A Kohonen neural network description of scoliosis fused regions and their corresponding Lenke classification.

PURPOSE: Surgical instrumentation for adolescent idiopathic scoliosis (AIS) is a complex procedure where selection of the appropriate curve segment to fuse, i.e., fusion region, is a challenging decision in scoliosis surgery. Currently, the Lenke classification model is used for fusion region evaluation and surgical planning. Retrospective evaluation of Lenke classification and fusion region results was performed. METHODS: Using a database of 1,776 surgically treated AIS cases, we investigated a topologically ordered self organizing Kohonen network, trained using Cobb angle measurements, to determine the relationship between the Lenke class and the fusion region selection. Specifically, the purpose was twofold (1) produce two spatially matched maps, one of Lenke classes and the other of fusion regions, and (2) associate these two maps to determine where the Lenke classes correlate with the fused spine regions. RESULTS: Topologically ordered maps obtained using a multi-center database of surgically treated AIS cases, show that the recommended fusion region agrees with the Lenke class except near boundaries between Lenke map classes. Overall agreement was 88%. CONCLUSION: The Lenke classification and fusion region agree in the majority of adolescent idiopathic scoliosis when reviewed retrospectively. The results indicate the need for spinal fixation instrumentation variation associated with the Lenke classification.

Pattern classification by a condensed neural network.

Neural networks have come to the fore as potent pattern classifiers. More amenable to parallel computation, they are much faster than the nearest neighbor classifier (NN), which, however, has distinctly outperformed them in several applications. The purpose of this study is to investigate a condensed neural network that combines the classification speed of neural networks and the low error rate of the nearest neighbor classifier. This condensed network is a fast, accurate classifier of simple architecture and function: it consists of a set of generalized perceptrons that draw maximal hyperspherical boundaries centered on patterns of memory units, each circumscribing reference patterns of a single category. The generalized perceptrons carry out classification, assisted by sporadic nearest neighbor matching to patterns of a small reference set. We compare the condensed network to a high performance neural network pattern classifier (Kohonen) and to NN in experiments on hand-printed character recognition.

Computation of surface orientation and structure of objects using grid coding.

In this correspondence, algorithms are introduced to infer surface orientation and structure of visible object surfaces using grid coding. We adopt the active lighting technique to spatially ``encode'' the scene for analysis. The observed objects, which can have surfaces of arbitrary shape, are assumed to rest on a plane (base plane) in a scene which is ``encoded'' with light cast through a grid plane. Two orthogonal grid patterns are used, where each pattern is obtained with a set of equally spaced stripes marked on a glass pane. The scene is observed through a camera and the object surface orientation is determined using the projected patterns on the object surface. If the surfaces under consideration obey certain smoothness constraints, a dense orientation map can be obtained through proper interpolation. The surface structure can then be recovered given this dense orientation map. Both planar and curved surfaces can be handled in a uniform manner. The algorithms we propose yield reasonably accurate results and are relatively tolerant to noise, especially when compared to shape-from-shading techniques. In contrast to other grid coding techniques reported which match the grid junctions for depth reconstruction under the stereopsis principle, our techniques use the direction of the projected stripes to infer local surface orientation and do not require any correspondence relationship between either the grid lines or the grid junctions to be specified. The algorithm has the ability to register images and can therefore be embedded in a system which integrates knowledge from multiple views.

On kineopsis and computation of structure and motion.

This correspondence deals with the computation of structure and motion of rigid objects in space from image positions and optical flow. A test for rigid motion of objects in space is introduced which yields a new formulation of the problem. Assuming a central projection model for the viewing system, it is shown that image positions and optical flow at four points can achieve this perception.

Detection of edges using range information.

Range data provide an important source of 3-D shape information. This information can be used to extract jump boundaries which correspond to occluding boundaries of objects in a scene and ``edges'' which correspond to points lying between significantly different regions on the surface of objects. We are mainly interested in range data obtained from sensors such as lasers. The main problem with this type of range finder is the fact that the accuracy of the measurements depends on the power of the signal that reaches the receiver. This study describes how a range edge detection procedure can be designed that has low sensitivity to noise and imbeds all the knowledge available on the range measurement accuracy.