Circular Foreign Object Detection in Chest X-ray Images

Zohora, Fatema Tuz; Santosh, K. C.

doi:10.1007/978-981-10-4859-3_35

Cited by 10 publications

(2 citation statements)

References 7 publications

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“…In general, detecting circles and lines is a fundamental task in computer vision and has been widely studied and developed in a variety of ways. Well-known techniques, such as the Hough transform or neural network approaches, are used to detect circle-like foreign objects in chest X-ray images [44][45][46] or line-like lanes for autonomous driving systems [47,48], to name recent applications.…”

Section: Approximation Of Shank Axismentioning

confidence: 99%

Real-time limb tracking in single depth images based on circle matching and line fitting

et al. 2021

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Modern lower limb prostheses neither measure nor incorporate healthy residual leg information for intent recognition or device control. In order to increase robustness and reduce misclassification of devices like these, we propose a vision-based solution for real-time 3D human contralateral limb tracking (CoLiTrack). An inertial measurement unit and a depth camera are placed on the side of the prosthesis. The system is capable of estimating the shank axis of the healthy leg. Initially, the 3D input is transformed into a stabilized coordinate system. By splitting the subsequent shank estimation problem into two less computationally intensive steps, the computation time is significantly reduced: First, an iterative closest point algorithm is applied to fit circular models against 2D projections. Second, the random sample consensus method is used to determine the final shank axis. In our study, three experiments were conducted to validate the static, the dynamic and the real-world performance of our CoLiTrack approach. The shank angle can be tracked at 20 Hz for one sixth of the entire human gait cycle with an angle estimation error below $$2.8\pm 2.1^{\circ }$$ 2.8 ± 2 . 1 ∘ . Our promising results demonstrate the robustness of the novel CoLiTrack approach to make “next-generation prostheses” more user-friendly, functional and safe.

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Section: Approximation Of Shank Axismentioning

confidence: 99%

Real-time limb tracking in single depth images based on circle matching and line fitting

et al. 2021

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“…It is a complicated problem to generate a report based on radiology images. First, it is necessary to accurately find the abnormal part of the image [2]- [6], and then describe it in the form of text. Most existing literature pertaining to radiology report generation problems is based on deep learning techniques, following the encoder-decoder architecture of the machine translation task.…”

Section: Introductionmentioning

confidence: 99%

Multi-Attention and Incorporating Background Information Model for Chest X-Ray Image Report Generation

Huang

Yan

et al. 2019

IEEE Access

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Chest X-ray images are widely used in clinical practice such as diagnosis and treatment. The automatic radiology report generation system can effectively reduce the rate of misdiagnosis and missed diagnosis. Previous studies were focused on the long text generation problem of image paragraph, ignoring the characteristics of the image and the auxiliary role of patient background information for diagnosis. In this paper, we propose a new hierarchical model with multi-attention considering the background information. The multi-attention mechanism can focus on the image's channel and spatial information simultaneously, and map it to the sentence topic. The patient's background information will be encoded by the neural network first, then it will be aggregated into a vector representation by a multi-layer perception and added to the pretrained vanilla word embedding, which finally forms a new word embedding after fusion. Our experimental results demonstrated that the model outperforms all baselines, achieving the state-of-the-art performance in terms of accuracy.

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