scholarly journals Medical Image Retrieval using Dual Tree Complex Wavelet Transform and Principal Component Analysis with Haralick Texture Features

2020 ◽  
Vol 8 (5) ◽  
pp. 3425-3435
Keyword(s):  
Principal Component Analysis ◽  
Wavelet Transform ◽  
Image Retrieval ◽  
Medical Image ◽  
Principal Component ◽  
Texture Features ◽  
Component Analysis ◽  
Complex Wavelet Transform ◽  
Medical Image Retrieval ◽  
Similar Images

Noise and distortion occurs in all types of medical images (Computed Tomography (CT), Magnetic Resonance Imaging (MRI.)..) and are unavoidable during the stages of image acquisition. We use medical image retrieval to extract the images from database by texture, shaptrix or color features. We use Dual Tree Complex Wavelet Transform (DTCWT) and Principal Component Analysis (PCA). DTCWT extracts the information of images. PCA compress the images. It also minimizes the feature vectors dimensions of all images. Haralick texture features are extracted from images with the co-occurrence matrix. This matrix describes the relationship of pixels. The similar images are found by calculating the similarity measure of the query image and all images in database by Mahalanobis distance. This method retrieves the similar images from database with respect to the input image provided by the user. The performance of the proposed algorithm can be found by precision and recall measures for evaluation. This system can be used in hospitals, clinics etc., for detecting diseases earlier

scholarly journals Medical Image Retrieval Approach by Texture Features Fusion Based on Hausdorff Distance

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Sun Xiaoming ◽  
Zhang Ning ◽  
Wu Haibin ◽  
Yu Xiaoyang ◽  
Wu Xue ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Image Retrieval ◽  
Hausdorff Distance ◽  
Medical Image ◽  
Medical Information ◽  
Medical Images ◽  
Brain Mri ◽  
Texture Features ◽  
Image Feature ◽  
Medical Image Retrieval

Medical images play an important role in the hospital diagnosis and treatment, which include a lot of valuable medical information. Manually annotated viewing is obviously not effective in managing large amounts of medical imaging data. Hence it is an important task to establish an efficient and accurate medical image retrieval system. In this paper, a medical image retrieval approach based on Hausdorff distance combining Tamura texture features and wavelet transform algorithm is proposed. The combination of Tamura texture features and wavelet transform features can extract the texture features of medical images more effectively, and Hausdorff distance can reflect the overall similarity of medical image feature set. In this paper, 6 group experiments of brain MRI database and the lung CT database were conducted separately. Experiments show that the proposed approach has higher accuracy than a single feature texture algorithm and is also higher than the approach of Tamura texture features and wavelet transform features combined with Euclidean distance.

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