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Statistical recursive minimum cross entropy for ultrasound image segmentation

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Abstract

Ultrasound imaging is one of the most widely used biomedical imaging modality for diagnostic purposes. Ultrasound image segmentation is an important step in order to achieve efficient qualitative measures, such as location of relevant structures, and quantitative measures, such as area of the structures for further analysis. However, segmentation of ultrasound images is considered a challenging task owing to its poor quality. Moreover, segmentation using global image statistics results in unsatisfactory results, making it difficult to use for further processing. In this study, we formulate a scheme to achieve successful segmentation of real time ultrasound images. The proposed method makes use of local statistics of the image in order to perform efficient segmentation. It considers histogram-based thresholding by making use of recursive minimum cross entropy on statistical properties (image integral and image gradient) derived from the instantaneous coefficient of variation images. To validate its excellence, results of the proposed method have been analysed by an expert and compared with some state-of-the-art methods. Results reveal that the proposed technique outperforms other techniques visually as well as quantitatively by an average of 13.67%. Thus, the technique is effective in segmenting ultrasound images efficiently by removing redundant information and leaving relevant information intact.

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Authors and Affiliations

  1. Department of Electronics & Communication Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, India

    Anterpreet Kaur Bedi & Ramesh Kumar Sunkaria

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  1. Anterpreet Kaur Bedi
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  2. Ramesh Kumar Sunkaria
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Correspondence to Anterpreet Kaur Bedi.

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Authors Anterpreet Kaur Bedi and Ramesh Kumar Sunkaria declare that they have no conflict of interest.

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Bedi, A.K., Sunkaria, R.K. Statistical recursive minimum cross entropy for ultrasound image segmentation. Multimed Tools Appl 81, 7873–7893 (2022). https://doi.org/10.1007/s11042-022-12050-8

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  • DOI: https://doi.org/10.1007/s11042-022-12050-8

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