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首页> 外文期刊>Multimedia Tools and Applications >Morphological edge detection and brain tumor segmentation in Magnetic Resonance (MR) images based on region growing and performance evaluation of modified Fuzzy C-Means (FCM) algorithm
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Morphological edge detection and brain tumor segmentation in Magnetic Resonance (MR) images based on region growing and performance evaluation of modified Fuzzy C-Means (FCM) algorithm

机译：基于区域生长的磁共振（MR）图像中的形态边缘检测和脑肿瘤分割和改进模糊C型（FCM）算法的性能评价

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摘要

The medical image processing has become indispensable with an increased demand for systematic and efficient detection of brain tumor in a short period of time. There are various techniques for medical image segmentation. Detecting a wide variety of brain images in terms of shape and intensity is a challenging and difficult task to bring out a reliable and authentic data for diagnosing brain tumor diseases. This paper presents an algorithm which combines Region of Interest (ROI), Region Growing and Morphological Operation (Dilation and Erosion). This method initially identifies the approximate Region Growing (RG). Region growing is a procedure that groups pixels into larger regions, which starts from the seed points. Region growing based techniques are better than the edge-based techniques in noisy images where edges are difficult to detect. The Morphological Edge Detection of the input image is done and the input image is reconstructed on the basis of dilation and erosion for the enhancement of the image. The proposed work is divided into preprocessing to reduce the noise, Fuzzy C-Means is used to Region growing, Morphological edge detection is to enhance the image. Then the morphological edge detection can be classified into two categories, one is dilation and another is Erosion. Finally apply Gaussian filter to get output. After that, Fuzzy C-Means clustering (FCM), followed by seeded region growing is applied to detect and segment the tumor from the brain MRI image.
机译：在短时间内，医学图像处理变得不可或缺的是对脑肿瘤的系统和有效检测的需求增加。医学图像分割有各种技术。在形状和强度方面检测各种脑图像是一个具有挑战性和艰巨的任务，可以为诊断脑肿瘤疾病带来可靠和真实的数据。本文介绍了一种结合兴趣区域（ROI），区域生长和形态学操作（扩张和侵蚀）的算法。该方法最初识别近似区域生长（RG）。区域生长是将像素分组到较大区域中的过程，该区域从种子点开始。基于地区的技术优于嘈杂的图像中的边缘技术优于边缘难以检测的边缘的技术。完成输入图像的形态边缘检测，并且基于扩张和侵蚀来重建输入图像以增强图像。所提出的工作分为预处理以降低噪声，模糊C-inse用于区域生长，形态边缘检测是增强图像。然后，形态边缘检测可以分为两类，一个是扩张，另一个是侵蚀。最后应用高斯过滤器以获取输出。之后，模糊C-Means聚类（FCM），然后施加种子地区生长以检测和分割脑MRI图像的肿瘤。

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