A Medical Image Registration Method Based on Progressive Images

Background. Medical image registration is an essential task for medical image analysis in various applications. In this work, we develop a coarse-to-fine medical image registration method based on progressive images and SURF algorithm (PI-SURF) for higher registration accuracy. Methods. As a first step, the reference image and the floating image are fused to generate multiple progressive images. Thereafter, the floating image and progressive image are registered to get the coarse registration result based on the SURF algorithm. For further improvement, the coarse registration result and the reference image are registered to perform fine image registration. The appropriate progressive image has been investigated by experiments. The mutual information (MI), normal mutual information (NMI), normalized correlation coefficient (NCC), and mean square difference (MSD) similarity metrics are used to demonstrate the potential of the PI-SURF method. Results. For the unimodal and multimodal registration, the PI-SURF method achieves the best results compared with the mutual information method, Demons method, Demons+B-spline method, and SURF method. The MI, NMI, and NCC of PI-SURF are improved by 15.5%, 1.31%, and 7.3%, respectively, while MSD decreased by 13.2% for the multimodal registration compared with the optimal result of the state-of-the-art methods. Conclusions. The extensive experiments show that the proposed PI-SURF method achieves higher quality of registration.

Measurement uncertainty assessment for virtual assembly

Virtual assembly (VA) is a method for datum definition and quality prediction of assemblies considering local form deviations of relevant geometries. Point clouds of measured objects are registered in order to recreate the objects' hypothetical physical assembly state. By VA, the geometrical verification becomes more accurate and, thus, increasingly function oriented. The VA algorithm is a nonlinear, constrained derivate of the Gaussian best fit algorithm, where outlier points strongly influence the registration result. In order to assess the robustness of the developed algorithm, the propagation of measurement uncertainties through the nonlinear transformation due to VA is studied. The work compares selected propagation methods distinguished from their levels of abstraction. The results reveal larger propagated uncertainties by VA compared to the unconstrained Gaussian best fit.

Prudential Principles in Regulation of Land Registration in Indonesia

Land registration is the first step for landowners to have legal ownership certificates and be recognized by the state in the form of land certificates, the raw errors in land registration result in multiple interpretations of legal certainty regarding land data that will harm landowners, so the principle of prudence in registration land is very important to be applied so that in the registration process there is no mistake and legal certainty is guaranteed from the certificate issued. This study aims to find out and analyze the extent o which the principle of prudence is regulated in land registration regulations so that the implementation of land registration is carried out correctly and appropriately. The type of research used in this research is normative legal research, namely research based on legal materials whose focus is on reading and studying the materials of primary law and secondary law. The results of this study indicate that the principle of prudence has been stipulated in the land registration law by observing the principle of prudence indicators contained in article per article in the land registration law such as the Law of the Republic of Indonesia Number 5 of 1960 concerning Regulations Basic Agrarian Principles, Republic of Indonesia Government Regulation Number 24 of 1997 concerning Land Registration, Agrarian Minister Regulation Number 3 of 1997 concerning Provisions for Implementing Government Regulation number 24 of 1997 concerning land registration, and Regulation of the Head of National Land Agency of the Republic of Indonesia number 1 of 2010 about Land Service and Regulatory Standards.

A New Image Registration Algorithm Based on Evidential Reasoning

Image registration is a crucial and fundamental problem in image processing and computer vision, which aims to align two or more images of the same scene acquired from different views or at different times. In image registration, since different keypoints (e.g., corners) or similarity measures might lead to different registration results, the selection of keypoint detection algorithms or similarity measures would bring uncertainty. These different keypoint detectors or similarity measures have their own pros and cons and can be jointly used to expect a better registration result. In this paper, the uncertainty caused by the selection of keypoint detector or similarity measure is addressed using the theory of belief functions, and image information at different levels are jointly used to achieve a more accurate image registration. Experimental results and related analyses show that our proposed algorithm can achieve more precise image registration results compared to several prevailing algorithms.

ANALISIS DAN PENERAPAN ALGORITHMA C45 DALAM DATA MINING UNTUK MENUNJANG STRATEGI PROMOSI PRODI INFORMATIKA UPGRIS

ABSTRAK Untuk menarik minat pendaftar mahasiswa baru memerlukan strategi khusus. Salah satu strategi adalah dengan melakukan analisa data dengan tujuan mengubah kumpulan data menjadi memiliki nilai bisnis melalui laporan analitik sehingga menghasilkan informasi yang akan diambil polanya menjadi pengetahuan [Kusrini, 2009]. Teknik klasifikasi merupakan pendekatan fungsi klasifikasi dalam data mining yang digunakan untuk melakukan prediksi atas informasi yang belum diketahui sebelumnya[Larose, 2005]. Pohon keputusan merupakan metode klasifikasi dan prediksi. pada penelitian ini algorithma yang dipakai untuk pembentukan pohon keputusan dengan mengunakan algoritma C45[Larose, 2005]. Data yang diproses adalah data mahasiswa baru angkatan 2014 dan angkatan 2015. Hasil penelitian ini menunjukkan bahwa variabel yang paling tinggi pengaruhnya terhadap hasil registrasi mahasiswa adalah Asal Sekolah dan Jenis Kelamin. Rata-rata berasal dari Semarang dengan jurusan SMU dari IPA dan yang berasal dari luar kota rata-rata berasal dari Batang dan Pati. Dari SMU jurusan IPS dan berjenis kelamin Laki-laki berasal dari Batang dan yang berjenis kelamin Perempuan berasal dari Pati.. Accuracy dari pembenukan model ini adalah sebesar 89.33 % (Good Classification). ABSTRACT To attract new student applicants requires a special strategy. One strategy is to perform data analysis with the aim of converting the data set to have business value through analytic reports so that the information will be taken into the pattern of knowledge [Kusrini, 2009]. The classification technique is an approximate classification function in data mining used to predict information previously unknown [Larose, 2005]. Decision tree is a method of classification and prediction. in this study the algorithm used for the formation of decision trees using the C45 algorithm [Larose, 2005]. Processed data are new student data of class of 2014 and class of 2015. The result of this research indicates that the variable that has the highest effect on student registration result is School Origin and Gender. The average comes from Semarang with high school majors from IPA and those coming from out of town on average come from Batang and Pati. Of SMU majoring in IPS and Male sex comes from the stem and the female sex is derived from Pati .. Accuracy of this model is 89.33% (Good Classification).

