BACKGROUND: Ultrasonography has shown to be useful in the diagnosis of constipation. OBJECTIVE: The aim of this study was to compare the rectal diameter and rectal wall thickness in children with and without constipation. METHODS: Children with the diagnosis of constipation according to Rome III criteria were included in the study. The children underwent transabdominal sonography for the evaluation of rectal diameter and rectal wall thickness. Ultrasonography was performed with a full bladder. Children without constipation who underwent abdominal sonography were assigned to the control group. RESULTS: The rectal diameter was larger in children with constipation than in children without constipation (31.72±9.63 mm vs 19.85±4.37 mm; P=0.001). The rectal wall was thinner in children with constipation than in children without constipation (1.75±0.33 mm vs 1.90±0.22 mm; P=0.032). There was no significant difference between boys and girls with constipation in terms of rectal diameter (31.02±8.57 mm 32.77±11.35 mm; P=0.63). CONCLUSION: Transabdominal rectal diameter measurement may be useful in the diagnosis of constipation.

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Detection of brain tumor's grade is a very important task in treatment plan design which was done using invasive methods such as pathological examination. This examination needs resection procedure and resulted in pain, hemorrhage and infection. The aim of this study is to provide an automated noninvasive method for estimation of brain tumor's grade using Magnetic Resonance Images (MRI). After pre-processing, using Fuzzy C-Means (FCM) segmentation method, tumor region was extracted from post-processed images. In feature extraction, texture, Local Binary Pattern (LBP) and fractal-based features were extracted using Matlab software. Then using Grasshopper Optimization Algorithm (GOA), parameters of three different classification methods including Random Forest (RF), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) were optimized. Finally, performance of three applied classifiers before and after optimization were compared. The results showed that the random forest with accuracy of 99.09% has achieved better performance comparing other classification methods.