Hybrid method combining superpixel, supervised learning, and random walk for glioma segmentation

Abstract

Currently, the analysis of magnetic resonance imaging (MRI) brain images of pathological patients is performed manually, both for the recognition of brain structures or lesions and for their characterization. Physicians sometimes encounter difficulties in interpreting these images for a reliable diagnosis of the patient's condition. This is due to the difficulty of detecting the nature of the lesions, particularly glioma. Glioma is one of the most common tumors, and one of the most difficult to detect because of its shape, irregularities, and ambiguous limits. The segmentation of these tumors is one of the most crucial steps for their classification and surgical planning. This article presents a new, accurate, and automatic approach for the precise segmentation of early gliomas (benign tumors), combining the random walk (RW) algorithm and the simple linear iterative clustering algorithm. The study was carried out in four steps. The first step consisted of decomposing the image into superpixels to obtain an initial outline of the tumor. The superpixels were generated using the SLIC algorithm. In the second step, for each superpixel, a set of statistical and multifractal characteristics were calculated (gray‐level co‐occurrence matrix, multifractal detrending moving average). In the third step, the superpixels were classified using a supervised random forest (RF) type classier into healthy or tumorous brain tissue. In the final step, the contour of the detected tumor was enhanced using the customized RW algorithm. The proposed method was evaluated using the Brain Tumor Image Segmentation Challenge 2013 database. The results obtained are competitive compared to other existing methods.

Real-Time Healthcare Resource Management Planning Using Advanced Machine Learning Methods

“…The ERC method is computationally costly as it estimates each relationship among the target variables and builds separate models for each relationship. Although, RF can efficiently handle the missing data problem, the memory consumption of RF is very high if parallelization is not utilized (Ait Mohamed et al, 2021). If time series input data comes with strong seasonality with an underlying trend, RF often underperforms.…”

Edge and anomaly detection of brain magnetic resonance images in a distributed environment

“…To produce a decision support tool, most approaches dealt with in the literature use the following methodology. Firstly, brain MRI image segmentation aims to form groups of pixels that share some resemblance measures, find the region of interest, or distinguish the foreground and background based on pixel similarities 8–13 . Secondly, the segmented image is interpreted by discriminating the data between the given classes as in references 4,14–17.…”