Non-invasive grading of brain tumors using online support vector machine with dynamic fuzzy rule-based parameters optimization.

Non-invasive grading of brain tumors provides a valuable understanding of tumor growth that helps choose the proper treatment. In this paper, an online method with an innovative optimization approach as well as a new and fast tumor segmentation method is proposed for the fully automated grading of brain tumors in magnetic resonance (MR) images. First, the tumor is segmented based on two characteristics of the tumor appearance (intensity and edges information). Second, the features of the tumor region are extracted. Then, the online support vector machine with the kernel (OSVMK) by dynamic fuzzy rule-based optimization of the parameters is used for the grading of tumors. The performance evaluation of the proposed tumor segmentation method was performed by manual segmentation using similarity criteria. Also, tumor grading results compared the proposed online method, the conventional online method, and the batch SVM with the kernel (batch SVMK) in terms of accuracy, precision, recall, specificity, and execution times. The segmentation results show a good correlation between the tumor segmented by the proposed method and by experts manually. Also, the grading results based on the accuracy, precision, recall, and specificity, 95.20%, 97.87%, 96.48%, and 96.45%, respectively, indicate the acceptable performance of the proposed method. The execution times of the introduced online method are much less than the batch SVMK. The method demonstrates the potential of fully automated tumor grading to provide a non-invasive diagnosis in order to determine the treatment strategy for the disease. So the physicians, according to the tumor's grade, can match the treatment of the brain tumor to the patient's individual needs and thus make the best course of treatment for each patient.

Right ventricle functional parameters estimation in arrhythmogenic right ventricular dysplasia using a robust shape based deformable model.

Assessment of cardiac right-ventricle functions plays an essential role in diagnosis of arrhythmogenic right ventricular dysplasia (ARVD). Among clinical tests, cardiac magnetic resonance imaging (MRI) is now becoming the most valid imaging technique to diagnose ARVD. Fatty infiltration of the right ventricular free wall can be visible on cardiac MRI. Finding right-ventricle functional parameters from cardiac MRI images contains segmentation of right-ventricle in each slice of end diastole and end systole phases of cardiac cycle and calculation of end diastolic and end systolic volume and furthermore other functional parameters. The main problem of this task is the segmentation part. We used a robust method based on deformable model that uses shape information for segmentation of right-ventricle in short axis MRI images. After segmentation of right-ventricle from base to apex in end diastole and end systole phases of cardiac cycle, volume of right-ventricle in these phases calculated and then, ejection fraction calculated. We performed a quantitative evaluation of clinical cardiac parameters derived from the automatic segmentation by comparison against a manual delineation of the ventricles. The manually and automatically determined quantitative clinical parameters were statistically compared by means of linear regression. This fits a line to the data such that the root-mean-square error (RMSE) of the residuals is minimized. The results show low RMSE for Right Ventricle Ejection Fraction and Volume (≤ 0.06 for RV EF, and ≤ 10 mL for RV volume). Evaluation of segmentation results is also done by means of four statistical measures including sensitivity, specificity, similarity index and Jaccard index. The average value of similarity index is 86.87%. The Jaccard index mean value is 83.85% which shows a good accuracy of segmentation. The average of sensitivity is 93.9% and mean value of the specificity is 89.45%. These results show the reliability of proposed method in these cases that manual segmentation is inapplicable. Huge shape variety of right-ventricle led us to use a shape prior based method and this work can develop by four-dimensional processing for determining the first ventricular slices.

Uterine segmentation and volume measurement in uterine fibroid patients' MRI using fuzzy C-mean algorithm and morphological operations.

BACKGROUND: Uterine fibroids are common benign tumors of the female pelvis. Uterine artery embolization (UAE) is an effective treatment of symptomatic uterine fibroids by shrinkage of the size of these tumors. Segmentation of the uterine region is essential for an accurate treatment strategy. OBJECTIVES: In this paper, we will introduce a new method for uterine segmentation in T1W and enhanced T1W magnetic resonance (MR) images in a group of fibroid patients candidated for UAE in order to make a reliable tool for uterine volumetry. PATIENTS AND METHODS: Uterine was initially segmented using Fuzzy C-Mean (FCM) method in T1W-enhanced images and some morphological operations were then applied to refine the initial segmentation. Finally redundant parts were removed by masking the segmented region in T1W-enhanced image over the registered T1W image and using histogram thresholding. This method was evaluated using a dataset with ten patients' images (sagittal, axial and coronal views). RESULTS: We compared manually segmented images with the output of our system and obtained a mean similarity of 80%, mean sensitivity of 75.32% and a mean specificity of 89.5%. The Pearson correlation coefficient between the areas measured by the manual method and the automated method was 0.99. CONCLUSIONS: The quantitative results illustrate good performance of this method. By uterine segmentation, fibroids in the uterine may be segmented and their properties may be analyzed.