Local Bit-Plane Decoded Pattern: A Novel Feature Descriptor for Biomedical Image Retrieval

A novel image feature descriptor based on the local bit-plane decoded pattern (LBDP) is introduced for indexing and retrieval of biomedical images in this paper. A local bit-plane transformation scheme is proposed to compute the local bit-plane transformed values for each image pixel from the bit-plane binary contents of its each neighboring pixels. The introduced LBDP is generated by finding a binary pattern using the difference of center pixel's intensity value with the local bit-plane transformed values. The efficacy of the LBDP is tested under biomedical image retrieval using average retrieval precision and average retrieval rate. Three benchmark databases Emphysema-CT, NEMA-CT, and Open Access Series of Imaging Studies magnetic resonance imaging are used for the evaluation and comparison of the proposed approach with recent state-of-art methods. The experimental results confirm the discriminative ability and the efficiency of the proposed LBDP for biomedical image indexing and retrieval and prove the outperformance of existing biomedical image retrieval approaches.

I. Introduction

The patient diagnosis in medical institutions and hospitals is becoming more challenging day by day due to hasty growth of biomedical images. X-ray, ultrasound, magnetic resonance imaging (MRI), computer tomography (CT), etc., are the common formats of the patient diagnosis database images. The biomedical data should be structured to allow the efficient search, access, and retrieval in order to facilitate the automatic decision support patient diagnosis using expert systems. Content-based biomedical image indexing and retrieval is turning up continuously to combat this problem on the basis of the visual cues representation of the image such as color, texture, shape, structure, faces, etc. Using biomedical image retrieval, the physician can point out the disorder present in the patient report by retrieving the most similar report from related reference reports. The existing biomedical image retrieval systems are depicted through published literature [1]– [7].