| BackgroundThe estimation of postmortem interval has been one of the most important and difficult issues in the field of criminal science and forensic medicine. It is of great importance to estimate PMI accurately and quickly both for criminal cases and civil cases. During the past decades, many new methods have been proposed to estimate PMI, both by theoretical and technologic means involving physics, chemistry, biology, immunohistochemistry and other basic sciences. But as yet, all the proposed methods and techniques are still far away from practical application, which is supported by the fact that studies on the reliability and precision of death time estimation are still scarce. Moreover, operating error tends to occur with complicated procedures.We therefore prefer to go back to the direct observation method and try to find quantified data by image analysis. Image processing and analyzing technique are employed to find features representing the quantified relationship between changes of corneal opacity and postmortem intervals. We applied K-Nearest Neighbor classifier, Adaptive Boosting classifier and Support Vector Machine classifier to evaluate the estimating result. This new technique is convenient and reliable, which is promising to solve the problem of lack of proper data for PMI estimation. Objective1. Establish a observation method to record the postmortem changes of rabbit's cornea with high definition camera continuously which could be cut by every 15 minutes to get digital images.2. With matlab software, use a gray-level histogram based method for segmenting corneal-pupil region (object region) from eye images and extract nine features to reflect the changes of cornea at different postmortem intervals, including four color-based features and five texture-based ones which could build up a feature database for corneal images3. Analyze the features primarily and establish models with classifiers.4. Compare the three classifiers with 9 features.Material and methods1. Preliminary experiment:One healthy rabbit was sentenced to death by air embolism. The body was put in a dark and closed room with temperature of about (25±1)℃and the relative humidity of 20%-60%. Meanwhile, the cornea was exposed to air by stretching eyelids by using haemostatic forceps and was lighted by a desk lamp. From the time point of pronounced death up to 47 h, videos was taken continuously at cornea by employing digital camera (SONY, HDR-SR12, Japan) and was cut by every 15 minutes to obtain digital images of rabbit's eye. The images gotten from 15 minutes to 47 hours after death were labeled with number one to 188.2.The eye images were segmented with gray level histogram method to divide into object region and nonobject region, then the object region was used.3.9 features were extracted with Matlab software, including GF, EL, G, S, C, J, Mean, Var and Ske. GF, gray-level based feature, presents the gray value difference of two regions in an images. MEAN, Means, the mean of gray value in the images, describes the average brightness of the whole images. VAR, Variance, the variance of images, in which, the lower of the variance means the changing of images is much smaller. SKE, Skewness,the Skewness of images, the greater asymmetry of distribution of images, the value of Skewness will be much bigger. EL, ratio of low-frequency energy to high-frequency energy, the higher El implies more energy is collected in low-frequency. G, contrast, textual features, the Contrast of images. S, Entropy, the informational entropy of images. The bigger value of entropy illuminates the distribution of texture in the image is more irregular. C, correlation, the correlation between the pixels in the images. J, Energy, the measure of uniformity of the gray-value distribution. The higher of the Energy's value, the scale of texture is much bigger.4. The features of all the images from all the rabbits were classified with K-Nearest' Neighbor classifier under 4-folds cross validations where the mean value was taken as accuracy rate. For each mission the experiment went through 5 rounds. The postmortem period from 15 minutes to 47 hours was divided into 3 intervals,4 intervals,5 intervals,6 intervals,8 intervals,10 intervals and 14 intervals (i.e. with 3 intervals there was about 15 hour for each interval in this 47 hours). The accuracy rate and predicting precision were evaluated under different interval length.5. Four healthy rabbits were labeled with R1, R2, R3 and R4, Their videos were taken and the images were obtained and analyzed with the same method stating in preliminary experiment. KNN classifier were applied as mentioned above.6. The predicting results were compared by using KNN classifier, Adaptive Boosting classifier and Support Vector Machine respectively with all features from images of four rabbits.4-folds cross validation methods were used and each experiment went 5 rounds.Results1. The classification model showed good results on the estimation of postmortem interval.2. The gray-level histogram based method effectively carried out segmentation in our experiment. Few images should be selected out and modified manually since the shadow under eye and the region of haemostatic forceps presents.3. All the nine features were capable for classification. The classification result of using single feature was weak. The combination use of 9 features improved the performance greatly.4. With the different amounts of intervals, there were good classification results both by using features from one rabbits alone and by features from the collection of four rabbits.5. As the amount of intervals increased, the accuracy rate decreased in each postmortem period by KNN classifier. While using features from each rabbit, the average accuracy rate was 97.1% with 3 intervals,88.5% with 8 intervals and 81.5% with 14 intervals. While using features from 4 rabbits, the accuracy rate was 96.9% with 3 intervals,87.6% with 8 intervals and 80.9% with 14 intervals.6. By adapting Adaboost classifier and using 9 features from each rabbit, the average accuracy rate was 94.4% with 3 intervals,85.3% with 8 intervals and 72.9% with 14 intervals; which were a little lower than KNN classifier. While using features from 4 rabbits, the accuracy rate was 85.1% with 3 intervals and 64.7% with 14 intervals, which were lower than KNN classifier.7. By adapting SVM classifier and using 9 features from each rabbit, the average accuracy rate was 88.3% with 3 intervals and 50.9% with 14 intervals; While using features from 4 rabbits, the accuracy rate was 78.9% with 3 intervals and 30.2% with 14 intervals. They were all much lower than the former two classifiers.Conclusions1. The classification model by using 9 features have been established successfully to estimate PMI.2. The gray-level based histogram method could segment the object region effectively.3. The 9 features extracted in our experiment could carry out PMI estimation reliably.4. In these experiments, KNN classifier is better than Adaboost classifier and SVM classifier. Background:Poisonings cause considerable morbidity and mortality, which influences the safety and healthy of humans beings worldwide. We collected the poisoning cases from our department and analyzed them in order to reflect the characteristic issues and the changing trends in central china with hope of helping improve public prevention and forensic examination.Material and methods:The records of 218 poisoning deaths in Hubei province of China from the Department of Forensic Medicine located at the middle region of China, Tongji Center for Medicolegal Expertise in Hubei (TCMEH), from 1999 to 2008 were retrospectively reviewed.Results:The majority (69.7%) of poisoning victims aged between the interval of 20 years old and 49 years old, and there was a male preponderance (male:female=1.7:1). The most common category of substances involved in poisoning deaths were rodenticide (19.7%), pesticide& herbicide (17.9%), carbon monoxide (16.5%), drugs (13.8%) and alcohols (12.4%). The manner of death, of the vast majority (64.7%), was accidental; suicidal intent was present in 25.2% of cases, homicide in 3.7%, and undetermined 4.1%. Ingestion was the predominant route of exposure (65.1%), followed in frequency by inhalation, injection and dermal. When compared with the former reports from the same institution, one for 1956-1984 and another for 1983-1999, an increase was found in the proportion of deaths due to rodenticides, CO, alcohols and drugs, as well as in accidental poisoning deaths. Conclusions:Poisoning deaths due to pesticides remain the major public health problem in China. Our government should carry out further regulatory enforcement to manage and restrict the application of pesticides and rodenticides which are were most dangerous to humans.
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