Segmentation of magnetic resonance brain image: integrating region growing and edge detection

Xuan, Jianhua; Adalı, Tülay; Wang, Yue

doi:10.1109/icip.1995.537692

Cited by 45 publications

(3 citation statements)

References 8 publications

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“…Computer-assisted Diagnosis (CAD) systems can analyze breast ultrasound images and provide auxiliary diagnosis results for benign or malignant tumors. Numerous research efforts have focused on the segmentation, feature extraction, feature selection and classification of breast images [11][12][13][14]. Xuan et al [12] proposed a technique that integrates region growing and edge detection for segmenting magnetic resonance (MR) images.…”

Section: Related Workmentioning

confidence: 99%

“…Numerous research efforts have focused on the segmentation, feature extraction, feature selection and classification of breast images [11][12][13][14]. Xuan et al [12] proposed a technique that integrates region growing and edge detection for segmenting magnetic resonance (MR) images. High feature counts increased computational costs and decelerated the classification process.…”

Section: Related Workmentioning

confidence: 99%

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Computer-Aided Diagnosis System for Breast Ultrasound Reports Generation and Classification Method Based on Deep Learning

2023

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Breast cancer is one of the most common malignancies that threaten women’s health. Ultrasound testing is a widespread technique employed for the early detection of tumors. However, after receiving the paper ultrasound report, most patients often have to wait for several days to receive the diagnosis results, which can increase their psychological burden and may cause treatment delay. Based on deep learning, this study designed a computer-aided diagnostic system that directly classifies benign and malignant tumors in breast ultrasound images on paper reports taken by patients, helping them obtain auxiliary diagnostic results as soon as possible. In order to segment and denoise ultrasound report images of patients, this paper proposes a breast ultrasound report generation method, which mainly includes a segmentation model, a rotating classification model and a generative model. With this method, multiple high-quality individual breast ultrasound images can be obtained from a single ultrasound report photo, improving the performance of the breast ultrasound image classification model. In order to utilize high-quality breast ultrasound images and improve classification performance, this paper proposed a breast ultrasound report classification model that includes a feature extraction module, a channel attention module and a classification module. The accuracy of the model reached 89.31%, recall rate reached 88.65%, specificity reached 89.57%, F1 score reached 89.42% and AUC reached 94.53% when input images contained noise. The method proposed in this article is more suitable for practical application scenarios and it can quickly and accurately assist patients in obtaining the benign and malignant classification results of ultrasound reports.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Computer-Aided Diagnosis System for Breast Ultrasound Reports Generation and Classification Method Based on Deep Learning

2023

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“…In the early days, when feature extraction algorithms were applied to image processing problems, quite basic features were used (e.g., edge detection, grey level information, color information, shape information, etc.) [6]. These low-level features had great difficulties in obtaining spatial information.…”

Section: Introductionmentioning

confidence: 99%

Hash Code Generation using Deep Feature Selection Guided Siamese Network for Content-Based Medical Image Retrieval

Öztürk

2021

Gazi University Journal of Science

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Highlights• An effective and short hash code technique is provided for content-based medical image retrieval.• Manhattan distance based Siamese network activation functions are changed to hyperbolic tanh.• Pre-hash code approach is introduced.• Short-length hash code generation with the iterative downsampling approach is introduced. • Higher performance is obtained compared to current state-of-the-art methods in the literature.

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Multi‐Scale 3D U‐Nets: An approach to automatic segmentation of brain tumor

Peng

Chen

Sun

et al. 2019

Int J Imaging Syst Tech

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Gliomas segmentation is a critical and challenging task in surgery and treatment, and it is also the basis for subsequent evaluation of gliomas. Magnetic resonance imaging is extensively employed in diagnosing brain and nervous system abnormalities. However, brain tumor segmentation remains a challenging task, because differentiating brain tumors from normal tissues is difficult, tumor boundaries are often ambiguous and there is a high degree of variability in the shape, location, and extent of the patient. It is therefore desired to devise effective image segmentation architectures. In the past few decades, many algorithms for automatic segmentation of brain tumors have been proposed. Methods based on deep learning have achieved favorable performance for brain tumor segmentation. In this article, we propose a Multi‐Scale 3D U‐Nets architecture, which uses several U‐net blocks to capture long‐distance spatial information at different resolutions. We upsample feature maps at different resolutions to extract and utilize sufficient features, and we hypothesize that semantically similar features are easier to learn and process. In order to reduce the computational cost, we use 3D depthwise separable convolution instead of some standard 3D convolution. On BraTS 2015 testing set, we obtained dice scores of 0.85, 0.72, and 0.61 for the whole tumor, tumor core, and enhancing tumor, respectively. Our segmentation performance was competitive compared to other state‐of‐the‐art methods.

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Segmentation of magnetic resonance brain image: integrating region growing and edge detection

Cited by 45 publications

References 8 publications

Computer-Aided Diagnosis System for Breast Ultrasound Reports Generation and Classification Method Based on Deep Learning

Computer-Aided Diagnosis System for Breast Ultrasound Reports Generation and Classification Method Based on Deep Learning

Hash Code Generation using Deep Feature Selection Guided Siamese Network for Content-Based Medical Image Retrieval

Multi‐Scale 3D U‐Nets: An approach to automatic segmentation of brain tumor

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