate with the local background. In an alternative algorithm we employ edge detection to identify the pixels that may potentially belong to microcalcifications. Region growing techniques are then applied and the resulting segmented objects are subjected to tests involving shape, size and gradient.

Paper Details

Date Published: 11 May 1994
PDF: 12 pages
Proc. SPIE 2167, Medical Imaging 1994: Image Processing, (11 May 1994); doi: 10.1117/12.175119
Show Author Affiliations
Farzin Aghdasi, Univ. of British Columbia and B.C. Cancer Agency (Canada)
Rabab K. Ward, Univ. of British Columbia (Canada)
Branko Palcic, Univ. of British Columbia and B.C. Cancer Agency (Canada)

Published in SPIE Proceedings Vol. 2167:
Medical Imaging 1994: Image Processing
Murray H. Loew, Editor(s)

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