• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권7호
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• pp.2400-2419
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• 2014

Pre-segmented pixels can reduce the difficulty of segmentation and promote the segmentation performance. This paper proposes a novel segmentation method based on merging texture superpixels by computing inner similarity. Firstly, we design a set of Gabor filters to compute the amplitude responses of original image and compute the texture map by a salience model. Secondly, we employ the simple clustering to extract superpixles by affinity of color, coordinates and texture map. Then, we design a normalized histograms descriptor for superpixels integrated color and texture information of inner pixels. To obtain the final segmentation result, all adjacent superpixels are merged by the homogeneity comparison of normalized color-texture features until the stop criteria is satisfied. The experiments are conducted on natural scene images and synthesis texture images demonstrate that the proposed segmentation algorithm can achieve ideal segmentation on complex texture regions.

• 대한전자공학회논문지SP
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• 제42권6호
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• pp.43-54
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• 2005

이본 논문에서는 Bayesian 영상 분할법과 SOM(Self Organization feature Map)을 이용한 텍스쳐(Texture) 분할 방법을 제안한다. SOM의 입력으로 다중 스케일에서의 웨이블릿 계수를 사용하고, 훈련된 SOM으로부터 관측 데이터에 대한 우도(尤度, likelihood)와 사후확률을 구하는 방법을 제시한다. 훈련된 SOM들로부터 구한 사후확률과 MAP(Maximum A Posterior) 분류법을 이용하여 텍스쳐 분할을 얻는다. 그리고 문맥 정보를 이용하여 텍스쳐 분할 결과를 개선하였다. 제안 방법은 HMT(Hidden Markov Tree)을 이용한 텍스쳐 분할보다 더 우수한 결과를 보여준다. 또한 SOM과 HMTseg라고 불리는 다중스케일 Bayesian 영상 분할 기법을 이용한 텍스쳐 분할 결과는 HMT와 HMTseg을 이용한 결과보다 더 우수한 성능을 보여준다.

• 자연과학논문집
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• 제4권
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• pp.191-220
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• 1991

Human Visual System(HVS)의 특성과 image의 textural regions의 roughness을 이용하여 image segmentation을 행하여 high compression에서도 고화질을 나타내는 새로운 image coder를 이 논문에서 논한다. 제안된 image coder는 constant segments를 가진 segmentation-based image coding technique의 문제들을 다음과 같은 방법론을 제안함으로써 해결하였다. Image를 HVS으로 보았을 때 degree of roughness에 관하여 textually homogeneous regions으로 segmentation하였다. Fractal dimension을 roughness of textural regions을 측정하기 위하여 사용하였다. Segmentation은 fractal dimension을 thresholding하여 textural regions이 three texture classes로 분류하였다(perceived constant intensity, smooth texture, and rough texture). High compression을 가지는 고질화의 image coder는 각각의 segment boundary와 각각의 texture class에 효율적인 coding technique를 적용 함으로 얻었다.

• Korean Journal of Radiology
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• 제22권11호
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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.

• 대한전자공학회논문지SP
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• 제42권4호
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• pp.39-48
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• 2005

본 논문에서는 Bayesian 추정법과 신경회로망을 이용한 새로운 결 분할 방법을 제안한다 신경회로망의 입력으로는 다중스케일을 가지는 웨이블릿 계수와 인접한 이웃 웨이블릿 계수들의 문맥정보를 사용하고, 신경회로망의 출력을 사후 확률로 모델링한다. 문맥정보는 HMT(Hidden Markov Tree) 모델을 이용하여 구한다. 제안 방법은 HMT를 이용한 ML(Maximum Likelihood) 분할 보다 더 우수한 결과를 보여준다. 또한 HMT를 이용한 결 분할 방법과 제안 방법을 이용한 결 분할 각각에 HMTseg라고 불리는 다중 스케일 Bayesian 영상 분할 기술을 이용하여 후처리를 행한 결 분할 또한 제안 방법이 우수함을 보여준다.

