216 research outputs found
Precise segmentation of densely interweaving neuron clusters using G-Cut
脑是宇宙间最为复杂的系统之一,成人的脑中有约1000亿个神经元,单个神经元通常与其它神经元有成千上万个“突触”连接节点,形成拥有百万亿级连接的极其复杂的脑神经网络。当前多数神经元三维重建和分析工具仅适用于单个神经元的形态学重建,难以从神经元簇图像中正确追踪重建出多个神经元,而神经元的重建质量又影响到量化分析神经元的形态学特征及其功能。针对这一问题,课题组提出一种新的三维神经元簇重建工具G-Cut。具体地,为了度量神经元胞体与神经突起间的关联性,课题组从已有的带有标注的大规模神经元形态学数据集统计分析得到其规律和形态学信息。然后将神经元簇的重建问题转化为神经突起之间连接所形成的拓扑连接图的图分割问题,并结合神经元形态学规律和信息,在所有的神经突起与神经元胞体的关联性中寻找重建问题的最优解。通过在不同的合成数据集以及真实的脑组织图像数据集上测试,和已有的方法相比,G-Cut在不同密度和不同规模的神经元簇图像上均获得了更高的重建正确率。该项研究工作由厦门大学,南加州大学,加州大学洛杉矶分校等高校课题组合作完成,厦门大学信息学院智能科学与技术系为第一完成单位,厦门大学博士生李睿和USC博士生Muye Zhu为论文共同第一作者,张俊松博士和南加州大学的Hong-Wei Dong教授为论文共同通讯作者。厦门大学周昌乐教授和南加州大学的Arthur Toga教授为研究提供了大力支持。【Abstract】Characterizing the precise three-dimensional morphology and anatomical context of neurons is crucial for neuronal cell type classification and circuitry mapping. Recent advances in tissue clearing techniques and microscopy make it possible to obtain image stacks of intact, interweaving neuron clusters in brain tissues. As most current 3D neuronal morphology reconstruction methods are only applicable to single neurons, it remains challenging to reconstruct these clusters digitally. To advance the state of the art beyond these challenges, we propose a fast and robust method named G-Cut that is able to automatically segment individual neurons from an interweaving neuron cluster. Across various densely interconnected neuron clusters, G-Cut achieves significantly higher accuracies than other state-of-the-art algorithms. G-Cut is intended as a robust component in a high throughput informatics pipeline for large-scale brain mapping projects.This work was supported by NIH/NIMH MH094360-01A1 (H.W.D.), MH094360-06 (H.W.D.), NIH/NCI U01CA198932-01 (H.W.D.), NIH/NIMH MH106008 (X.W.Y. and H.W.D.), National Nature Science Foundation of China No. 61772440 (J.S.Z.), and National Basic Research Program of China 2013CB329502 (J.S.Z. and C.L.Z.). We thank a support of Graduate Student International Exchange Project of Xiamen University to R.L. and State Scholarship Fund of China Scholarship Council (No. 201406315023) to J.S.Z.
该项研究得到国家自然科学基金、国家重点基础研究发展计划973项目、国家留学基金、厦门大学研究生国际交流项目、美国脑计划和NIH等课题资助
Tumor Extraction for Brain Magnetic Resonance Imaging Using Modified Gaussian Distribution
Magnetic Resonance Imaging (MRI) is extensively used in the study of brain.
Segmentation of MR brain images is necessary for a number of clinical
investigations of various complexity, change detection, cortical labeling, and
visualization in surgical planning. The volume of enhancing lesions, following the
administration of paramagnetic contrast agent is an important indicator of pathology
in multiple sclerosis (MS). Manual estimation of enhancing lesion volumes
introduces significant errors, and operator bias, besides being time consuming and
subjective. Therefore, there is a need for automatic identification and estimation of
volumes of the present MS lesions specially by dealing with a large number of
images that are typically acquired in multi-center clinical trials.
In the developed techniques, 150 T1- and T2-weighted spin echo images were taken
from the routine scans of Kuala Lumpur General Hospital.Multiple sclerosis lesions visualized by morphological MRI are classified through a
feature map technique on T1 weighted MRI tissue. Gray level morphology methods
are used to make tissue types in the images more homogenous and minimize
difficulties with connections to outside tissue. A method for hzzy connectedness
and combinations of the different segmentation techniques were experimented. A
gain-based correction method; probability density function model are used to cluster
white and gray matters, cerebrospinal fluid, and meninges. Results of segmentation
have been validated by a group of neuro-radiologists.
