Investigating the Effect of Some Selected Distance Measures on the Performance of Query-By-Image-Content Over Mammogram Images
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Date
2023-12-08
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Abstract
Introduction: Generating signatures of images and comparing them with those stored in a database for the purpose of retrieval of similar content is termed Query by image content. This art is helpful for detection of various diseases such as breast cancer, brain tumor and spine disorder. The image data are obtained through Computerized Tomography scan, Magnetic Resonance Imaging and mammogram, and are misunderstood in routine screening of mammograms despite the recent scientific improvements.
Aim: This study aims to determine the most suitable distance measure for retrieval of similar images by experimenting state-of-the-art distance measures on mammographic images.
Materials and Methods: The procedure was benchmarked on mini mammographic image analysis society (mini-MIAS) and breast cancer digital repository datasets. The experimental process includes thresholding and extraction of Region of Interest using gray level co-occurrence matrix. The extracted features were tested on Euclidean distance, Minkowski distance, Hamming distance, Mahalanobis distance, Cosine Similarity and Manhattan distance measures. The performance of the system on the distance measure was evaluated on the datasets to determine the distance metric that best identify abnormality in the samples.
Results: The empirical results reveal that Mahalanobis distance measure outperforms the others in terms of retrieval time (1.26s and 1.14s) and minimal error (0.004 and 0.002) respectively for both the mini-MIAS dataset and the BCDR dataset, based on the similarity of images retrieved compared to queried images.
Conclusion: Researchers can take appropriate decision on the choice of distance measure based on these findings
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Babatunde et. al., 2023