Identification of body fat tissues in MRI data

In recent years non-invasive medical diagnostic techniques have been used widely in medical investigations. Among the various imaging modalities available, Magnetic Resonance Imaging is very attractive as it produces multi-slice images where the contrast between various types of body tissues such as muscle, ligaments and fat is well defined. The aim of this paper is to describe the implementation of an unsupervised image analysis algorithm able to identify the body fat tissues from a sequence of MR images encoded in DICOM format. The developed algorithm consists of three main steps. The first step pre-processes the MR images in order to reduce the level of noise. The second step extracts the image areas representing fat tissues by using an unsupervised clustering algorithm. Finally, image refinements are applied to reclassify the pixels adjacent to the initial fat estimate and to eliminate outliers. The experimental data indicates that the proposed implementation returns accurate results and furthermore is robust to noise and to greyscale in-homogeneity

A case report of a rare intramuscular granular cell tumor

Background: Granular cell tumors (GCTs) were firstly described by Weber in 1854 and 70 years later by Abrikossoff and classified as benign tumors. Originally considered muscle tumors, they have been identified as neural lesions, due to their close association with nerve and to their immunohystochemical characteristics. GCTs are uncommon tumors and they may arise in any part of the body; they have been mainly observed in tongue, chest wall and upper extremities; less frequent sites are larynx, gastrointestinal tract, breast, pituitary stalk and the female anogenital region. Here we report a case of GCT showing an uncommon localization such as the upper third of the right rectus muscle of the abdominal wall. Case presentation: A 45 year-old woman of Caucasian origin presented to the surgeon with a 6-month history of light pain in the upper third of the abdominal wall. Radiological exams (Ultrasonography, Computed Tomography and Contrast magnetic resonance imaging) showed a localized in the right rectus abdominis muscle. After excision, histological and immunohystochemical analysis, with the support of electron microscopy, allowed making diagnosis of granular cell tumor. Discussion: After fist description by Abrikosoff in 1926 of GCT like mesenchymal tumor of unknown origin, in recent years immunohystochemical techniques definitely demonstrated the histogenetic derivation of GCT from Schwann cells. Granular cell tumors are rare, small, slow-growing, solitary and painless subcutaneous nodules which behave in a benign fashion, but can have a tendency to recur; in rare cases they can metastasize, when they became malignant; there are some clinical and histological criteria to suspect the malignance of this tumor. Conclusion: It is important that clinicians, radiologists and pathologists are aware of the clinical presentation and histopathology of GCT for appropriate management, counselling and follow-up. In our case we had a complete radiological, morphological and immunohystochemical characterization of the lesion and a definitive diagnosis of benignity confirmed by electron microscopy

Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

Recent trends, technical concepts and components of computer-assisted orthopedic surgery systems: A comprehensive review

Computer-assisted orthopedic surgery (CAOS) systems have become one of the most important and challenging types of system in clinical orthopedics, as they enable precise treatment of musculoskeletal diseases, employing modern clinical navigation systems and surgical tools. This paper brings a comprehensive review of recent trends and possibilities of CAOS systems. There are three types of the surgical planning systems, including: systems based on the volumetric images (computer tomography (CT), magnetic resonance imaging (MRI) or ultrasound images), further systems utilize either 2D or 3D fluoroscopic images, and the last one utilizes the kinetic information about the joints and morphological information about the target bones. This complex review is focused on three fundamental aspects of CAOS systems: their essential components, types of CAOS systems, and mechanical tools used in CAOS systems. In this review, we also outline the possibilities for using ultrasound computer-assisted orthopedic surgery (UCAOS) systems as an alternative to conventionally used CAOS systems.Web of Science1923art. no. 519