Current Medical Imaging - Volume 12, Issue 2, 2016

Image segmentation is considered to be the most important practical aspect of image processing. It is bethought to have its application in medical imaging and also it acts as a clinical diagnostic tool. Medical image segmentation (MIS) is facilitated by automating the depiction of anatomical structures and other region of interest. In case of Computer Aided Diagnosis, MIS is considered to be an initial and essential step. Accuracy of image segmentation algorithms are focused more behind the success of any medical image analysis. Whatever may be the application either in radiotherapy planning or clinical diagnosis and treatment, exact segmentation of medical images are cared. Hence, it remains challenging, unsolved, and sometimes seems to be a complex task too. Various MIS algorithms have emerged, which are not suitable for all images. In this paper, different MIS approaches are categorized with their sub methods and sub fields. Recent techniques for every category are also discussed and the comparison of these approaches with pros and cons is summarized. Using these techniques to develop a new hybrid algorithm will be of very much use in medical diagnosis.

In recent years, more and more detector systems utilizing Compton scattering effect to detect gamma rays have been developed due to their potential for improved image sensitivity. However, the standard non-electron-tracking Compton based detectors can restrict the origin of the photon only to a cone. Therefore, one key challenge in the data analysis process is the reconstruction of the source distribution, from the measured data. Several different techniques have been developed, ranging from basic back-projections, iterative maximum likelihood and Bayesian techniques, as well as stochastic and analytic methods. This paper reviews the existing image reconstruction techniques developed for Compton imaging, their basic approach, their application areas, their restrictions and difficulties.

Hybrid PET/MRI represents an innovative diagnostic technology for non-invasive in vivo imaging of cancer. Although the current clinical experience is limited to few clinical centres investing in such an ambitious technology, preliminary results are showing potentials for hybrid PET/MRI to enter in the clinical setting as imaging technology that can profoundly impact on the pool of available in vivo multimodal imaging studies. The higher contrast increases detectability of oncological lesions in specific applications; the reduction of radiation exposure leads benefits for children and young adults and for follow-up studies; the multi-functionality of PET/MRI opens up to several opportunities for assessing cancer disease and for addressing oncological patients to optimal care.

Calcium Pyrophosphate Deposition (CPPD) is an extremely common and treatable cause of acute and chronic arthritis. Its radiographic presentation should be recognized in order to suggest the diagnosis to the clinician. This review encompasses its broad variety of radiographic presentations as well as its appearance on CT, MRI, and ultrasound. A review of its nomenclature, epidemiology, pathophysiology, diagnosis, treatment, and clinical presentation is included as well.

Image modeling can be concerned as a basic core of many medical image analysis/ processing systems. Indeed, proposed model for a medical image defines the required process such as coding, compression, contrast enhancement, denoising, feature extraction, classification, etc. In this paper, we present a comprehensive classification of the models used in medical image processing. Assortment of various models can be done in different manners. In a wide categorization, each model can be applied in spatial or transform domain. In transform domain, we divide models into two subgroups subject to the choice of the basis function as a data adaptive and non-data adaptive transform models. Beside this classification, we categorize all the models in both spatial or transform domain, as a deterministic, stochastic, geometric, or partial differential equation (PDE) based models. After describing each of these models, we provide a tree structure figure to display the classification of these models and the relations between them. Although we attempt to present an all-around classification of different models used in medical images, it is necessary to note that these models are not entirely distinct from each other and in some cases they may overlap with each other. In addition, we try to illustrate by two examples on retinal Optical Coherence Tomography (OCT) and color fundus images that how this categorization of models can be used in different image processing applications and conclude that considering this classification can help researchers to have a cognitive selection of models for their own specified goals.

Introduction: Surgical treatment of tumors, particularly metastases to the spine, has become increasingly common owing to the progress in anesthesiology and spinal surgery and greater detectability. The patients qualified for surgeries are those with mechanical pain, fracture or at risk of vertebral fracture or neurological complications. The basis for qualification for different types of surgeries is clinical and imaging examination, particularly MRI and CT. Qualification should always be multidisciplinary and requires understanding and knowledge of its most essential aspects. When carrying out imaging examinations, it is necessary to assess the size and the type of the tumor, taking into account of differential diagnosis. One should also consider the factors indicating spinal instability or the onset of neurological deficits. The criteria developed by Kostiuk-Weinstain and Taneichi are used for that purpose. The aim of the present study was to evaluate the correspondence between the most essential elements of clinical and MRI examination of the spine and the intraoperative status of patients with spinal tumors. Materials and Methods: We carried out prospective examination assessing the correspondence between the clinical status and MR images and the intraoperative spine. We introduced algorithm to describe the morphology of neoplastic lesions within the spine. Results: The information obtained from the clinical examination and the intraoperative status of the spine corresponded with the MRI examination with the exception of the assessment of neoplastic infiltration to soft tissues, dura mater and nerve roots. It was also found that there are no clear-cut MRI features allowing differentiation of metastatic lesions from primary tumors and osteitis. Furthermore, MRI examination does not allow for the assessment of the quality of bone tissue in the vicinity of the tumor.