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A Semiautomatic Image Processing-Based Method for Binary Segmentation of Lungs in Computed Tomography Images

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Abstract

Precise biomedical image segmentation is pivotal in medical diagnosis and treatment. Among various methodologies, image processing-based techniques are useful due to their swift and consistent results, free from the dependency on extensive training data required by deep learning approaches. This study introduces a tri-phase, semi-automated pipeline for lung computed tomography image segmentation, beginning with a preprocessing phase that includes contrast enhancement, noise reduction, and edge detection to improve image quality. This is followed by the application of the region growing method for segmentation and is completed with morphological closing to refine the final output. Comparative tests reveal that our technique surpasses prevalent methods like split and merge, Otsu’s technique, and basic region growing in terms of the dice coefficient (DC) and intersection over union (IoU). Specifically, our approach achieved an average DC of 0.9633, contrasting with 0.4668, 0.005, and 0.4067 recorded by Otsu’s, split and merge, and basic region growing techniques respectively. Similarly, the average IoU for our method was 0.9341, in comparison to 0.3105, 0.0660, and 0.2605 for the aforementioned methods. Additionally, comparative analysis with contemporary segmentation models demonstrates that our approach is competitive, even without relying on large training datasets. These results highlight our method’s efficiency and practicality, distinguishing it as a compelling option in medical scenarios where access to extensive training data is limited.

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Data Availability

The datasets generated during and/or analysed during the current study are available in Kaggle, https://www.kaggle.com/kmader/finding-lungs-in-ct-data.

Notes

  1. https://www.kaggle.com/kmader/finding-lungs-in-ct-data.

  2. https://github.com/Leo-Thomas/IP-Lung-Segmentation.

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Authors and Affiliations

  1. School of Mathematical and Computational Sciences, Yachay Tech University, Urcuquí, 100119, Ecuador

    Leo Ramos

  2. Deep Learning for Autonomous Driving, Robotics, and Computer Vision (DeepARC) Research Group, Yachay Tech University, Urcuquí, 100119, Ecuador

    Leo Ramos

  3. Kauel Inc., Menlo Park (Silicon Valley), CA, 94025, USA

    Leo Ramos

  4. Computer Vision Center, Universitat Autònoma de Barcelona, Bellaterra, 08193, Spain

    Leo Ramos

  5. College of Sciences and Engineering, Universidad San Francisco de Quito, Quito, 170901, Ecuador

    Israel Pineda

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Ramos, L., Pineda, I. A Semiautomatic Image Processing-Based Method for Binary Segmentation of Lungs in Computed Tomography Images. SN COMPUT. SCI. 5, 689 (2024). https://doi.org/10.1007/s42979-024-03047-1

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