Article: Robust liver segmentation using marker controlled watershed transform Journal: International Journal of Medical Engineering and Informatics (IJMEI) 2025 Vol.17 No.3 pp.255 - 266 Abstract: The liver is the body's largest organ, and it is largely responsible for metabolism and detoxification. In computer vision-based biomedical image analysis, liver segmentation is a critical step in detecting liver cancer. Due to the complicated structure of abdominal computed tomography (CT) images, noise, and textural differences across the image, liver segmentation is a key task that results in under-segmentation and over-segmentation. This paper uses a marker-based watershed transform to segment the liver in abdominal CT images. The double stage Gaussian filter with texture and contrast enhancement (DSGFTCE) is used to improve image quality at the pre-processing stage. The performance of the proposed segmentation is assessed using various performance evaluation metrics such as dice score (DS), volume overlapping error (VOE), Jacquard index (JI) and relative volume difference on LiTS dataset. The performance comparison with previous state of arts shows that proposed liver segmentation scheme provides better results (DS = 0.968, VOE = 0.089, JI = 0.9379, RVD = 0.09) compared with existing techniques. Inderscience Publishers - linking academia, business and industry through research

Title: Robust liver segmentation using marker controlled watershed transform

Authors: Mohammad Anwarul Siddique; Shailendra Kumar Singh; Moin Hasan

Addresses: School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab – 144401, India ' School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab – 144401, India ' School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab – 144401, India

Abstract: The liver is the body's largest organ, and it is largely responsible for metabolism and detoxification. In computer vision-based biomedical image analysis, liver segmentation is a critical step in detecting liver cancer. Due to the complicated structure of abdominal computed tomography (CT) images, noise, and textural differences across the image, liver segmentation is a key task that results in under-segmentation and over-segmentation. This paper uses a marker-based watershed transform to segment the liver in abdominal CT images. The double stage Gaussian filter with texture and contrast enhancement (DSGFTCE) is used to improve image quality at the pre-processing stage. The performance of the proposed segmentation is assessed using various performance evaluation metrics such as dice score (DS), volume overlapping error (VOE), Jacquard index (JI) and relative volume difference on LiTS dataset. The performance comparison with previous state of arts shows that proposed liver segmentation scheme provides better results (DS = 0.968, VOE = 0.089, JI = 0.9379, RVD = 0.09) compared with existing techniques.

Keywords: liver segmentation; contrast enhancement; texture smoothening; watershed transform; Gaussian filtering; computer tomography.

DOI: 10.1504/IJMEI.2025.145848

International Journal of Medical Engineering and Informatics, 2025 Vol.17 No.3, pp.255 - 266

Received: 19 Jun 2022
Accepted: 19 Aug 2022
Published online: 30 Apr 2025 *

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Title: Robust liver segmentation using marker controlled watershed transform

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