Texture-Based Medical Image Compression

Image processing is one of the most researched areas these days due to the flooding of the internet with an overload of images. The noble medicine industry is not left untouched. It has also suffered with an excess of patient record storage and maintenance. With the advent of automation of the industries in the world, the medicine industry has sought to change and provide a more portable feel to it, leading to the fields of telemedicine and such. Our algorithm comes in handy in such scenarios where large amount of data needs to be transmitted over the network for perusal by another consultant. We aim for a visual quality approach in our algorithm rather than pixel-wise fidelity. We utilize parameters of edges and textures as the basic parameters in our compression algorithm.

[1]  Dong Liu,et al.  Edge-Based Inpainting and Texture Synthesis for Image Compression , 2007, 2007 IEEE International Conference on Multimedia and Expo.

[2]  Guillermo Sapiro,et al.  Image inpainting , 2000, SIGGRAPH.

[3]  Guillermo Sapiro,et al.  Simultaneous structure and texture image inpainting , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[4]  Lena Costaridou,et al.  Texture-Based Identification and Characterization of Interstitial Pneumonia Patterns in Lung Multidetector CT , 2010, IEEE Transactions on Information Technology in Biomedicine.

[5]  Patrick Pérez,et al.  Poisson image editing , 2003, ACM Trans. Graph..

[6]  Guillermo Sapiro,et al.  Simultaneous structure and texture image inpainting , 2003, IEEE Trans. Image Process..

[7]  D. Jemi Florinabel,et al.  Efficient block prediction-based coding of computer screen images with precise block classification , 2011 .

[8]  Dong Liu,et al.  Image Compression With Edge-Based Inpainting , 2007, IEEE Transactions on Circuits and Systems for Video Technology.

[9]  L. Brooke.  The National Library of Medicine. , 1980, Hospital libraries.

[10]  Guillermo Sapiro,et al.  Structure and texture filling-in of missing image blocks in wireless transmission and compression applications , 2003, IEEE Trans. Image Process..

[11]  Francisco José Madrid-Cuevas,et al.  Determining Hysteresis Thresholds for Edge Detection by Combining the Advantages and Disadvantages of Thresholding Methods , 2010, IEEE Transactions on Image Processing.

[12]  Zongben Xu,et al.  Image Inpainting by Patch Propagation Using Patch Sparsity , 2010, IEEE Transactions on Image Processing.

[13]  Irfan A. Essa,et al.  Graphcut textures: image and video synthesis using graph cuts , 2003, ACM Trans. Graph..

[14]  Feng Wu,et al.  Compression with vision technologies , 2006 .

[15]  Guillermo Sapiro,et al.  Filling-in by joint interpolation of vector fields and gray levels , 2001, IEEE Trans. Image Process..