• The Transactions of the Korea Information Processing Society
• /
• v.7 no.1
• /
• pp.286-295
• /
• 2000

We present a novel statistically adaptive method using the Minimum Description Length(MDL) principle for unsupervised segmentation of magnetic resonance(MR) images. In the method, Markov random filed(MRF) modeling of tissue region accounts for random noise. Intensity measurements on the local region defined by a window are modeled by a finite Gaussian mixture, which accounts for image inhomogeneities. The segmentation algorithm is based on an iterative conditional modes(ICM) algorithm, approximately finds maximum ${\alpha}$ posteriori(MAP) estimation, and estimates model parameters on the local region. The size of the window for parameter estimation and segmentation is estimated from the image using the MDL principle. In the experiments, the technique well reflected image characteristic of the local region and showed better results than conventional methods in segmentation of MR images with inhomogeneities, especially.

• Journal of Magnetics
• /
• v.21 no.4
• /
• pp.603-615
• /
• 2016

In this study, in order to determine the validity and accuracy of MR imaging of 3D gradient dual echo 2-point DIXON technique for measuring abdominal adipose tissue volume and distribution, the measurements obtained by CT were set as a reference for comparison and their correlations were evaluated. CT and MRI scans were performed on each subject (17 healthy male volunteers who were fully informed about this study) to measure abdominal adipose tissue volume. Two skilled investigators individually observed the images acquired by CT and MRI in an independent environment, and directly separated the total volume using region-based thresholding segmentation method, and based on this, the total adipose tissue volume, subcutaneous adipose tissue volume and visceral adipose tissue volume were respectively measured. The correlation of the adipose tissue volume measurements with respect to the observer was examined using the Spearman test and the inter-observer agreement was evaluated using the intra-class correlation test. The correlation of the adipose tissue volume measurements by CT and MRI imaging methods was examined by simple regression analysis. In addition, using the Bland-Altman plot, the degree of agreement between the two imaging methods was evaluated. All of the statistical analysis results showed highly statistically significant correlation (p<0.05) respectively from the results of each adipose tissue volume measurements. In conclusion, MR abdominal adipose volumetry using the technique of 3D gradient dual echo 2-point DIXON showed a very high level of concordance even when compared with the adipose tissue measuring method using CT as reference.

• The Transactions of the Korea Information Processing Society
• /
• v.6 no.1
• /
• pp.253-260
• /
• 1999

In this paper, we suggest a new direct method for mage segmentation using texture and color information combined through a multivariate linear discriminant algorithm. The color texture is computed in nin 3${\times}$3 masks obtained from each 3${\times}$3${\times}$3 spatio-spectral neighborhood in the image using the classical haralick and Pressman texture features. Among these 9${\times}$28 texture features the best set was extracted from a training set. The resulting set of 10 features were used to segment an image into four different regions. The resulting segmentation was Compared to classical color and texture segmentation methods using both box classifiers and maximum likelihood classification. It compared favourably on the test image from a Fastred-Lightgreen stained prostatic histological tissue section based on visual inspection. The classification accuracy of 97.5% for the new method obtained on the training data was also among the best of the tested methods. If these results hold for a larger set of images, this method should be a useful tool for segmenting images where both color and texture are relevant for the segmentation process.

• Journal of Biomedical Engineering Research
• /
• v.24 no.4
• /
• pp.259-266
• /
• 2003

Non-rigid registration between different modality images with shape deformation can be used to diagnosis and study for inter-patient image registration, longitudinal intra-patient registration, and registration between a patient image and an atlas image. This paper proposes a hierarchical registration method using bodily tissue based segmentation for registration between color images and CT images of the Visible Human leg areas. The cross-sectional color images and the axial CT images are segmented into three distinctive bodily tissue regions, respectively: fat, muscle, and bone. Each region is separately registered hierarchically. Bounding boxes containing bodily tissue regions in different modalities are initially registered. Then, boundaries of the regions are globally registered within range of searching space. Local boundary segments of the regions are further registered for non-rigid registration of the sampled boundary points. Non-rigid registration parameters for the un-sampled points are interpolated linearly. Such hierarchical approach enables the method to register images efficiently. Moreover, registration of visibly distinct bodily tissue regions provides accurate and robust result in region boundaries and inside the regions.

• Journal of the Korea Computer Graphics Society
• /
• v.29 no.3
• /
• pp.127-135
• /
• 2023

Accurate segmentation of histopathology whole slide images (WSIs) is a crucial task for disease diagnosis and treatment planning. However, conventional automated segmentation algorithms may not always be applicable to WSI segmentation due to their large size and variations in tissue appearance, staining, and imaging conditions. Recent advances in interactive segmentation, which combines human expertise with algorithms, have shown promise to improve efficiency and accuracy in WSI segmentation but also presented us with challenging issues. In this paper, we propose a novel interactive segmentation method, ZoomISEG, that leverages multi-resolution WSIs. We demonstrate the efficacy and performance of the proposed method via comparison with conventional single-scale methods and an ablation study. The results confirm that the proposed method can reduce human interaction while achieving accuracy comparable to that of the brute-force approach using the highest-resolution data.

