Automated delineation of thyroid nodules in ultrasound images using spatial neutrosophic clustering and level set

作者：Koundal Deepika^*; Gupta Savita; Singh Sukhwinder

来源：Applied Soft Computing, 2016, 40: 86-97.

DOI：10.1016/j.asoc.2015.11.035

• Thyroid gland
• Ultrasound images
• Segmentation
• Neutrosophic clustering
• Computer-aided detection
• Level set

An accurate contour estimation plays a significant role in classification and estimation of shape, size, and position of thyroid nodule. This helps to reduce the number of false positives, improves the accurate detection and efficient diagnosis of thyroid nodules. This paper introduces an automated delineation method that integrates spatial information with neutrosophic clustering and level-sets for accurate and effective segmentation of thyroid nodules in ultrasound images. The proposed delineation method named as Spatial Neutrosophic Distance Regularized Level Set (SNDRLS) is based on Neutrosophic L-Means (NLM) clustering which incorporates spatial information for Level Set evolution. The SNDRLS takes rough estimation of region of interest (ROI) as input provided by Spatial NLM (SNLM) clustering for precise delineation of one or more nodules. The performance of the proposed method is compared with level set, NLM clustering, Active Contour Without Edges (ACWE), Fuzzy C-Means (FCM) clustering and Neutrosophic based Watershed segmentation methods using the same image dataset. To validate the SNDRLS method, the manual demarcations from three expert radiologists are employed as ground truth. The SNDRLS yields the closest boundaries to the ground truth compared to other methods as revealed by six assessment measures (true positive rate is 95.45 +/- 3.5%, false positive rate is 7.32 +/- 5.3% and overlap is 93.15 +/- 5. 2%, mean absolute distance is 1.8 +/- 1.4 pixels, Hausdorff distance is 0.7 +/- 0.4 pixels and Dice metric is 94.25 +/- 4.6%). The experimental results show that the SNDRLS is able to delineate multiple nodules in thyroid ultrasound images accurately and effectively. The proposed method achieves the automated nodule boundary even for low-contrast, blurred, and noisy thyroid ultrasound images without any human intervention. Additionally, the SNDRLS has the ability to determine the controlling parameters adaptively from SNLM clustering.

• 出版日期2016-3