Foreign Body Shape Classification Using Intuitionistic Fuzzy Rule Based Approach on Pediatric Radiography Images

Vasumathy M., Mythili T.
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Abstract

Segmentation is the significant key stage in image analysis towards partitioning an image into different regions which have homogeneous features such as color, shape, and texture which is very important in classifying different region shapes in an image. In general, images are considered fuzzy due to the uncertainty present in terms of vagueness. The regions contain imprecise gray levels and uncertain data values which makes the task of defining the membership function difficult due to lack of precise knowledge. The intuitionistic fuzzy rule-based shape classification approach is used to classify the different shapes, such as circular, polygon, sharp, and irregular of the aspired foreign body on pediatric radiography images. Experimental results show the effectiveness of the proposed method in contrast to conventional fuzzy rule base algorithm.
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基于直觉模糊规则的儿童x线影像异物形状分类方法
图像分割是图像分析中重要的关键步骤,它将图像分割成具有颜色、形状和纹理等同质特征的不同区域,对图像中不同区域形状的分类非常重要。一般来说,图像被认为是模糊的,因为不确定性存在于模糊性方面。这些区域包含不精确的灰度和不确定的数据值,由于缺乏精确的知识,使得定义隶属函数的任务变得困难。采用基于直觉模糊规则的形状分类方法,对儿童x线影像上吸入异物的圆形、多边形、尖锐、不规则等不同形状进行分类。实验结果表明,与传统的模糊规则库算法相比,该方法是有效的。
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