A Systematic Approach To Decision Making In The Compression Of Medical DICOM Images
Medical imaging is the primary factor for precise and successful clinical diagnosis and investigation of diseases.
X-Ray images captured from the radiography machine have to be stored and transferred to the required destination
effectively without any loss of data, for medical analysis and various operations by the radiologists. As the amount of
medical imaging data increases, a proper storing mechanism using a good compression technique has to be implemented for
storing huge amount of data, without compromising with quality of the medical images. This paper discusses various
parameters and the preprocessing methods which play a very important role in analyzing the medical image size before and
after compression for different cases. Based on the sets of results obtained for various cases, decision is taken at multi levels.
This in turn helps in pointing at the parameter which acts as the deciding factor to obtain a good compression. The
parameters considered in this paper for our analysis are: (1) signal to noise ratio values calculated between a set of test and
reference images (2) correlation coefficients obtained for a sequence of source and target images (3) slice thickness for
multi-slice modality images. The preprocessing method employed here is, subtracting the images in a sequential order and
storing the resultant images. The unprocessed original and preprocessed images are later compressed using a lossless
compression algorithm. Based on these compression results acquired, further processing using average and compression
technique is done to check for betterment in the compression results. This positive change in results is explained, and also
the parameter which bought about this improvement in results is identified. Hence, a detailed study in understanding which
factor can be used to determine, if subtraction is a better preprocessing method that supports good compression in medical
imaging; is called for. We also intend to find if the parameters correlation or signal to noise ratio, can be used as a sole factor
for this analysis, by considering examples for many case conditions. Thus, this paper discusses a systematic approach for
decision making which helps in obtaining a good compression based on the exhaustive sets of the experimental results
obtained. Using ImageJ software, correlation and signal to noise ratio values have been obtained. Software coding for
preparation, subtraction and averaging of images has been done using Visual Studio 2010 software. JPEG2000 lossless
compression has been chosen for compressing all the medical images that have been considered in this research work.