• Title/Summary/Keyword: Automatic Segmentation

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A Study of Automatic Medical Image Segmentation using Independent Component Analysis (Independent Component Analysis를 이용한 의료영상의 자동 분할에 관한 연구)

  • Bae, Soo-Hyun;Yoo, Sun-Kook;Kim, Nam-Hyun
    • The Transactions of the Korean Institute of Electrical Engineers D
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    • v.52 no.1
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    • pp.64-75
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    • 2003
  • Medical image segmentation is the process by which an original image is partitioned into some homogeneous regions like bones, soft tissues, etc. This study demonstrates an automatic medical image segmentation technique based on independent component analysis. Independent component analysis is a generalization of principal component analysis which encodes the higher-order dependencies in the input in addition to the correlations. It extracts statistically independent components from input data. Use of automatic medical image segmentation technique using independent component analysis under the assumption that medical image consists of some statistically independent parts leads to a method that allows for more accurate segmentation of bones from CT data. The result of automatic segmentation using independent component analysis with square test data was evaluated using probability of error(PE) and ultimate measurement accuracy(UMA) value. It was also compared to a general segmentation method using threshold based on sensitivity(True Positive Rate), specificity(False Positive Rate) and mislabelling rate. The evaluation result was done statistical Paired-t test. Most of the results show that the automatic segmentation using independent component analysis has better result than general segmentation using threshold.

Semi-Automatic Segmentation based on Color Information (색상 정보를 이용한 반자동 영상분할 기법)

  • 김민호;최재각;호요성
    • 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.

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3D INTERACTIVE SEGMENTATION OF BRAIN MRI

  • Levinski, Konstantin;Sourin, Alexei;Zagorodnov, Vitali
    • 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.

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Optimization of Multi-Atlas Segmentation with Joint Label Fusion Algorithm for Automatic Segmentation in Prostate MR Imaging

  • Choi, Yoon Ho;Kim, Jae-Hun;Kim, Chan Kyo
    • Investigative Magnetic Resonance Imaging
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    • v.24 no.3
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    • pp.123-131
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    • 2020
  • Purpose: Joint label fusion (JLF) is a popular multi-atlas-based segmentation algorithm, which compensates for dependent errors that may exist between atlases. However, in order to get good segmentation results, it is very important to set the several free parameters of the algorithm to optimal values. In this study, we first investigate the feasibility of a JLF algorithm for prostate segmentation in MR images, and then suggest the optimal set of parameters for the automatic prostate segmentation by validating the results of each parameter combination. Materials and Methods: We acquired T2-weighted prostate MR images from 20 normal heathy volunteers and did a series of cross validations for every set of parameters of JLF. In each case, the atlases were rigidly registered for the target image. Then, we calculated their voting weights for label fusion from each combination of JLF's parameters (rpxy, rpz, rsxy, rsz, β). We evaluated the segmentation performances by five validation metrics of the Prostate MR Image Segmentation challenge. Results: As the number of voxels participating in the voting weight calculation and the number of referenced atlases is increased, the overall segmentation performance is gradually improved. The JLF algorithm showed the best results for dice similarity coefficient, 0.8495 ± 0.0392; relative volume difference, 15.2353 ± 17.2350; absolute relative volume difference, 18.8710 ± 13.1546; 95% Hausdorff distance, 7.2366 ± 1.8502; and average boundary distance, 2.2107 ± 0.4972; in parameters of rpxy = 10, rpz = 1, rsxy = 3, rsz = 1, and β = 3. Conclusion: The evaluated results showed the feasibility of the JLF algorithm for automatic segmentation of prostate MRI. This empirical analysis of segmentation results by label fusion allows for the appropriate setting of parameters.

Uncertain Region Based User-Assisted Segmentation Technique for Object-Based Video Editing System (객체기반 비디오 편집 시스템을 위한 불확실 영역기반 사용자 지원 비디오 객체 분할 기법)

  • Yu Hong-Yeon;Hong Sung-Hoon
    • 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.

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A Study on Endpoint Detection and Syllable Segmentation System Using Ramp Edge Detection (Ramp Edge Detection을 이용한 끝점 검출과 음절 분할에 관한 연구)

  • 유일수;홍광석
    • Proceedings of the IEEK Conference
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    • 2003.07e
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    • pp.2216-2219
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    • 2003
  • Accurate speech region detection and automatic syllable segmentation is important part of speech recognition system. In automatic speech recognition system, they are needed for the purpose of accurate recognition and less computational complexity, In this paper, we Propose improved syllable segmentation method using ramp edge detection method and residual signal Peak energy. These methods were used to ensure accuracy and robustness for endpoint detection and syllable segmentation system. They have almost invariant response to various background noise levels. As experimental results, we obtained the rate of 90.7% accuracy in syllable segmentation in a condition of accurate endpoint detection environments.

