Fusion of spatial gray level dependency and fractal texture features for the characterization of thyroid lesions

Raghavendra, U.; Acharya, U. Rajendra; Gudigar, Anjan; Tan, Jen Hong; Fujita, Hamido; Hagiwara, Yuki; Molinari, Filippo; Kongmebhol, Pailin; Ng, Kwan Hoong

doi:10.1016/j.ultras.2017.02.003

Cited by 57 publications

(27 citation statements)

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“…They reported a performance of 93.59% accuracy using a quadratic discriminant analysis classifier. Raghavendra et al (2017) explored the fusion of different texture features and extracted various entropies for the discrimination of benign and malignant thyroid lesions. Similarly, they constructed a thyroid clinical risk index to distinguish the two classes using numerical values.…”

Section: Discussionmentioning

confidence: 99%

Towards precision medicine: from quantitative imaging to radiomics

Acharya

Hagiwara

Sudarshan

et al. 2018

J. Zhejiang Univ. Sci. B

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Radiology (imaging) and imaging-guided interventions, which provide multi-parametric morphologic and functional information, are playing an increasingly significant role in precision medicine. Radiologists are trained to understand the imaging phenotypes, transcribe those observations (phenotypes) to correlate with underlying diseases and to characterize the images. However, in order to understand and characterize the molecular phenotype (to obtain genomic information) of solid heterogeneous tumours, the advanced sequencing of those tissues using biopsy is required. Thus, radiologists image the tissues from various views and angles in order to have the complete image phenotypes, thereby acquiring a huge amount of data. Deriving meaningful details from all these radiological data becomes challenging and raises the big data issues. Therefore, interest in the application of radiomics has been growing in recent years as it has the potential to provide significant interpretive and predictive information for decision support. Radiomics is a combination of conventional computer-aided diagnosis, deep learning methods, and human skills, and thus can be used for quantitative characterization of tumour phenotypes. This paper discusses the overview of radiomics workflow, the results of various radiomics-based studies conducted using various radiological images such as computed tomography (CT), magnetic resonance imaging (MRI), and positron-emission tomography (PET), the challenges we are facing, and the potential contribution of radiomics towards precision medicine.

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Section: Discussionmentioning

confidence: 99%

Towards precision medicine: from quantitative imaging to radiomics

Acharya

Hagiwara

Sudarshan

et al. 2018

J. Zhejiang Univ. Sci. B

Self Cite

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“…The thyroid is an important organ located in the human neck that produces and secretes two important hormones, namely triiodothyronine and thyroxine, which are responsible for the regulation of metabolism in the human body. Due to its important role in the human body, diagnosing and treatment of thyroid disease has become important [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]. As reported in the previous studies, one important problem commonly experienced in the thyroid region is the appearance of nodules that cause thyroid cancer.…”

Section: Introductionmentioning

confidence: 89%

“…This kind of system uses and processes one or more captured medical images of some organs such as X-ray, CT, and MRI scans, and yields its decision that can assist doctors in diagnosing diseases. Due to its purpose, CAD systems have been widely developed and used in real-life applications such as for diagnosing the brain [2,3,7,12], breasts [4,8,[13][14][15][16], lungs [10], and thyroid diseases [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

“…Using the ultrasound thyroid nodule images, there have been several previous studies on CAD for the thyroid nodule detection and classification problems. In contrast to the conventional thyroid diagnosis methods mentioned above, the CAD methods for thyroid nodules use ultrasound thyroid nodule images as inputs and produce thyroid nodule regions and/or the status of nodules (benign or malign) [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36]. Similar to normal image processing systems, CAD systems for thyroid nodule use several image processing techniques to extract information from input images for detection/classification purposes.…”

Section: Introductionmentioning

confidence: 99%

“…As indicated by its name, this kind of method uses several handcrafted image feature extraction methods that are designed by experts based on their knowledge of specific problems to extract efficient features from input images for image-based processing systems. For the thyroid nodule CAD, the handcrafted feature-based method has also been used previously [22,24,31]. Chang et al [24] used up to 78 texture features extracted from ultrasound thyroid nodule images for the thyroid nodule classification problem.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Ultrasound Image-Based Diagnosis of Malignant Thyroid Nodule Using Artificial Intelligence

