Support vector machines based analysis of brain SPECT images for determining cerebral abnormalities in asymptomatic diabetic patients
| Autor: | Dionisis Cavouras, Ioannis Kalatzis, N. Piliouras, D. Pappas |
|---|---|
| Rok vydání: | 2003 |
| Předmět: |
Adult
Male Health Informatics Image processing Brain damage Asymptomatic Diabetic Neuropathies Health Information Management Histogram Healthy volunteers Humans Medicine General Nursing Tomography Emission-Computed Single-Photon Greece business.industry Diabetes mellitus type II Brain Middle Aged Support vector machine Diabetes Mellitus Type 2 Radiographic Image Interpretation Computer-Assisted Female Health Services Research medicine.symptom business Nuclear medicine Classifier (UML) |
| Zdroj: | Medical Informatics and the Internet in Medicine. 28:221-230 |
| ISSN: | 1463-9238 |
| DOI: | 10.1080/14639230310001613449 |
| Popis: | An image processing method was developed to investigate whether brain SPECT images of patients with diabetes mellitus type II (DMII) and no brain damage differ from those of normal subjects.Twenty-five DMII patients and eight healthy volunteers underwent brain 99mTc-Bicisate SPECT examination. A semi-automatic method, allowing for physician's interaction, was developed to delineate specific brain regions (ROIs) on the SPECT images. Twenty-eight features from the grey-level histogram and the spatial-dependence matrix were computed from numerous small image-samples collected from each specific ROI. Classification into 'diabetics' and 'non-diabetics' was performed for each ROI separately. The classical least squares-minimum distance (LSMD) classifier and the recently developed support vector machines (SVM) classifier were used. System performance was evaluated by means of the leave-one-out method; one sample was left out, the classifier was trained by the rest of the samples, and the left-out sample was classified. By repeating for all samples, the classifier's performance could be tested on data not incorporated in its design.Highest classification accuracies (LSMD: 97.8%, SVM: 99.1%) were achieved at the right occipital lobule employing two features, the standard deviation and entropy. For the rest of the ROIs classification accuracies ranged between 84.5 and 98.6%.Our findings indicate cerebral blood flow disruption in patients with DMII. The proposed system may assist physicians in evaluating cerebral blood flow in patients with DMII undergoing brain SPECT. |
| Databáze: | OpenAIRE |
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