Classification of compressed DICOM liver tissue images

The paper presents an experimental analysis of the classification compressed liver tissue images. The classification algorithm is trained on a sample set S of 400 positive (abnormal findings) and 400 negative (normal liver tissue) examples and uses a local search strategy. The examples are fragments of CT images of size n=14161=119×119 derived from the DICOM standard. The images are encoded with different parameter settings of JPEG2000. In our computational experiments, the algorithm is trained with decoded images and tested on sets of 100+100 examples (disjoint from the learning set) of decoded and original images. Results show that the classification is robust against different levels of compression and performs a correct classification of about 97%.