In image analysis software, the user manually adjusts a sliding grayvalue stick, until the nucleus appears dense and the cytoplasm and background is whited out. Estimation of the nuclear edge depends upon this manual threshold, which is not necessarily repeatable from observer to observer, or even within the same observer. Subjective variations in nuclear edge determination may, in turn, result in differences of estimated size, shape, and texture of the nuclear edge. We propose an automated thresholding method, which removes some of the subjectivity in nuclear edge determination. User makes a tracing to delineate a workspace, which includes the nucleus and a surrounding rim of cytoplasm and background.
Software makes a histogram of all pixels in the workspace.
At each division point between two consecutive bars of the histogram, software calculates the pooled standard deviation for the Student t test. This initial method is typically flawed by small, dark artefacts outside the nucleus, and by large, light artefacts inside the nucleus. The distinction of artefact versus true feature is somewhat arbitrary. For each inside point, every dark pixel which has a shared edge or shared corner with that inside point, likewise becomes an inside point. Step 3 is repeated to exhaustion.