Intestinal Polyp Recognition Based on Salient Codebook Locality-Constrained Linear Coding with Annular Spatial Pyramid Matching

Abstract

Gastrointestinal endoscopy has become a widely used technique to diagnose intestinal anomalies like polyp. However, the vast quantities of images produced in the detection process greatly increase the burden of clinicians and the misdiagnosis rate. To tackle this problem, a new computer-assisted detection system called Salient Codebook Locality-Constrained Linear Coding (SCLLC) with Annular Spatial Pyramid Matching (ASPM) is proposed in this paper to classify intestinal polyp images automatically. Firstly, SIFT features are extracted from images and k-means clustering method is utilized on the patch features to obtain the initial codebook. Secondly, the proposed SCLLC algorithm is employed to achieve the salient codebook and encode the features by emphasizing the salient basis vectors in feature coding process. Then, a max-pooling strategy of annular region segmentation based on Spatial Pyramid Matching is proposed to improve the effectiveness of processing for intestinal images. Finally, SVM classifier is developed to carry out polyp images classification tasks. The experimental results exhibit promising 94.10% accuracy, 91.20% sensitivity and 97.01% specificity. In addition, the 0.97 s detecting time indicates the feasibility of clinical application. Our proposed method can effectively improve the overall performance of intestinal polyp recognition by integrating the internal base vector correlation of codebook and considering the annular structure of the intestinal images.