基于超像素方法的间质性肺疾病图像处理

Anni.U. Gupta, Sarita Singh Bhadauria
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引用次数: 0

摘要

医学图像分割是一种从医学图像信息中提取理想部分和亮点的策略。分类阶段的表现取决于预处理和分割等初始阶段。传统的模糊c均值(FCM)聚类算法通常用于灰度和彩色图像分割。在这项工作中,我们提出了一种基于超像素的FCM聚类算法,该算法在图像分割的聚类算法中比最佳算法更出色。首先利用轮廓精确的超像素图像获取预处理阶段,进行背景分离。与传统的固定大小和形状的邻域窗口不同,超像素图像具有更好的自适应和不规则的局部空间邻域,有助于改善间质性肺疾病(ILD)图像的分割。然后将结果与自适应中值滤波预处理进行比较,以消除对ILD图像的噪声影响,然后进行对比度有限的自适应直方图均衡化(CLAHE)增强以提高图像质量,然后进行FCM分割。利用超像素方法对不同数量的聚类进行FCM分割,结果与传统的FCM和Otsu方法在ILD图像上的分割结果相比有明显改善。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
FCM with Super-Pixel Approach for Interstitial Lung Disease Image Processing
Medical image segmentation is a strategy for extricating the ideal parts and highlights from the info medical image information. The presentation of classification stage depends on initial stages like preprocessing and segmentation. The traditional Fuzzy c-means (FCM) clustering algorithms have been generally utilized for grayscale and color image segmentation. In this work, we propose a super-pixel based FCM clustering algorithm that is altogether more hearty than best in clustering algorithms for image segmentation. We initially acquire a preprocessing stage by super-pixel image with exact contour for background separation. As opposed to customary neighboring window of fixed size and shape, the super-pixel image gives better adaptive and irregular local spatial neigh-borhoods that are helpful for improving Interstitial lung disease (ILD) image segmentation. Also after that the results are compared with preprocessing performed by adaptive median filtering to stay away from the noise effect on ILD images followed by Contrast-limited adaptive histogram equalization (CLAHE) enhancement to improve the image quality and then segmented by FCM. The outcomes are obtained for various number of clusters segmented with FCM with super-pixel approach and result are improve as contrast to conventional FCM and Otsu method on ILD images.
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