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Background: An abnormal sternocleidomastoid muscle in congenital muscular torticollis can be classified into one of the four types via sonography. However, this categorization lacks quantitative measurements. The purpose of the study was to determine quantitative measurements of the sonograms via image analysis., Methods: Infants younger than 12 months of age suspected of having congenital muscular torticollis were included. Intraclass correlation coefficient estimates for interobserver reliability and a simple regression analysis for criterion validity were calculated. Spearman correlation analysis was then performed. The analyzed parameters included cervical passive range of motion for lateral flexion and rotation, area, brightness, max/min Feret's diameters, and muscular width/thickness., Results: Of the 29 (4.0 ± 2.6 months) screened infants, 13 (1.9 ± 1.7 months) were included. Nine were male, and 4 were female. Seven infants with mass were ultrasonographically classified into type I, and the other six infants were classified into type II. The affected/unaffected side ratios of cervical passive range of motion for lateral flexion and rotation were 0.92 ± 0.13 and 0.88 ± 0.16, respectively. The parameters measured on the sonograms were reliable, and the max/min Feret's diameters were valid measurements. The affected/unaffected side ratio of cervical passive range of motion for rotation significantly correlated with the affected/unaffected side ratios of the sternocleidomastoid muscle sonogram on area (r = -0.62, p = 0.03) and min Feret's diameter (r = -0.69, p = 0.01)., Conclusions: The area and min Feret's diameter were efficacious parameters for image analysis on sternocleidomastoid sonograms, and the min Feret's diameter would be more suitable than thickness for measuring the thickening SCM in transverse view. A healthy control group, more data and follow-up would be needed to confirm the changes on the SCM sonograms for clinical decision., (Copyright © 2018 Chang Gung University. Published by Elsevier B.V. All rights reserved.)

Face recognition is one of the most intensively studied topics in computer vision and pattern recognition, but few are focused on how to robustly recognize faces with expressions under the restriction of one single training sample per class. A constrained optical flow algorithm, which combines the advantages of the unambiguous correspondence of feature point labeling and the flexible representation of optical flow computation, has been developed for face recognition from expressional face images. In this paper, we propose an integrated face recognition system that is robust against facial expressions by combining information from the computed intraperson optical flow and the synthesized face image in a probabilistic framework. Our experimental results show that the proposed system improves the accuracy of face recognition from expressional face images.

Automatic extraction of vertebra regions from a spinal magnetic resonance (MR) image is normally required as the first step to an intelligent spinal MR image diagnosis system. In this work, we develop a fully automatic vertebra detection and segmentation system, which consists of three stages; namely, AdaBoost-based vertebra detection, detection refinement via robust curve fitting, and vertebra segmentation by an iterative normalized cut algorithm. In order to produce an efficient and effective vertebra detector, a statistical learning approach based on an improved AdaBoost algorithm is proposed. A robust estimation procedure is applied on the detected vertebra locations to fit a spine curve, thus refining the above vertebra detection results. This refinement process involves removing the false detections and recovering the miss-detected vertebrae. Finally, an iterative normalized-cut segmentation algorithm is proposed to segment the precise vertebra regions from the detected vertebra locations. In our implementation, the proposed AdaBoost-based detector is trained from 22 spinal MR volume images. The experimental results show that the proposed vertebra detection and segmentation system can achieve nearly 98% vertebra detection rate and 96% segmentation accuracy on a variety of testing spinal MR images. Our experiments also show the vertebra detection and segmentation accuracies by using the proposed algorithm are superior to those of the previous representative methods. The proposed vertebra detection and segmentation system is proved to be robust and accurate so that it can be used for advanced research and application on spinal MR images.

In this paper, we present a robust image alignment algorithm based on matching of relative gradient maps. This algorithm consists of two stages; namely, a learning-based approximate pattern search and an iterative energy-minimization procedure for matching relative image gradient. The first stage finds some candidate poses of the pattern from the image through a fast nearest-neighbor search of the best match of the relative gradient features computed from training database of feature vectors, which are obtained from the synthesis of the geometrically transformed template image with the transformation parameters uniformly sampled from a given transformation parameter space. Subsequently, the candidate poses are further verified and refined by matching the relative gradient images through an iterative energy- minimization procedure. This approach based on the matching of relative gradients is robust against nonuniform illumination variations. Experimental results on both simulated and real images are shown to demonstrate superior efficiency and robustness of the proposed algorithm over the conventional normalized correlation method.