We investigated differences in corpus callosum shape at the midsagittal plane using MRI for different subjects: normal males, normal females, and subjects with gender identity disorder (GID). We first represented callosal shapes with Fourier descriptors of callosal contours. Using linear support vector machines with soft-margin, we next determined a hyperplane that separates normal males and females most optimally in the vector space spanned by Fourier descriptors. We then proposed a measure that has prominent sex difference: it is defined as the coordinate of a given callosal shape on the subspace orthogonal to the obtained hyperplane. Use of the measure provides discrimination of someone's sex with 74.17% accuracy. We further showed that the value of the measure for GID more strongly reflects their mental sex, i.e. gender, than their physical sex.

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This article proposes an approximated Bayesian entropy estimator for a discrete random variable. An entropy estimator that achieves least square error is obtained through Bayesian estimation of the occurrence probabilities of each value taken by the discrete random variable. This Bayesian entropy estimator requires large amount of calculation cost if the random variable takes numerous sorts of values. Therefore, the present article proposes a practical method for calculating an Bayesian entropy estimate; the proposed method utilizes approximation of the entropy function by a truncated Taylor series. Numerical experiments demonstrate that the proposed entropy estimation method improves estimation precision of entropy remarkably in comparison to the conventional entropy estimation method.

Although it has been deemed irrelevant to address ejaculation time in terms of mean values, our study was designed as a preliminary step in determining the normal range of ejaculation time (from the start of stimulation of the erect penis to ejaculation) as a criterion for assessing the degree of ejaculation disorder. Ejaculation experiments were performed with informed and consenting healthy volunteers about 20 years of age, using identical manual stimulation by the same woman, and ejaculation time was measured. The mean +/- standard deviation for the ejaculation time was 156.5 +/- 80.7 seconds, which was shown to be erection time dependent.

The detection of nystagmus using video oculography experiences accuracy problems when patients who complain of dizziness have difficulty in fully opening their eyes. Pupil detection and tracking in this condition affect the accuracy of the nystagmus waveform. In this research, we design a pupil detection method using a pattern matching approach that approximates the pupil using a Mexican hat-type ellipse pattern, in order to deal with the aforementioned problem. We evaluate the performance of the proposed method, in comparison with that of a conventional Hough transform method, for eye movement videos retrieved from Gifu University Hospital. The performance results show that the proposed method can detect and track the pupil position, even when only 20% of the pupil is visible. In comparison, the conventional Hough transform only indicates good performance when 90% of the pupil is visible. We also evaluate the proposed method using the Labelled Pupil in the Wild (LPW) data set. The results show that the proposed method has an accuracy of 1.47, as evaluated using the Mean Square Error (MSE), which is much lower than that of the conventional Hough transform method, with an MSE of 9.53. We conduct expert validation by consulting three medical specialists regarding the nystagmus waveform. The medical specialists agreed that the waveform can be evaluated clinically, without contradicting their diagnoses.

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The mean-shift method is a convenient mode-seeking method. Using a principle of the sample mean over an analysis window, or kernel, in a data space where samples are distributed with bias toward the densest direction of sample from the kernel center, the mean-shift method is an attempt to seek the densest point of samples, or the sample mode, iteratively. A smaller kernel leads to convergence to a local mode that appears because of statistical fluctuation. A larger kernel leads to estimation of a biased mode affected by other clusters, abnormal values, or outliers if they exist other than in the major cluster. Therefore, optimal selection of the kernel size, which is designated as the bandwidth in many reports of the literature, represents an important problem. As described herein, assuming that the major cluster follows a Gaussian probability density distribution, and, assuming that the outliers do not affect the sample mode of the major cluster, and, by adopting a Gaussian kernel, we propose a new mean-shift by which both the mean vector and covariance matrix of the major cluster are estimated in each iteration. Subsequently, the kernel size and shape are updated adaptively. Numerical experiments indicate that the mean vector, covariance matrix, and the number of samples of the major cluster can be estimated stably. Because the kernel shape can be adjusted not only to an isotropic shape but also to an anisotropic shape according to the sample distribution, the proposed method has higher estimation precision than the general mean-shift.

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