Paper Info
Title
Two-Phase Compression of Histological Images with MDL Ranking of Segmentation Images
Abstract
We propose several methods for minimizing the codelength achievable when encoding histological images in two phases, where a segmentation of the image is encoded in the first phase and the image conditional on its segmentation is encoded in the second phase. The main goal of the paper is to establish the class of models suitable to be used in minimum description based analysis of histological images, and to compare the compression performance achievable by several predictive coding schemes, which we derive specifically for the second phase. The most efficient of the publicly available general image compressors, JPEG-LS and CALIC, are also compared in the one phase coding scenario. We conclude with a preliminary study on setting automatically the optimal parameters of one segmentation algorithm so that the compressed description has minimum length.
Year
DOI
Venue
2013
10.1109/CSCS.2013.12
CSCS '13 Proceedings of the 2013 19th International Conference on Control Systems and Computer Science
Keywords
Field
DocType
biological tissues,data compression,image coding,image segmentation,medical image processing,CALIC compressor,JPEG-LS compressor,MDL ranking,histological image,image conditional,image segmentation,minimum description length,one-phase coding scenario,predictive coding scheme,segmentation algorithm,two-phase image compression,computer assisted diagnostics,histological images,image segmentation,minimum description length principle
Scale-space segmentation,Pattern recognition,Computer science,Image texture,Image processing,Segmentation-based object categorization,Image segmentation,Region growing,Artificial intelligence,Data compression,Minimum spanning tree-based segmentation
Conference
ISSN
Citations 
PageRank 
2379-0474
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
I. Tabus18710.32
J. Hukkanen200.34
I. Schiopu3378.04