Abstract
Background: With the expeditious development of current medical imaging technology, the availability of histopathological images has been increased in a large number. Hence, histopathological image classification and annotation have emerged as the prime research fields in the pathological diagnosis and clinical practices. Several methods are available for the automation of image classification.
Methods: Recently, the bag-of-features appeared as a successful histopathological image classification method. However, all the extracted keypoints in bag-of-features are not relevant and generally have very high dimensions, which degrade the performance of a classifier. Therefore, this paper introduces a new Grey relational analysis-based bag-of-features method to search the relevant keypoints.
Results: The efficacy of the proposed method has been analyzed on animal diagnostics lab histopathological image datasets having healthy and inflamed images of three organs. The average accuracy of the proposed method is 88.3%, which is the highest among other state-of-the-art methods.
Conclusion: This paper introduced a new Grey relational analysis-based bag-of-features which improves the efficiency of vector quantization step of the standard bag-of-features method. The method used Grey relational analysis for similarity measure in vector quantization method of bag-offeatures. The proposed method has been validated in terms of precision, recall, G-mean, F1 score, and radar charts on three datasets, Kidney, Lung, and Spleen of ADL histopathological images.
Keywords: Histopathological image classification, Bag-of-features, Grey relational analysis, keypoints selection, clustering, keypoints detection.
Graphical Abstract
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