Urban image classification with semisupervised multiscale cluster kernels

This paper presents a semisupervised support vector machine (SVM) that integrates the information of both labeled and unlabeled pixels efficiently. Method's performance is illustrated in the relevant problem of very high resolution image classification of urban areas. The SVM is trained with the linear combination of two kernels: a base kernel working only with labeled examples is deformed by a likelihood kernel encoding similarities between labeled and unlabeled examples. Results obtained on very high resolution (VHR) multispectral and hyperspectral images show the relevance of the method in the context of urban image classification. Also, its simplicity and the few parameters involved make the method versatile and workable by unexperienced users.

Automatic Mallampati Classification Using Active Appearance Models

Difficult tracheal intubation assessment is an important research topic in anesthesia as failed intubations are important causes of mortality in anesthetic practice. The modified Mallampati score is widely used, alone or in conjunction with other criteria, to predict the difficulty of intubation. This work presents an automatic method to assess the modified Mallampati score from an image of a patient with the mouth wide open. For this purpose we propose an active appearance models (AAM) based method and use linear support vector machines (SVM) to select a subset of relevant features obtained using the AAM. This feature selection step proves to be essential as it improves drastically the performance of classification, which is obtained using SVM with RBF kernel and majority voting. We test our method on images of 100 patients undergoing elective surgery and achieve 97.9% accuracy in the leave-one-out crossvalidation test and provide a key element to an automatic difficult intubation assessment system.

Image guided critical screw placement precision in the cervical spine : important or incidental? : results of a series of 91 consecutive cases using OVEM

Study design: A retrospective study of image guided cervical implant placement precision. Objective: To describe a simple and precise classification of cervical critical screw placement. Summary of Background Data: "Critical" screw placement is defined as implant insertion into a bone corridor which is surrounded circumferentially by neurovascular structures. While the use of image guidance has improved accuracy, there is currently no classification which provides sufficient precision to assess the navigation success of critical cervical screw placement. Methods: Based on postoperative clinical evaluation and CT imaging, the orthogonal view evaluation method (OVEM) is used to classify screw accuracy into grade I (no cortical breach), grade la (screw thread cortical breach), grade II (internal diameter cortical breach) and grade III (major cortical breach causing neural or vascular injury). Grades II and III are considered to be navigation failures, after accounting for bone corridor / screw mismatch (minimal diameter of targeted bone corridor being smaller than an outer screw diameter). Results: A total of 276 screws from 91 patients were classified into grade I (64.9%), grade la (18.1%), and grade II (17.0%). No grade III screw was observed. The overall rate of navigation failure was 13%. Multiple logistic regression indicated that navigational failure was significantly associated with the level of instrumentation and the navigation system used. Navigational failure was rare (1.6%) when the margin around the screw in the bone corridor was larger than 1.5 mm. Conclusions: OVEM evaluation appears to be a useful tool to assess the precision of critical screw placement in the cervical spine. The OVEM validity and reliability need to be addressed. Further correlation with clinical outcomes will be addressed in future studies.