Aiming to solve the inefficient segmentation in traditional C-V model for complex topography image and time-consuming process caused by the level set function solving with partial differential, an improved Chan-Vese m...Aiming to solve the inefficient segmentation in traditional C-V model for complex topography image and time-consuming process caused by the level set function solving with partial differential, an improved Chan-Vese model is presented in this paper. With the good per）brmances of maintaining topological properties of the traditional level set method and avoiding the numerical so- lution of partial differential, the same segmentation results could be easily obtained. Thus, a stable foundation tbr rapid segmenta- tion-based on image reconstruction identification is established.展开更多
Liver segmentation in CT images is an important step for liver volumetry and vascular evaluation in liver pre-surgical planning. In this paper, a segmentation method based on distance regularized level set evolution(D...Liver segmentation in CT images is an important step for liver volumetry and vascular evaluation in liver pre-surgical planning. In this paper, a segmentation method based on distance regularized level set evolution(DRLSE) model was proposed, which incorporated a distance regularization term into the conventional Chan-Vese (C-V) model. In addition, the region growing method was utilized to generate the initial liver mask for each slice, which could decrease the computation time for level-set propagation. The experimental results show that the method can dramatically decrease the evolving time and keep the accuracy of segmentation. The new method is averagely 15 times faster than the method based on conventional C-V model in segmenting a slice.展开更多
An adaptive narrowband two-phase Chan-Vese （ANBCV） model is proposed for improving the shadow regions detection performance of sonar images. In the first noise smoothing step, the anisotropic second-order neighborho...An adaptive narrowband two-phase Chan-Vese （ANBCV） model is proposed for improving the shadow regions detection performance of sonar images. In the first noise smoothing step, the anisotropic second-order neighborhood MRF （Markov Random Field, MRF） is used to describe the image texture feature parameters. Then, initial two-class segmentation is processed with the block mode k-means clustering algorithm, to estimate the approximate position of the shadow regions. On this basis, the zero level set function is adaptively initialized by the approximate position of shadow regions. ANBCV model is provided to complete local optimization for eliminating the image global interference and obtaining more accurate results. Experimental results show that the new algorithm can efficiently remove partial noise, increase detection speed and accuracy, and with less human intervention.展开更多
The incident rate of the Gastrointestinal-Disease(GD)in humans is gradually rising due to a variety of reasons and the Endoscopic/Colonoscopic-Image(EI/CI)supported evaluation of the GD is an approved practice.Extract...The incident rate of the Gastrointestinal-Disease(GD)in humans is gradually rising due to a variety of reasons and the Endoscopic/Colonoscopic-Image(EI/CI)supported evaluation of the GD is an approved practice.Extraction and evaluation of the suspicious section of the EI/CI is essential to diagnose the disease and its severity.The proposed research aims to implement a joint thresholding and segmentation framework to extract the Gastric-Polyp(GP)with better accuracy.The proposed GP detection system consist;(i)Enhancement of GP region using Aquila-Optimization-Algorithm supported tri-level thresholding with entropy(Fuzzy/Shannon/Kapur)and between-class-variance(Otsu)technique,(ii)Automated(Watershed/Markov-Random-Field)and semi-automated(Chan-Vese/Level-Set/Active-Contour)segmentation of GPfragment,and(iii)Performance evaluation and validation of the proposed scheme.The experimental investigation was performed using four benchmark EI dataset(CVC-ClinicDB,ETIS-Larib,EndoCV2020 and Kvasir).The similarity measures,such as Jaccard,Dice,accuracy,precision,sensitivity and specificity are computed to confirm the clinical significance of the proposed work.The outcome of this research confirms that the fuzzyentropy thresholding combined with Chan-Vese helps to achieve a better similarity measures compared to the alternative schemes considered in this research.展开更多
Traditional texture region location methods with Gabor features are often limited in the selection of Gabor filters and fail to deal with the target which contains both texture and non-texture parts.Thus,to solve this...Traditional texture region location methods with Gabor features are often limited in the selection of Gabor filters and fail to deal with the target which contains both texture and non-texture parts.Thus,to solve this problem,a two-step new model was proposed.In the first step,the original features extracted by Gabor filters are applied to training a self-organizing map(SOM) neural network and a novel merging scheme is presented to achieve the clustering.A back propagation(BP) network is used as a classifier to locate the target region approximately.In the second step,Chan-Vese active contour model is applied to detecting the boundary of the target region accurately and morphological processing is used to create a connected domain whose convex hull can cover the target region.In the experiments,the proposed method is demonstrated accurate and robust in localizing target on texture database and practical barcode location system as well.展开更多
文摘Aiming to solve the inefficient segmentation in traditional C-V model for complex topography image and time-consuming process caused by the level set function solving with partial differential, an improved Chan-Vese model is presented in this paper. With the good per）brmances of maintaining topological properties of the traditional level set method and avoiding the numerical so- lution of partial differential, the same segmentation results could be easily obtained. Thus, a stable foundation tbr rapid segmenta- tion-based on image reconstruction identification is established.