Nguyen

Kang

Pham

et al. 2020

Sensors

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Computer-aided diagnosis systems have been developed to assist doctors in diagnosing thyroid nodules to reduce errors made by traditional diagnosis methods, which are mainly based on the experiences of doctors. Therefore, the performance of such systems plays an important role in enhancing the quality of a diagnosing task. Although there have been the state-of-the art studies regarding this problem, which are based on handcrafted features, deep features, or the combination of the two, their performances are still limited. To overcome these problems, we propose an ultrasound image-based diagnosis of the malignant thyroid nodule method using artificial intelligence based on the analysis in both spatial and frequency domains. Additionally, we propose the use of weighted binary cross-entropy loss function for the training of deep convolutional neural networks to reduce the effects of unbalanced training samples of the target classes in the training data. Through our experiments with a popular open dataset, namely the thyroid digital image database (TDID), we confirm the superiority of our method compared to the state-of-the-art methods.

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Utility of a Deep-Learning Algorithm to Guide Novices to Acquire Echocardiograms for Limited Diagnostic Use

Narang

Bae

Hong³

et al. 2021

JAMA Cardiol

208

143

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IMPORTANCE Artificial intelligence (AI) has been applied to analysis of medical imaging in recent years, but AI to guide the acquisition of ultrasonography images is a novel area of investigation. A novel deep-learning (DL) algorithm, trained on more than 5 million examples of the outcome of ultrasonographic probe movement on image quality, can provide real-time prescriptive guidance for novice operators to obtain limited diagnostic transthoracic echocardiographic images.OBJECTIVE To test whether novice users could obtain 10-view transthoracic echocardiographic studies of diagnostic quality using this DL-based software. DESIGN, SETTING, AND PARTICIPANTSThis prospective, multicenter diagnostic study was conducted in 2 academic hospitals. A cohort of 8 nurses who had not previously conducted echocardiograms was recruited and trained with AI. Each nurse scanned 30 patients aged at least 18 years who were scheduled to undergo a clinically indicated echocardiogram at Northwestern Memorial Hospital or Minneapolis Heart Institute between March and May 2019. These scans were compared with those of sonographers using the same echocardiographic hardware but without AI guidance.INTERVENTIONS Each patient underwent paired limited echocardiograms: one from a nurse without prior echocardiography experience using the DL algorithm and the other from a sonographer without the DL algorithm. Five level 3-trained echocardiographers independently and blindly evaluated each acquisition.MAIN OUTCOMES AND MEASURES Four primary end points were sequentially assessed: qualitative judgement about left ventricular size and function, right ventricular size, and the presence of a pericardial effusion. Secondary end points included 6 other clinical parameters and comparison of scans by nurses vs sonographers.RESULTS A total of 240 patients (mean [SD] age, 61 [16] years old; 139 men [57.9%]; 79 [32.9%] with body mass indexes >30) completed the study. Eight nurses each scanned 30 patients using the DL algorithm, producing studies judged to be of diagnostic quality for left ventricular size, function, and pericardial effusion in 237 of 240 cases (98.8%) and right ventricular size in 222 of 240 cases (92.5%). For the secondary end points, nurse and sonographer scans were not significantly different for most parameters.CONCLUSIONS AND RELEVANCE This DL algorithm allows novices without experience in ultrasonography to obtain diagnostic transthoracic echocardiographic studies for evaluation of left ventricular size and function, right ventricular size, and presence of a nontrivial pericardial effusion, expanding the reach of echocardiography to clinical settings in which immediate interrogation of anatomy and cardiac function is needed and settings with limited resources.

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Fusion of spatial gray level dependency and fractal texture features for the characterization of thyroid lesions

Cited by 57 publications

References 45 publications

Towards precision medicine: from quantitative imaging to radiomics

Towards precision medicine: from quantitative imaging to radiomics

Ultrasound Image-Based Diagnosis of Malignant Thyroid Nodule Using Artificial Intelligence

Utility of a Deep-Learning Algorithm to Guide Novices to Acquire Echocardiograms for Limited Diagnostic Use

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