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In recent days, automated demarcation of biased nuclei of follicular lymphoma has become a standard framework in the pipeline of quantitative histopathology. It has received substantial consideration due to subjective variability between different oncologists. This difference can occasionally result in erroneous conclusions, distinct prognostic reports, and inconsistent treatment. This paper provides additional input to an oncologist to ease the prognosis process. This approach defines a local criterion fitting function in neighbourhood of each point based on image intensity. An assumption is pixel intensity will remain constant next to the point. Integration of these local neighbourhood centres leads us to define the global criterion of image segmentation. Segmentation accuracy is evaluated using region-based measures and the optimal segmentation accuracy of 98.6% is achieved using the DICE coefficient. By using locally formulated energy, execution time is also reduced in comparison to the contour algorithm. This technique is independent of the initialization.

Accurate demarcation of a biased nucleus from H&E-stained follicular lymphoma tissues samples