Objective To study and solve the repairing and reconstruction characteristic for chronic radioactivity ulceration on chest wall.Methods Using transverse rectus abdominis musculocutaneous flaps (TRAM) to repair and reconstruct 12 cases serious chronic radioactivity ulceration after radical operation of mastocarcinoma.Including 6 cases using single pedicle TRAM flaps, 6 cases using double pedicle TRAM flaps.Results All 12 cases were applied successfully with 100% survived and were followed-up for 1 to 4 years.There were better colour, texture, elasticity on flap and obviously improvement for cicatricial tissue round ulceration.Conclusion It is better choice for repairing chronic radioactivity ulceration on chest wall with transverse rectus abdominis musculocutaneous flaps and it is also reliable method for flap circulation using double pedicle TRAM flaps by vascular anastomosis.

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.
Bayes Theorem ; Color ; Discriminant Analysis ; Drugs, Chinese Herbal ; analysis ; Medicine, Chinese Traditional

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.

Objective To propose a novel algorithm model based on YOLOv7 for detecting small lesions in ultrasound images of hepatic cystic echinococcosis.Methods The original feature extraction backbone was replaced with a lightweight feature extraction backbone network GhostNet for reducing the quantity of model parameters.To address the problem of low detection accuracy when the evaluation index CIoU of YOLOv7 was used as a loss function,ECIoU was substituting for CIoU,which further improved the model detection accuracy.Results The model was trained on a self-built dataset of small lesion ultrasound images of hepatic cystic echinococcosis.The results showed that the improved model had a size of 59.4 G and a detection accuracy of 88.1%for mAP@0.5,outperforming the original model and surpassing other mainstream detection methods.Conclusion The proposed model can detect and classify the location and category of lesions in ultrasound images of hepatic cystic echinococcosis more efficiently.

Cranial defects may result from clinical brain tumor surgery or accidental trauma. The defect skulls require hand-designed skull implants to repair. The edge of the skull implant needs to be accurately matched to the boundary of the skull wound with various defects. For the manual design of cranial implants, it is time-consuming and technically demanding, and the accuracy is low. Therefore, an informer residual attention U-Net (IRA-Unet) for the automatic design of three-dimensional (3D) skull implants was proposed in this paper. Informer was applied from the field of natural language processing to the field of computer vision for attention extraction. Informer attention can extract attention and make the model focus more on the location of the skull defect. Informer attention can also reduce the computation and parameter count from N ² to log( N). Furthermore,the informer residual attention is constructed. The informer attention and the residual are combined and placed in the position of the model close to the output layer. Thus, the model can select and synthesize the global receptive field and local information to improve the model accuracy and speed up the model convergence. In this paper, the open data set of the AutoImplant 2020 was used for training and testing, and the effects of direct and indirect acquisition of skull implants on the results were compared and analyzed in the experimental part. The experimental results show that the performance of the model is robust on the test set of 110 cases fromAutoImplant 2020. The Dice coefficient and Hausdorff distance are 0.940 4 and 3.686 6, respectively. The proposed model reduces the resources required to run the model while maintaining the accuracy of the cranial implant shape, and effectively assists the surgeon in automating the design of efficient cranial repair, thereby improving the quality of the patient's postoperative recovery.
Humans ; Computer-Aided Design ; Skull/surgery* ; Prostheses and Implants ; Head

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.
Algorithms ; China ; Databases, Factual ; Echinococcosis, Hepatic ; diagnostic imaging ; Humans ; Image Processing, Computer-Assisted ; methods ; Information Storage and Retrieval ; methods ; Tomography, X-Ray Computed ; methods