This paper explores brain CT slices segmentation technique and some related problems, including contours segmentation algorithms, edge detector, algorithm evaluation and experimental results. This article describes a ...This paper explores brain CT slices segmentation technique and some related problems, including contours segmentation algorithms, edge detector, algorithm evaluation and experimental results. This article describes a method for contour-based segmentation of anatomical structures in 3D medical data sets. With this method, the user manually traces one or more 2D contours of an anatomical structure of interest on parallel planes arbitrarily cutting the data set. The experimental results showes the segmentation based on 3D brain volume and 2D CT slices. The main creative contributions in this paper are: (1) contours segmentation algorithm; (2) edge detector; (3) algorithm evaluation.展开更多
In lung CT images, the edge of a tumor is frequently fuzzy because of the complex relationship between tumors and tissues, especially in cases that the tumor adheres to the chest and lung in the pathology area. This m...In lung CT images, the edge of a tumor is frequently fuzzy because of the complex relationship between tumors and tissues, especially in cases that the tumor adheres to the chest and lung in the pathology area. This makes the tumor segmentation more difficult. In order to segment tumors in lung CT images accurately, a method based on support vector machine(SVM) and improved level set model is proposed. Firstly, the image is divided into several block units; then the texture, gray and shape features of each block are extracted to construct eigenvector and then the SVM classifier is trained to detect suspicious lung lesion areas; finally, the suspicious edge is extracted as the initial contour after optimizing lesion areas, and the complete tumor segmentation can be obtained by level set model modified with morphological gradient. Experimental results show that this method can efficiently and fast segment the tumors from complex lung CT images with higher accuracy.展开更多
A new separation algorithm based on contour segments and ellipse fitting is proposed to separate the ellipse-like touching grain kernels in digital images.The image is filtered and converted into a binary image first....A new separation algorithm based on contour segments and ellipse fitting is proposed to separate the ellipse-like touching grain kernels in digital images.The image is filtered and converted into a binary image first.Then the contour of touching grain kernels is extracted and divided into contour segments (CS) with the concave points on it.The next step is to merge the contour segments,which is the main contribution of this work.The distance measurement (DM) and deviation error measurement (DEM) are proposed to test whether the contour segments pertain to the same kernel or not.If they pass the measurement and judgment,they are merged as a new segment.Finally with these newly merged contour segments,the ellipses are fitted as the representative ellipses for touching kernels.To verify the proposed algorithm,six different kinds of Korean grains were tested.Experimental results showed that the proposed method is efficient and accurate for the separation of the touching grain kernels.展开更多
Purpose-One of the challenging issues in computer vision and pattern recognition is face image recognition.Several studies based on face recognition were introduced in the past decades,but it has few classification is...Purpose-One of the challenging issues in computer vision and pattern recognition is face image recognition.Several studies based on face recognition were introduced in the past decades,but it has few classification issues in terms of poor performances.Hence,the authors proposed a novel model for face recognition.Design/methodology/approach-The proposed method consists of four major sections such as data acquisition,segmentation,feature extraction and recognition.Initially,the images are transferred into grayscale images,and they pose issues that are eliminated by resizing the input images.The contrast limited adaptive histogram equalization(CLAHE)utilizes the image preprocessing step,thereby eliminating unwanted noise and improving the image contrast level.Second,the active contour and level set-based segmentation(ALS)with neural network(NN)or ALS with NN algorithm is used for facial image segmentation.Next,the four major kinds of feature descriptors are dominant color structure descriptors,scale-invariant feature transform descriptors,improved center-symmetric local binary patterns(ICSLBP)and histograms of gradients(HOG)are based on clour and texture features.Finally,the support vector machine(SVM)with modified random forest(MRF)model for facial image recognition.Findings-Experimentally,the proposed method performance is evaluated using different kinds of evaluation criterions such as accuracy,similarity index,dice similarity coefficient,precision,recall and F-score results.However,the proposed method offers superior recognition performances than other state-of-art methods.Further face recognition was analyzed with the metrics such as accuracy,precision,recall and F-score and attained 99.2,96,98 and 96%,respectively.Originality/value-The good facial recognition method is proposed in this research work to overcome threat to privacy,violation of rights and provide better security of data.展开更多
This paper presents an algorithm for extracting lines from hand drawings.It starts from contour pixel tracing, fits them into contour segments, and thenextracts skeleton lines from the contour segments. The algorithm ...This paper presents an algorithm for extracting lines from hand drawings.It starts from contour pixel tracing, fits them into contour segments, and thenextracts skeleton lines from the contour segments. The algorithm finds all con-tours in one scan of the input matrix without detecting and marking multiplepixels. In line extraction, the method Elastic Contour Segment nacing is pro-posed which extracts lines by referring to the contour segments at both sides,overcoming noise and passing through blotted areas by fitting and extrapolation.Experiments on free hand mechanical drawings, sketches, letter/numerals,as well as Chinese characters are carried out and satisfactory results are achieved.展开更多
文摘This paper explores brain CT slices segmentation technique and some related problems, including contours segmentation algorithms, edge detector, algorithm evaluation and experimental results. This article describes a method for contour-based segmentation of anatomical structures in 3D medical data sets. With this method, the user manually traces one or more 2D contours of an anatomical structure of interest on parallel planes arbitrarily cutting the data set. The experimental results showes the segmentation based on 3D brain volume and 2D CT slices. The main creative contributions in this paper are: (1) contours segmentation algorithm; (2) edge detector; (3) algorithm evaluation.
