PET medical image retrieval using fuzzy connectedness image segmentation and geometric moments

Abstract

In medical imaging DICOM (Digital Imaging and Communications in Medicine) format is the most commonly used format. Various medical imaging sources generate images in this format, which are collected in large database repository [1]. Various modalities of medical images such as CT scan, X-Ray, Ultrasound, Pathology, MRI, Microscopy, etc [2] are used to collect these images. From the analysis of these medical images proper diagnosis of different diseases can provided to the patients. This paper presents an approach for efficient image retrieval of angiograms, ultrasound and x-ray medical images from the huge medical image datasets. This paper presents the proposed fuzzy connectedness image segmentation with geometric moment approach which provides more precise retrieval results with less computational complexity. This paper compares the various techniques for DICOM medical image retrieval and shows that the proposed fuzzy connectedness image segmentation with geometric moments based image feature extraction and image retrieval approach performs better as compared to other approaches. The proposed method produced results with the precision of 95%. 