脑MRI图像增强与肿瘤分割

Aye Min, Zin Mar Kyu
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引用次数: 16

摘要

脑肿瘤是肿瘤细胞在脑部的异常生长。在医学领域,由于脑肿瘤结构复杂,对其进行脑区域分割和检测是一项极具挑战性的任务。磁共振成像(MRI)提供了大脑解剖学的详细信息。利用脑磁共振图像对脑肿瘤进行正确的分割,有助于准确识别脑肿瘤的大小和形状,有助于脑肿瘤的诊断和治疗。然而,磁共振数据的人工分割是一项耗时的任务,并且在MRI中仍然难以检测到脑肿瘤区域。脑肿瘤检测面临的主要挑战是肿瘤区域检测和肿瘤区域分割的准确性较低。该系统提出了图像增强的结果融合方法,并结合自适应k均值聚类和形态学操作进行肿瘤分割。所有实验结果将在BRATS多模态图像的脑肿瘤分割基准数据集上进行测试。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MRI Images Enhancement and Tumor Segmentation for Brain
Brain tumor is the abnormal growth of cancerous cells in Brain. In medical field, segmentation of brain regions and detection of brain tumor are very challenging tasks because of its complex structure. Magnetic resonance imaging (MRI) provides the detailed information about brain anatomy. Proper brain tumor segmentation using MR brain images helps in identifying exact size and shape of Brain tumor, this intern helps in diagnosis and treatment of brain tumor. However, manual segmentation in magnetic resonance data is a time-consuming task and is still being difficult to detect brain tumor area in MRI. The main challenges of brain tumor detection are less of accuracy to detect tumor area and to segment the tumor area. The system proposed the results fusion method for image enhancement and combination of adaptive k-means clustering and morphological operation for tumor segmentation. All of the experimental results will be tested on BRATS multimodal images of brain tumor Segmentation Benchmark dataset.
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