Segmentation of Nucleus and Cytoplasm from H&E-Stained Follicular Lymphoma

This paper proposes a noble image segment technique to differentiate between large malignant cells called centroblasts vs. centrocytes. A new approach is introduced, which will provide additional input to an oncologist to ease the prognosis. Firstly, a H&E-stained image is projected onto L*a*b* color space to quantify the visual differences. Secondly, this transformed image is segmented with the help of k-means clustering into its three cytological components (i.e., nuclei, cytoplasm, and extracellular), followed by pre-processing techniques in the third step, where adaptive thresholding and the area filling function are applied to give them proper shape for further analysis. Finally, the demarcation process is applied to pre-processed nuclei based on the local fitting criterion function for image intensity in the neighborhood of each point. Integration of these local neighborhood centers leads us to define the global criterion of image segmentation. Unlike active contour models, this technique is independent of initialization. This paper achieved 92% sensitivity and 88.9% specificity in comparing manual vs. automated segmentation.