3D-local oriented zigzag ternary co-occurrence fused pattern for biomedical CT image retrieval

Abstract

In this letter, a new feature descriptor called three dimensional local oriented zigzag ternary co-occurrence fused pattern ($3D{\text{-}}LOZTCoFP$) is proposed for computed tomography (CT) image retrieval. Unlike the conventional local pattern based approaches, where the relationship between the reference and its neighbors in a circular shaped neighborhood are captured in a 2-D plane, the proposed descriptor encodes the relationship between the reference and it’s neighbors within a local 3D block drawn from multiscale Gaussian filtered images employing a new 3D zigzag sampling structure. The proposed 3D zigzag scan around a reference not only provides an effective texture representation by capturing non-uniform and uniform local texture patterns but the fine to coarse details are also captured via multiscale Gaussian filtered images. In this letter, we have introduced three unique 3D zigzag patterns in four diverse directions. In $3D{\text{-}}LOZTCoFP$, we first calculate the 3D local ternary pattern within a local 3D block around a reference using proposed 3D zigzag sampling structure at both radius 1 and 2. Then the co-occurrence of similar ternary edges within the local 3D cube is computed to further enhance the discriminative power of the descriptor. A quantization and fusion based scheme is introduced to reduce the feature dimension of the proposed descriptor. Experiments are conducted on popular NEMA and TCIA-CT image databases and the results demonstrate superior retrieval efficiency of the proposed $3D{\text{-}}LOZTCoFP$ descriptor over many local pattern based approaches in terms of average retrieval precision and average retrieval recall in CT image retrieval.