Knowledge-Dependent Pattern Classification of Human Nasal Profiles

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Abstract
Objective: (1) To determine feature vector representations (geometric pattern parameters) that are effective in describing human nasal profiles, (2) to determine the number of code vectors (typical nasal patterns) that are mathematically optimized by applying the vector quantization method to each feature vector extracted for each subject, and (3) to determine the morphological traits of each code. Materials and Methods: Lateral facial photographs of 200 Japanese women recorded for orthodontic diagnosis were selected. Five anatomic landmarks were identified on each image together with a set of data points that constituted the contour of the facial profile. An eight-dimensional feature vector effective in distinguishing differences in nasal profile patterns was extracted from the data set using experts' knowledge of the anatomic traits of the nose. The vector quantization technique was applied to the feature vectors to provide the optimum number of nasal profile patterns. Results: The number of code vectors mathematically optimized was six, and the differences between vectors were maximized by morphological traits of the root, dorsum, tip, and base of the nose. Proportions of the number of image records classified into each code were 25.5%, 24.5%, 21.5%, 15.0%, 10.0%, and 3.5% from code 1 to code 6, respectively. Conclusions: Classifying nasal profile patterns based on knowledge from a linguistic description was found to be more effective than a method based on uniform sectioning. The differences between vectors were maximized by morphological traits of the root, the dorsum, the tip, and the base of the nose.

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