Adaptive Diffeomorphic Multiresolution Demons and Their Application to Same Modality Medical Image Registration with Large Deformation

Diffeomorphic demons can guarantee smooth and reversible deformation and avoid unreasonable deformation. However, the number of iterations needs to be set manually, and this greatly influences the registration result. In order to solve this problem, we proposed adaptive diffeomorphic multiresolution demons in this paper. We used an optimized framework with nonrigid registration and diffeomorphism strategy, designed a similarity energy function based on grey value, and stopped iterations adaptively. This method was tested by synthetic image and same modality medical image. Large deformation was simulated by rotational distortion and extrusion transform, medical image registration with large deformation was performed, and quantitative analyses were conducted using the registration evaluation indexes, and the influence of different driving forces and parameters on the registration result was analyzed. The registration results of same modality medical images were compared with those obtained using active demons, additive demons, and diffeomorphic demons. Quantitative analyses showed that the proposed method’s normalized cross-correlation coefficient and structural similarity were the highest and mean square error was the lowest. Medical image registration with large deformation could be performed successfully; evaluation indexes remained stable with an increase in deformation strength. The proposed method is effective and robust, and it can be applied to nonrigid registration of same modality medical images with large deformation.

Assessment of Iterative Closest Point Registration Accuracy for Different Phantom Surfaces Captured by an Optical 3D Sensor in Radiotherapy

An optical 3D sensor provides an additional tool for verification of correct patient settlement on a Tomotherapy treatment machine. The patient’s position in the actual treatment is compared with the intended position defined in treatment planning. A commercially available optical 3D sensor measures parts of the body surface and estimates the deviation from the desired position without markers. The registration precision of the in-built algorithm and of selected ICP (iterative closest point) algorithms is investigated on surface data of specially designed phantoms captured by the optical 3D sensor for predefined shifts of the treatment table. A rigid body transform is compared with the actual displacement to check registration reliability for predefined limits. The curvature type of investigated phantom bodies has a strong influence on registration result which is more critical for surfaces of low curvature. We investigated the registration accuracy of the optical 3D sensor for the chosen phantoms and compared the results with selected unconstrained ICP algorithms. Safe registration within the clinical limits is only possible for uniquely shaped surface regions, but error metrics based on surface normals improve translational registration. Large registration errors clearly hint at setup deviations, whereas small values do not guarantee correct positioning.

Research on WebGIS-Based Sport Games Information System

The organization and management of the sports tournament relate to the sports competition area of mission in each aspect. Taking the competition as a core, it is essential to establish sports tournament information system with the personnel registration, result processing, the audience services ,the competition information and data sharing etc. In this paper ,GIS-Based Sports Tournament Information System plays an important role in Digital Sport construction. This system is under the support of the geographic information system, network system, communication system and the database system. So it can provide the multi-source and multi-level data and information for the organization and management sections of the sports and games. The successful construction and realization of the system can enhance the sports tournament organization and management efficiency.

A New Fast Similarity Metric Algorithm Based on Contour

The similarity metric is a key on image registration. This paper divides similarity metric algorithms into two classes: similarity metrics based on pixels (or voxels) and similarity metrics based on image features. For those images that acquired contours easily, this paper proposes a new fast similarity metric arithmetic based on scan line. This algorithm is insensitive to illumination change and is robust without considering gray level of pixels (or voxels). In addition, this arithmetic does not consider all pixels (or voxels) in image, but consider pixels (or voxels) in the range of contour. So it is very simple and fast. It is not only suitable for 2D images but also suitable for higher dimension images. In experiment we use Laplacian pyramid to decompose image and use snake model to detect image contour. Lastly we give a novel registration result.

Registration and Integration of Automobile-Bodies Scattered Point Cloud Based on K-D Tree

As the automobile-bodies point cloud had the traits of large geometric dimension, huge data and rigor reverse precision, the improved iterative closet point algorithm (ICP) is put forward. The searching structure is generated using k-D tree. The closet points are searched by k-Dimensional sphere. The mapped relationship between points is generated. The matched points are filtered by the principle of geometric similarity. The solving of ICP algorithm is speeded and the registration precision of ICP algorithm is improved. The registration algorithm based on least-square and quaternion is used to calculate accurate registration result. The algorithm has certain theoretical and practical significance for improving of the efficiency and precision of registration. The reliability and accuracy of the algorithm are proved by experimentation.