Computer-aided measurement of liver volumes in CT by means of geodesic active contour segmentation coupled with level-set algorithms.
نویسندگان
چکیده
PURPOSE Computerized liver extraction from hepatic CT images is challenging because the liver often abuts other organs of a similar density. The purpose of this study was to develop a computer-aided measurement of liver volumes in hepatic CT. METHODS The authors developed a computerized liver extraction scheme based on geodesic active contour segmentation coupled with level-set contour evolution. First, an anisotropic diffusion filter was applied to portal-venous-phase CT images for noise reduction while preserving the liver structure, followed by a scale-specific gradient magnitude filter to enhance the liver boundaries. Then, a nonlinear grayscale converter enhanced the contrast of the liver parenchyma. By using the liver-parenchyma-enhanced image as a speed function, a fast-marching level-set algorithm generated an initial contour that roughly estimated the liver shape. A geodesic active contour segmentation algorithm coupled with level-set contour evolution refined the initial contour to define the liver boundaries more precisely. The liver volume was then calculated using these refined boundaries. Hepatic CT scans of 15 prospective liver donors were obtained under a liver transplant protocol with a multidetector CT system. The liver volumes extracted by the computerized scheme were compared to those traced manually by a radiologist, used as "gold standard." RESULTS The mean liver volume obtained with our scheme was 1504 cc, whereas the mean gold standard manual volume was 1457 cc, resulting in a mean absolute difference of 105 cc (7.2%). The computer-estimated liver volumetrics agreed excellently with the gold-standard manual volumetrics (intraclass correlation coefficient was 0.95) with no statistically significant difference (F = 0.77; p(F < or = f) = 0.32). The average accuracy, sensitivity, specificity, and percent volume error were 98.4%, 91.1%, 99.1%, and 7.2%, respectively. Computerized CT liver volumetry would require substantially less completion time (compared to an average of 39 min per case by manual segmentation). CONCLUSIONS The computerized liver extraction scheme provides an efficient and accurate way of measuring liver volumes in CT.
منابع مشابه
A New Computer-Aided Detection System for Pulmonary Nodule in CT Scan Images of Cancerous Patients
Introduction: In the lung cancers, a computer-aided detection system that is capable of detecting very small glands in high volume of CT images is very useful.This study provided a novelsystem for detection of pulmonary nodules in CT image. Methods: In a case-control study, CT scans of the chest of 20 patients referred to Yazd Social Security Hospital were examined. In the two-dimensional and ...
متن کاملA New Algorithm for Skin Lesion Border Detection in Dermoscopy Images
Background: With advances in medical imaging systems, digital dermoscopy has become one of the major imaging modalities in the analysis of skin lesions. Thus, automated segmentation or border detection has a great impact on the subsequent steps of skin cancer computer-aided diagnosis using demoscopy images. Since dermoscopy images suffer from artifacts such as shading and hair, there is a need ...
متن کاملA Hybrid Method for Segmentation and Visualization of Teeth in Multi-Slice CT scan Images
Introduction: Various computer assisted medical procedures such as dental implant, orthodontic planning, face, jaw and cosmetic surgeries require automatic quantification and volumetric visualization of teeth. In this regard, segmentation is a major step. Material and Methods: In this paper, inspired by our previous experiences and considering the anatomical knowledge of teeth and jaws, we prop...
متن کاملSegmentation of Liver in Low-Contrast Images Using K-Means Clustering and Geodesic Active Contour Algorithms
In this paper, we present an algorithm to segment the liver in low-contrast CT images. As the first step of our algorithm, we define a search range for the liver boundary. Then, the EM algorithm is utilized to estimate parameters of a ‘Gaussian Mixture’ model that conforms to the intensity distribution of the liver. Using the statistical parameters of the intensity distribution, we introduce a ...
متن کاملEvaluation of Semi-automatic Segmentation Methods for Persistent Ground Glass Nodules on Thin-Section CT Scans
OBJECTIVES This work was a comparative study that aimed to find a proper method for accurately segmenting persistent ground glass nodules (GGN) in thin-section computed tomography (CT) images after detecting them. METHODS To do this, we first applied five types of semi-automatic segmentation methods (i.e., level-set-based active contour model, localized region-based active contour model, seed...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Medical physics
دوره 37 5 شماره
صفحات -
تاریخ انتشار 2010