Complete, Fully Automatic Detection and Classification of Benign and Malignant Breast Tumors Based on CT Images Using Artificial Intelligent and Image Processing.

Breast cancer is the most common type of cancer in women, and early detection is important to significantly reduce its mortality rate. This study introduces a detection and diagnosis system that automatically detects and classifies breast tumors in CT scan images. First, the contours of the chest wall are extracted from computed chest tomography images, and two-dimensional image characteristics and three-dimensional image features, together with the application of active contours without edge and geodesic active contours methods, are used to detect, locate, and circle the tumor. Then, the computer-assisted diagnostic system extracts features, quantifying and classifying benign and malignant breast tumors using a greedy algorithm and a support vector machine. The study used 174 breast tumors for experiment and training and performed cross-validation 10 times (k-fold cross-validation) to evaluate performance of the system. The accuracy, sensitivity, specificity, and positive and negative predictive values of the system were 99.43%, 98.82%, 100%, 100%, and 98.89% respectively. This system supports the rapid extraction and classification of breast tumors as either benign or malignant, helping physicians to improve clinical diagnosis.

In Vivo Automatic and Quantitative Measurement of Adult Human Larynx and Vocal Fold Images.

INTRODUCTION: Qualitative laryngoscopy belongs to a diagnostic routine. Nevertheless, quantitative morphometric measurements of laryngeal structures remain challenging. This study aimed to introduce a special laser projection device that can facilitate computer-assisted digitalized analysis and provide important quantitative information for diagnostics and treatment planning. MATERIALS AND METHODS: The laryngeal images were captured with our device, which contained two parallel laser beams in order to provide the scaling reference. The maximum length of the vocal fold during respiration and vibration (phonation), vocal width at midpoint, total fold area, maximum cross-sectional area of the glottic space, and maximum vocal fold angle were determined and calculated. These parameters were analyzed and compared on the basis of age, sex, body height, body weight and body mass index. RESULTS: A total of 87 subjects were enrolled in this study, comprising 39 males and 48 females. The age range for all subjects was 21 to 80 years old. The maximum value of the glottic area and vocal angle showed no significant gender difference. Both the respiration and vibration vocal fold length was significantly longer in males than in females. The vocal width revealed no gender difference, but the fold area during both respiration and phonation was significantly larger in men than in women. As for the respiration-to-vibration ratio of the vocal length, there was a trend, but without statistical significance (P = 0.06), toward a higher length compression ratio in men than in women. Meanwhile, age was found to have a strong relationship with vocal width during phonation. The width of vibration vocal fold decreased with aging significantly. CONCLUSION: Our innovative module can provide reference parameters, which makes it possible to directly estimate the objective absolute values of relevant laryngeal structures. Our non-invasive approach can be used during routine laryngoscopy and the findings easily documented. In future, we can extend its clinical application to measure subtle laryngeal or hypopharyngeal changes, which are difficult to objectively quantify.

Automated Optical Inspection for Defect Identification and Classification in Actual Woven Fabric Production Lines.

This paper presents a turnkey integrated system that can be operated in real time for real textile manufacturers. Eight types of defects in woven fabric, including stain, broken end, broken weft, hole, nep, double pick, kinky weft and float can be recognized and classified. First, an image is captured by a CMOS industrial camera with a pixel size of 4600 × 600 above the batcher at 20 m/min. After that, the four-stage image processing procedure is applied to detect defects and for classification. Stage 1 is image pre-processing; the filtration of the image noise is carried out by a Gaussian filter. The light source is corrected to reduce the uneven brightness resulting from halo formation. The improved mask dodging algorithm is used to reduce the standard deviation of the corrected original image. Afterwards, the background texture is filtered by an averaging filter, and the mean value is corrected for histogram shifting, so that this system is robust to the texture and color changes of woven fabric. The binary segmentation threshold is determined using the mean value and standard deviation of an image with a normal sample. Stage 2 uses adaptive binarization for separation of the background and defects and to filter the noise. In Stage 3, the morphological processing is used before the defect contour is circled, i.e., four features of each block, including the defect area, the aspect ratio of the defect, the average gray level of the defect and the defect orientation, which are calculated according to the range of contour. The image defect recognition dataset consists of 2246 images. The results show that the detection success rate is 96.44%, and the false alarm rate is 3.21%. In Stage 4, the defect classification is implemented. The support vector machine (SVM) is used for classification, 230 defect images are used as training samples, and 206 are used as test samples. The experimental results show that the overall defect recognition rate is 96.60%, providing that the software and hardware equipment designed in this study can implement defect detection and classification for woven fabric effectively.

Quantitative Measurement of Adult Human Larynx post General Anesthesia with Intubation.

