Automated Nuclei Segmentation Using Shearlet Based Unsharp Masking

Accurate nuclei segmentation is essential in pathology since evaluations and diagnoses are mostly dependent on the measurement, identification, and counting of nuclei. Automated segmentation in digital pathology is a difficult task due to several factors, including, hazy nucleus boundaries, low cont...

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Bibliographic Details
Published inInternational Conference on Signal Processing and Communication (Online) pp. 637 - 641
Main Authors Mishra, Shivam, Vishwakarma, Amit, Vimal, Ronak, Kumar, Anil
Format Conference Proceeding
LanguageEnglish
Published IEEE 20.02.2025
Subjects
Accuracy
Cancer
Feature extraction
Image segmentation
Monitoring
Nuclear measurements
Nuclei segmentation
Pathology
Shape
Shearlet transform
Signal processing
Transforms
U-Net
Un-sharp masking
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Summary:Accurate nuclei segmentation is essential in pathology since evaluations and diagnoses are mostly dependent on the measurement, identification, and counting of nuclei. Automated segmentation in digital pathology is a difficult task due to several factors, including, hazy nucleus boundaries, low contrast between nuclei and background, varying nuclei sizes and shapes, and imaging modalities. In this work, a new nuclei segmentation method is proposed which incorporates the shearlet-based unsharp masking (SBUM) technique to pre-process the source images together with the CNN-based U-Net architecture. The SBUM method primarily enhances the source image's high-frequency details. The effectiveness of a developed nuclei segmentation technique is compared to the existing techniques using a common dataset. The developed method achieves IOU, Accuracy, F1Score, and Precision values as 0.8146, 95.93%, 0.8953, and 0.8725 respectively. The empirical findings demonstrate that the developed method outperforms the existing methods.
ISSN:2643-444X
DOI:10.1109/ICSC64553.2025.10969034