Detection of Cerebrovascular Diseases using Novel Discrete Component Wavelet Cosine Transform.

IF 1.5 4区 医学 Q4 CHEMISTRY, MEDICINAL
Bandana Pal, Shruti Jain
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引用次数: 0

Abstract

Aims: Detecting and classifying a brain tumor amid a sole image can be problematic for doctors, although improvements can be made with medical image fusions.

Background: A brain tumor develops in the tissues surrounding the brain or the skull and has a major impact on human life. Primary tumors begin within the brain, whereas secondary tumors, identified as brain metastasis tumors, are generated outside the brain.

Objective: This paper proposes hybrid fusion techniques to fuse multi-modal images. The evaluations are based on performance metrics, and the results are compared with conventional ones.

Methods: In this paper, pre-processing is done considering enhancement methods like Binarization, Contrast Stretching, Median Filter, & Contrast Limited Adaptive Histogram Equalization (CLAHE). Authors have proposed three techniques, PCA-DWT, DCT-PCA, and Discrete ComponentWaveletCosine Transform (DCWCT), which were used to fuse CT-MR images of brain tumors.

Results: The different features were evaluated from the fused images, which were classified using various machine learning approaches. Maximum accuracy of 97.9% and 93.5% is obtained using DCWCT for Support Vector Machine (SVM) and k Nearest Neighbor (kNN), respectively, considering the combination of both feature's shape & Grey Level Difference Statistics. The model is validated using another online dataset.

Conclusion: It has been observed that the classification accuracy for detecting cerebrovascular disease is better after employing the proposed image fusion technique.

基于新型离散分量小波余弦变换的脑血管疾病检测。
目的:对医生来说,在单一图像中检测和分类脑肿瘤是有问题的,尽管医学图像融合可以改进。背景:脑肿瘤发生在大脑或颅骨周围的组织中,对人类生活有重大影响。原发性肿瘤起源于脑内,而继发性肿瘤,即脑转移瘤,则产生于脑外。目的:提出一种多模态图像融合的混合融合技术。评估基于绩效指标,并将结果与常规评估结果进行比较。方法:本文采用二值化、对比度拉伸、中值滤波和对比度有限自适应直方图均衡化(CLAHE)等增强方法进行预处理。作者提出了三种技术,PCA-DWT, DCT-PCA和离散分量小波余弦变换(DCWCT),用于融合脑肿瘤的CT-MR图像。结果:从融合图像中评估不同的特征,使用各种机器学习方法对融合图像进行分类。结合特征的形状和灰度差统计,采用DCWCT对支持向量机(SVM)和k近邻(kNN)分别获得97.9%和93.5%的最大准确率。该模型使用另一个在线数据集进行验证。结论:采用本文提出的图像融合技术对脑血管疾病的分类准确率有较好的提高。
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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来源期刊
Current computer-aided drug design
Current computer-aided drug design 医学-计算机:跨学科应用
CiteScore
3.70
自引率
5.90%
发文量
46
审稿时长
>12 weeks
期刊介绍: Aims & Scope Current Computer-Aided Drug Design aims to publish all the latest developments in drug design based on computational techniques. The field of computer-aided drug design has had extensive impact in the area of drug design. Current Computer-Aided Drug Design is an essential journal for all medicinal chemists who wish to be kept informed and up-to-date with all the latest and important developments in computer-aided methodologies and their applications in drug discovery. Each issue contains a series of timely, in-depth reviews, original research articles and letter articles written by leaders in the field, covering a range of computational techniques for drug design, screening, ADME studies, theoretical chemistry; computational chemistry; computer and molecular graphics; molecular modeling; protein engineering; drug design; expert systems; general structure-property relationships; molecular dynamics; chemical database development and usage etc., providing excellent rationales for drug development.
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