Highly accurate grey neural network classifier for an abdominal aortic aneurysm classification based on image processing approach

A. Bose, Vasuki Ramesh
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Abstract

An Abdominal Aorta Aneurysm (AAA) is an abnormal focal dilation of the aorta. Most un-ruptured AAAs are asymptomatic, which leads to the problem of having abdominal malignancy, kidney damage, heart attack and even death. As it is ominous, it requires an astute scrutinizing approach. The significance of this proposed work is to scrutinize the exact location of the ruptured region and to make astute report of the pathological condition of AAA by computing the Ruptured Potential Index (RPI). To determine these two factors, image processing is performed in the retrieved image of aneurysm. Initially, it undergoes a process to obtain a high-quality image by making use of Adaptive median filter. After retrieving high quality image, segmentation is carried out using Artificial Neural Network-based segmentation. After segmenting the image into samples, 12 features are extracted from the segmented image by Gray Level Co-Occurrence Matrix (GLCM), which assists in extracting the best feature out of it. This optimization is performed by using Particle Swarm Optimization (PSO). Finally, Grey Neural Network (GNN) classifier is applied to analogize the trained and test set data. This classifier helps to achieve the targeted objective with high accuracy.
基于图像处理方法的腹主动脉瘤高精度灰色神经网络分类器
腹主动脉动脉瘤(AAA)是一种异常局灶性扩张的主动脉。大多数未破裂的AAAs是无症状的,这可能导致腹部恶性肿瘤、肾脏损害、心脏病发作甚至死亡。由于这是一个不祥之兆,它需要一种敏锐的审视方法。这项工作的意义在于通过计算破裂电位指数(ruptured Potential Index, RPI)来精确地检查破裂区域的确切位置,并对AAA的病理状况做出准确的报告。为了确定这两个因素,对检索到的动脉瘤图像进行图像处理。首先,利用自适应中值滤波器获得高质量图像。在检索到高质量图像后,采用基于人工神经网络的分割方法进行分割。将图像分割成样本后,利用灰度共生矩阵(GLCM)从分割后的图像中提取12个特征,帮助提取最佳特征。该算法采用粒子群算法(PSO)进行优化。最后,利用灰色神经网络(GNN)分类器对训练集和测试集数据进行模拟。该分类器有助于以较高的准确率实现目标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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