Fibroid Detection in Ultrasound Uterus Images Using Image Processing

Dilna, K. T.; Hemanth, D. Jude

doi:10.1007/978-981-15-1286-5_15

Cited by 3 publications

(2 citation statements)

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“…Various optimization techniques [26][27][28][29] are utilized in feature extraction to enhance the quality, efficiency, and discriminative power of the extracted features. Dilna and Jude 30 introduced an approach that involves a sequential process encompassing the segmentation of fibroid regions, succeeded by the extraction of features based on shape for further characterization.…”

Section: Literature Surveymentioning

confidence: 99%

Efficient feature extraction and hybrid deep learning for early identification of uterine fibroids in ultrasound images

Lekshmanan Chinna,

Pathrose Mary

2024

Int J Imaging Syst Tech

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Non‐cancerous growths called uterine fibroids develop in the uterus. They can vary in size, location, and number, and can produce symptoms including excessive menstrual flow, pelvic discomfort, and reproductive problems. Early detection of uterine fibroids is important because it allows for timely intervention and appropriate management strategies. Extracting meaningful features from ultrasound (US) images requires robust and effective techniques. However, different feature extraction methods may yield varying results, and the choice of technique can influence the accuracy of fibroid detection. This paper presents an efficient approach for early detection of uterine fibroids in US images. Our proposed technique combines efficient feature extraction methods and a hybrid deep learning approach. Firstly, we employ the well‐known canny edge detection algorithm to accurately identify the edges of the fibroid region in the US images. This step helps in segmenting the target edge portion for further analysis. Additionally, we introduce an improved bird swarm optimization (IBSO) algorithm to extract a comprehensive set of features, utilizing both known features and newly obtained features. This approach enhances the accuracy of fibroid detection in uterine images. The proposed method uses a convolutional recurrent neural network (CRNN) to accurately detect and treat uterine fibroids, ensuring timely diagnosis and treatment. Our proposed IBSO‐CRNN model produces accuracy rates of 99.897% and 97.568% on the augmented and original datasets, respectively.

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Section: Literature Surveymentioning

confidence: 99%

Efficient feature extraction and hybrid deep learning for early identification of uterine fibroids in ultrasound images

Lekshmanan Chinna,

Pathrose Mary

2024

Int J Imaging Syst Tech

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“…Then, the selected plane and contour are used as a start point of fitting elliptical contours in semi-axial planes [4]. Another group, focused on use of conventional image processing techniques for automatic detection and segmentation of uterine fibroid [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Automatic 3D Ultrasound Segmentation of Uterus Using Deep Learning

Behboodi¹,

Rivaz²,

Lalondrelle³

et al. 2021

Preprint

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On-line segmentation of the uterus can aid effective image-based guidance for precise delivery of dose to the target tissue (the uterocervix) during cervix cancer radiotherapy. 3D ultrasound (US) can be used to image the uterus, however, finding the position of uterine boundary in US images is a challenging task due to large daily positional and shape changes in the uterus, large variation in bladder filling, and the limitations of 3D US images such as low resolution in the elevational direction and imaging aberrations. Previous studies on uterus segmentation mainly focused on developing semi-automatic algorithms where require manual initialization to be done by an expert clinician. Due to limited studies on the automatic 3D uterus segmentation, the aim of the current study was to overcome the need for manual initialization in the semi-automatic algorithms using the recent deep learning-based algorithms. Therefore, we developed 2D UNet-based networks that are trained based on two scenarios. In the first scenario, we trained 3 different networks on each plane (i.e., sagittal, coronal, axial) individually. In the second scenario, our proposed network was trained using all the planes of each 3D volume. Our proposed schematic can overcome the initial manual selection of previous semi-automatic algorithm.

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