Fuzzy C-Means Algorithm-Based ARM-Linux-Embedded System Combined with Magnetic Resonance Imaging for Progression Prediction of Brain Tumors.

Haibo Wang,Lei Yan,Lei Han,Tieshi Song,Liying Wang

doi:10.1155/2022/4224749

Haibo Wang, Lei Yan + Show 3 more

Open Access

https://doi.org/10.1155/2022/4224749

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Abstract

The aim of this research was to analyze the application of fuzzy C-means (FCM) algorithm-based ARM-Linux-embedded system in magnetic resonance imaging (MRI) images for prediction of brain tumors. The optimized FCM (OFCM) algorithm was proposed based on kernel function, and the ARM-Linux-embedded imaging system was designed under ARM9 chip and Linux recorder, which were applied in MRI images of brain tumor patients. It was found that the sensitivity, specificity, and accuracy of the OFCM algorithm (90.46%, 88.97%, and 97.46%) were greater obviously than those of the deterministic C-means clustering algorithm (80.38%, 77.98%, and 85.24%) and the traditional FCM algorithm (83.26%, 79.56%, and 86.45%), and the difference was statistically substantial (P < 0.05). The ME and running time of the OFCM algorithm decreased sharply in contrast to those of the deterministic C-means clustering algorithm and the traditional FCM algorithm (P < 0.05). There were great differences in fraction anisotropy (FA) and mean diffusion (MD) of tumor parenchymal area, surrounding edema area, and normal white matter area (P < 0.05). FA of stage III+IV was smaller than those of stage I and II (P < 0.05), while the apparent diffusion coefficient (ADC) of stage III+IV was greater than that of stage I and II (P < 0.05). In conclusion, the poor update data processing and low data clustering efficiency of FCM were solved by OFCM. Moreover, computational efficiency of ARM-Linux-embedded imaging system was improved, so as to better realize the prediction of brain tumor patients through ARM-Linux-embedded system based on adaptive FCM incremental clustering algorithm.

Highlights

Brain tumors are all kinds of intracranial tumors, which can be divided into benign and malignant tumors
The sensitivity, specificity, and accuracy of the optimized FCM (OFCM) algorithm (90.46%, 88.97%, and 97.46%) were greater obviously than those of the deterministic C-means clustering algorithm (80.38%, 77.98%, and 85.24%) and the traditional fuzzy C-means (FCM) algorithm (83.26%, 79.56%, and 86.45%), and the difference was statistically substantial (P < 0:05)
It was found that the Misclassified error (ME) and running time of the OFCM algorithm decreased sharply in contrast to those of the deterministic C-means clustering algorithm and the traditional FCM algorithm (P < 0:05)

Summary

Introduction

Brain tumors are all kinds of intracranial tumors, which can be divided into benign and malignant tumors. Malignant tumors tend to grow faster, adhere to surrounding tissues, and are difficult to achieve the complete surgical resection They are prone to recurrence after surgery and distant metastasis and cause invasions to the surrounding brain tissues, blood vessels, and nerves, resulting in serious neurological dysfunction [3]. The kernel function and the traditional FCM algorithm were first introduced in this study to construct an adaptive incremental image segmentation algorithm, and an ARM-Linux-embedded MRI system was designed through the ARM9 chip and Linux recorder. This algorithm was compared with the HCM algorithm and the traditional FCM algorithm, and the ARM-Linux-embedded MRI system was applied to the MRI images of patients with glioma to comprehensively evaluate the predictive value of the ARM-Linux-embedded system combined with MRI images in the progression of patients with brain tumors

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