Efficient Image Retrieval approach for Large-scale Chest X Ray data using Hand-Crafted Features and Machine Learning Algorithms
DOI:
https://doi.org/10.26438/ijcse/v6i11.890896Keywords:
Medical image retrieval, pneumonia detection, hand-crafted features, classification, Histogram of Gradient, feature reduction, clusteringAbstract
The rapid growth in digital imaging techniques have resulted in the generation of large volume of diverse medical images. Most of these image corpus is either unlabeled or partially annotated. To ex-tract relevant information from such largescale image corpus, it is necessary to have an efficient and scalable image retrieval techniques. In this article, we present an effective approach for retrieving images from large-scale Chest X-Ray dataset that have the similar disease conditions or severity as that of the query image. We tested our approach on NIH chest x-ray image dataset, that contains images of pneumonia affected patients. The Histogram of Gradients (HoG) features are found to give better results in classifying the disease. The dimensionality of dense HoG features is reduced by using level decomposition of Haar wavelet and using random projection. The performance degradation happened due to the feature reduction is rectified by using a hybrid approach. The proposed features are compact and capable of conveniently outperforming several existing approaches in image retrieval. To find the nearest match to the query image, the feature space is reduced further by applying k-means clustering. The implementation results are presented to test efficacy of the proposed approach
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