Classification of glioma and solitary brain metastasis using magnetic resonance imaging radiomics feature / 解剖学报

Objective To analyze the difference of radiomics features between solitary brain metastasis and glioma using routine 3T TI, T2 and fluid attenuation inversion recovery (FLAIR) magnetic resonance imaging, to explore the significance of texture features constructed in different directions and angles in tumor regions in distinguishing the two kinds of tumors, and to explore a feasible method for high-precision classification of solitary brain metastases and gliomas. Methods Given the multimodal images of 43 patients with glioma and 45 age- and sex- matched patients with solitary brain metastasis, the gray level co-occurrence matrices of different angles of each slice were constructed from the transverse, coronal and sagittal directions of the tumor regions of these images, and the texture spatial relationship features (including contrast, correlation, energy and homogeneity) were calculated. Wilcoxon rank sum test was used to eliminate redundant features and select features with strong distinguishing ability. Finally, SVM linear kernel classifier was used to classify the selected features to achieve the identification of the two kinds of tumors. Results When classifying glioma and solitary brain metastasis, the precision, recall, Fl score and accuracy of multimodal and multidirectional combination features were 0.8857, 0.9114, 0.8944 and 0.8922, respectively. The area under the receiver operating characteristic curve obtained by linear kernel SVM classifier was 0. 9602. Totally 40 of the 45 patients with solitary brain metastases were correctly classified, and 39 of the 43 gliomas were correctly classified. Conclusion The multimodal and multi-directional combination features of tumor areas can be classified by linear kernel SVM classifier to distinguish gliomas from solitary brain metastases, which can be used as a second opinion to effectively assist doctors in making diagnosis.

Multi class disorder detection of magnetic resonance brain images using composite features and neural network

Brain disorder recognition has becoming a promising area of study. In reality, some disorders share similar features and signs, making the task of diagnosis and treatment challenging. This paper presents a rigorous and robust computer aided diagnosis system for the detection of multiple brain abnormalities which can assist physicians in the diagnosis and treatment of brain diseases. In this system, we used energy of wavelet sub bands, textural features of gray level co-occurrence matrix and intensity feature of MR brain images. These features are ranked using Wilcoxon test. The composite features are classifi ed using back propagation neural network. Bayesian regulation is adopted to fi nd the optimal weights of neural network. The experimentation is carried out on datasets DS-90 and DS-310 of Harvard Medical School. To enhance the generalization capability of the network, fi vefold stratifi ed cross validation technique is used. The proposed system yields multi class disease classifi cation accuracy of 100% in diff erentiating 90 MR brain images into 18 classes and 97.81% in diff erentiating 310 MR brain images into 6 classes. The experimental results reveal that the composite features along with BPNN classifi er create a competent and reliable system for the identifi cation of multiple brain disorders which can be used in clinical applications. The Wilcoxon test outcome demonstrates that standard deviation feature along with energies of approximate and vertical sub bands of level 7 contribute the most in achieving enhanced multi class classifi cation performance results.

The value of MRI texture analysis based on gray level co-occurrence matrix to predict cervical lymph node metastasis in tongue carcinoma patients / 中华放射学杂志

Objective To evaluate the value of MRI texture analysis based on gray level co-occurrence matrix to predict cervical lymph node metastasis in patients with tongue carcinoma. Methods A total of 70 patients with tongue carcinoma confirmed by pathology were analyzed retrospectively. The patients were divided into cervical lymph node (LN) metastasis group (unilateral LN+, n=18;bilateral LN+,n=22) and no cervical lymph node metastasis(LN-,n=30). T1W, T2W and contrast-enhanced T1W images of the largest section of tumor were selected. ROI of the lesion was manually drew and GLCM texture parameters (energy, contrast, correlation, inverse difference and entropy) were extracted. The tumor length, thickness and para-lingual distance between tumor and tongue midline were also measured.Differences of all parameters were compared between LN+ group and LN- group, unilateral and bilateral cervical lymph node metastasis group, the parameters with statistically significant difference in predicting the efficiency of cervical lymph node metastasis were analyzed. The diagnostic efficiency of lymph node metastasis was calculated. Results The correlation, inverse difference and entropy based on T2WI showed significant difference (Zcor elation=2.97, tinverse dif erence=5.14, tentropy=2.41; P<0.05), entropy showed the best diagnostic efficiency, the area under the ROC curve (AUC) was 0.90, the cut off value was 7.19, the sensitivity was 80.0％, specificity was 86.7％. Only entropy showed significant difference between unilateral LN+group and bilateral LN+group (P<0.05), the AUC was 0.82, the cut off value was 7.47, the sensitivity was 90.9％, specificity was 78.8％. The index of tumor length, thickness and para-lingual distance between tumor and tongue midline all showed significant difference between LN+group and LN-group (P<0.05), the thickness showed the best diagnostic efficiency, the AUC value was 0.81, the cut off value was 11.19, the sensitivity was 78.0％, specificity was 81.7％. The index of tumor length, thickness and para-lingual distance between tumor and tongue midline showed no significant difference between unilateral LN+ group and bilateral LN+ group (P>0.05). The diagnostic sensitivity of radiologists was 65.0％ (26/40), the specificity was 80.0％ (24/32) on cervical lymph node metastasis. Conclusion Texture analysis based on T2WI can predict cervical lymph node metastasis in patients with tongue carcinoma. Entropy has certain value in predicting bilateral cervical lymph node metastasis.

