Analysis of color feature extraction techniques for pathology image retrieval system

Medical imaging has become an important tool not only in documenting patient presentation and clinical findings, but also understanding and managing various diseases. Image data provides tangible visual evidence of disease manifestation. The number of digital images that needs to be acquired, analyzed, classified, stored and retrieved in the medical centers is exponentially growing with the advances in medical imaging technology. The goal of this work is to develop a medical image retrieval system for pathology images that implements recent improvements in feature representation, efficient indexing, and similarity matching. The feature representation is analyzed using various color feature extraction techniques in HSV, CIE L*u*v*, CIE L*a*b* color space. The image indexing is implemented based on the color descriptors. The efficiency of similarity matching is analyzed by means of Euclidean distance, Histogram intersection and hamming distance. The experimental results are compared for the color feature extraction techniques discussed in this paper. The image database considered for retrieval process consists of pathology images belonging to dermatology, dental, hematology, gastroscopic and cervical cancer. This paper suggests an optimal color feature extraction technique suitable for different pathology image categories chosen for the study.