The maximum likelihood identification method applied to insect morphometric data

To distinguish species or populations using morphometric data is generally processed through multivariate analyses, in particular the discriminant analysis. We explored another approach based on the maximum likelihood method. Simple statistics based on the assumption of normal distribution at a single variable allows to compute the chance of observing a particular data （or sample） in a given reference group. When data are described by more than one variable, the maximum likelihood （MLi） approach allows to combine these chances to find the best fit for the data. Such approach assumes independence between variables. The assumptions of normal distribution of variables and independence between them are frequently not met in morphometrics, but improvements may be obtained after some mathematical transformations. Provided there is strict anatomical correspondence of variables between unknown and reference data, the MLi classification produces consistent classification. We explored this approach using various input data, and compared validated classification scores with the ones obtained after the Mahalanobis distance-based classification. The simplicity of the method, its fast computation, performance and versatility, make it an interesting complement to other classification techniques.

Multiple 2D approaches to human sexual dimorphism of the distal end of femur

Various studies on the difference of distal femoral condyles between genders have been reported recently in order to provide anatomic information for knee prosthesis design and surgical planning in total knee arthroplasty. They also had the objective to be used as a sex recognition character, as may be needed in forensic medicine. Except for a recent 3D approach on the distal femur, most of the studies used dimensional information or aspect ratio but not shape. Our 2D study aimed to determine the size and shape variation of femoral condyles in Thais, considering age, sex and sides. One hundred and twenty-four cadaveric femurs （male 84 legs and female 40 legs） were dissected. The specimens were photographed by digital camera and images were analyzed using three geometric techniques： （i） the landmark-based method （5 landmarks）, （ii） with or without addition of 23 sliding semilandmark and （iii） the outline-based methods. From the resulting geometric coordinates, size and shape were extracted for comparisons between genders and sides. Between sides, directional asymmetry could be detected only for shape variation, and only when introducing curves in the analyses （either through the semilandmarks technique or through the outline-based one）. Non-directional asymmetry, probably fluctuating asymmetry, was detected for size, as well as for shape, in both genders. Sex discrimination was performed for each geometric technique using two classification methods： the Mahalanobis distance classification and the Maximum likelihood classification. The latter provided much more satisfactory gender validated reclassification （87%） than shape （72%）.