The use of 3D surface fitting for robust polyp detection and classification in CT colonography

Automatic rectum limit detection by anatomical markers correlation

Several diseases take place at the end of the digestive system. Many of them can be diagnosed by means of different medical imaging modalities together with computer aided detection (CAD) systems. These CAD systems mainly focus on the complete segmentation of the digestive tube. However, the detection of limits between different sections could provide important information to these systems. In this paper we present an automatic method for detecting the rectum and sigmoid colon limit using a novel global curvature analysis over the centerline of the segmented digestive tube in different imaging modalities. The results are compared with the gold standard rectum upper limit through a validation scheme comprising two different anatomical markers: the third sacral vertebra and the average rectum length. Experimental results in both magnetic resonance imaging (MRI) and computed tomography colonography (CTC) acquisitions show the efficacy of the proposed strategy in automatic detection of rectum limits. The method is intended for application to the rectum segmentation in MRI for geometrical modeling and as contextual information source in virtual colonoscopies and CAD systems.

AUTOMATIC DETECTION OF COLORECTAL POLYPS IN STATIC IMAGES

Colorectal cancer continues to be one of the leading causes of mortality worldwide. Scanning using colorectal endoscopy is a useful and common method in clinical examinations. However, the scanning and polyps detections are performed by physicians. Failures to detect polyps might be caused due to lack of experience or knowledge. The purpose of this paper is to discover a scheme able to distinguish polyps from normal tissue in static images off-line.Texture features are studied for the discrimination between polyps and normal tissue. Two useful and simple features are proposed. The student's t-test is applied in selecting useful features to reduce the computation time. The support vector machine is used as a classifier to identify the position of polyps. A study on the numbers in the training patterns is done in order to select an optimal ratio between the polyps and non-polyps sub-images. Seventy-four colonoscopic images are collected to test the system. Half are used as training images and half for testing. The experimental result shows the system can identify all polyps if the colonoscopic images contain a single polyp. The sensitivity is 86.2% and the false-positive rate is 1.26 marks per image.

Improving Polyp Detection Algorithms for CT Colonography: Pareto Front Approach

We investigated a Pareto front approach to improving polyp detection algorithms for CT colonography (CTC). A dataset of 56 CTC colon surfaces with 87 proven positive detections of 53 polyps sized 4 to 60 mm was used to evaluate the performance of a one-step and a two-step curvature-based region growing algorithm. The algorithmic performance was statistically evaluated and compared based on the Pareto optimal solutions from 20 experiments by evolutionary algorithms. The false positive rate was lower (p<0.05) by the two-step algorithm than by the one-step for 63% of all possible operating points. While operating at a suitable sensitivity level such as 90.8% (79/87) or 88.5% (77/87), the false positive rate was reduced by 24.4% (95% confidence intervals 17.9-31.0%) or 45.8% (95% confidence intervals 40.1-51.0%) respectively. We demonstrated that, with a proper experimental design, the Pareto optimization process can effectively help in fine-tuning and redesigning polyp detection algorithms.

Improving initial polyp candidate extraction for CT colonography

Reducing the number of false positives (FPs) as much as possible is a challenging task for computer-aided detection (CAD) of colonic polyps. As part of a typical CAD pipeline, an accurate and robust process for segmenting initial polyp candidates (IPCs) will significantly benefit the successive FP reduction procedures, such as feature-based classification of false and true positives (TPs). In this study, we introduce an improved scheme for segmenting IPCs. It consists of two main components. One is geodesic distance-based merging, which merges suspicious patches (SPs) for IPCs. Based on the merged SPs, another component, called convex dilation, grows each SP beyond the inner surface of the colon wall to form a volume of interest (VOI) for that IPC, so that the inner border of the VOI beyond the colon inner surface could be segmented as convex, as expected. The IPC segmentation strategy was evaluated using a database of 50 patient studies, which include 100 scans at supine and prone positions with 84 polyps and masses sized from 6 to 35 mm. The presented IPC segmentation strategy (or VOI extraction method) demonstrated improvements, in terms of having no undesirably merged true polyp and providing more helpful mean and variance of the image intensities rooted from the extracted VOI for classification of the TPs and FPs, over two other VOI extraction methods (i.e. the conventional method of Nappi and Yoshida (2003 Med. Phys. 30 1592-601) and our previous method (Zhu et al 2009 Cancer Manag. Res. 1 1-13). At a by-polyp sensitivity of 0.90, these three methods generated the FP rate (number of FPs per scan) of 4.78 (new method), 6.37 (Nappi) and 7.01 (Zhu) respectively.

Computer-aided detection for CT colonography: update 2007

Computed tomographic colonography (CTC) is an emerging technique for polyp detection in the colon. However, lesion detection can be challenging due to insufficient patient preparation, chosen CT technique or reader imperfection. The primary goal of computer-aided detection (CAD) for CTC is locating possible polyps, and presenting the reader with these polyp candidates. Other goals are sensitivity improvement and reduction of reading time and inter-observer variability. The multistep CAD procedure typically consists of segmentation of the colonic wall (e.g. region growing); selection of intermediate polyp candidates (curvature analysis, sphere fitting, normal analysis, slope density function ...); classification of final candidates for detection and listing suspicious polyps (location, size and volume). Remaining task for the radiologist is the validation or rejection of the polyp candidates. State-of-the-art CAD systems should require minimal or even no user interaction for the extraction of the colonic wall, offer a computation time less than 10-20 min and high sensitivity and specificity for different polyp sizes and shapes, with a low number of false positives. These systems have the potential to increase radiologist's performance and to decrease inter-reader variability. Besides CAD key techniques we also discuss new developments in CAD and describe recent applications facilitating CTC.

An ellipse-fitting based method for efficient registration of breast masses on two mammographic views

When reading mammograms, radiologists routinely search for and compare suspicious breast lesions identified on two corresponding craniocaudal (CC) and mediolateral oblique (MLO) views. Automatically identifying and matching the same true-positive breast lesions depicted on two views is an important step for developing successful multiview based computer-aided detection (CAD) schemes. The authors developed a method to automatically register breast areas and detect matching strips of interest used to identify the matched mass regions depicted on CC and MLO views. The method uses an ellipse based model to fit the breast boundary contour (skin line) and set a local Cartesian coordinate system for each view. One intersection point between the major/minor axis and the fitted ellipse perimeter passed through breast boundary is selected as the origin and the majoraxis and the minoraxis of the ellipse are used as the two axis of the Cartesian coordinate system. When a mass is identified on one view, the scheme computes its position in the local coordinate system. Then, the distance is mapped onto the local coordinate of the other view. At the end of the mapped distance a registered centerline of the matching strip is created. The authors established an image database that includes 200 test examinations each depicting one verified mass visible on the two views. They tested whether the registered centerline identified on another view can be used to locate the matched mass region. The experiments show that the average distance between the mass region centers and the registered centerlines was +/- 8.3 mm and in 91% of testing cases the registered centerline actually passes through the matched mass regions. A matching strip width of 47 mm was required to achieve 100% sensitivity for the test database. The results demonstrate the feasibility of the proposed method to automatically identify masses depicted on CC and MLO views, which may improve future development of multiview based CAD schemes.