3D visualization has been implemented for the segmented regions as well as brain
lesion. The visualization of the segmented structures uses a combination of volume
rendering and surface rendering.
The mutual information algorithms used in this work has been developed and
experimented in the system and has proven to yield more accurate and stable results
than other algorithms.
Currently testing the validation of the proposed segmentation in a validation study
that compares resulting MS lesion as well as gray and white matter tissue structures
with Neural Network expert segmentation system. The proposed method versus
Neural Network rater validation showed an average validation score of overlap ratio
of >85% for gray and white matters tissue segmentation and for MS lesion the rater
validation showed an average overlap ratio of > 87%
Segmentasi Aksara Pada Tulisan Aksara Jawa Menggunakan Adaptive Threshold
Penelitian mengenai Aksara Jawa sudah banyak digunakan. Salah
satunya adalah penelitian mengenai naskah pada Aksara Jawa. Kondisi naskah
Aksara Jawa sebagian besar dalam kondisi baik meskipun masih terdapat
beberapa halaman yang robek dan warna kertas yang memudar. Hal ini
disebabkan karena umur kertas yang sudah puluhan tahun lebih dan bahan kertas
yang kurang baik.
Penelitian ini difokuskan hanya untuk membagi aksara pada citra tulisan
tangan menjadi karakter-karakter aksara yang dapat digunakan dalam pengenalan
Aksara Jawa pada penelitian selanjutnya. Penelitian ini terdapat lima proses, yaitu
akuisi citra, proses preprocessing, proses segmentasi, dilasi, dan pelabelan
Aksara. Pada proses segmentasi, penelitian ini menggunakan adaptive threshold.
Metode adaptive threshold dapat digunakan pada segmentasi citra
Aksara Jawa karena metode ini memilih nilai threshold berdasarkan variasi
intensitas tiap lokal window. Hasil nilai akurasi yang didapat dari penelitian ini
yaitu sebesar 88.60% dari 30 data citra Aksara Jawa.
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Research on character java there have been many used. One of them is
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mostly in good condition although there is still several pages torn and color paper
faded. This is because age paper already dozens of more years and materials paper
a less well.
Research is focused only to divide character in image handwriting be the
characters characters that can be used in the introduction of character java in the
next research. This research there are five the process, image aquatition, the
preprocessing, the process segmentation, dilations, and the labeling character. To
the process segmentation, this research using adaptive threshold.
A method of adaptive threshold can be used on segmentation image
character java because this method choose threshold value based on variations in
intensity every local window. The results of value accuracy obtained from the
study is as much as 88.60% of 30 image data character java
A comparative evaluation for liver segmentation from spir images and a novel level set method using signed pressure force function
Thesis (Doctoral)--Izmir Institute of Technology, Electronics and Communication Engineering, Izmir, 2013Includes bibliographical references (leaves: 118-135)Text in English; Abstract: Turkish and Englishxv, 145 leavesDeveloping a robust method for liver segmentation from magnetic resonance images is a challenging task due to similar intensity values between adjacent organs, geometrically complex liver structure and injection of contrast media, which causes all tissues to have different gray level values. Several artifacts of pulsation and motion, and partial volume effects also increase difficulties for automatic liver segmentation from magnetic resonance images. In this thesis, we present an overview about liver segmentation methods in magnetic resonance images and show comparative results of seven different liver segmentation approaches chosen from deterministic (K-means based), probabilistic (Gaussian model based), supervised neural network (multilayer perceptron based) and deformable model based (level set) segmentation methods. The results of qualitative and quantitative analysis using sensitivity, specificity and accuracy metrics show that the multilayer perceptron based approach and a level set based approach which uses a distance regularization term and signed pressure force function are reasonable methods for liver segmentation from spectral pre-saturation inversion recovery images. However, the multilayer perceptron based segmentation method requires a higher computational cost. The distance regularization term based automatic level set method is very sensitive to chosen variance of Gaussian function. Our proposed level set based method that uses a novel signed pressure force function, which can control the direction and velocity of the evolving active contour, is faster and solves several problems of other applied methods such as sensitivity to initial contour or variance parameter of the Gaussian kernel in edge stopping functions without using any regularization term
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