Organ Recognition using Gabor Filters

The aim of this research is to investigate the possibility of using medical image information to extract unique features and classify different patients ’ organ tissues, such as the prostate, based on concepts related to what is already done in iris recognition. This paper therefore presents a new ap-proach in medical imaging, an organ recognition system, tested on a standard database of grey scale prostate images in order to validate its performance. In this research, features of the prostate image were en-coded by convolving the normalized organ region with a 2D Gabor filter and then quantizing its phase in order to pro-duce a bit-wise biometric template. Our experiments prove that prostate patterns have a low degree of freedom to be used in organ recognition systems and inter-class and intra-class distributions are highly correlated. However, there are still open issues that need to be addressed for future work on organ recognition, including precise segmentation and intensive computation cost.