Thyroid classification and segmentation in ultrasound images using machine learning algorithms

Selvathi, D.; Sharnitha, V.S.

doi:10.1109/icsccn.2011.6024666

Cited by 48 publications

(20 citation statements)

References 8 publications

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“…In 2011, Selvathi et al [22] developed an automatic system that classified the thyroid images and segmented the thyroid gland using machine learning algorithms. The classifiers such as SVM, ELM are used.…”

Section: Hybrid Techniquesmentioning

confidence: 99%

Computer-Aided Diagnosis of Thyroid Nodule: A Review

Koundal¹,

Gupta²,

Singh³

2012

IJCSES

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Section: Hybrid Techniquesmentioning

confidence: 99%

Computer-Aided Diagnosis of Thyroid Nodule: A Review

Koundal¹,

Gupta²,

Singh³

2012

IJCSES

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“…A thyroid ultrasound examination provides an objective and precise method for detection of a change in the size of the nodule, used to evaluate the US features, which include size, echogenicity (hypo echoic or hyper echoic), and composition (cystic, solid, or mixed), as well as presence or absence of coarse or, a halo and irregular margins [1]. Segmentation plays an important role in medical imaging to obtain the location of the object of interest as well as to detect the area, volume or the analysis of dynamic behaviour of anatomical structure over time [11]. Thus by segmentation process the affected or the region of interest can be separated from other tissues.…”

Section: Introductionmentioning

confidence: 99%

A segmentation method and classification of diagnosis for thyroid nodules

Singh¹

2012

IOSRJCE

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Heterogeneous features of thyroid nodules in ultrasound images is very difficult task whenradiologists and physicians manually draw a complete shape of nodule, size and shape, image or distinguish what type of nodule is exist. Segmentation and classification is important methods for medical image processing. Ultrasound imaging is the best way to prediction of which type of thyroid is there. In this paper, uses the groups Benign (non-cancerous) and Malignant (cancerous) Thyroid Nodules images were used. The texture feature method like GLCM are very useful for classifying texture of images and these features are used to train the classifiers such as SVM, KNN and Bayesian. The experimental result shows the performance of the classifiers and shows the best predictive value and positively identify the percentage of the non-cancerous or cancerous people and shows the best performance accuracy using the SVM classifier as compare to the KNN and Bayesian classifier.

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“…Ultrasonography is the modality of choice for evaluation and follow-up, because of its sensitivity and convenience. Ultrasound continues to make significant contributions to patient care by reassuring patients and enhancing their quality of life by helping physicians understand their anatomy in ways not possible with other techniques [2].Ultrasound images does not have any hazardous effects on the patient. Thyroid Scan (Scintigraphy) is done in nuclear medical department.…”

Section: Introductionmentioning

confidence: 99%

“…Several methods for segmenting objects from Medical images have been developed. Several methods for segmenting objects from medical images have been developed such as Active contour without edges [3], Variable Background Active Contour (VBAC) [4], Minimization of Region-Scalable Fitting Energy [5], ELM [6], RBF [7]. Among all the Segmentation algorithms, ACWE, Localized Region Based Active Contour [8], and Distance Regularized Level Set (DRLSE) [9] have been discussed in this paper with their application to Thyroid Gland.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Thyroid Segmentation Algorithms in Ultrasound and Scintigraphy Images

Kaur¹,

Jindal²

2012

IJCA

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To integrate medical images and image processing, image segmentation plays an essential role. Image segmentation is an operation that separates the image into different segments. There are several image segmentation algorithms that are at present available. In this paper, three segmentation algorithms have been implemented and discussed namely: Active Contour without edges, Localized region Based active contour and Distance Regularized Level Set. To detect the thyroid disorders various imaging modalities are used: MRI, Scintigraphy, SPECT and Ultrasound.Out of these, Ultrasound Imaging and Scintigraphy have been discussed in this paper. The segmentation algorithms have been implemented on these two modalities to segment the thyroid gland.

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Thyroid classification and segmentation in ultrasound images using machine learning algorithms

Cited by 48 publications

References 8 publications

Computer-Aided Diagnosis of Thyroid Nodule: A Review

Computer-Aided Diagnosis of Thyroid Nodule: A Review

A segmentation method and classification of diagnosis for thyroid nodules

Comparison of Thyroid Segmentation Algorithms in Ultrasound and Scintigraphy Images

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