Full-Automatic Liver Segmentation on Abdominal MR Images

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Abstract

Liver segmentation process is a challenging field in computer-aided medical image analysis. This study implemented liver segmentation on Abdominal MR images. The liver was automatically segmented from images by morphological methods with high performance. Liver segmentation process was implemented on 56 MR images and the segmentation results were examined. In this study, an effective and fast method was proposed. Seven evaluation metrics (sensitivity, specificity, accuracy, precision, Dice coefficient, Jaccard rate, Structural Similarity Index (SSIM)) were used to test the performance of the proposed method. Mean Dice coefficient value was obtained as 91.701%, mean Jaccard rate value was obtained as 85.052% on 56 images. Segmentation duration for an image (T1 and T2 phases) was found as 2.828 seconds with the proposed method.

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Keywords

Segmentation, Sørensen–Dice Coefficient, Image Segmentation, Artificial Intelligence, Jaccard Index, MR images, Evaluation metrics, morphological methods, liver segmentation

Fields of Science

03 medical and health sciences, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

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OpenCitations Citation Count
1

Volume

28

Issue

Start Page

1

End Page

5
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Scopus : 1

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Mendeley Readers : 2

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