MEDIAL LONGITUDINAL ARCH CHANGE IN DIABETIC PERIPHERAL NEUROPATHY

Objective: To describe and compare foot anthropometry in healthy and diabetic subjects using Medial Longitudinal Arch (MLA) classificatory indexes: Arch Index (AI), Chippaux-Smirak Index (CSI) and (A) over cap Angle ((A) over cap), as well as to compare the classification of these methods in each group. Materials and Methods: Control Group (CG) composed by 21 healthy subjects and Diabetic Group (DG), with 46 diabetic neuropathy subjects. The indexes were calculated from footprints. Results: A larger proportion of flat feet was seen in DG for the three indexes (At: 32,2%, CSI: 59,7%, A: 17,5%), while highly arched feet acted oppositely. The groups were statistically different for the proportion of flat feet in (A) over cap (p=0,0080) and CSI (p=0,0000) and high feet in A (p=0,0036). There were significant differences when compared GC and GD in the three indexes: IA (p 0,0027), CSI (p=0,0064), (A) over cap (p=0,0296). Conclusion: Data showed motor and orthopedic changes originated by peripheral neuropathy, which is responsible for foot changes, causing longitudinal arch crumbling. It was seen that A Angle strongly disagreed when compared with the arch classification made by the other two indexes and therefore, its application needs care.

Exploring the knowledge contained in neuroimages: Statistical discriminant analysis and automatic segmentation of the most significant changes

Objective: The aim of this article is to propose an integrated framework for extracting and describing patterns of disorders from medical images using a combination of linear discriminant analysis and active contour models. Methods: A multivariate statistical methodology was first used to identify the most discriminating hyperplane separating two groups of images (from healthy controls and patients with schizophrenia) contained in the input data. After this, the present work makes explicit the differences found by the multivariate statistical method by subtracting the discriminant models of controls and patients, weighted by the pooled variance between the two groups. A variational level-set technique was used to segment clusters of these differences. We obtain a label of each anatomical change using the Talairach atlas. Results: In this work all the data was analysed simultaneously rather than assuming a priori regions of interest. As a consequence of this, by using active contour models, we were able to obtain regions of interest that were emergent from the data. The results were evaluated using, as gold standard, well-known facts about the neuroanatomical changes related to schizophrenia. Most of the items in the gold standard was covered in our result set. Conclusions: We argue that such investigation provides a suitable framework for characterising the high complexity of magnetic resonance images in schizophrenia as the results obtained indicate a high sensitivity rate with respect to the gold standard. (C) 2010 Elsevier B.V. All rights reserved.