Level set approach based on Parzen Window and floor of log for edge computing object segmentation in digital images

Scientific research into methodologies and algorithms to improve support for medical diagnoses remains in high demand on the agenda. Computer-aided diagnostic systems that use the Internet of Things (IoT) enable greater accessibility and integration between patients and specialists. One of the steps covered by IoT systems is the segmentation of Regions of Interest (ROI) in digital images. The challenge to segment these ROI in IoT systems is to apply methods that have low computational and storage costs with reliable results. Thus this work proposes the use of edge computing with a new approach based on active contours to target the ROI in medical images called FLog Parzen Level Set (FPLS). The method can be divided into two stages. First is the initialization of the seed point using Parzen Window and clusterization using Floor of Log. Second the growth and refinement of the region with probabilistic estimates of the regional contour using the Parzen Window. The proposed method was evaluated using the metrics of Accuracy, Precision, Sensitivity, Specificity, Matthews Coefficient Correlation, Hausdorff distance, Dice, and Jaccard Similarity Coefficient. The stable and satisfactory results can be highlighted despite the low computational times and costs. The proposed method was submitted to stroke, lung, and skin disease datasets. The proposed method achieved the fastest mean segmentation time of 1.64s for the three datasets used in this work. The method obtained the highest values for Sensitivity (98.57%), Accuracy (98.77%) and MCC (94.73%) in the stroke dataset, the lowest value for Hausdorff distance (4.24) in the lung dataset, and the highest Dice Coefficient value (92.49%) in the skin dataset. In conclusion, the proposed method is a promising tool for an edge computing system that segments regions of interest with the high precision of a level set and a fast convergence rate.