Local features integration for content-based image retrieval based on color, texture, and shape

Imaging techniques like computed tomography (CT) and ultrasound are employed to provide valuable information for physicians, including size, contour, and internal organs' anatomical information. Information retrieval systems can be used to deliver on-time information to the radiologists when some sections of scans are lost. In this study, a new content-based image retrieval (CBIR) model based on an effective combination of color, texture, and shape features is proposed to reconstruct these images' corrupted portions. For this purpose, image scans are normalized, and their noise is reduced by employing a median filter. Then, the color channel shift is modified utilizing the Simple Linear Iterative Clustering (SLIC) superpixel. Afterward, a Histogram of Oriented Gradients (HOG) descriptor is introduced to enhance image contrast and feature extraction. Finally, local thresholding based on Local Binary Patterns (LBP) is performed to separate the image details into three components to examine the light and edge intensity. The proposed method is experimented on several images by evaluating the texture, color, and shape morphology of the reconstructed images compared to the ground truth. The highest content retrieval rate of 90.54% on a liver CT scan image demonstrates the proposed method's efficiency compared with former state-of-the-art approaches.