Automated classification of static ultrasound images of ovarian tumours based on decision level fusion

Khazendar, Shan; Al-Assam, Hisham; Du, Hongbo; Jassim, Sabah; Sayasneh, Ahmad; Bourne, T.; Kaijser, Jeroen; Timmerman, Dirk

doi:10.1109/ceec.2014.6958571

Cited by 17 publications

(12 citation statements)

References 12 publications

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“…52 Attempts were made to adapt the LBP texture features for distinguishing benign and malignant ovarian masses from 2D US images. 24,55 SVM classifiers were trained and tested using a dataset of 187 ultrasound scan images from 177 patients. Classification accuracy rates of 69%, 81%, and 90% were achieved at three different confidence levels (low, medium, and high) respectively, but the overall accuracy remained at 77%.…”

Section: Discussionmentioning

confidence: 99%

An Evaluation of the Effectiveness of Image-based Texture Features Extracted from Static B-mode Ultrasound Images in Distinguishing between Benign and Malignant Ovarian Masses

Al-karawi¹,

Al-Assam

et al. 2021

Ultrason Imaging

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Significant successes in machine learning approaches to image analysis for various applications have energized strong interest in automated diagnostic support systems for medical images. The evolving in-depth understanding of the way carcinogenesis changes the texture of cellular networks of a mass/tumor has been informing such diagnostics systems with use of more suitable image texture features and their extraction methods. Several texture features have been recently applied in discriminating malignant and benign ovarian masses by analysing B-mode images from ultrasound scan of the ovary with different levels of performance. However, comparative performance evaluation of these reported features using common sets of clinically approved images is lacking. This paper presents an empirical evaluation of seven commonly used texture features (histograms, moments of histogram, local binary patterns [256-bin and 59-bin], histograms of oriented gradients, fractal dimensions, and Gabor filter), using a collection of 242 ultrasound scan images of ovarian masses of various pathological characteristics. The evaluation examines not only the effectiveness of classification schemes based on the individual texture features but also the effectiveness of various combinations of these schemes using the simple majority-rule decision level fusion. Trained support vector machine classifiers on the individual texture features without any specific pre-processing, achieve levels of accuracy between 75% and 85% where the seven moments and the 256-bin LBP are at the lower end while the Gabor filter is at the upper end. Combining the classification results of the top k ( k = 3, 5, 7) best performing features further improve the overall accuracy to a level between 86% and 90%. These evaluation results demonstrate that each of the investigated image-based texture features provides informative support in distinguishing benign or malignant ovarian masses.

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Section: Discussionmentioning

confidence: 99%

An Evaluation of the Effectiveness of Image-based Texture Features Extracted from Static B-mode Ultrasound Images in Distinguishing between Benign and Malignant Ovarian Masses

Al-karawi¹,

Al-Assam

et al. 2021

Ultrason Imaging

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show abstract

“…The system was implemented on 187 ultrasound images from 177 patients. Based on classification decisions of high, medium and low confidence, respectively, the method provided accuracies of 90%, 81% and 69% [29,30]. Although this model was developed using a relatively large cohort of patients, numerous images were considered ineligible for inclusion in the high-confidence range [32].…”

Section: Discussionmentioning

confidence: 99%

Logistic models and artificial intelligence in the sonographic assessment of adnexal masses – a systematic review of the literature

et al. 2020

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Adnexal masses are common, yet challenging, in gynecological practice. Making the differential diagnosis between their benign and malignant condition is essential for optimal surgical management, but reliable pre-surgical differentiation is sometimes difficult using clinical features, ultrasound examination, or tumor markers alone. A possible way to improve the diagnosis is using artificial intelligence (AI) or logistic models developed based on compiling and processing clinical, ultrasound, and tumor marker data together. Ample research has already been conducted in this regard that medical practitioners could benefit from. In this systematic review, we present logistic models and methods using AI, chosen based on their demonstrated high performance in clinical practice. Although some external validation of these models has been performed, further prospective studies are needed in order to select the best model or to create a new, more efficient, one for the pre-surgical evaluation of ovarian masses.

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“…Also the suspected abnormality was displayed in a heat map. Traditional benign/malignancy classification of ovarian cyst depended only on manually designed features [76]. In a more recent study by Zhang et al [77], a diagnosis system to determine Ovarian Cyst in colored Doppler ultrasound image was proposed to reduce unnecessary fine needle aspiration (FNA) evaluation.…”

Section: A C C E P T E D a R T I C L Ementioning

confidence: 99%

Technology trends and applications of deep learning in ultrasonography: image quality enhancement, diagnostic support, and improving workflow efficiency

Kang

Kwon

et al. 2021

Ultrasonography

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Reviews of the most recent applications of deep learning on ultrasound imaging applications are presented. Architectures of deep learning networks are briefly explained for medical imaging application categories of classification, detection, segmentation, and generation. Ultrasonography applications are then reviewed and summarized for image processing and diagnosis along with some representative study cases of breast, thyroid, heart, kidney, liver, and fetal head. Efforts on workflow enhancement are also reviewed with emphasis on view recognition, scanning guide, image quality assessment, and quantification and measurement. Finally some future prospects are presented on image quality Enhancement, diagnostic support, and improving workflow efficiency, along with remarks on hurdles, benefits, and necessary collaborations.

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Automated classification of static ultrasound images of ovarian tumours based on decision level fusion

Cited by 17 publications

References 12 publications

An Evaluation of the Effectiveness of Image-based Texture Features Extracted from Static B-mode Ultrasound Images in Distinguishing between Benign and Malignant Ovarian Masses

An Evaluation of the Effectiveness of Image-based Texture Features Extracted from Static B-mode Ultrasound Images in Distinguishing between Benign and Malignant Ovarian Masses

Logistic models and artificial intelligence in the sonographic assessment of adnexal masses – a systematic review of the literature

Technology trends and applications of deep learning in ultrasonography: image quality enhancement, diagnostic support, and improving workflow efficiency

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