Automatic Noise Reduction in Ultrasonic Computed Tomography Image for Adult Bone Fracture Detection

Noise reduction in medical image analysis is still an interesting hot topic, especially in the field of ultrasonic images. Actually, a big concern has been given to automatically reducing noise in human-bone ultrasonic computed tomography (USCT) images. In this chapter, a new hardware prototype, called USCT, is used but images given by this device are noisy and difficult to interpret. Our approach aims to reinforce the peak signal-to-noise ratio (PSNR) in these images to perform an automatic segmentation for bone structures and pathology detection. First, we propose to improve USCT image quality by implementing the discrete wavelet transform algorithm. Second, we focus on a hybrid algorithm combining the k-means with the Otsu method, hence improving the PSNR. Our assessment of the performance shows that the algorithmic approach is comparable with recent methods. It outperforms most of them with its ability to enhance the PSNR to detect edges and pathologies in the USCT images. Our proposed algorithm can be generalized to any medical image to carry out automatic image diagnosis due to noise reduction, and then we have to overcome classical medical image analysis by achieving a short-time process

Auto -Organiser Neural Network Application for Ultrasound Computed Tomographic Image Classification

International audienceImage classification still be a crucial topic of interest in the field of medical imaging diagnostic. In this paper, we have used a new Ultrasonic Computed Tomography (USCT) prototype giving us USCT bone images. However, given images are difficult to interpret and to classify. As a solution, an Auto Organiser Neural Network has been proposed to classify them automatically into pathologic bone images and healthy images. As a result, we have achieved an accuracy of 94.2% and an excellent value of gradient descent. Obtained results outperforms related works

Improved USCT of Paired Bones Using Waveletbased Image Processing

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Automatic USCT Image Processing Segmentation for Osteoporosis Detection

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Automatic recognition processing in ultrasound computed tomography of bone

International audienceUltrasound Computed Tomography (USCT) of soft biological tissues today provides images with a high-level of resolution. The signal acquisition system using multichannel and/or multifrequency arrays performs in circular mode and the main (linear) inversion algorithms are based on compression wave propagation modeling. The main limits of these methods for bone imaging are due to the large impedance contrast between tissues, and to propagative phenomena generated through periosteal interfaces (mode conversion, attenuation). The linear inversion methods fail to provide high-level resolution images. Despite their performance and robustness, the non-linear methods are still today unsuitable for clinical applications because of the high computation time required. However, in the special case of children bone imaging, acquisition steps must be as fast as possible, with short-time exposure and low-intensity sonication. In this context, we have developed a fast-acquisition setup (1 sec.) based on a cylindrical-focusing ring antenna, and a protocol (< 5 sec.) using classical Born approximation and spatial Fourier transform. Unfortunately, the result today is a poor contrast-to-noise ratio (CNR) image. Previous work done to improve CNR used signal and image processing. This work focuses on this last point, and an automatic edge detection procedure, using Haar wavelet 2D-decompositon, combining k-means and Ostu algorithms. Results will be presented on ex vivo real bone samples and on geometrical mimicking bone phantom (Sawbones TM). An example of bone defect imaging will be presented and discussed

Automatic recognition processing in ultrasound computed tomography of bone

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