Image processing, medical image segmentation, PET-CT

Background: Positron emission tomography (PET) images with amyloid tracers show normal uptake in healthy white matter, which suggests that amyloid tracers are potentially useful for studying such white matter diseases as multiple sclerosis (MS). Methods: Twelve patients diagnosed with MS (5 with RRMS, 5 with SPMS, and 2 with PPMS) and 3 healthy controls underwent studies with MRI and 18 F-florbetaben-PET imaging. Images were preprocessed using Statistical Parametric Mapping software. We analysed 18 F-florbetaben uptake in demyelinating plaques (appearing as hyperintense lesions in FLAIR sequences), in normal-appearing white matter, and in grey matter. Results: Mean standardized uptake value relative to cerebellum was higher in normally appearing white matter (NAWM) (1.51 ± 0.12) than in damaged white matter (DWM) (1.24 ± 0.12; P = .002). Mean percentage of change between NAWM and DWM was −17.56 % ± 6.22 %. This percentage of change correlated negatively with EDSS scores (r = −0.61, p < .05) and with age (r = −0.83, p < 0.01). Progressive forms of MS showed a more pronounced reduction of the uptake in DWM in comparison to relapsing-remitting form. Conclusions: Uptake of 18 F-florbetaben in damaged white matter is lower than that occurring in normally-appearing white matter. These findings indicate that amyloid tracers may be useful in studies of MS, although further research is needed to evaluate the utility of amyloid-PET in monitoring MS progression.

In the digestion of amino acids, carbohydrates, and lipids, as well as protein synthesis from the consumed food, the liver has many diverse responsibilities and functions that are to be performed. Liver disease may impact the hormonal and nutritional balance in the human body. The earlier diagnosis of such critical conditions may help to treat the patient effectively. A computationally efficient AW-HARIS algorithm is used in this paper to perform automated segmentation of CT scan images to identify abnormalities in the human liver. The proposed approach can recognize the abnormalities with better accuracy without training, unlike in supervisory procedures requiring considerable computational efforts for training. In the earlier stages, the CT images are pre-processed through an Adaptive Multiscale Data Condensation Kernel to normalize the underlying noise and enhance the image's contrast for better segmentation. Then, the preliminary phase's outcome is being fed as the input for the Anisotropic Weighted-Heuristic Algorithm for Real-time Image Segmentation algorithm that uses texture-related information, which has resulted in precise outcome with acceptable computational latency when compared to that of its counterparts. It is observed that the proposed approach has outperformed in the majority of the cases with an accuracy of 78%. The smart diagnosis approach would help the medical staff accurately predict the abnormality and disease progression in earlier ailment stages.

One of the main objectives of the IAEA Radioisotope Production and Radiation Technology programme is to enhance the expertise and capability of IAEA Member States in deploying emerging radioisotope products and generators for medical and industrial applications in order to meet national needs as well as to assimilate new developments in radiopharmaceuticals for diagnostic and therapeutic applications. This will ensure local availability of these applications within a framework of quality assurance.

Background: The Free and Cued Selective Reminding Test (FCSRT) is the most accurate test for the diagnosis of prodromal Alzheimer's disease (AD). Recently, a novel cognitive test, the Loewenstein-Acevedo Scale for Semantic Interference and Learning (LASSI-L), has been developed in order to provide an early diagnosis.

Medical image segmentation plays an important role in treatment planning, identifying tumors, tumor volume, patient follow up and computer guided surgery. There are various techniques for medical image segmentation. This paper presents a image segmentation technique for locating brain tumor(Astrocytoma-A type of brain tumor).Proposed work has been divided in two phases-In the first phase MRI image database(Astrocytoma grade I to IV) is collected and then preprocessing is done to improve quality of image. Second-phase includes three steps-Feature extraction, Feature selection and Image segmentation. For feature extraction proposed work uses GLCM (Grey Level co-occurrence matrix).To improve accuracy only a subset of feature is selected using hybrid Genetic algorithm(Genetic Algorithm+fuzzy rough set) and based on these features fuzzy rules and membership functions are defined for segmenting brain tumor from MRI images of .ANFIS is a adaptive network which combines benefits of both fuzzy and neural network .Finally, a comparative analysis is performed between ANFIS, neural network, Fuzzy ,FCM,K-NN, DWT+SOM,DWT+PCA+KN, Texture combined +ANN, Texture Combined+ SVM in terms of sensitivity ,specificity ,accuracy.