Extraction of Texture Feature Parameter of Transverse Section in Chinese Herbal Medicine by Gray-Level Co-occurrence Matrix / 世界科学技术-中医药现代化

Digitalization is an important method for the objectification and quantification of quality control on Chi-nese herbal medicine. To solve the problem of texture online visualization of Chinese herbal medicine and the estab-lishment of automatic identification method based on the texture, 12 Chinese herbal medicines were selected to cap-ture the images of their transverse sections. A total of 11 features were extracted from images using Gray-level Co-occurrence Matrix (GLCM) method. Through analyzing the influence of distances and angles between pixels on identi-fication results, it was concluded that when the distance was d = 3 and the angle was ? = 0o, the features extracted were suitable for expressing the texture of the transverse sections. The results indicated the feasibility of establishing the automatic identification method on Chinese herbal medicine based on image of transverse section. It will provide new technologies for the objectification and quantification of identification on Chinese herbal medicine.

Impact of Rotation Sampling on Parameters of Texture Feature in Chinese Herbal Medicine Images / 世界科学技术-中医药现代化

This study was aimed to investigate the impact of rotation sampling on feature parameters of texture im-ages of Chinese herbal medicine. Four Chinese herb medicine with various shape and texture feature were taken as research materials. Images of complete and incomplete herbal medicine were collected respectively after different ro-tation angles. The 26 parameters were extracted by gray-level co-occurrence matrix and grayscale gradient matrix. The impact of rotation sampling on feature parameters was investigated through analysis of variation tendency and range of 26 parameters. The results showed that if the Chinese herbal medicine was complete, the 26 parameters were not impacted by the rotation angle, whereas the 26 parameters were impacted by the rotation angle and the im-pact will be more obvious when the shape of Chinese herb medicine was irregular. It was concluded that in order to get a high quality of images and construct a well identification model based on the parameter of texture features, we must consider the impact of rotation angle on the parameters to Chinese herbal medicine with various shapes and tex-ture features.

Research on Parameters of Texture Feature of Transverse Section in Incomplete Chinese Herbal Medicine / 世界科学技术-中医药现代化

This study was aimed to explore the impact of integrality of Chinese herbal medicine on parameters of tex-ture feature in transverse section images. Three Chinese herbal medicine of Semen Arecae, Radix et Rhizoma Rhei and Radix A tractylodis Macrocephalae with different texture features were taken as research materials. Parts of Chi-nese herbal medicine were cut off from the whole by equal proportions. The 26 parameters were extracted by gray-level co-occurrence matrix and grayscale gradient matrix. The similarities and differences of 26 parameters of texture feature in the parts and whole, rectangular and fan-shaped Chinese herbal medicine were compared. The results showed that parameters of Semen A recae and Radix et Rhizoma Rhei with radial or annular texture had better con-sistency in whole and fan-shaped parts. Parameters of Radix A tractylodis Macrocephalae with irregular texture fea-ture were approximately the same in the whole and rectangular parts. It was concluded that whether parameters of texture features in parts Chinese herbal medicine can present the whole were related to its texture feature and the shape of the parts. This study provided the basis for collection of Chinese herbal medicine when sampling images. It also laid a foundation for the extraction of accurate parameter of texture feature.

Comparative Studies on Parameters of Texture Feature of Transverse Section in Different Chinese Herbal Medicine / 世界科学技术-中医药现代化

This study was aimed to compare the difference of parameters of texture feature in the transverse section images of the same and different Chinese herbal medicine. A total of 26 parameters of herbal medicines were ex-tracted by gray-level co-occurrence matrix and grayscale gradient matrix. The graph of mutative curve was drawn. And differences of 26 parameters of texture feature in the same and different Chinese herbal medicine were com-pared. The results showed that parameters of texture feature extracted by gray-level co-occurrence matrix and grayscale gradient matrix had similarities and differences in the same and different Chinese herbal medicine. It was concluded that the method can show the texture feature scientifically and quantitatively. It also laid a foundation for the establishment of an automatic identification model, but the parameters still had instability. All these remind us to find some parameters which can show the details of texture feature preferably, thus perfect the extracted method of texture features in Chinese herbal medicine.

Texture analysis of SPIO-enhanced MR imaging in rat models of hepatocellular carcinoma and hepatocirrhosis based on gray level co-occurrence matrix / 中国医学影像技术

Objective To analyze the texture features of SPIO-enhanced MR imaging in rat models of hepatocellular carcinoma (HCC) and hepatocirrhosis with gray level co-occurrence matrix (GLCM). Methods HCC and hepatocirrhosis models were established in rats. SPIO-enhanced MR images were obtained. A total of 161 regions of interests (ROIs, 81 of HCC and 80 of hepatocirrhosis) were selected manually. Feature values as angular second moment, contrast, correlation, inverse difference moment, entropy, variance were extracted based on GLCM. The differences of feature values between two groups were statistically analyzed. Results In SPIO-enhanced MR images, hypointense signal changes were found in hepatocirrhosis, as well as hyperintensity in HCC nodules and intermixed intensity in larger HCC nodules. Correlation and entropy values of HCC group were higher than that of hepatocirrhosis group, while the angular second moment, contrast, inverse difference moment, and variance values were lower than hepatocirrhosis group. Conclusion The feature values based on GLCM could be used for the further computer aided diagnosis of SPIO-enhanced MR images in rat models of HCC and hepatocirrhosis.

Medical Image Segmentation Method Based on Texture Character and GRBF Network / 医疗卫生装备

Objective To get a better medical image segmentation result by studing a new image segmentation method. Methods Medical images were segmented using image segmentation method based on texture character and generalized radial basis function neural networks. The texture character parameters were obtained according to gray level co-occurrence matrix. The parameters were input to the generalized radial basis function neural networks to train the network. Results Comparatively, perfect binary images were obtained by using this new image segmentation method. Conclusion The emulational results show that the method is an effective medical image segmentation method.