• Korean Journal of Radiology
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• v.22 no.11
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• pp.1797-1808
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• 2021

Objective: To determine whether volumetric CT texture analysis (CTTA) using fully automatic tumor segmentation can help predict recurrence-free survival (RFS) in patients with intrahepatic mass-forming cholangiocarcinomas (IMCCs) after surgical resection. Materials and Methods: This retrospective study analyzed the preoperative CT scans of 89 patients with IMCCs (64 male; 25 female; mean age, 62.1 years; range, 38-78 years) who underwent surgical resection between January 2005 and December 2016. Volumetric CTTA of IMCCs was performed in late arterial phase images using both fully automatic and semi-automatic liver tumor segmentation techniques. The time spent on segmentation and texture analysis was compared, and the first-order and second-order texture parameters and shape features were extracted. The reliability of CTTA parameters between the techniques was evaluated using intraclass correlation coefficients (ICCs). Intra- and interobserver reproducibility of volumetric CTTAs were also obtained using ICCs. Cox proportional hazard regression were used to predict RFS using CTTA parameters and clinicopathological parameters. Results: The time spent on fully automatic tumor segmentation and CTTA was significantly shorter than that for semi-automatic segmentation: mean ± standard deviation of 1 minutes 37 seconds ± 50 seconds vs. 10 minutes 48 seconds ± 13 minutes 44 seconds (p < 0.001). ICCs of the texture features between the two techniques ranged from 0.215 to 0.980. ICCs for the intraobserver and interobserver reproducibility using fully automatic segmentation were 0.601-0.997 and 0.177-0.984, respectively. Multivariable analysis identified lower first-order mean (hazard ratio [HR], 0.982; p = 0.010), larger pathologic tumor size (HR, 1.171; p < 0.001), and positive lymph node involvement (HR, 2.193; p = 0.014) as significant parameters for shorter RFS using fully automatic segmentation. Conclusion: Volumetric CTTA parameters obtained using fully automatic segmentation could be utilized as prognostic markers in patients with IMCC, with comparable reproducibility in significantly less time compared with semi-automatic segmentation.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.619-622
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• 1999

This paper describes a new semi-automatic segmentation algorithm based on color information. Semi-automatic segmentation mainly consists of intra-frame segmentation and inter-frame segmentation. While intra-frame segmentation extracts video objects of interest from boundary information provided by the user and intensity information of the image, inter-frame segmentation partitions the image into the video objects and background by tracking the motion of video objects. For inter-frame segmentation, color information (Y, Cb and Cr) of the current frame can be used efficiently in order to find the exact boundary of the video objects. In this paper we propose a new region growing algorithm which can maximize the ability of region differentiation, while preserving features of each color component.

• Journal of Biomedical Engineering Research
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• v.30 no.2
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• pp.153-161
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• 2009

Mammogram is one of the important techniques for mass detection, which is the early diagnosis stage of a breast cancer. Especially, the CAD(Computer Aided Diagnosis) using mammogram improves the working performance of radiologists as it offers an effective mass detection. There are two types of CAD systems using mammogram; automatic and semi-automatic CAD systems. However, the automatic segmentation is limited in performance due to the difficulty of obtaining an accurate segmentation since mass occurs in the dense areas of the breast tissue and has smoother boundaries. Semi-automatic CAD systems overcome these limitations, however, they also have problems including high FP (False Positive) rate and a large amount of training data required for training a classifier. The proposed system which overcomes the aforementioned problems to detect mass is composed of the suspected area selection, the level set segmentation and SVM (Support Vector Machine) classification. To assess the efficacy of the system, 60 test images from the FFDM (Full-Field Digital Mammography) are analyzed and compared with the previous semi-automatic system, which uses the ANN classifier. The experimental results of the proposed system indicate higher accuracy of detecting mass in comparison to the previous systems.

• Journal of Korea Multimedia Society
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• v.9 no.5
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• pp.529-541
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• 2006

In this paper, we propose a semi-automatic segmentation method which can be used to generate video object plane (VOP) for object based coding scheme and multimedia authoring environment. Semi-automatic segmentation can be considered as a user-assisted segmentation technique. A user can initially mark objects of interest around the object boundaries and then the selected objects are continuously separated from the un selected areas through time evolution in the image sequences. The proposed segmentation method consists of two processing steps: partially manual intra-frame segmentation and fully automatic inter-frame segmentation. The intra-frame segmentation incorporates user-assistance to define the meaningful complete visual object of interest to be segmentation and decides precise object boundary. The inter-frame segmentation involves boundary and region tracking to obtain temporal coherence of moving object based on the object boundary information of previous frame. The proposed method shows stable and efficient results that could be suitable for many digital video applications such as multimedia contents authoring, content based coding and indexing. Based on this result, we have developed objects based video editing system with several convenient editing functions.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.55-58
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• 2009