A chest x-ray screening system for pulmonary pathologies such as tuberculosis (TB) is of paramount importance due to the increasing mortality rate of patients with undiagnosed TB, especially in densely-populated developing countries. As a first step toward developing such screening systems, this paper presents a novel computer vision module that automatically segments the lungs from posteroanterior digital chest x-ray images. The segmentation task is non-trivial, due to poor image contrast and occlusion of the lung region by ribs, clavicle, heart, and by non-TB abnormalities associated with pulmonary diseases. In the proposed procedure, we first compute a lung shape model by employing a level set based technique for registration up to a homography. Next, we use this computed mean lung shape to initialize the level set that is based on a best fit measure obtained in a heuristically estimated search space for the projective transform parameters. Once the level set is initialized, a suite of customized lower level image features and higher level shape features up to a homography evolve the level set function at a lower resolution in order to achieve a coarse segmentation of the lungs. Finally, a fine segmentation step is performed by adding additional shape variation constraints and evolving the level set in a higher resolution. We processed the standard Japanese Society of Radiological Technology (JSRT) dataset, comprised of 247 images, using this scheme. The promising results (92% accuracy) demonstrate the viability and efficacy of the proposed approach. © (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

Content-Based Image Retrieval (CBIR) locates, retrieves and displays images alike to one given as a query, using a set of features. It demands accessible data in medical archives and from medical equipment, to infer meaning after some processing. A problem similar in some sense to the target image can aid clinicians. CBIR complements text-based retrieval and improves evidence-based diagnosis, administration, teaching, and research in healthcare. It facilitates visual/automatic diagnosis and decision-making in real-time remote consultation/screening, store-and-forward tests, home care assistance and overall patient surveillance. Metrics help comparing visual data and improve diagnostic. Specially designed architectures can benefit from the application scenario. CBIR use calls for file storage standardization, querying procedures, efficient image transmission, realistic databases, global availability, access simplicity, and Internet-based structures. This chapter recommends important and complex aspects required to handle visual content in healthcare.

This report describes the design, implementation, evaluation and original enhancements to the Live-Wire method for 2D and 3D image segmentation. Live-Wire 2D employs a semi-automatic paradigm; the user is asked to select a few boundary points of the object to segment, to steer the process in the right direction, while the result is displayed in real time. In our implementation segmentation is extended to three dimensions by performing this process on a slice-by-slice basis. User's time and involvement is further reduced by allowing him to specify object contours in planes orthogonal to the slices. If these planes are chosen strategically, Live-Wire 3D can perform 2D segmentation in the plane of each slice automatically. This report also proposes two improvements to the original method, path heating and a new graph edge feature function based on variance of path properties along the boundary. We show that these improvements lead up to a 33% reduction in interaction with the user, and improved delineation in presence of strong interfering edges.

Pancreatic cancer diagnosis and staging can be difficult in 10-20% of patients. Positron emission tomography (PET)/computed tomography (CT) adds precise anatomical localisation to functional data. The use of PET/CT may add further value to the diagnosis and staging of pancreatic cancer.
OBJECTIVE:

To determine the incremental diagnostic accuracy and impact of PET/CT in addition to standard diagnostic work-up in patients with suspected pancreatic cancer.
DESIGN:

A multicentre prospective diagnostic accuracy and clinical value study of PET/CT in suspected pancreatic malignancy.
PARTICIPANTS:

Patients with suspected pancreatic malignancy.
INTERVENTIONS:

All patients to undergo PET/CT following standard diagnostic work-up.
MAIN OUTCOME MEASURES:

