Design CNN On Bone Spine Segmention TO Methodes Image Processing

This thesis proposes a deep learning approach to bone segmentation in abdominal CNN+PG. Segmentation is a common initial step in medical images analysis, often fundamental for computer-aided detection and diagnosis systems. The extraction of bones in PG is a challenging task, which if done manually by experts requires a time consuming process and that has not today a broadly recognized automatic solution. The method presented is based on a convolutional neural network, inspired by the U-Net and trained end-to-end, that performs a semantic segmentation of the data. The training dataset is made up of 21 abdominal PG+CNN, each one containing between 0 and 255 2D transversal images. Those images are in full resolution, 4*4*50 voxels, and each voxel is classified by the network into one of the following classes: background, femoral bones, hips, sacrum, sternum, spine and ribs. The output is therefore a bone mask where the bones are recognized and divided into six different classes. In the testing dataset, labeled by experts, the best model achieves a Dice coefficient as average of all bone classes of 0.8980. This work demonstrates, to the best of my knowledge for the first time, the feasibility of automatic bone segmentation and classification for PG using a convolutional neural network

Representing junctions through asymmetric tensor diffusion

Gradient-based junctions form key features in such applications as object classification, motion segmentation, and image enhancement. Asymmetric junctions arise from the merging of an odd number of contour end-points such as at a 'Y' junction. Without an asymmetric representation of such a structure, it will be identified in the same category as 'X' junctions. This has severe consequences when distinguishing between features in object classification, discerning occlusion from disocclusion in motion segmentation and in properly modeling smoothing boundaries in image enhancement.Current junction analysis methods include convolution, which applies a mask over a sub-region of the image, and diffusion, which propagates gradient information from point-to-point based on a set of rules.A novel method is proposed that results in an improved approximation of the underlying contours, through the use of asymmetric junctions. The method combines the ability to represent asymmetric information, as do a number of convolution methods, with the robustness of local support obtained from diffusion schemes. This work investigates several different design paradigms of the asymmetric tensor diffusion algorithm. The proposed approach proved superior to existing techniques by properly accounting for asymmetric junctions over a wide range of scenarios