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Noise reduction in medical image analysis is still an interesting hot topic, es-pecially in the field of ultrasonic images. Actually, a big concern has been given to automatically reduce noise in human-bone Ultrasonic Computed Tomography (USCT) images. In this paper, a new hardware prototype, called USCT, is used but images given by this device are noisy and difficult to inter-pret. 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 Ostu method, hence improving the PSNR . Our assessment of the performance shows that the algorithmic approach is comparable to 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.

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