Automatic segmentation of cervical region in colposcopic images using K-means.

Colposcopy is an important imaging modality for the detection of cervical lesions. The analysis of colposcopic images, especially the effective segmentation of the cervical region, has important clinical value in clinical application. A cervical segmentation method based on the HSV color mode is proposed, which can divide and extract the cervical region in the medical and anatomical sense. Firstly, the histogram threshold method is used to analyze the histogram (Y) of the colposcopic image. In order to achieve the removal of the mirror reflection pretreatment operation in the colposcopy image. Secondly, the Preprocessed RGB images is used. Then, the colposcopic image is converted into the HSV color space, and the V component is extracted using the K-means algorithm. Finally, using the area filter to smooth the edge, the segmented cervical region can be obtained. In our study, 110 standard colposcopy images, which were tagged by experts, were tested and verified. The segmentation results were analyzed and compared using dice coefficients, Jaccard coefficients, structural segmentation accuracy specificity, sensitivity, positive predictive value, and negative predictive value. Our experimental results show that the accuracy, specificity and sensitivity of the method are 87.25%, 81.99% and 96.70%, respectively. The effectiveness of the method in clinical segmentation was verified. Our study has demonstrated that cervical regional segmentation of colposcopic images based on HSV color space using K-means has high clinical utility and can help medical specialists in the diagnosis of cervical cancer.

[Study on spectral calibration of discrimination of corn variety using near-infrared spectra based on DS algorithm].

From the perspective of calibration, the present paper studies the model stability problem in qualitative analysis of NIR. Aiming at the issue of model failure caused by different data acquisition time, 13 varieties of corn were used as experimental material, and learning from the idea of model calibration transfer between the two instruments in quantitative analysis of NIR, the DS (direct standardization) algorithm was used to calibrate the spectra acquired at different times with the same instrument, that made the varieties identification model established one time able to be applied to identify the test data at different acquisition time. First, transfer set was selected from the master spectrum set by Kennard/Stone algorithm, the corresponding number spectrums in slave spectrum set were selected, and then DS algorithm was applied to transfer set to calculate the transformation function between the two sets of data. Finally, the remaining slave spectrums were transformed so that they could apply to the model. This study does some experiment to discuss the impact of the number of transfer set and the location of calibration on the calibration results. Respectively, the experiment results were analyzed from two aspects, one is the correct discrimination rate in qualitative analysis, and the other is the distribution distance between master spectrums and slave spectrums before and after calibration. The experiment results indicate that this approach is effective to solve the spectra drift produced by sampling over time, can bring higher recognition rate on different sampling time test sets, also improves the robustness and application scope of the identification model, and the experiment results also indicate that the best result can be obtained with calibration locating after feature extraction.

[Effects of Yifuning capsule on blood lipids of ovariectomized hyperlipidemia rats].

OBJECTIVE: To observe the effects of Yifuning (YEN) capsule on blood lipids of ovariectomized hyperlipidemia rats. METHOD: Fifty-six female mature Sprague-Dawley rats were randomized into 7 groups: normal control group, model control group, diethylstilbestrol tablets (DT) group, Xuezhikang group, YFN high, middle and low dose groups. The ovariectomized rats were fed on high fat diet and administrated with the drugs for 3 weeks, then were killed and estimated body weight, liver index and five items of blood lipid (TC, TG, HDL-C, LDL-C, VLDL) by test kit. Enzyme (such as HP, LDL, and whole lipase) was detected too. RESULT: The weight and liver index of model control group increased obviously as compared with normal group. YFN could reduce TG, TC, and LDL-C (P < 0.05) of ovariectomized hyperlipidemia rats obviously and increase HP, LDL and whole lipase (P < 0.05) on the other hand. CONCLUSION: YFN can ameliorate blood lipids of ovariectomized hyperlipidemia rats.