DICOM IMAGE COMPRESSION BASED ON SPATIAL FUZZY CLUSTERING USING WAVELET BASED CONTOURLET TRANSFORM

Rupa Sajin

Abstract: Medical imaging has a great impact on diagnosis, so high resolution is mandatory for the representation of such nontrivial images. On the other hand coding and transmission of medical images by preserving the clinically nontrivial information with reduction in storage space differ from standard image coding as it incorporates the integrity of CROI and bandwidth. The main concern in medical image compression lies in compressing a huge amount of visual data into a low bit rate stream. The proposed strategy intends to safeguard the clinically basic data with change in pressure proportion (CR) and crest sign to commotion proportion (PSNR) In the proposed work altered spatial introduction tree (MSPIHT) is utilized to encode the wavelet based contourlet coefficients of the clinical area of interest (CROI) and SPIHT to encode the wavelet coefficients for encoding the foundation, portioned utilizing spatial fluffy C implies bunching (SFCM).The proposed calculation indicates change in PSNR and give effective representation of edges in DICOM (Digital Imaging and Communications in Medicine) pictures.

Keywords: DICOM Images, SFCM, Clinical Region of Interest ,WBCT and Modified SPIHT

DOI: https://doi.org/10.15623/ijret.2016.0516056