ABSTRACT
<p><b>OBJECTIVE</b>To explore the temporal pattern of postmortem color changes in the pupil region of the cornea for noninvasive estimation of the postmortem interval (PMI).</p><p><b>METHODS</b>Two rabbit models of air embolism and drowning were established in a dark room at a temperature of 20 ℃ with a relative humidity of 30%. The corneal images of the rabbits were acquired using a digital camera at two-hour intervals within 72 h after death. The pupil region on the corneal images was segmented using computer image processing technique (MATLAB), and the parameters of 6 image color features (RGBHSV) were extracted. Regression analysis was used to analyze the relationship between these parameters and the PMI, and the effects of different death causes on the changes of the corneal color features were also assessed.</p><p><b>RESULTS</b>Within 72 h after death from different causes, the R, G and B values of the pupil region on the corneal images all tended to increase with the PMI, showing a good fitting with the PMI ( < 0.01). No significant correlation was found between the values of H, S and V and the PMI (>0.05). The R, G and B values in the pupil region on the corneal images showed consistent variation trends after death from the two causes, and their correlations with PMI were also similar. The measured values of R, G and B in air embolism group were greater than those in the drowning group.</p><p><b>CONCLUSIONS</b>The postmortem color changes of the pupil region on corneal images follow an identifiable temporal pattern and can vary across different causes of death. The regression equations established in this study provide references for non-invasive and objective estimation of the PMI.</p>
ABSTRACT
OBJECTIVE: The most widely used method for quantifying new blood vessel growth in tumor angiogenesis is the determination of microvessel density, which is reported to be associated with tumor progression and metastasis, and a prognostic indicator of patient outcome. In this study, we propose a method for the determination of microvessel density by image analysis, to improve the accuracy and the objectivity of determination of the microvessel density. METHODS: Four-micron-thick tissue sections of renal cell carcinoma samples were stained immunohistochemically for CD34. The regions with a high degree of vascularization were selected by an expert for digitization. Each image was digitized as a 24-bits/pixel image file with a resolution of 640x480 pixels. First, segmentation of the microvessels based on pixel classification using color features in hybrid color space was performed. After use of a correction process for microvessels with discontinuities and separation of touching microvessels, we counted the number of microvessels for the microvessel density measurement. RESULTS: The result was evaluated by comparison with manual quantification of the same images. The comparison revealed that our computerized microvessel quantification was highly correlated with manual counting by a pathologist. CONCLUSION: The results indicate that our method is better than the conventional computerized image analysis methods.