Automatic segmentation of brain MRI data usually leaves some segmentation errors behind that are to be subsequently removed interactively, using computer graphics tools. This interactive removal is normally performed by operating on individual 2D slices. It is very tedious and still leaves some segmentation errors which are not visible on the slices. We have proposed to perform a novel 3D interactive correction of brain segmentation errors introduced by the fully automatic segmentation algorithms. We have developed the tool which is based on 3D semi-automatic propagation algorithm. The paper describes the implementation principles of the proposed tool and illustrates its application.

• Journal of the Korea Society of Computer and Information
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• v.11 no.4 s.42
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• pp.283-289
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• 2006

The present image segmentation is what user want to segment image and has been studied for technology in composition of segment object with other images. In this paper, we propose a method of novel semi-automatic image segmentation using gradual region merging and genetic algorithm. Proposed algorithm is edge detection of object using genetic algorithm after selecting object which user want. We segment region of object which user want to based on detection edge using watershed algorithm. We separated background and object in indefinite region using gradual region merge from Segment object. And, we have applicable value which user want by making interface based on GUI for efficient perform of algorithm development. In the experiments, we analyzed various images for proving superiority of the proposed method.

• International Journal of Contents
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• v.9 no.1
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• pp.6-10
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• 2013

Foreground estimation in object segmentation has been an important issue for last few decades. In this paper we propose a GrabCut based automatic foreground estimation method using block clustering. GrabCut is one of popular algorithms for image segmentation in 2D image. However GrabCut is semi-automatic algorithm. So it requires the user input a rough boundary for foreground and background. Typically, the user draws a rectangle around the object of interest manually. The goal of proposed method is to generate an initial rectangle automatically. In order to create initial rectangle, we use Gabor filter and Saliency map and then we use 4 features (amount of area, variance, amount of class with boundary area, amount of class with saliency map) to categorize foreground and background. From the experimental results, our proposed algorithm can achieve satisfactory accuracy in object segmentation without any prior information by the user.

• Journal of Biomedical Engineering Research
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• v.26 no.6
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• pp.407-412
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• 2005

Convenience is one of the most important factors in medical image segmentation. Convenience is defined by compiling opinions from radiologists, and can be described as controllable maximum automation on the condition of producing only accurate results. The components of convenience are inclusive automation and inclusive modification. Inclusive modification consists of verify-and-confirm, undo-redo, exchange of segmentation methods, and intelligent modification tools. Inclusive automation is composed of automatic selection of a method, automatic selection of a confident segment, and automated chores. The convenient segmentation tool has been developed to segment X-ray images for orthopedic surgery, and has received an excellent evaluation from radiologists.

• Journal of Biomedical Engineering Research
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• v.28 no.6
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• pp.791-797
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• 2007

This paper proposes a semiautomatic vertebra segmentation method that overcomes limitations of both manual segmentation requiring tedious user interactions and fully automatic segmentation that is sensitive to initial conditions. The proposed method extracts fence surfaces between vertebrae, and segments a vertebra using fence-limited region growing. A fence surface is generated by a deformable model utilizing valley information in a valley emphasized Gaussian image. Fence-limited region growing segments a vertebra using gray value homogeneity and fence surfaces acting as barriers. The proposed method has been applied to ten patient data sets, and produced promising results accurately and efficiently with minimal user interaction.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.11
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• pp.1096-1100
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• 2010

In this paper, we propose an automatic segmentation of the meniscus based on active shape model using interpolated shape information in MR images. First, the statistical shape model of meniscus is constructed to reflect the shape variation in the training set. Second, the generation technique of interpolated shape information by using the weight according to shape similarity is proposed to robustly segment the meniscus with large variation. Finally, the automatic meniscus segmentation is performed through the active shape model fitting. For the evaluation of our method, we performed the visual inspection, accuracy measure and processing time. For accuracy evaluation, the average distance difference between automatic segmentation and semi-automatic segmentation are calculated and visualized by color-coded mapping. Experimental results show that the average distance difference was $0.54{\pm}0.16mm$ in medial meniscus and $0.73{\pm}0.39mm$ in lateral meniscus. The total processing time was 4.87 seconds on average.