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Active Contour Extension Basing on Haralick Texture Features, Multi-gene Genetic Programming, and Block Matching to Segment Thyroid in 3D Ultrasound Images

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Abstract

The segmentation and estimation of thyroid volume in 3D ultrasound images have attracted the research community’s attention because of their great importance in clinical diagnosis. Usually, thyroid volume estimation is based on the segmentation of 3D ultrasound images, which is difficult due to various disorders, including non-homogeneous texture distribution within the thyroid region, artifacts, speckles, and the nature of the thyroid shape. This paper presents an approach to segmenting all individual slices and then reconstructing them into a 3D object to overcome these difficulties. The process involves four techniques. The VOI initialization encompasses the probable thyroid gland; it greatly affects the segmentation results. Multi-gene genetic programming determines the appropriate textural features. The block-matching technique estimates the thyroid gland’s change in size and location from slice to slice. Finally, the ITKSNAP software reconstructs the 3D volume. The proposed method is compared with state-of-the-art methods to prove its effectiveness in medical image analysis. Sixteen 3D images from an ultrasound thyroid image dataset were used for the experiments. The analysis of the results based on performance evaluation metrics shows that the proposed method is more efficient than the state-of-the-art methods.

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  1. Laboratory of LESIA, University of Biskra, 07000, Biskra, Algeria

    Fatma Zohra Benabdallah & Leila Djerou

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  1. Fatma Zohra Benabdallah
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Correspondence to Fatma Zohra Benabdallah.

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Benabdallah, F.Z., Djerou, L. Active Contour Extension Basing on Haralick Texture Features, Multi-gene Genetic Programming, and Block Matching to Segment Thyroid in 3D Ultrasound Images. Arab J Sci Eng 48, 2429–2440 (2023). https://doi.org/10.1007/s13369-022-07286-3

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