• 한국멀티미디어학회논문지
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• 제11권12호
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• pp.1635-1648
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• 2008

The extraction of important features in cancer cell image analysis is a key process in grading renal cell carcinoma. In this study, we applied three-dimensional (3D) texture feature extraction methods to cell nuclei images and evaluated the validity of them for computerized cell nuclei grading. Individual images of 2,423 cell nuclei were extracted from 80 renal cell carcinomas (RCCs) using confocal laser scanning microscopy (CLSM). First, we applied the 3D texture mapping method to render the volume of entire tissue sections. Then, we determined the chromatin texture quantitatively by calculating 3D gray-level co-occurrence matrices (3D GLCM) and 3D run length matrices (3D GLRLM). Finally, to demonstrate the suitability of 3D texture features for grading, we performed a discriminant analysis. In addition, we conducted a principal component analysis to obtain optimized texture features. Automatic grading of cell nuclei using 3D texture features had an accuracy of 78.30%. Combining 3D textural and 3D morphological features improved the accuracy to 82.19%. As a comparative study, we also performed a stepwise feature selection. Using the 4 optimized features, we could obtain more improved accuracy of 84.32%. Three dimensional texture features have potential for use as fundamental elements in developing a new nuclear grading system with accurate diagnosis and predicting prognosis.

• 대한원격탐사학회지
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• 제24권3호
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• pp.267-272
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• 2008

Tree species-specific estimates with spacebome high-resolution imagery improve estimation of forest biomass which is needed to predict the long term planning for the sustainable forest management(SFM). This paper is a contribution to develop crown distinguishing coniferous species, Pinus densiflora and Pinus koraiensis, from QuickBird imagery. The proposed feature analysis derived from shape parameters and first and second-order statistical texture features of the same test area were compared for the two species separation and delineation. As expected, initial studies have shown that both formfactor and compactness shape parameters provided the successful differentiating method between the pine species within the compartment for single crown identification from spaceborne high resolution imagery. Another result revealed that the selected texture parameters - the mean, variance, angular second moment(ASM) - in the infrared band image could produce good subset combination of texture features for representing detailed tree crown outline.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권10호
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• pp.5197-5218
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• 2019

Large-scale retrieval algorithm is problem for visual analyses applications, along its research track. In this paper, we propose a high-efficiency region division-based image retrieve approaches, which fuse low-level local color histogram feature and texture feature. A novel image region division is proposed to roughly mimic the location distribution of image color and deal with the color histogram failing to describe spatial information. Furthermore, for optimizing our region division retrieval method, an image descriptor combining local color histogram and Gabor texture features with reduced feature dimensions are developed. Moreover, we propose an extended Canberra distance method for images similarity measure to increase the fault-tolerant ability of the whole large-scale image retrieval. Extensive experimental results on several benchmark image retrieval databases validate the superiority of the proposed approaches over many recently proposed color-histogram-based and texture-feature-based algorithms.

• Journal of information and communication convergence engineering
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• 제20권1호
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• pp.58-64
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• 2022

Breast ultrasonic reading is critical as a primary screening test for the early diagnosis of breast cancer. However, breast ultrasound examinations show significant differences in diagnosis based on the difference in image quality according to the ultrasonic equipment, experience, and proficiency of the examiner. Accordingly, studies are being actively conducted to analyze the texture characteristics of normal breast tissue, positive tumors, and malignant tumors using breast ultrasonography and to use them for computer-assisted diagnosis. In this study, breast ultrasonography was conducted to select 247 ultrasound images of 71 normal breast tissues, 87 fibroadenomas among benign tumors, and 89 malignant tumors. The selected images were calculated using a statistical method with 21 feature parameters extracted using the gray level co-occurrence matrix algorithm, and classified as normal breast tissue, benign tumor, and malignancy. In addition, we proposed five feature parameters that are available for computer-aided diagnosis of breast cancer classification. The average classification rate for normal breast tissue, benign tumors, and malignant tumors, using this feature parameter, was 82.8%.

• 대한전자공학회논문지SP
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• 제49권2호
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• pp.102-112
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• 2012

본 논문에서는 웨이브렛 영역의 BDIP(block difference of inverse probabilities)와 BVLC(block variance of local correlation coefficients) 특징, 그리고 WPCA(whitened principal component analysis) 분류기를 이용한 질감 분류 방법을 제안한다. 제안된 방법에서는 먼저 질의 영상에 웨이브렛 변환을 적용한다. 그런 다음 웨이브렛 영역의 각 부대역에 BDIP와 BVLC 연산자를 적용한다. 이어서 각 BDIP, BVLC 부대역에 대하여 전역 통계치를 계산하고 그 결과들을 벡터화하여 특징 벡터로 사용한다. 분류 단계에서는 얼굴 인식에 주로 사용되는 WPCA를 분류기로 하여 질의 특징 벡터와 가장 유사한 학습 특징 벡터를 찾는다. 실험 결과 제안된 방법은 3가지의 실험 질감 영상 DB에 대하여 낮은 특징 벡터 차원으로 매우 우수한 질감 분류 성능을 보여준다.

