Easy-GT: Open-Source Software to Facilitate Making the Ground Truth for White Blood Cells Nucleus

The nucleus of white blood cells (WBCs) plays a significant role in their detection and classification. Appropriate feature extraction of the nucleus is necessary to fit a suitable artificial intelligence model to classify WBCs. Therefore, designing a method is needed to segment the nucleus accurately. The detected nuclei should be compared with the ground truths identified by a hematologist to obtain a proper performance evaluation of the nucleus segmentation method. It is a time-consuming and tedious task for experts to establish the ground truth manually. This paper presents an intelligent open-source software called Easy-GT to create the ground truth of WBCs nucleus faster and easier. This software first detects the nucleus by employing a new otsus thresholding based method with a dice similarity coefficient (DSC) of 95.42 %; the hematologist can then create a more accurate ground truth, using the designed buttons to modify the threshold value. This software can speed up ground truths forming process more than six times.

Contour Integration using Graph-Cut and Non-Classical Receptive Field

Many edge and contour detection algorithms give a soft-value as an output and the final binary map is commonly obtained by applying an optimal threshold. In this paper, we propose a novel method to detect image contours from the extracted edge segments of other algorithms. Our method is based on an undirected graphical model with the edge segments set as the vertices. The proposed energy functions are inspired by the surround modulation in the primary visual cortex that help suppressing texture noise. Our algorithm can improve extracting the binary map, because it considers other important factors such as connectivity, smoothness, and length of the contour beside the soft-values. Our quantitative and qualitative experimental results show the efficacy of the proposed method.