Differential diagnosis of thyroid nodules with ultrasound elastography based on support vector machines

Ma, Jieming; Si, Luo; Dighe, Manjiri; Lim, Dong-Jun; Kim, Yongmin

doi:10.1109/ultsym.2010.5935482

Cited by 14 publications

(9 citation statements)

References 9 publications

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“…The image is updated according to the indeterminacy value, 'I', which is compared to indeterminacy threshold value, ' I T ' as shown in Eq. (17). The description of selecting the best value of I T is given in Section 3.4.3.…”

Section: Spatial Neutrosophic L-means Clusteringmentioning

confidence: 99%

“…Later, thyroid nodule detector (TND) system was presented for nodule detection in TUS images and videos with more than 95% accuracy [16]. One of the thyroid nodule segmentation methods for ultrasound images is given in [17]. The method is consisted of contrast enhancement, smoothing and segmentation based on edge based active contour model.…”

Section: Introductionmentioning

confidence: 99%

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

Koundal

Gupta

Singh

2016

Applied Soft Computing

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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.

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Section: Spatial Neutrosophic L-means Clusteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Koundal

Gupta

Singh

2016

Applied Soft Computing

View full text Add to dashboard Cite

show abstract

“…Where denotes the intensity of a pixel in ROI block which passed through the Haar transformation, and B denotes a block size of [8].…”

Section: Feature Extraction and Selectionmentioning

confidence: 99%

“…Normalized Multiscale Intensity Difference Feature: NMSID feature is defined as differences between the pixel pairs with horizontal, vertical, diagonal and asymmetric diagonal directions [8].…”

Section: )mentioning

confidence: 99%