基于分割、阈值和形态学操作的磁共振成像分析脑肿瘤检测

Misha Urooj Khan, Hareem Khan, A. Arshad, N. K. Baloch, Aneela Shaheen, Faizan Tariq
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引用次数: 0

摘要

脑肿瘤是世界上最危险、最致命的疾病之一。几乎95%的病人死于这种疾病。脑肿瘤主要分为良性和恶性两类。治疗的第一个失败是发现和诊断较晚。研究人员正在寻找和发现一种易于获得和简单的方法来早期发现它,以便在危及生命的情况出现之前控制其无法控制的传播。MRI(磁共振成像)是图像处理的最佳技术,它使检测大脑肿瘤区域变得更容易。本文采用预处理、分割、阈值分割、特征提取和分类等方法对MRI图像中的脑肿瘤进行检测。还设计了GUI(图形用户界面)来加载图像、绘制结果,并告诉用户/医生MRI是健康受试者还是肿瘤患者。总算法在5秒内给出完整的分类结果,分类准确率达到98.99%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Brain Tumor Detection based on Magnetic Resonance Imaging Analysis Using Segmentation, Thresholding and Morphological Operations
The brain tumor is one of the most dangerous and deadly diseases in the world. Almost 95% of the patient die due to this disease. The main two classes of brain tumors are benign and malignant tumors. The first failure towards their cure is the late detection and diagnosis. Researchers are on their way to find and discover an accessible and easy method for its earlier detection so that its uncontrollable spread can be controlled before a life-threatening situation. MRI (Magnetic Resonance Imaging) is the best technique for image processing and makes it easier to detect a tumor area in the brain. In this paper, we used preprocessing, segmentation, thresholding, feature extraction, and classification to detect brain tumors in MRI images. A GUI (Graphical User Interface) is also designed to load the images, plot the results, and tell the user/doctor whether the MRI is of a healthy subject or a Tumor patient. The total algorithm takes less than 5 seconds to give complete classification results and accurately classifies 98.99% times.
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