Characterization of Normal Pineal Gland in Sudanese Population Using Magnetic Resonance Images

Abstract

This study aim to estimation the volume of the pineal gland using disc summation method (DSM) and identification of shapes in normal Sudanese adult with classification to the region using MRI; defining the corpus, pineal gland, tectum, and third ventricle were carried out using Interactive Data Language (IDL) program as platform for the generated codes, 159 consecutive patients scanned for Brian MRI to pineal region using 1.5 Tesla, the sequence was3D-T1. Detailed Demographic Information of Population including; age, gender, weight, height, and BMI was recorded. DSM used to measure the normal pineal gland in normal individuals, and the shapes of pineal gland were evaluated in Axial & Sagittal images. On the other hand the features extraction was done by First Order Statistics. The results showed that the pineal gland volume showed inconclusive difference between gender 0.135±0.0063 0.137±0.0063 cm3, for female and male respectively and using analysis of variance test for the age with body mass index BMI and the pineal gland volume were the p.value show that there is significant difference between the body mass index 0.000 and pineal gland volume 0.037 with age. The pineal gland volume can be estimated using the following linear equations: Pineal gland Volume = 0.0004 (Age/ys) + 0.1262andPineal gland Volume = 0.0012 (BMI) + 0.1104. The distribution of shape of pineal gland showed three different shapes; pear, fusiform and cone shape. Where the pear shape found in 13.8%, of the cases, the fusiform shape 38.4% cases and the cone shape in47.8%. The result this study also showed that the mean ± S.D pineal gland volumes using Disk Summation Method were found to be 0.136 ± 0.007 cm3.The classification showed that the pineal gland areas were classified well from the rest of the tissues although it has characteristics mostly similar to surrounding tissue. also the Gray Level variation and features give varies classification accuracy. Texture features are introduced using Gray Level variation, features and the FOS. FOS gives a classification score matrix generated by linear discriminate analysis with overall classification accuracy of 92.7%, where the classification accuracy of corpus 93.3%, pineal gland 97.9%, tectum 89.9%, while the third Ventricle showed classification accuracy of 88.5%. In conclusion these relationships are stored in a Texture Dictionary that can be later used to automatically annotate new MRI with the appropriate pineal gland.