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In this paper, we propose a new registration method for prone and supine computed tomographic colonography scans using graph matching. We formulate 3-D colon registration as a graph matching problem and propose a new graph matching algorithm based on mean field theory. In the proposed algorithm, we solve the matching problem in an iterative way. In each step, we use mean field theory to find the matched pair of nodes with highest probability. During iterative optimization, one-to-one matching constraints are added to the system in a step-by-step approach. Prominent matching pairs found in previous iterations are used to guide subsequent mean field calculations. The proposed method was found to have the best performance with smallest standard deviation compared with two other baseline algorithms called the normalized distance along the colon centerline (NDACC) ( p = 0.17) with manual colon centerline correction and spectral matching ( p < 1e−5). A major advantage of the proposed method is that it is fully automatic and does not require defining a colon centerline for registration. For the latter NDACC method, user interaction is almost always needed for identifying the colon centerlines. [ABSTRACT FROM PUBLISHER]

A multiobjective genetic algorithm is designed to optimize a computer-aided detection (CAD) system for identifying colonic polyps. Colonic polyps appear as elliptical protrusions on the inner surface of the colon. Curvature-based features for colonic polyp detection have proved to be successful in several CT colonography (CTC) CAD systems. Our CTC CAD program uses a sequential classifier to form initial polyp detections on the colon surface. The classifier utilizes a set of thresholds on curvature-based features to cluster suspicious colon surface regions into polyp candidates. The thresholds were previously chosen experimentally by using feature histograms. The chosen thresholds were effective for detecting polyps sized 10 mm or larger in diameter. However, many medium-sized polyps, 6–9 mm in diameter, were missed in the initial detection procedure. In this paper, the task of finding optimal thresholds as a multiobjective optimization problem was formulated, and a genetic algorithm to solve it was utilized by evolving the Pareto front of the Pareto optimal set. The new CTC CAD system was tested on 792 patients. The sensitivities of the optimized system improved significantly, from 61.68% to 74.71% with an increase of 13.03% (95% CI [6.57%, 19.5%], p=7.78×10-5) for the size category of 6–9 mm polyps, from 65.02% to 77.4% with an increase of 12.38% (95% CI [6.23%, 18.53%], p=7.95×10-5) for polyps 6 mm or larger, and from 82.2% to 90.58% with an increase of 8.38% (95%CI [0.75%, 16%], p=0.03) for polyps 8 mm or larger at comparable false positive rates. The sensitivities of the optimized system are nearly equivalent to those of expert radiologists. [ABSTRACT FROM AUTHOR]

Virtual colonoscopy (VC) is becoming a more prevalent method to detect and diagnose colorectal cancer. An essential component of using VC to detect cancerous polyps, especially in conjunction with computer-aided diagnosis, is the accurate calculation of the centerline of the colon. While the colon is often modeled as a simple cylinder, the amount of colonic distention may vary between patients and within the same patient often causing loops and multiple disconnected segments to be present in the colon segmentation. These variations have caused previous centerline algorithms to fail to capture a complete and accurate centerline for all colons. We have developed an automatic method to determine from a computed tomography (CT) VC a subvoxel precise centerline that is accurate even in cases of over-distended or under-distended colons. In this algorithm, the loops in the colon caused by over-distention are detected and removed when the centerline calculation is performed. Also, a newly developed method for the detection and segmentation of the outer wall of the colon is used to connect collapsed portions of the colon where the lumen segmentation fails to produce a continuous centerline. These two methods allow for a complete and accurate centerline to be calculated in uniformly distended colons as well as in colons containing segments which are over-distended and/or under-distended. We have demonstrated successfully the effectiveness of our algorithm on 50 cases, 25 of which resulted in erroneous solutions by previous centerline algorithms due to variability in the colon distention. [ABSTRACT FROM AUTHOR]