Ketut E. Purnama

Automatic Cobb angle determination from radiographic images.

Study Design. Automatic measurement of Cobb angle in patients with scoliosis. Objective. To test the accuracy of an automatic Cobb angle determination method from frontal radiographical images. Summary of Background Data. Thirty-six frontal radiographical images of patients with scoliosis. Methods. A modified charged particle model is used to determine the curvature on radiographical spinal images. Three curve fitting methods, piece-wise linear, splines, and polynomials, each with 3 variants were used and evaluated for the best fit. The Cobb angle was calculated out of these curve fit lines and compared with a manually determined Cobb angle. The best-automated method is determined on the basis of the lowest mean absolute error and standard deviation, and the highest R2. Results. The error of the manual Cobb angle determination among the 3 observers, determined as the mean of the standard deviations of all sets of measurements, was 3.37°. For the automatic method, the best piece-wise linear method is the 3-segments method. The best spline method is the 10-steps method. The best polynomial method is poly 6. Overall, the best automatic methods are the piece-wise linear method using 3 segments and the polynomial method using poly 6, with a mean absolute error of 4,26° and 3,91° a standard deviation of 3,44° and 3,60°, and a R2 of 0.9124 and 0.9175. The standard measurement error is significantly lower than the upper bound found in the literature (11.8°). Conclusion. The automatic Cobb angle method seemed to be better than the manual methods described in the literature. The piece-wise linear method using 3 segments and the polynomial method using poly 6 yield the 2 best results because the mean absolute error, standard deviation, and R2 are the best of all methods. Level of Evidence: 3

A framework for human spine imaging using a freehand 3D ultrasound system

The use of 3D ultrasound imaging to follow the progression of scoliosis, i.e., a 3D deformation of the spine, is described. Unlike other current examination modalities, in particular based on X-ray, its non-detrimental effect enables it to be used frequently to follow the progression of scoliosis which sometimes may develop rapidly. Furthermore, 3D ultrasound imaging provides information in 3D directly in contrast to projection methods. This paper describes a feasibility study of an ultrasound system to provide a 3D image of the human spine, and presents a framework of procedures to perform this task. The framework consist of an ultrasound image acquisition procedure to image a large part of the human spine by means of a freehand 3D ultrasound system and a volume reconstruction procedure which was performed in four stages: bin-filling, hole-filling, volume segment alignment, and volume segment compounding. The overall results of the procedures in this framework show that imaging of the human spine using ultrasound is feasible. Vertebral parts such as the transverse processes, laminae, superior articular processes, and spinous process of the vertebrae appear as clouds of voxels having intensities higher than the surrounding voxels. In sagittal slices, a string of transverse processes appears representing the curvature of the spine. In the bin-filling stage the estimated mean absolute noise level of a single measurement of a single voxel was determined. Our comparative study for the hole-filling methods based on rank sum statistics proved that the pixel nearest neighbour (PNN) method with variable radius and with the proposed olympic operation is the best method. Its mean absolute grey value error was less in magnitude than the noise level of a single measurement.

Multi-parameter dynamic difficulty game's scenario using Box-Muller of Gaussian distribution

Scenario is an important aspect in a game. It controls players experience according to the scenario that has been composed. Diversity design of scenario and unpredicted event make the game more challenging. This paper investigates the usage of multi-parameter Box-Muller method of Gaussian distribution in adjusting dynamically game scenario. Those parameters are mean (μ) and standard deviation (σ). Scenario is designed at cave stage of pedagogical game Reog Ponorogo using mathematics problems as games challenge. Challenges are defined as six categories cognitive domain of Bloom taxonomy. Those categories are: knowledge, comprehension, application, analysis, synthesis, and evaluation. Problem domain includes the following: sequences and series, probability and mathematical logic. Box-Muller method is used to select five of ten available problems at random, and Gaussian distribution was used to dynamically adjusting difficulty level of problems in order to match players skill.

Estimation of wire phantom's position in ultrasound probe calibration based on polynomial equation

The primary problem in the reconstruction of the 3D ultrasound image is the calibration of the probe. This paper presents a method of estimating wire phantoms position within the calibration system of the ultrasounds probe. Calibration process was done by probe scanning above the phantom, in this case using copper wires. Depends on the position and orientation probe to wires phantom, scanning process produced some types of images of phantom. The phantom will generate random distribution intensity in the B-mode ultrasound images. The intensity of the image often exceed a maximum value and produce image saturation. The saturated image, usually includes many saturated pixels. To find the accurate position of a phantom in the B-mode images, it requires modeling the distribution of saturated pixels. Data distribution in the ultrasound image was approximated using 5th degree polynomial equation to compute the coordinate of image maximum value. This experiments show that one centimeter is equivalent to 122.72 pixels, and the average error is (17x10-5) %. By calculating of the center of mass of wire image, obtained that one centimeter is 123.01 pixels, and an average error is (20.9x10-5) %.