文摘Liver segmentation in CT images is an important step for liver volumetry and vascular evaluation in liver pre-surgical planning. In this paper, a segmentation method based on distance regularized level set evolution(DRLSE) model was proposed, which incorporated a distance regularization term into the conventional Chan-Vese (C-V) model. In addition, the region growing method was utilized to generate the initial liver mask for each slice, which could decrease the computation time for level-set propagation. The experimental results show that the method can dramatically decrease the evolving time and keep the accuracy of segmentation. The new method is averagely 15 times faster than the method based on conventional C-V model in segmenting a slice.
基金supported by the National Natural Science Foundation of China(41306086)Technology Innovation Talent Special Foundation of Harbin(2014RFQXJ105)the Fundamental Research Funds for the Central Universities(HEUCF100606)
文摘An adaptive narrowband two-phase Chan-Vese （ANBCV） model is proposed for improving the shadow regions detection performance of sonar images. In the first noise smoothing step, the anisotropic second-order neighborhood MRF （Markov Random Field, MRF） is used to describe the image texture feature parameters. Then, initial two-class segmentation is processed with the block mode k-means clustering algorithm, to estimate the approximate position of the shadow regions. On this basis, the zero level set function is adaptively initialized by the approximate position of shadow regions. ANBCV model is provided to complete local optimization for eliminating the image global interference and obtaining more accurate results. Experimental results show that the new algorithm can efficiently remove partial noise, increase detection speed and accuracy, and with less human intervention.
基金Authors of this research thanks the database contributors of CVC-ClinicDB,ETIS-Larib,EndoCV2020,Kvasir for providing the open access to the dataset for research purpose and thank to Deanship of Scientific Research at Majmaah University for supporting this work under the Project No.155/46683.This research work was partially supported by Chiang Mai University.
文摘The incident rate of the Gastrointestinal-Disease(GD)in humans is gradually rising due to a variety of reasons and the Endoscopic/Colonoscopic-Image(EI/CI)supported evaluation of the GD is an approved practice.Extraction and evaluation of the suspicious section of the EI/CI is essential to diagnose the disease and its severity.The proposed research aims to implement a joint thresholding and segmentation framework to extract the Gastric-Polyp(GP)with better accuracy.The proposed GP detection system consist;(i)Enhancement of GP region using Aquila-Optimization-Algorithm supported tri-level thresholding with entropy(Fuzzy/Shannon/Kapur)and between-class-variance(Otsu)technique,(ii)Automated(Watershed/Markov-Random-Field)and semi-automated(Chan-Vese/Level-Set/Active-Contour)segmentation of GPfragment,and(iii)Performance evaluation and validation of the proposed scheme.The experimental investigation was performed using four benchmark EI dataset(CVC-ClinicDB,ETIS-Larib,EndoCV2020 and Kvasir).The similarity measures,such as Jaccard,Dice,accuracy,precision,sensitivity and specificity are computed to confirm the clinical significance of the proposed work.The outcome of this research confirms that the fuzzyentropy thresholding combined with Chan-Vese helps to achieve a better similarity measures compared to the alternative schemes considered in this research.
基金Supported by Tianjin Natural Science Fundation (No.07JCZDJC05800)
文摘Traditional texture region location methods with Gabor features are often limited in the selection of Gabor filters and fail to deal with the target which contains both texture and non-texture parts.Thus,to solve this problem,a two-step new model was proposed.In the first step,the original features extracted by Gabor filters are applied to training a self-organizing map(SOM) neural network and a novel merging scheme is presented to achieve the clustering.A back propagation(BP) network is used as a classifier to locate the target region approximately.In the second step,Chan-Vese active contour model is applied to detecting the boundary of the target region accurately and morphological processing is used to create a connected domain whose convex hull can cover the target region.In the experiments,the proposed method is demonstrated accurate and robust in localizing target on texture database and practical barcode location system as well.
