DMLLE: a large-scale dimensionality reduction method for detection of polyps in CT colonography

Abstract

Computer-aided diagnosis systems have been shown to be feasible for polyp detection on computed tomography (CT) scans. After 3-D image segmentation and feature extraction, the dataset of colonic polyp candidates has large-scale and high dimension characteristics. In this paper, we propose a large-scale dimensionality reduction method based on Diffusion Map and Locally Linear Embedding for detection of polyps in CT colonography. By selecting partial data as landmarks, we first map the landmarks into a low dimensional embedding space using Diffusion Map. Then by using Locally Linear Embedding algorithm, non-landmark samples are embedded into the same low dimensional space according to their nearest landmark samples. The local geometry of samples is preserved in both the original space and the embedding space. We applied the proposed method called DMLLE to a colonic polyp dataset which contains 58336 candidates (including 85 6-9mm true polyps) with 155 features. Visual inspection shows that true polyps with similar shapes are mapped to close vicinity in the low dimensional space. FROC analysis shows that SVM with DMLLE achieves higher sensitivity with lower false positives per patient than that of SVM using all features. At the false positives of 8 per patient, SVM with DMLLE improves the average sensitivity from 64% to 75% for polyps whose sizes are in the range from 6 mm to 9 mm (p < 0.05). This higher sensitivity is comparable to unaided readings by trained radiologists.