基金supported by the National Natural Science Foundation of China(No.61261029)Jinchuan Company Research Foundation(No.JCYY2013009)
文摘In lung CT images, the edge of a tumor is frequently fuzzy because of the complex relationship between tumors and tissues, especially in cases that the tumor adheres to the chest and lung in the pathology area. This makes the tumor segmentation more difficult. In order to segment tumors in lung CT images accurately, a method based on support vector machine(SVM) and improved level set model is proposed. Firstly, the image is divided into several block units; then the texture, gray and shape features of each block are extracted to construct eigenvector and then the SVM classifier is trained to detect suspicious lung lesion areas; finally, the suspicious edge is extracted as the initial contour after optimizing lesion areas, and the complete tumor segmentation can be obtained by level set model modified with morphological gradient. Experimental results show that this method can efficiently and fast segment the tumors from complex lung CT images with higher accuracy.
基金Project supported by the Grant of the Korean Ministry of Education,Science and Technology under the Regional Core Research Program
文摘A new separation algorithm based on contour segments and ellipse fitting is proposed to separate the ellipse-like touching grain kernels in digital images.The image is filtered and converted into a binary image first.Then the contour of touching grain kernels is extracted and divided into contour segments (CS) with the concave points on it.The next step is to merge the contour segments,which is the main contribution of this work.The distance measurement (DM) and deviation error measurement (DEM) are proposed to test whether the contour segments pertain to the same kernel or not.If they pass the measurement and judgment,they are merged as a new segment.Finally with these newly merged contour segments,the ellipses are fitted as the representative ellipses for touching kernels.To verify the proposed algorithm,six different kinds of Korean grains were tested.Experimental results showed that the proposed method is efficient and accurate for the separation of the touching grain kernels.
文摘Purpose-One of the challenging issues in computer vision and pattern recognition is face image recognition.Several studies based on face recognition were introduced in the past decades,but it has few classification issues in terms of poor performances.Hence,the authors proposed a novel model for face recognition.Design/methodology/approach-The proposed method consists of four major sections such as data acquisition,segmentation,feature extraction and recognition.Initially,the images are transferred into grayscale images,and they pose issues that are eliminated by resizing the input images.The contrast limited adaptive histogram equalization(CLAHE)utilizes the image preprocessing step,thereby eliminating unwanted noise and improving the image contrast level.Second,the active contour and level set-based segmentation(ALS)with neural network(NN)or ALS with NN algorithm is used for facial image segmentation.Next,the four major kinds of feature descriptors are dominant color structure descriptors,scale-invariant feature transform descriptors,improved center-symmetric local binary patterns(ICSLBP)and histograms of gradients(HOG)are based on clour and texture features.Finally,the support vector machine(SVM)with modified random forest(MRF)model for facial image recognition.Findings-Experimentally,the proposed method performance is evaluated using different kinds of evaluation criterions such as accuracy,similarity index,dice similarity coefficient,precision,recall and F-score results.However,the proposed method offers superior recognition performances than other state-of-art methods.Further face recognition was analyzed with the metrics such as accuracy,precision,recall and F-score and attained 99.2,96,98 and 96%,respectively.Originality/value-The good facial recognition method is proposed in this research work to overcome threat to privacy,violation of rights and provide better security of data.
文摘This paper presents an algorithm for extracting lines from hand drawings.It starts from contour pixel tracing, fits them into contour segments, and thenextracts skeleton lines from the contour segments. The algorithm finds all con-tours in one scan of the input matrix without detecting and marking multiplepixels. In line extraction, the method Elastic Contour Segment nacing is pro-posed which extracts lines by referring to the contour segments at both sides,overcoming noise and passing through blotted areas by fitting and extrapolation.Experiments on free hand mechanical drawings, sketches, letter/numerals,as well as Chinese characters are carried out and satisfactory results are achieved.
