Feature Extraction and Classification on Esophageal X-Ray Images of Xinjiang Kazak Nationality.

Esophageal cancer is one of the fastest rising types of cancers in China. The Kazak nationality is the highest-risk group in Xinjiang. In this work, an effective computer-aided diagnostic system is developed to assist physicians in interpreting digital X-ray image features and improving the quality of diagnosis. The modules of the proposed system include image preprocessing, feature extraction, feature selection, image classification, and performance evaluation. 300 original esophageal X-ray images were resized to a region of interest and then enhanced by the median filter and histogram equalization method. 37 features from textural, frequency, and complexity domains were extracted. Both sequential forward selection and principal component analysis methods were employed to select the discriminative features for classification. Then, support vector machine and K-nearest neighbors were applied to classify the esophageal cancer images with respect to their specific types. The classification performance was evaluated in terms of the area under the receiver operating characteristic curve, accuracy, precision, and recall, respectively. Experimental results show that the classification performance of the proposed system outperforms the conventional visual inspection approaches in terms of diagnostic quality and processing time. Therefore, the proposed computer-aided diagnostic system is promising for the diagnostics of esophageal cancer.

Xinjiang Uygur Medicine Image Feature Extraction and Discriminant Analysis Based on Color and Textural Features / 中国中医药信息杂志

Objective To extract Xinjiang Uyghur medicine image features and analyze the features; To investigate the image classification effect of the researched features; To find the suitable features for Xinjiang Uyghur medicine image classification; To lay the foundation for content-based medical image retrieval system of Xinjiang Uyghur medicine images.Methods The flowers and leaves of Xinjiang Uyghur medicine were treated as the research objects. First, images were under preprocessing. Then color and textural features were extracted as original features and statistics method was used to analyze the features. Maximum classification distance was used to analyze the main features obtained from image classification. At last, the classification ability of features was evaluated by Bayes discriminant analysis.Results Color and textural features were selected and classified. The correct classification rate of flower images was 85% and the correct classification rate of leaf images was 62%. The classification effect of flower images used by selected features was better than classification effect of original feature.Conclusion Compared with the classification of original features, the classification accuracy of flower medicine is higher through selected features. This research can lay a certain foundation for the further researches on Xinjiang Uyghur medicine images and the improvement of feature extraction methods.

[Image Feature Extraction and Discriminant Analysis of Xinjiang Uygur Medicine Based on Color Histogram].

Image feature extraction is an important part of image processing and it is an important field of research and application of image processing technology. Uygur medicine is one of Chinese traditional medicine and researchers pay more attention to it. But large amounts of Uygur medicine data have not been fully utilized. In this study, we extracted the image color histogram feature of herbal and zooid medicine of Xinjiang Uygur. First, we did preprocessing, including image color enhancement, size normalizition and color space transformation. Then we extracted color histogram feature and analyzed them with statistical method. And finally, we evaluated the classification ability of features by Bayes discriminant analysis. Experimental results showed that high accuracy for Uygur medicine image classification was obtained by using color histogram feature. This study would have a certain help for the content-based medical image retrieval for Xinjiang Uygur medicine.

[The comparison of algorithms on the CT image retrieval of Xinjiang local liver hydatid disease].

Xinjiang local liver hydatid disease is an infectious parasitic disease in Xinjiang pastoral areas. Based on the image features, selecting the appropriate distance algorithms to retrieve the image quickly and accurately, different distance algorithms have been induced in this area, which can greatly assist the doctors to early detect, diagnose and cure the liver hydatid disease. This paper compared the performance of different distance algorithms to retrieve the image when using the liver hydatid disease medical image texture features. The results showed that: for the liver hydatid disease medical images retrieval based on gray level cocurrence matrix (GLCM) texture features, the Mahalanobis distance algorithm is superior to other distance algorithms.

Speaker identification based on Mel frequency cepstrum coefficient and complexity measure / 生物医学工程学杂志

In present, the most basically used parameters for speaker identification are linear predictive coding (LPC) parameter, Mel frequency cepstrum coefficient(MFCC), etc. First in this paper only MFCC was used as the parameter and then Lempel-Ziv Complexity was combined with MFCC as parameters. The text-dependent recognition rate of 50 speakers increased from 42% to 80% and the text-independent recognition rate of 50 speakers increased from 60% to 72%. This test shows that Lempel-Ziv complexity, as a new parameter, can be applied to speaker identification.

A method of iris image quality evaluation / 生物医学工程学杂志

Iris image quality evaluation plays a very important part in iris computer recognition. An iris image quality evaluation method was introduced into this study to distinguish good image from bad image caused by pupil distortion, blurred boundary, two circles appearing not concentric, and severe occlusion by eyelids and eyelashes. The tests based on this method gave good results.

Studying psychology test EEG based on information transmission of cerebral cortex / 生物医学工程学杂志

23 subjects' 8-lead (Fp1, Fp2, Cp3, Cp4, T7, T8, P7, P8) electroencephalogram (EEG) was recorded when they were doing mental arithmetic at four different levels. We calculated the information transmission time series in human cerebral cortex basing on EEG, and the Lempel-Ziv complexity and C1C2 complexity of these time series. When 20 subjects were doing the most difficult mental arithmetic, the information transmission series between lead at left-brain (Cp3, T7, P7) and other leads was of more complexity than others; a light "cross" could be seen after the information transmission matrix was converted to image; when complexity was calculated, the difference was more significant by use of C1 complexity than by other complexity measures.