Computer-aided Diagnosis applied to MRI images of Brain Tumor using Spatial Fuzzy Level Set and ANN Classifier

IF 0.9 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING
S. Virupakshappa, Sachinkumar Veerashetty, N. Ambika
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引用次数: 1

Abstract

The most vital organs in the human body are the brain, heart, and lungs. Because the brain controls and coordinates the operations of all other organs, normal brain function is vital. Brain tumour is a mass of tissues which interrupts the normal functioning of the brain, if left untreated will lead to the death of the subject. The classification of multiclass brain tumours using spatial fuzzy based level sets and artificial neural network (ANN) techniques is proposed in this paper. In the proposed method, images are preprocessed using Median Filtering technique, the boundaries of the Brain Tumor are obtained using Spatial Fuzzy based Level Set method, features are extracted using Gabor Wavelet and Gray-Level Run Length Matrix (GLRLM) methods. Finally ANN technique is used for the classification of the image into Normal or Benign Tumor or Malignant Tumor. The proposed method was implemented in the MATLAB working platform and achieved classification accuracy of 94%, which is significant compared to state-of-the-art classification techniques. Thus, the proposed method assist in differentiating between benign and malignant brain tumours, enabling doctors to provide adequate treatment.
空间模糊水平集与神经网络分类器在脑肿瘤MRI图像计算机辅助诊断中的应用
人体最重要的器官是大脑、心脏和肺。因为大脑控制和协调所有其他器官的运作,正常的大脑功能是至关重要的。脑瘤是一团组织,它会干扰大脑的正常功能,如果不及时治疗将导致患者死亡。本文提出了一种基于空间模糊水平集和人工神经网络的多类脑肿瘤分类方法。该方法采用中值滤波技术对图像进行预处理,采用基于空间模糊的水平集方法获得脑肿瘤的边界,采用Gabor小波和灰度运行长度矩阵(GLRLM)方法提取特征。最后利用人工神经网络技术对图像进行正常、良性、恶性肿瘤的分类。该方法在MATLAB工作平台上实现,分类准确率达到94%,与目前的分类技术相比有显著提高。因此,所提出的方法有助于区分良性和恶性脑肿瘤,使医生能够提供适当的治疗。
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来源期刊
Scalable Computing-Practice and Experience
Scalable Computing-Practice and Experience COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
2.00
自引率
0.00%
发文量
10
期刊介绍: The area of scalable computing has matured and reached a point where new issues and trends require a professional forum. SCPE will provide this avenue by publishing original refereed papers that address the present as well as the future of parallel and distributed computing. The journal will focus on algorithm development, implementation and execution on real-world parallel architectures, and application of parallel and distributed computing to the solution of real-life problems.
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