Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Automated tissue image analysis is a process by which computer-controlled automatic test equipment to evaluate tissue samples, using computations to derive quantitative measurements from an image to avoid subjective errors. In a typical application, automated tissue image analysis could be used to measure the aggregate activity of cancer cells in a biopsy of a cancerous tumor taken from a patient.
The existing methods and devices of degassing fuel liquid, special liquids in weightlessness conditions are expensive and economically not advantageous, since degassing is achieved by a complex, not effective method on the technically bulky devices. The developed methods of degassing fuel liquid and special liquids in the power supply and life-support systems of automatic spacecraft using the controlled vibration or the specially created temperature gradient in the liquid are simple on its construction, economically highly effective and productive. The developed methods permit to produce the unique gasfilled materials with uniform distribution of fine dispersed gas phase using the application of electric field and controlled vibration or electric field and inertial forces in the conditions of space flight. The developed method of processing in weightlessness of antitumorigenic medicines with uniform distribution of spherical drops of cytostatic, which permit during implanting of medicine inside of body parts of patient,which feed by blood tumor, with calculated dose of cytostatic to act directly on tumor cells at the moment of their mitosis.
The Automatic analysis of Medical images using computer analysis diagnosis is one of the most interesting field in biomedical image processing. The proposed system gives techniques related to MRI analysis. A statistical structure analysis based on tumor segmentation scheme is presented, which focuses on the structural analysis in both normal and abnormal tissues,will help doctors to avoid the human error in manual interpretation of medical content. In this study, an enhanced thresholding algorithm is applied to extract the abnormal part from the 2D MRI. Samples of different ages and cases are taken from the AL-Imammain Al-Kadhimain Medical city and the Radiology Institute.Calculating the area of the abnormal tissue (tumor), the Wavelet transformation is then applied which is a signal estimation technique that exploits the capabilities to denoising the signal. A statistical feature has been obtained, then a hybrid method is applied in which k-mean clustering is a method of cluster analysis which aims to partitioned the images into clusters. Finally,an algorithm has been created to colored images depending on the boundary. This helps to separate the abnormal part into k clusters.
Since the beginning of Radiation Therapy, the Radiation Mold Technology has continued to develop. It continues to find new applications, most notably, so far as mold technology, in the development of delivery a homogenous dose of radiation to an accurately localized target volume in order to produce tumor control with minimal effect on surrounding normal tissues. Mold technology itself has also moved forward. No self-respecting mold room will now wish to be seen without Simple or complex immobilization devices and even automatic Styrofoam cutters are to be found in some departments. The automatic Styrofoam offers real advantages for lead blocks cutting. The manual Styrofoam is now supplanted by fully digital approaches, offering the advantage of greater reliability and the ability to handle accurate foam cutting. Radiation Mold Technology's role in Radiotherapy is increasing, for example in immobilization of the affected part of the patient, Localization to assess the size and relationship of the tumor volumes to the skin, Protection of vital structures adjacent to the tumor volume and Reproducibility of treatment technique.
Accurate tumor extraction is a critical task in the case of brain tumor due to the complex structure of the brain. Segmentation is an important process to extract suspicious region from complex medical images. Automatic detection of brain tumor through MRI can provide valuable outlook and accuracy of earlier brain tumor detection. This book focuses on how one can provide optimal cluster from MRI Images using Machine Learning Techniques with example data sets.
In the book is represented the computer model of the behavior of gas bubble both in the Earth's conditions and in the conditions of the acceleration of gravity change. The computer model of the behavior of gas bubbles in the vibrating liquid with a change of the acceleration of the gravity is shown. The developed computer models have been proved experimentally during the process of the conducted tests on the board of the flying laboratory (FL) IL-76 K and showed the possibility of their practical use for the simulation of the behavior of real gas bubbles in the life-support systems and power supply technological processes of automatic spacecraft, in the conditions of real space flight. The computer system of optimal interpolation prognosis illustrates, which permits to create the medico-mathematical model of tumor, numerically reflecting all laws governing the flow of tumor process after the conducted treatment, to plan the optimum tactics of postoperative control, of the selection of the periods of test survey and regimes of the preventive treatment of oncological patients, to determine the effectiveness of the used methods of treatment of the oncological patients.
A new proposed method of fully automatic processing frameworks is given based on Geodesic Graph-cut Active Contour algorithms. The algorithm is applied to image segmentation using two different kinds of local neighborhoods in constructing the graph. The major problem with Graph-Cut approach is the incorrect selection of Liver Region with coloring similar to user's scribbles being identified as a tumor region. Results can be improved by using the proposed new technique based on Geodesic Graph-Cut method. This system has concentrated on finding a fast and interactive segmentation method for liver and tumor segmentation. In the preprocessing stage, the CT image process is carried over with mean shift filter and statistical thresholding method for reducing processing area with improving detection rate. Second stage is Liver Segmentation, the liver region has been segmented using the algorithm of the proposed method. In the next stage, Tumor segmentation also followed the same steps. Finally the liver and tumor regions are separately segmented from the computer tomography image.
Intra-cardiac masses identification in echocardiograms is an important task in cardiac disease diagnosis. To improve the diagnostic accuracy, the tumor and thrombi is identified using an automatic classification method based on the sparse representation. A region of interest is cropped to define the mass area. Then, a globally demising method is employed to remove the speckle and preserve the structures. Subsequently, the contour of the mass and its connected aerial wall are described by our proposed adaptive co segmentation localized region level set model. These methods are used to distinguish intra cardiac tumor and thrombi in echocardiography.