Magnetic Resonance Imaging

Kallmann syndrome (KS) is a rare genetic disorder characterized by hypogonadtrophic hypogonadism associated with altered sense of smell. KS is due to failure of intrauterine migration of olfactory axons and gonadotropin releasing hormone (GnRH) neurons from olfactory plate to the hypothalamus. There is defective hypothalamic gonadotropin releasing hormone (GnRH) synthesis and agenesis or hypoplasia of olfactory bulbs and olfactory sulcus. The prevalence is estimated at one in 10,000 males and one in 50,000 females. We described a case of 22 years male patient who presented with delayed puberty, characterized by absence of facial and axillary hair and sparse pubic hair, micropenis and bilateral small testes and associated with decrease smelling capacity. Diagnostic evaluation consist of hormonal evaluation which revealed revealed low levels of testosterone, LH & FSH with normal levels of TSH, prolactine and cortisol. MRI shows agenesis of olfactory bulbs and grooves, absence of the olfactory sulcus resulting in fused gyrus rectus and medial orbital gyrus forming a single gyrus. Furthermore, this patient had partial empty sella, which is one of the anomalies that are associated to this syndrome. It is planned to manage this case with Hormonal replacement therapy to induce puberty and later on pulsatile GnRH will be administered when fertility will be desired.

A brain tumor is formed when abnormal cells get accumulated within the brain. These cells multiply in an uncontrolled manner and damage the brain tissues. Magnetic Resonance Image scans are commonly used to diagnose brain tumors. However, segmenting and detecting the brain tumor manually is a tedious task for the radiologists. Hence, there is a need for automatic systems which yield accurate results. A fully automatic method is introduced to detect brain tumors. It consists of five stages Image Acquisition, Preprocessing, Segmentation, using Fuzzy C-means technique; Harris Corner Detection based feature extraction and classification using K-NN. Performance metrics such as accuracy, precision, sensitivity and specificity are used to evaluate the performance.