Balan J A Alex Rajju, Rajan S Edward
Vins Christian College of Engineering, Nagercoil, Tamil Nadu, India.
Department of Electrical and Electronics Engineering, Mepco Schlenk Engineering College, Sivakasi, Tamil Nadu, India.
Technol Health Care. 2014;22(1):13-25. doi: 10.3233/THC-130767.
Volumes of medical images are rapidly generated in medical field and to manage them effectively has become a great challenge. This paper studies the development of innovative medical image retrieval based on texture features and accuracy.
The objective of the paper is to analyze the image retrieval based on diagnosis of healthcare management systems.
This paper traces the development of innovative medical image retrieval to estimate both the image texture features and accuracy. The texture features of medical images are extracted using MDCT and multi SVM. Both the theoretical approach and the simulation results revealed interesting observations and they were corroborated using MDCT coefficients and SVM methodology.
All attempts to extract the data about the image in response to the query has been computed successfully and perfect image retrieval performance has been obtained. Experimental results on a database of 100 trademark medical images show that an integrated texture feature representation results in 98% of the images being retrieved using MDCT and multi SVM.
Thus we have studied a multiclassification technique based on SVM which is prior suitable for medical images. The results show the retrieval accuracy of 98%, 99% for different sets of medical images with respect to the class of image.
医学领域中医疗图像的数量迅速增长，有效管理这些图像已成为一项巨大挑战。本文研究基于纹理特征和准确性的创新型医学图像检索的发展。
本文的目的是分析基于医疗管理系统诊断的图像检索。
本文追溯创新型医学图像检索的发展，以估计图像纹理特征和准确性。使用MDCT和多支持向量机提取医学图像的纹理特征。理论方法和模拟结果均显示出有趣的发现，并使用MDCT系数和支持向量机方法进行了验证。
所有响应查询提取图像数据的尝试均已成功计算，并获得了完美的图像检索性能。在一个包含100幅商标医学图像的数据库上的实验结果表明，使用MDCT和多支持向量机进行集成纹理特征表示可检索出98%的图像。
因此，我们研究了一种基于支持向量机的多分类技术，该技术特别适用于医学图像。结果显示，针对不同类别的医学图像集，检索准确率分别为98%和99%。
