Classification of thyroid ultrasound images based on shape features analysis

Zulfanahri,; Nugroho, Hanung Adi; Nugroho, Anan; Frannita, Eka Legya; Ardiyanto, Igi

doi:10.1109/bmeicon.2017.8229106

Cited by 8 publications

(4 citation statements)

References 13 publications

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“…Our irregularity method and the methods developed in 5,7,12,14 both measure global irregularity of the nodule, but our method uses convexity and ellipticity variance, providing a more robust and accurate assessment of nodule irregularity without being excessive. Furthermore, our method has a new feature extraction step that incorporates and measures of local irregularity of margin such as lobulation and angulation.…”

Section: Methodsmentioning

confidence: 99%

“…Several studies to automatically quantify features based on the standardized TIRADS categories for classifying thyroid nodules have been reported, 12,13 but the work on extracting correct TIRADS features from ultrasound images for annotation purposes remains limited. Zulfanahri et al 5 analyzed and classified the margin irregularity of thyroid nodule into regular or irregular class using rectangularity, convexity, and tortuosity features with an SVM classifier. The study reported an accuracy of 91.52% (91.80% sensitivity and 91.35% specificity) over a set of 165 images.…”

Section: Background and Related Workmentioning

confidence: 99%

“…) is the decision for each intensity profile within the region r S ,| | is the number of intensity profiles in region r and t p is the intensity profile classification threshold experimentally as 80%. The overall margin smoothness of a nodule is classified using equation (5).…”

Section: Ipd Intensity Profile Decision (mentioning

confidence: 99%

“…Several studies have been conducted to analyze US image characteristics of thyroid nodules, but most of them extracted such characteristics for classifying a nodule as benign or malignant rather than accurately detecting and evaluating the characteristics for report generation. [5][6][7] In this paper, we present a comprehensive translation of the US TIRADS characteristics, aiming at an automated process of describing clinical findings in thyroid nodules. The proposed methods provide an effective, efficient, deterministic, and consistent annotation of the TIRADS terms to reduce subjectivity and increase precision in nodule examination and reporting.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Detection of Thyroid Nodule Characteristics From 2D Ultrasound Images

Han,

Ibrahim,

et al. 2023

Ultrason Imaging

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Thyroid cancer is one of the common types of cancer worldwide, and Ultrasound (US) imaging is a modality normally used for thyroid cancer diagnostics. The American College of Radiology Thyroid Imaging Reporting and Data System (ACR TIRADS) has been widely adopted to identify and classify US image characteristics for thyroid nodules. This paper presents novel methods for detecting the characteristic descriptors derived from TIRADS. Our methods return descriptions of the nodule margin irregularity, margin smoothness, calcification as well as shape and echogenicity using conventional computer vision and deep learning techniques. We evaluate our methods using datasets of 471 US images of thyroid nodules acquired from US machines of different makes and labeled by multiple radiologists. The proposed methods achieved overall accuracies of 88.00%, 93.18%, and 89.13% in classifying nodule calcification, margin irregularity, and margin smoothness respectively. Further tests with limited data also show a promising overall accuracy of 90.60% for echogenicity and 100.00% for nodule shape. This study provides an automated annotation of thyroid nodule characteristics from 2D ultrasound images. The experimental results showed promising performance of our methods for thyroid nodule analysis. The automatic detection of correct characteristics not only offers supporting evidence for diagnosis, but also generates patient reports rapidly, thereby decreasing the workload of radiologists and enhancing productivity.

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