• Journal of Advanced Information Technology and Convergence
• /
• v.10 no.1
• /
• pp.25-36
• /
• 2020

Motivation: Fibers as the extracellular filamentous structures determine the shape of the cytoskeletal structures. Their characterization and reconstruction from a 3D cellular image represent very useful quantitative information at the cellular level. In this paper, we presented a novel automatic method to extract fiber diameter distribution through a pipeline to reconstruct fibers from 3D fluorescence confocal images. The pipeline is composed of four steps: segmentation, skeletonization, template fitting and fiber tracking. Segmentation of fiber is achieved by defining an energy based on tensor voting framework. After skeletonizing segmented fibers, we fit a template for each seed point. Then, the fiber tracking step reconstructs fibers by finding the best match of the next fiber segment from the previous template. Thus, we define a fiber as a set of templates, based on which we calculate a diameter distribution of fibers.

• 제어로봇시스템학회:학술대회논문집
• /
• 2004.08a
• /
• pp.101-106
• /
• 2004

In this paper, a blood vessel in an angiographic image, which plays an importance role in the diagnose diseases including in the eyes, brain and heart, is enhanced by using a directed diffusion technique. A fundamental component of the angiographic analysis is vessel segmentation that the proposed method provides a preprocessing of the image into a form suitable for human analysis, or more importantly, for machine analysis such the segmentation. Vessel enhancement is a challenging problem due to the complex nature of vascular trees and to imaging imperfections. Some parts of the inherent imperfections in angiography are the intensity inhomogeneity between the larger and smaller vessels, and another imperfection is the leakage of contrast agent into the background tissue that provides to low contrast between vessels and tissue. In the proposed scheme, the directed diffusion solves the problem by formulating a local geometric structure, which consists of direction and scale of the blood vessels. The diffusion process uses the local structure to enhance by a diffusivity tensor. The proposed algorithm can be applied to maintain sharpness and coherence-smooth the intra-regions into homogeneity better than traditional diffusion methods, which are Gaussian regulation and coherence enhancing diffusion.

• Journal of Biosystems Engineering
• /
• v.39 no.3
• /
• pp.174-183
• /
• 2014

Purpose: A robust, efficient auto-grading computer vision system for meat carcasses is in high demand by researchers all over the world. In this paper, we discuss our study, in which we developed a system to speed up line processing and provide reliable results for pork grading, comparing the results of our algorithms with visual human subjectivity measurements. Methods: We differentiated fat and lean using an entropic correlation algorithm. We also developed a self-designed robust segmentation algorithm that successfully segmented several porkcut samples; this algorithm can help to eliminate the current issues associated with autothresholding. Results: In this study, we carefully considered the key step of autoextracting lean tissue. We introduced a self-proposed scheme and implemented it in over 200 pork-cut samples. The accuracy and computation time were acceptable, showing excellent potential for use in online commercial systems. Conclusions: This paper summarizes the main results reported in recent application studies, which include modifying and smoothing the lean area of pork-cut sections of commercial fresh pork by human experts for an auto-grading process. The developed algorithms were implemented in a prototype mobile processing unit, which can be implemented at the pork processing site.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2010.04a
• /
• pp.455-458
• /
• 2010

Extracting the brain from magnetic resonance imaging head scans is an essential preprocessing step of which the accuracy greatly affects subsequent image analysis. The currently popular Brain Extraction Tool produces a brain mask which may be too smooth for practical use to reduce the accuracy. This paper presents a novel and indirect brain extraction method based on non-brain tissue segmentation. Based on ITK, the proposed method allows a non-brain contour by using region growing to match with the original image naturally and extract the brain tissue. Experiments on two set of MRI data and 2D brain image in horizontal plane and 3D brain model indicate successful extraction of brain tissue from a head.

• Journal of the Institute of Convergence Signal Processing
• /
• v.13 no.2
• /
• pp.70-76
• /
• 2012

In this paper, an automatic extraction method of glomerulus region from human renal tissue image is presented. The important information reflecting the state of kidneys richly included in the glomeruli, so it should be the first step to extract the glomerulus region from the renal tissue image for the further quantitative analysis of the renal condition. Especially, there is no clear difference between the glomerulus and other tissues, so the glomerulus region can not be easily extracted from its background by the existing segmentation methods. The outer edge of a glomerulus region is regarded as a common property for the regions of this kind ; a two- dimensional Gaussian distribution is used to convolve with an original image first and then the image is thresholded at this blurred image ; a closed curve corresponding to the outer edge can be obtained by usual pattern processing skills like thinning, branch-cutting, hole-filling etc., Finally, the glomerulus region can be obtained by extracting the area in the original image surrounded by the closed curve. The glomerulus regions are correctly extracted by 85 percentages and experimental results show the proposed method is effective.