• 한국생산제조학회지
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• 제9권2호
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• pp.144-150
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• 2000

Surface roughness is one of the most important parameters to estimate quality of products. As this reason so many studies were car-ried out through various attempts that were contact or non-contact using computer vision. Even through these efforts there were few good results in this research., however texture analysis making a important role to solve these problems in various fields including universe aviation living thing and fibers. In this study feature value of co-occurrence matrix was calculated by statistic method and roughness value of worked surface was classified, of it. Experiment was carried out using input vector of neural network with characteristic value of texture calculated from worked surface image. It's found that recognition rate of 74% was obtained when adapting texture features. In order to enhance recogni-tion rate combination type in characteristics value of texture was changed into input vector. As a result high recognition rate of 92.6% was obtained through these processes.

• 한국멀티미디어학회논문지
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• 제22권8호
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• pp.832-843
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• 2019

Prostate cancer is a high-risk with a high incidence and is a disease that occurs only in men. Accurate diagnosis of cancer is necessary as the incidence of cancer patients is increasing. Prostate cancer is also a disease that is difficult to predict progress, so it is necessary to predict in advance through prognosis. Therefore, in this paper, grade classification is attempted based on texture feature extraction. There are two main methods of classification: Uses One-way Analysis of Variance (ANOVA) to determine whether texture features are significant values, compares them with all texture features and then uses only one classification i.e. Benign versus. The second method consisted of more detailed classifications without using ANOVA for better analysis between different grades. Results of both these methods are compared and analyzed through the machine learning models such as Support Vector Machine and K-Nearest Neighbor. The accuracy of Benign versus Grade 4&5 using the second method with the best results was 90.0 percentage.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.2059-2064
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• 2004

Texture analysis is an important role for automatic visual insfection. This paper presents an application of wavelet extension and Gray level co-occurrence matrix (GLCM) for detection of defect encountered in textured images. Texture characteristic in low quality images is not to easy task to perform caused by noise, low frequency and small dimension. In order to solve this problem, we have developed a procedure called wavelet image extension. Wavelet extension procedure is used to determine the frequency bands carrying the most information about the texture by decomposing images into multiple frequency bands and to form an image approximation with higher resolution. Thus, wavelet extension procedure offers the ability to robust feature extraction in images. Then the features are extracted from the co-occurrence matrices computed from the sub-bands which performed by partitioning the texture image into sub-window. In the detection part, Mahalanobis distance classifier is used to decide whether the test image is defective or non defective.

• 융합신호처리학회논문지
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• 제16권2호
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• pp.44-49
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• 2015

텍스처가 부족한 장면이나 카메라 포즈 변화가 클 경우, 기존의 텍스처 기반의 특징 추적 방법의 신뢰도는 크게 떨어진다. LLAH와 같은 특징 사이의 기하 정보를 활용하는 토폴로지 기반 특징 기술 방법이 좋은 대안이 될 수 있으나, 특징 검출방법의 성능에 크게 영향을 받는다. 본 논문에서는 토폴로지 기반 특징 기술을 위한 효과적인 특징 검출 방법을 마련하기 위한 기초 연구로, OpenCV 라이브러리에서 제공되는 특징 검출 방법들의 반복성(repeatability) 분석을 통해 토폴로지 기반 특징 기술에의 적용 가능성을 살펴본다. 실험을 통해, FAST의 성능이 가장 우수함을 확인하였다.

• 정보처리학회논문지B
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• 제10B권3호
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• pp.311-320
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• 2003

고유의 텍스쳐 성분에만 최적 반응을 하는 최적 필터(optimal filter)는 다중 텍스쳐 영상으로부터 원하는 텍스쳐 성분을 추출하기 위한 가장 뛰어난 기술이다. 그러나 기존의 최적필터 설계 방법들은 영상에 내재된 텍스쳐 정보가 사전에 주어지는 교사적 방법이 대부분이며, 내재된 텍스쳐 인식을 기반으로 하는 완전 비교사적인 방법에 관한 연구는 거의 이루어지고 있지 않은 실정이다. 따라서 본 논문에서는 효율적인 텍스쳐 분석을 위한 비교사 학습 방법과 가버필터의 주파수 대역 통과형 특징을 이용한 새로운 최적 필터 설계 방법을 제안한다. 제안한 방법은 자기조직형 신경회로망에 의해서 영상에 내재된 텍스쳐 영역을 블록 단위로 군화(clustering)하며, 가버필터의 최적 주파수는 인식된 텍스쳐 오브젝트(texture objects)의 공간 주파수를 분석한 최적 주파수에 동조(turning)한다. 그리고 설계된 최적 가버필터의 성능 평가를 위해서는 다양한 형태의 다중 텍스쳐 영상을 생성하여 내재된 텍스쳐 오브젝트를 추출함으로써 성공적인 결과를 보인다.