Computer-aided assessment of regional abdominal fat with food residue removal in CT.

Abdominal obesity is strongly related to metabolic disorders, hypertension, and cardiovascular disease (1,2). Separate quantification of visceral and subcutaneous fat in the abdomen is important because of their different metabolic consequences and because visceral fat more than subcutaneous fat has been associated with numerous age-related problems, such as insulin resistance, chronic inflammation, and cardiac diastolic dysfunction. Computed tomography (CT) is a widely adopted technique for assessing abdominal fat. Measurements of subcutaneous and visceral fat areas are typically computed from CT images using manual or semimanual analyses operated by clinical specialists. Even though these techniques usually provide reliable results, they are time-consuming and affected by intra- and inter-operator variability. Therefore, a regional fat quantification approach is highly desirable to increase the analysis throughput and reproducibility, especially in large epidemiological studies (3–6). Earlier abdomen fat quantification approaches (7,8) required user intervention to define a region of interest and detected the fat voxels using density thresholding techniques in the domain of Hounsfield units (HUs). More recent techniques make use of joint intensity and texture information in a fuzzy affinity framework to identify the fat class (9), employ shape and appearance models for quantification of regional fat and muscle compartments (10), or use radial analysis to delineate the boundary between subcutaneous and visceral fat (11). Despite the high level of sophistication and accuracy that has been achieved by the state-of-the-art methods, the presence of food residues in the intestines that can greatly reduce precision and even introduce a bias in the measure has not been explicitly addressed in the literature. The pixels of the food particles have HUs that greatly overlap with those of the visceral fat voxels and are usually identified as fat when simple intensity criteria are used. The quantification error induced by food residues can introduce a relevant bias in studies of aging because the permanence of food residues is likely to occur in larger quantities in older than in younger individuals. Herein we present a method for automated quantification of abdominal fat in CT that concentrates on reducing false positives in the estimation of visceral fat resulting from food residues. The fat identification and separation stage uses intensity-based clustering and a gradient vector flow (GVF) active contour model. In several instances, the detected visceral fat includes regions of food residues. To exclude these areas from the quantification of visceral fat, we applied a machine learning scheme that employs texture, local shape features, and a support vector machine (SVM) classifier to identify and remove the food residues within the intestines. The food residue removal stage is applied as a final step in our methodology. In the experiment section, we report performance evaluation of our method against semimanually segmented reference masks of fat regions and draw conclusions on the applicability of this approach in a large-scale study.