• Journal of Electrical Engineering and Technology
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• 제12권1호
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• pp.436-442
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• 2017

A novel method of Skin lesion segmentation based on the combination of Texture and Neural Network is proposed in this paper. This paper combines the textures of different pixels in the skin images in order to increase the performance of lesion segmentation. For segmenting skin lesions, a two-step process is done. First, automatic border detection is performed to separate the lesion from the background skin. This begins by identifying the features that represent the lesion border clearly by the process of Texture analysis. In the second step, the obtained features are given as input towards the Recurrent Echo state neural networks in order to obtain the segmented skin lesion region. The proposed algorithm is trained and tested for 862 skin lesion images in order to evaluate the accuracy of segmentation. Overall accuracy of the proposed method is compared with existing algorithms. An average accuracy of 98.8% for segmenting skin lesion images has been obtained.

• International Journal of Computer Science & Network Security
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• 제23권7호
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• pp.61-70
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• 2023

Image segmentation is a very crucial step in effective digital image processing. In the past decade, several research contributions were given related to this field. However, a general segmentation algorithm suitable for various applications is still challenging. Among several image segmentation approaches, graph-based approach has gained popularity due to its basic ability which reflects global image properties. This paper proposes a methodology to partition the image with its pixel, region and texture along with its intensity. To make segmentation faster in large images, it is processed in parallel among several CPUs. A way to achieve this is to split images into tiles that are independently processed. However, regions overlapping the tile border are split or lost when the minimum size requirements of the segmentation algorithm are not met. Here the contributions are made to segment the image on the basis of its pixel using min-cut/max-flow algorithm along with edge-based segmentation of the image. To segment on the basis of the region using a homogenous optimum cut algorithm with boundary segmentation. On the basis of texture, the object type using spectral partitioning technique is identified which also minimizes the graph cut value.

• Computers and Concrete
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• 제14권3호
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• pp.315-325
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• 2014

Concrete is an important material in most of civil constructions. Many properties of concrete can be determined through analysis of concrete images. Image segmentation is the first step for the most of these analyses. An automated system for segmentation of concrete images into microstructures using texture analysis is proposed. The performance of five different classifiers has been evaluated and the results show that using an Artificial Neural Network classifier is the best choice for an automatic image segmentation of concrete.

• 한국통신학회논문지
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• 제25권1B호
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• pp.156-166
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• 2000

본 논문에서는 반복적 병합의 율왜곡 기반 비용을 계산할 때 일반화된 질감 근사화 과정을 도입함으로써 질감 특성이 고려된 율왜곡 기반 영상 분할 기법을 제시하였다. 이때 영역 내부의 질감은 SA-DCT를 적용한 후, 양자화 간격이 고정된 두 개의 균일 양자화기에 의해 DC 성분 및 AC 성분을 각각 근사화하여 율왜곡 기반 비용을 구하고, 이러한 율왜곡 기반 비용에 근거하여 반복적 병합을 이용한 영상 분할을 수행하였다. 256$\times$256 Lena에 대하여 실험한 결과, 제안한 분할법에 의한 영역기반 부호화는 DC 성분만으로 근사화하여 율왜곡 기반 영상분할에 의한 것과 JPEG에 비하여 각각 0.8~1.0 ㏈, 1.2~1.5㏈ 정도의 PSNR 성능 개선이 있는 것으로 나타났다.

• 대한전자공학회논문지
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• 제24권5호
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• pp.914-922
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• 1987

An image coding technique based on a segmentation, which utilizes a simplified description of regions composing an image, is investigated in this paper. The proposed coding technique consists of 3 stages: segmentation, contour coding. In this paper, emphasis was given to texture coding in order to improve a quality of an image. Split-and-merge method was employed for a segmentation. In the texture coding, a linear predictive coding(LPC), along with approximation technique based on a two-dimensional polynomial function was used to encode texture components. Depending on a size of region and a mean square error between an original and a reconstructed image, appropriate texture coding techniques were determined. A computer simulation on natural images indicates that an acceptable image quality at a compression ratio as high as 15-25 could be obtained. In comparison with a discrete cosine transform coding technique, which is the most typical coding technique in the first-generation coding, the proposed scheme leads to a better quality at compression ratio higher than 15-20.