Introduction: Post-anaesthetic sore throat (PAST) is a well-recognized consequence of tracheal intubation; however, quantitative morphometric measurements remain challenging. This study aimed to introduce a special laser projection device that can facilitate computer-assisted, digitalized analysis and provide important information on laryngeal mucosa change, pre and post-surgery under general anesthesia with intubation. Materials and methods: The laryngeal images were captured and divided into the control group and the intubation group. Image processing techniques were used to quantify the post-extubation laryngeal variation, with its distinct color space and texture features. Meanwhile, the maximum length of the vocal fold, vocal width at the midpoint, and maximum cross-sectional area of the glottic space were determined and calculated. These parameters were analyzed and compared pre and post-surgery. Results: A total of 69 subjects were enrolled in this study, comprising 32 subjects in the healthy group and 37 subjects in the intubation group. The color space and texture analysis with contrast and correlation profiles all shows trend toward higher measures in the intubation group than in the healthy group, with statistical significance and outstanding discrimination ability, especially in the interarytenoid region. The incidence of PAST was approximately 46% (17 patients). The gender difference, type of surgery, and the fixation position of the tube were not significantly related to the PAST occurrence. All the eigenvalues showed significant differences pre and post-surgery in the interarytenoid region and a significant trend toward red and increased contrast texture profiles was revealed. Furthermore, the glottic area showed a significant decrease of 25.29%, while the vocal width showed a significant increase post extubation. Conclusion: Our equipment and processing can measure subtle laryngeal changes that would allow a clinician to diagnose postoperative laryngeal inflammation in a simpler and less invasive way. The trend toward red, the increased contrast texture and vocal width, and the reduced glottic space were all compatible with post-intubation inflammatory response, especially in the interarytenoid region. This is important to know so that one can take appropriate steps to alleviate PAST in the future.

Semi-Supervised Deep Learning Semantic Segmentation for 3D Volumetric Computed Tomographic Scoring of Chronic Rhinosinusitis: Clinical Correlations and Comparison with Lund-Mackay Scoring.

BACKGROUND: The traditional Lund-Mackay score (TLMs) is unable to subgrade the volume of inflammatory disease. We aimed to propose an effective modification and calculated the volume-based modified LM score (VMLMs), which should correlate more strongly with clinical symptoms than the TLMs. METHODS: Semi-supervised learning with pseudo-labels used for self-training was adopted to train our convolutional neural networks, with the algorithm including a combination of MobileNet, SENet, and ResNet. A total of 175 CT sets, with 50 participants that would undergo sinus surgery, were recruited. The Sinonasal Outcomes Test-22 (SNOT-22) was used to assess disease-specific symptoms before and after surgery. A 3D-projected view was created and VMLMs were calculated for further comparison. RESULTS: Our methods showed a significant improvement both in sinus classification and segmentation as compared to state-of-the-art networks, with an average Dice coefficient of 91.57%, an MioU of 89.43%, and a pixel accuracy of 99.75%. The sinus volume exhibited sex dimorphism. There was a significant positive correlation between volume and height, but a trend toward a negative correlation between maxillary sinus and age. Subjects who underwent surgery had significantly greater TLMs (14.9 vs. 7.38) and VMLMs (11.65 vs. 4.34) than those who did not. ROC-AUC analyses showed that the VMLMs had excellent discrimination at classifying a high probability of postoperative improvement with SNOT-22 reduction. CONCLUSIONS: Our method is suitable for obtaining detailed information, excellent sinus boundary prediction, and differentiating the target from its surrounding structure. These findings demonstrate the promise of CT-based volumetric analysis of sinus mucosal inflammation.

Quantitative laryngoscopy with computer-aided diagnostic system for laryngeal lesions.

Laryngoscopes are widely used in the clinical diagnosis of laryngeal lesions, but such diagnosis relies heavily on the physician's subjective experience. The purpose of this study was to develop a computer-aided diagnostic system for the detection of laryngeal lesions based on objective criteria. This study used the distinct features of the image contour to find the clearest image in the laryngoscopic video. First to reduce the illumination problem caused by the laryngoscope lens, which could not fix the position of the light source, this study proposed image compensation to provide the image with a consistent brightness range for better performance. Second, we also proposed a method to automatically screen clear images from laryngoscopic film. Third, we used ACM to segment automatically them based on structural features of the pharynx and larynx, using hue and geometric analysis in the vocal cords and other zones. Finally, the support vector machine was used to classify laryngeal lesions based on a decision tree. This study evaluated the performance of the proposed system by assessing the laryngeal images of 284 patients. The accuracy of the detection for vocal cord polyps, cysts, leukoplakia, tumors, and healthy vocal cords were 93.15%, 95.16%, 100%, 96.42%, and 100%, respectively. The cross-validation accuracy for the five classes were 93.1%, 94.95%, 99.4%, 96.01% and 100%, respectively, and the average test accuracy for the laryngeal lesions was 93.33%. Our results showed that it was feasible to take the hue and geometric features of the larynx as signs to identify laryngeal lesions and that they could effectively assist physicians in diagnosing laryngeal lesions.

Exposure to Guava Affects Citrus Olfactory Cues and Attractiveness to Diaphorina citri (Hemiptera: Psyllidae).

Intercropping can reduce agricultural pest incidence, and represents an important sustainable alternative to conventional pest control methods. Understanding the ecological mechanisms for intercropping could help optimize its use, particularly in tropical systems which present a large number of intercropping possibilities. Citrus is threatened worldwide by greening disease (huanglongbing, HLB) vectored by the Asian citrus psyllid, Diaphorina citri Kuwayama (Hemiptera: Psyllidae). Control of HLB and citrus psyllid can be partially achieved through intercropping with guava, Psidium guajava L., but the mechanisms remain unclear. We tested the hypothesis that guava olfactory cues affect psyllid behavior by altering the attractiveness of citrus through plant-plant interactions. In choice and no-choice cage experiments, psyllid settlement was reduced on citrus shoots that had been exposed to guava shoot odors for at least 2 h. In Y-tube olfactometer experiments, psyllids oriented to odors of unexposed, compared with guava-exposed, citrus shoots. These behavioral results indicate that a mechanism for the success of guava intercropping for sustainable, ecological disease management may be the indirect effect of guava on citrus attractiveness.