The primary outcome was the incremental diagnostic value of PET/CT in addition to standard diagnostic work-up with multidetector computed tomography (MDCT). Secondary outcomes were (1) changes in patients' diagnosis, staging and management as a result of PET/CT; (2) changes in the costs and effectiveness of patient management as a result of PET/CT; (3) the incremental diagnostic value of PET/CT in chronic pancreatitis; (4) the identification of groups of patients who would benefit most from PET/CT; and (5) the incremental diagnostic value of PET/CT in other pancreatic tumours.
RESULTS:

Between 2011 and 2013, 589 patients with suspected pancreatic cancer underwent MDCT and PET/CT, with 550 patients having complete data and in-range PET/CT. Sensitivity and specificity for the diagnosis of pancreatic cancer were 88.5% and 70.6%, respectively, for MDCT and 92.7% and 75.8%, respectively, for PET/CT. The maximum standardised uptake value (SUVmax.) for a pancreatic cancer diagnosis was 7.5. PET/CT demonstrated a significant improvement in relative sensitivity (p = 0.01) and specificity (p = 0.023) compared with MDCT. Incremental likelihood ratios demonstrated that PET/CT significantly improved diagnostic accuracy in all scenarios (p < 0.0002). PET/CT correctly changed the staging of pancreatic cancer in 56 patients (p = 0.001). PET/CT influenced management in 250 (45%) patients. PET/CT stopped resection in 58 (20%) patients who were due to have surgery. The benefit of PET/CT was limited in patients with chronic pancreatitis or other pancreatic tumours. PET/CT was associated with a gain in quality-adjusted life-years of 0.0157 (95% confidence interval -0.0101 to 0.0430). In the base-case model PET/CT was seen to dominate MDCT alone and is thus highly likely to be cost-effective for the UK NHS. PET/CT was seen to be most cost-effective for the subgroup of patients with suspected pancreatic cancer who were thought to be resectable.
CONCLUSION:

PET/CT provided a significant incremental diagnostic benefit in the diagnosis of pancreatic cancer and significantly influenced the staging and management of patients. PET/CT had limited utility in chronic pancreatitis and other pancreatic tumours. PET/CT is likely to be cost-effective at current reimbursement rates for PET/CT to the UK NHS. This was not a randomised controlled trial and therefore we do not have any information from patients who would have undergone MDCT only for comparison. In addition, there were issues in estimating costs for PET/CT. Future work should evaluate the role of PET/CT in intraductal papillary mucinous neoplasm and prognosis and response to therapy in patients with pancreatic cancer.

Image segmentation is a critical step in computer vision tasks constituting an essential issue for pattern recognition and visual interpretation. In this paper, we study the behavior of entropy in digital images through an iterative algorithm of mean shift filtering. The order of a digital image in gray levels is defined. The behavior of Shannon entropy is analyzed and then compared, taking into account the number of iterations of our algorithm, with the maximum entropy that could be achieved under the same order. The
use of equivalence classes it induced, which allow us to interpret entropy as a hyper-surface in real mdimensional space. The difference of the maximum entropy of order n and the entropy of the image is used to group the the iterations, in order to caractrizes the performance of the algorithm.

Apply the Image processing techniques to analyse the medical images may assist medical professionals as well as patients, especially in this research apply the algorithms to diagnose the liver tumours from the abdominal CT image. This research proposes a software solution to illustrate the automated liver segmentation and tumour detection using artificial intelligent techniques. Evaluate the results of the liver segmentation and tumour detection, in-cooperation with the radiologists by using the prototype of the proposed system. This research overcomes the challenges in medical image processing. The 100 samples collected from ten patients and received 90% accuracy rate.

Although 18F-FDG is now a days the universal tracer in PET for oncology, it is a marker of metabolic activity that is not specific enough to give positive results in a variety of benign physiological and pathological conditions. Not all cancer pathologies are detected by PET, which should be taken into account at the time of interpretation. In the case presented below, with the use of intravenous furosemide, it was possible to reduce the artifacts caused by the physiological excretion of FDG-F18 by the renal system, which allowed to identify one of the lesions that was close to the left ureter And that it was not informed in the previous CT study, for which the PET-CT was prescribed for confirmation of the diagnosis and planning of the SBRT.