Gabor barcodes for medical image retrieval

Abstract

In recent years, advances in medical imaging have led to the emergence of massive databases, containing images from a diverse range of modalities. This has significantly heightened the need for automated annotation of the images on one side, and fast and memory-efficient content-based image retrieval systems on the other side. Binary descriptors have recently gained more attention as a potential vehicle to achieve these goals. One of the recently introduced binary descriptors for tagging of medical images are Radon barcodes (RBCs) that are driven from Radon transform via local thresholding. Gabor transform is also a powerful transform to extract texture-based information. Gabor features have exhibited robustness against rotation, scale, and also photometric disturbances, such as illumination changes and image noise in many applications. This paper introduces Gabor Barcodes (GBCs), as a novel framework for the image annotation. To find the most discriminative GBC for a given query image, the effects of employing Gabor filters with different parameters, i.e., different sets of scales and orientations, are investigated, resulting in different barcode lengths and retrieval performances. The proposed method has been evaluated on the IRMA dataset with 193 classes comprising of 12,677 x-ray images for indexing, and 1,733 x-rays images for testing. A total error score as low as 351 (≈ 80% accuracy for the first hit) was achieved.

Combining real-valued and binary Gabor-Radon features for classification and search in medical imaging archives

“…Highlighted results all use Gabor and/or Radon barcodes in some way. The proposed method, inspired by [5], [6] and [14], delivers the best result among this class of algorithms.…”

“…In this paper, we used the algorithm as proposed in [6] to extract Gabor-Radon barcode features (GRBFs) but we also extracted Gabor Radon features (GRFs) by adding new variable to the algorithm (see Algorithm 1). GRFs are extracted by taking the magnitude or absolute value of each ψ (u,v) (x, y) and then converted to a vector.…”

“…For speeding-up the image retrieval process we split the CBIR system into two stages: classification and retrieval. We combine two ideas: 1) using the same feature for a SVM classification before converting it to a barcode (as proposed in [14]), and 2) combining Gabor and Radon features (as proposed in [5], [6]).…”

“…Radon barcodes based on Radon transform for encoding the local image information was introduced recently [11]. Using Gabor filters to extract binary codes from Radon transformed image was proposed by Nouresanesh et al [5], [6]. Camlica et al used an SVM classifier trained with LBP features derived from saliency image regions [12].…”

A Comparative Study of Testing Parameters of Medical Images Processing

“…al., [1] presents Gabor transform having the important property of its strength of measuring of translation, scale, rotation, and photometric turbulences, like noise or illumination changes. Arrieta et.…”