Open Access

ARTICLE

Material-SAM: Adapting SAM for Material XCT

Xuelong Wu¹, Junsheng Wang^1,*, Zhongyao Li¹, Yisheng Miao¹, Chengpeng Xue¹, Yuling Lang², Decai Kong², Xiaoying Ma², Haibao Qiao²

1 School of Materials Science and Engineering, Beijing Institute of Technology, Beijing, 100081, China
2 Senior Engineer at the Simulation Center, Citic Dicastal Co., Ltd., Qinhuangdao, 066010, China

* Corresponding Author: Junsheng Wang. Email:

Computers, Materials & Continua 2024, 78(3), 3703-3720. https://doi.org/10.32604/cmc.2024.047027

Received 22 October 2023; Accepted 24 January 2024; Issue published 26 March 2024

Abstract

X-ray Computed Tomography (XCT) enables non-destructive acquisition of the internal structure of materials, and image segmentation plays a crucial role in analyzing material XCT images. This paper proposes an image segmentation method based on the Segment Anything model (SAM). We constructed a dataset of carbide in nickel-based single crystal superalloys XCT images and preprocessed the images using median filtering, histogram equalization, and gamma correction. Subsequently, SAM was fine-tuned to adapt to the task of material XCT image segmentation, resulting in Material-SAM. We compared the performance of threshold segmentation, SAM, U-Net model, and Material-SAM. Our method achieved 88.45% Class Pixel Accuracy (CPA) and 88.77% Dice Similarity Coefficient (DSC) on the test set, outperforming SAM by 5.25% and 8.81%, respectively, and achieving the highest evaluation. Material-SAM demonstrated lower input requirements compared to SAM, as it only required three reference points for completing the segmentation task, which is one-fifth of the requirement of SAM. Material-SAM exhibited promising results, highlighting its potential as a novel method for material XCT image segmentation.

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Keywords

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