A Method of Breast Tumour MRI Segmentation and 3D Reconstruction

Yin, Dong; Renwei, Lu

doi:10.1109/itme.2015.117

Cited by 8 publications

(3 citation statements)

References 5 publications

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“…Selecting an appropriate bounding box can reduce the number of rays during rendering [28]. The bounding box should be as small as possible and should accommodate the 3D data field.…”

Section: ) Setting Of Spherical Bounding Boxmentioning

confidence: 99%

3D Reconstruction for Super-Resolution CT Images in the Internet of Health Things Using Deep Learning

Zhang

Gong

et al. 2020

IEEE Access

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The Internet of Health Things (IoHT) enables health devices to connect to the Internet and communicate with each other, which provides the high-accuracy and high-security diagnosis result in the medical area. As essential parts of the IoHT, computed tomography (CT) images help doctors diagnose disease. In the traditional disease diagnosing process, low-resolution medical CT images produce lowaccuracy diagnosis results for microlesions. Moreover, CT images can only provide 2D information about organs, and doctors should estimate the 3D shape of a lesion based on experience. To solve these problems, we propose a 3D reconstruction method for secure super-resolution computed tomography (SRCT) images in the IoHT using deep learning. First, we use deep learning to obtain secure SRCT images from lowresolution images in the IoHT. To this end, we adopt a conditional generative adversarial network (CGAN) based on the edge detection loss function (EDLF) in the deep learning process, namely EDLF-CGAN algorithm. In this algorithm, the CGAN is employed to generate SRCT images with luminance and contrast as the input auxiliary conditions, which can improve the accuracy of super-resolution (SR) images. An EDLF is proposed to consider the edge features in the generated SRCT images, which reduces the deformation of generated image. Second, we apply the secure SR images generated from the deep learning method to perform 3D reconstruction. An advanced ray casting 3D reconstruction algorithm that can reduce the number of rays by selecting the appropriate bounding box is proposed. Compared with the traditional algorithm, the proposed ray casting 3D reconstruction algorithm can reduce the time and memory cost. The experimental results show that our EDLF-CGAN has a better SR reconstruction effect than other algorithms via the indicators of the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). In addition, our advanced ray casting 3D reconstruction algorithm greatly improves the efficiency compared with the traditional ray casting algorithm.

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“…Selecting an appropriate bounding box can reduce the number of rays during rendering [28]. The bounding box should be as small as possible and should accommodate the 3D data field.…”

Section: ) Setting Of Spherical Bounding Boxmentioning

confidence: 99%

3D Reconstruction for Super-Resolution CT Images in the Internet of Health Things Using Deep Learning

Zhang

Gong

et al. 2020

IEEE Access

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“…In the second stage, the obtained initial segmentation is refined by a 3D level set method. Yin and Lu [12] have interest in the 3D reconstruction of the breast tumor from 2D segmented images. The level set method based on energy and gradient is adopted to segment the tumor.…”

Section: Contour-based Approachesmentioning

confidence: 99%

“…Nevertheless, extraction of breast tumor from MR images is a challenging task due to their considerable shape variations, intensity inhomogeneity, overlap with the normal breast tissue, etc. Several segmentation methods are proposed in the literature to address these issues including contour-based [11][12][13][14] and region-based [15][16][17][22][23][24][25][26][27] approaches. Unfortunately, none of the state-of-the-art methods produced rigorous results and the challenge remains open.…”

Section: Introductionmentioning

confidence: 99%

Levels Propagation Approach to Image Segmentation: Application to Breast MR Images

Bouchebbah

Slimani

2019

J Digit Imaging

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Accurate segmentation of a breast tumor region is fundamental for treatment. Magnetic resonance imaging (MRI) is a widely used diagnostic tool. In this paper, a new semi-automatic segmentation approach for MRI breast tumor segmentation called Levels Propagation Approach (LPA) is introduced. The introduced segmentation approach takes inspiration from tumor propagation and relies on a finite set of nested and non-overlapped levels. LPA has several features: it is highly suitable to parallelization and offers a simple and dynamic possibility to automate the threshold selection. Furthermore, it allows stopping of the segmentation at any desired limit. Particularly, it allows to avoid to reach the breast skin-line region which is known as a significant issue that reduces the precision and the effectiveness of the breast tumor segmentation. The proposed approach have been tested on two clinical datasets, namely RIDER breast tumor dataset and CMH-LIMED breast tumor dataset. The experimental evaluations have shown that LPA has produced competitive results to some state-of-the-art methods and has acceptable computation complexity.

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