Neural networks in segmentation of mammographic microcalcifications
Farzin Aghdasi, Rabab Kreidieh Ward, Branko Palcic · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1994
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.