Automatic segmentation system for liver tumors based on the multilevel thresholding and electromagnetism optimization algorithm

Abstract

In this article, we propose an automated segmentation system for liver tumors using magnetic resonance imaging and computed tomography. The proposed system is based on the algorithm of multilevel thresholding with electromagnetism optimization (EMO). The system starts with visualizing a patient's digital communication in medicine (DICOM) abdominal data set in three views. Two‐stage active contour segmentation methods that integrate region‐based local and global techniques using the active geodesic contour technique are proposed to segment the liver. To increase the accuracy and speed of segmentation for liver images, we identify the optimal threshold of the image segmentation method based on EMO with Otsu and Kapur algorithms. EMO offers interesting search capabilities while keeping a low computational cost. The proposed system was tested using a set of five DICOM data sets. All images were of the same size and stored in JPEG format (512 × 512 pixels). Experimental results illustrate that the proposed system outperforms state‐of‐the‐art methods such as the watershed algorithm. The average sensitivity, specificity, and accuracy of the segmented liver using the active contour model were 97.05%, 99.88%, and 98.47%, respectively. Moreover, the average sensitivity, specificity, and accuracy of the segmented liver tumor results were 94.15%, 99.57%, and 96.86%, respectively.

Deep learning-aided 3D proxy-bridged region-growing framework for multi-organ segmentation

“…Threshold-based methods are popular techniques for organ segmentation, which subdivide the image into several cohesive regions based on the intensity of the pixels 16 , 17 . Numerous algorithms have been proposed in this direction over recent years, including grayscale threshold 18 , interactive pixel classification 19 , and fuzzy rule algorithms 20 . Although such methods perform well and have fast computational speed for simple tasks, they fail to take consideration of the spatial correlation information between voxels and are highly influenced by external disturbances, such as noise.…”

Liver Tumor Detection Using Image Processing Technique: A Survey

“…Further evaluation of the proposed technique on a larger set of CT scans is needed in the future. Lamia N. Mahdy et al, [11] This article presents a novel technique for liver tumor segmentation from MRI and CT scans using an electromagnetism optimization (EMO) algorithm. The technique uses a two-stage active contour segmentation approach to first segment the liver and then separate it from surrounding tissues, organs, and bones.…”