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FIFE: fast and indented feature extractor for medical imaging based on shape features

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Abstract

The advancement in technology also upgraded the biomedical field and enhanced the diagnostic system performance, where medical imaging plays its inevitable role. Several image processing techniques created the medical systems automated and meticulous. These systems nowadays support physicians in identifying adverse diseases like cancer, tumor, and skin diseases. In this imaging, features have a significant role in visually performing the diagnostic and finding the suffered area. Moreover, it also improves the performance of the automatic recognition systems used in the medical field. The researchers proposed various feature extraction techniques that use color, texture, and shape-based features in image processing systems but not every feature is best suited for medical imaging. This paper covers the objective of providing a Fast and Indented Feature Extractor (FIFE) for the health care sector’s future. This proposed extractor extracts only relevant shape features for processing to reduce processing time and improve the result’s quality. The image samples are taken for a skin disease named skin lesions for this work. The dataset of ISIC and PH2 is used in this work for analysis. This FIFE works on multiple feature extraction approaches and blends them to form a distinct extraction level. The indentation level one, two, three, and four are used in this work using four different shape descriptors, namely SURF, FAST, BRISK, and ORB. The performance comparison of the proposed indentation levels is based on the number of features and time. The experimentation results are evaluated with qualitative and quantitative measures that reveal the proposed extractor’s efficiency.

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Authors and Affiliations

  1. Yadavindra College of Engineering, Punjabi University, Guru Kashi Campus, Talwandi Sabo, Dist Bathinda, Punjab, 151302, India

    Balkrishan Jindal

  2. Department of Electronics and Communication, Punjabi University, Patiala, 147002, India

    Shelly Garg

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  1. Balkrishan Jindal
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Correspondence to Shelly Garg.

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Jindal, B., Garg, S. FIFE: fast and indented feature extractor for medical imaging based on shape features. Multimed Tools Appl 82, 6053–6069 (2023). https://doi.org/10.1007/s11042-022-13589-2

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