We introduce the adaptive speckle reducing anisotropic diffusion(SRAD) that uses wavelet for enhancement of medical ultrasound images. This method first modifies coarse-to-fine classification to decide the homogeneous region that operated as diffusion threshold in SRAD works. Then, SRAD are played on each scale of decomposed wavelet domain with determined homogeneous regions as called "speckle scale function". From this process, homogenous region can considered without manual selection or preliminary exponential decay function. As a result, conventional SRAD will modify as adaptive one. Moreover, variety pattern of speckle is reduced by the proposed modified SRAD on wavelet decomposing. The proposed method can improve the image quality for ultrasound images enhancement. Finally, we validate this method to compare with conventional filters group using artificial speckle image and real ultrasound images. The experimental results show that the proposed method performed effectively both terms of speckle reduction and edge preservation
Background: We investigated the feasibility of a deep learning algorithm (DLA) based on apparent diffusion coefficient (ADC) maps for the segmentation and discrimination of clinically significant cancer (CSC, Gleason score ≥ 7) from non-CSC in patients with prostate cancer (PCa). Methods: Data from a total of 149 consecutive patients who had undergone 3T-MRI and been pathologically diagnosed with PCa were initially collected. The labelled data (148 images for GS6, 580 images for GS7) were applied for tumor segmentation using a convolutional neural network (CNN). For classification, 93 images for GS6 and 372 images for GS7 were used. For external validation, 22 consecutive patients from five different institutions (25 images for GS6, 70 images for GS7) representing different MR machines were recruited. Results: Regarding segmentation and classification, U-Net and DenseNet were used, respectively. The tumor Dice scores for internal and external validation were 0.822 and 0.7776, respectively. As for classification, the accuracies of internal and external validation were 73 and 75%, respectively. For external validation, diagnostic predictive values for CSC (sensitivity, specificity, positive predictive value and negative predictive value) were 84, 48, 82 and 52%, respectively. Conclusions: Tumor segmentation and discrimination of CSC from non-CSC is feasible using a DLA developed based on ADC maps (b2000) alone.
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