A desk top computer program for visualized statistical analysis of lesional images in intracerebral hemorrhage

Statistically identified information on the relationships between the sites of lesions in intracerebral hemorrhage (ICH), risk factors such as a smoking or drinking habit, anamnesis, and biochemical data through blood tests will extend assistance to neuromedical clinicians on their daily clinical duties. It will provide them with a useful guide to determine the method of treatment. Also, it will be a basic research material for their clinical studies on diagnosis, progress, or prognosis in ICH. In order to obtain such statistics with the help of the computer, we need to have a computationally effective image database system. As is generally known, medical image data especially requires a great amount of storage; high-speed processing techniques are therefore also needed to deal with such data effectively. In addition, it is desired that we have outputs from the analysis edited with well-visualized effect, using 3D computer graphics, etc. These are why most existing image processing systems have been designed to work on comparatively large-scale computers. So far as we know, it is hard to find a practical and inexpensive personal computer-based application system for visualized statistical analysis of lesional images in ICH. We have developed a desk top computer-based program for statistical analysis of lesional image data of ICH. With this system, we can organize a medical image database that consists of the personal data of patients with ICH (sex, age, occupation, diagnosis, symptoms, part of physical disorder, etc.), risk factors, anamnesis (cerebral apoplexy, hypertension, hypotension, corpulence, diabetes, hyperlipidemia, atrial fibrillation, valvular endocarditis, etc.), biochemical data of blood, and lesional image data from CT or MRI. This system consists of the following components: 1) database management, 2) information retrieval (IR), 3) lesional image processing, 4) statistical analysis, and 5) prognostic prediction. The images are drawn manually on prescribed data sheets by tracing CT or MRI films and are read through the image scanner; then the compressed data of the digitized images is recorded in the database. Each recorded image data consists of the following two components: the frame image that corresponds to the contour of tissues of interest on the corresponding sliced section, and the actual image that corresponds to the lesion itself. In our system, these two images are separately stored and managed so that we can effectively perform subsequent image analysis. Other variables in the database (risk factors, anamnesis, etc.) are mainly used as search keys for making the aggregate of image data by the IR subsystem. In any aggregate, its elements, namely image data, have common medical background descriptions with the search keys. These aggregates can be used as input for the lesional image processing subsystem. With this subsystem, we can obtain the accumulated distribution of frequencies within a specified range of any sliced section, display planar color maps and profiles associated with the distribution, reconstruct it in 3D form, perform transformations of 3D images (zooming, enhancement, rotation, etc.), and test the significant difference of frequencies between any two different sites. We have been making practical use of this system to find the neurological relationship between the symptom (dysarthria, and paralysis of upper/lower limbs) and the site of lesion with cerebral infarction in pons. This study is quite important since the distributions of pyramidal tract related to the above symptom in pons are not well-known compared to those in cerebral cortex, internal capsule, or cerebral peduncle. With our system, we have obtained several findings expected to be helpful for this study. However, since this study is still in the initial phases, we will only present the outcome as a working example of our system. Our system was originally developed for analyzing lesional images with ICH. However, it could