Segmentation of liver, its vessels and lesions from CT images for surgical planning

<p>Abstract</p> <p>Background</p> <p>Cancer treatments are complex and involve different actions, which include many times a surgical procedure. Medical imaging provides important information for surgical planning, and it usually demands a proper segmentation, i.e., the identification of meaningful objects, such as organs and lesions. This study proposes a methodology to segment the liver, its vessels and nodules from computer tomography images for surgical planning.</p> <p>Methods</p> <p>The proposed methodology consists of four steps executed sequentially: segmentation of liver, segmentation of vessels and nodules, identification of hepatic and portal veins, and segmentation of Couinaud anatomical segments. Firstly, the liver is segmented by a method based on a deformable model implemented through level sets, of which parameters are adjusted by using a supervised optimization procedure. Secondly, a mixture model is used to segment nodules and vessels through a region growing process. Then, the identification of hepatic and portal veins is performed using liver anatomical knowledge and a vein tracking algorithm. Finally, the Couinaud anatomical segments are identified according to the anatomical liver model proposed by Couinaud.</p> <p>Results</p> <p>Experiments were conducted using data and metrics brought from the liver segmentation competition held in the Sliver07 conference. A subset of five exams was used for estimation of segmentation parameter values, while 15 exams were used for evaluation. The method attained a good performance in 17 of the 20 exams, being ranked as the 6^thbest semi-automatic method when comparing to the methods described on the Sliver07 website (2008). It attained visual consistent results for nodules and veins segmentation, and we compiled the results, showing the best, worst, and mean results for all dataset.</p> <p>Conclusions</p> <p>The method for liver segmentation performed well, according to the results of the numerical evaluation implemented, and the segmentation of liver internal structures were consistent with the anatomy of the liver, as confirmed by a specialist. The analysis provided evidences that the method to segment the liver may be applied to segment other organs, especially to those whose distribution of voxel intensities is nearly Gaussian shaped.</p

Using Mixture Covariance Matrices to Improve Face and Facial Expression Recognitions

Abstract. In several pattern recognition problems, particularly in image recognition ones, there are often a large number of features available, but the number of training examples for each pattern is significantly less than the dimension of the feature space. This statement implies that the sample group covariance matrices often used in the Gaussian maximum probability classifier are singular. A common solution to this problem is to assume that all groups have equal covariance matrices and to use as their estimates the pooled covariance matrix calculated from the whole training set. This paper uses an alternative estimate for the sample group covariance matrices, here called the mixture covariance, given by an appropriate linear combination of the sample group and pooled covariance matrices. Experiments were carried out to evaluate the performance associated with this estimate in two biometric applications: face and facial expression. The average recognition rates obtained by using the mixture covariance matrices were higher than the usual estimates

A New Quadratic Classifier applied to Biometric Recognition

In biometric recognition applications, the number of training examples per class is limited and consequently the conventional quadratic classifier either performs poorly or cannot be calculated. Other non-conventional quadratic classifiers have been used in limited sample and high dimensional classification problems. In this paper, a new quadratic classifier called Maximum Entropy Covariance Selection (MECS) is presented. This classifier combines the sample group covariance matrices and the pooled covariance matrix under the principle of maximum entropy. This approach is a direct method that not only deals with the singularity and instability of the maximum likelihood covariance estimator, but also does not require an optimisation procedure. In order to evaluate the MECS effectiveness, experiments on face and fingerprint recognition were carried out and compared with other similar classifiers, including the Reguralized Discriminant Analysis (RDA), the Leave-One-Out Covariance estimator (LOOC) and the Simplified Quadratic Discriminant Function (SQDF)

Using Mixture Covariance Matrices to Improve Face and Facial Expression Recognitions

In several pattern recognition problems, particularly in image recognition ones, there are often a large number of features available, but the number of training samples for each pattern is significantly less than the dimension of the feature space. This statement implies that the sample group covariance matrices often used in the Gaussian maximum probability classifier are singular. A common solution to this problem is to assume that all groups have equal covariance matrices and to use as their estimates the pooled covariance matrix calculated from the whole training set. This paper uses an alternative estimate for the sample group covariance matrices, here called the mixture covariance, given by an appropriate linear combination of the sample group and pooled covariance matrices. Experiments were carried out to evaluate the performance associated with this estimate in two recognition applications: face and facial expression. The average recognition rates obtained by using the mixture covariance matrices were higher than the usual estimates.

Small Sample Problem in Bayes Plug-in Classifier for Image Recognition

applied to discriminate high dimensional data. This classifier is based on similarity measures that involve the inverse of the sample group covariance matrices. These matrices, however, are singular in &quot;small sample size&quot; problems. Therefore, other methods of covariance estimation have been proposed where the sample group covariance estimate is replaced by covariance matrices of various forms. In this paper, a new covariance estimator is proposed and compared with two covariance estimators known as RDA and LOOC. The new estimator does not require an optimisation procedure, but an eigenvector-eigenvalue ordering process to select information from the projected sample group covariance matrices whenever possible and the pooled covariance otherwise. The effectiveness of the method is shown by experimental results carried out on face and facial expression recognition, using different databases for each application