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Automatic Liver Segmentation on Abdominal Contrast-enhanced CT Images for the Pre-surgery Planning of Living Donor Liver Transplantation

  • Jang, Yujin;Hong, Helen;Chung, Jin Wook
    • Journal of International Society for Simulation Surgery
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    • v.1 no.1
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    • pp.37-40
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    • 2014
  • Purpose For living donor liver transplantation, liver segmentation is difficult due to the variability of its shape across patients and similarity of the density of neighbor organs such as heart, stomach, kidney, and spleen. In this paper, we propose an automatic segmentation of the liver using multi-planar anatomy and deformable surface model in portal phase of abdominal contrast-enhanced CT images. Method Our method is composed of four main steps. First, the optimal liver volume is extracted by positional information of pelvis and rib and by separating lungs and heart from CT images. Second, anisotropic diffusing filtering and adaptive thresholding are used to segment the initial liver volume. Third, morphological opening and connected component labeling are applied to multiple planes for removing neighbor organs. Finally, deformable surface model and probability summation map are performed to refine a posterior liver surface and missing left robe in previous step. Results All experimental datasets were acquired on ten living donors using a SIEMENS CT system. Each image had a matrix size of $512{\times}512$ pixels with in-plane resolutions ranging from 0.54 to 0.70 mm. The slice spacing was 2.0 mm and the number of images per scan ranged from 136 to 229. For accuracy evaluation, the average symmetric surface distance (ASD) and the volume overlap error (VE) between automatic segmentation and manual segmentation by two radiologists are calculated. The ASD was $0.26{\pm}0.12mm$ for manual1 versus automatic and $0.24{\pm}0.09mm$ for manual2 versus automatic while that of inter-radiologists was $0.23{\pm}0.05mm$. The VE was $0.86{\pm}0.45%$ for manual1 versus automatic and $0.73{\pm}0.33%$ for manaual2 versus automatic while that of inter-radiologist was $0.76{\pm}0.21%$. Conclusion Our method can be used for the liver volumetry for the pre-surgery planning of living donor liver transplantation.

Automatic Detection of the Middle Tooth Crown Part for Full Automatic Tooth Segmentation in Dental CT Images

  • Lee, Chan-Woo;Chae, Ok-Sam
    • Journal of the Korea Society of Computer and Information
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    • v.23 no.3
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    • pp.17-23
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    • 2018
  • In this paper, we propose the automatic detection method which find the middle part of tooth crown to start individual tooth segmentation. There have been many studies on the automation of individual tooth segmentation, but there are still many problems for full automation. Detection of middle part of tooth crown used as initial information for individual tooth segmentation is closely related to performance, but most studies are based on the assumption that they are already known or they can be represented by using a straight line. In this study, we have found that the jawbone curve is similar to the tooth alignment curve by spatially analyzing the CT image, and propose a method to automatically detect the middle part of tooth crown. The proposed method successfully uses the jawbone curves to successfully create a tooth alignment curve that is difficult to detect. As the middle part of tooth crown is in the form of a tooth alignment curve, the proposed method detects the middle part of tooth crown successfully. It has also been verified by experiments that the proposed method works well on real dental CT images.

Improvement of an Automatic Segmentation for TTS Using Voiced/Unvoiced/Silence Information (유/무성/묵음 정보를 이용한 TTS용 자동음소분할기 성능향상)

  • Kim Min-Je;Lee Jung-Chul;Kim Jong-Jin
    • MALSORI
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    • no.58
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    • pp.67-81
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    • 2006
  • For a large corpus of time-aligned data, HMM based approaches are most widely used for automatic segmentation, providing a consistent and accurate phone labeling scheme. There are two methods for training in HMM. Flat starting method has a property that human interference is minimized but it has low accuracy. Bootstrap method has a high accuracy, but it has a defect that manual segmentation is required In this paper, a new algorithm is proposed to minimize manual work and to improve the performance of automatic segmentation. At first phase, voiced, unvoiced and silence classification is performed for each speech data frame. At second phase, the phoneme sequence is aligned dynamically to the voiced/unvoiced/silence sequence according to the acoustic phonetic rules. Finally, using these segmented speech data as a bootstrap, phoneme model parameters based on HMM are trained. For the performance test, hand labeled ETRI speech DB was used. The experiment results showed that our algorithm achieved 10% improvement of segmentation accuracy within 20 ms tolerable error range. Especially for the unvoiced consonants, it showed 30% improvement.

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Unconstrained Object Segmentation Using GrabCut Based on Automatic Generation of Initial Boundary

  • Na, In-Seop;Oh, Kang-Han;Kim, Soo-Hyung
    • 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.