Segmenting the Dynamic Contrast-Enhanced Breast Magnetic Resonance Images(DCE-BMRI)is an extremely important task to diagnose the disease because it has the highest specificity when acquired with high temporal and spa...Segmenting the Dynamic Contrast-Enhanced Breast Magnetic Resonance Images(DCE-BMRI)is an extremely important task to diagnose the disease because it has the highest specificity when acquired with high temporal and spatial resolution and is also corrupted by heavy noise,outliers,and other imaging artifacts.In this paper,we intend to develop efficient robust segmentation algorithms based on fuzzy clustering approach for segmenting the DCE-BMRs.Our proposed segmentation algorithms have been amalgamated with effective kernel-induced distance measure on standard fuzzy c-means algorithm along with the spatial neighborhood information,entropy term,and tolerance vector into a fuzzy clustering structure for segmenting the DCE-BMRI.The significant feature of our proposed algorithms is its capability tofind the optimal membership grades and obtain effective cluster centers automatically by minimizing the proposed robust objective functions.Also,this article demonstrates the superiority of the proposed algorithms for segmenting DCE-BMRI in comparison with other recent kernel-based fuzzy c-means techniques.Finally the clustering accuracies of the proposed algorithms are validated by using silhouette method in comparison with existed fuzzy clustering algorithms.展开更多
Finding subtypes of cancer in breast cancer database is an extremely dificult task because ofheavy noise by measurement error.Most of the recent clustering techniques for breast cancerdatabase to achieve cancerous and...Finding subtypes of cancer in breast cancer database is an extremely dificult task because ofheavy noise by measurement error.Most of the recent clustering techniques for breast cancerdatabase to achieve cancerous and noncancerous often weigh down the interpretability of thestructure.Hence,this paper tries to find effective Fuzzy C-Means-based clustering techniques toidentify the proper subtypes of cancer in breast cancer database,This paper obtains the objectivefunction of ffective Fuzzy C-Means clustering techriques by incorporating the kermel induceddist ance function,Renyi's entropy function,weighted dist ance measure and neighborhood ternsbased spatial context.The efectiveness of the proposed methods are proved through the ex-perimental works on Lung cancer database,IRIS dataset,Wine dat aset,Checkerboard dataset,Time Series dataset and Yeast dataset.Finlly,the proposed methods are implemented suc-cesfully to cluster the breast cancer dat abase into cancerous and noncancerous.The clusteringaccuracy has been validat ed through error matrix and silhouette method.展开更多
基金This work was supported by DG CSIR(Ref.No.:39-35/2010(SR)),India.
文摘Segmenting the Dynamic Contrast-Enhanced Breast Magnetic Resonance Images(DCE-BMRI)is an extremely important task to diagnose the disease because it has the highest specificity when acquired with high temporal and spatial resolution and is also corrupted by heavy noise,outliers,and other imaging artifacts.In this paper,we intend to develop efficient robust segmentation algorithms based on fuzzy clustering approach for segmenting the DCE-BMRs.Our proposed segmentation algorithms have been amalgamated with effective kernel-induced distance measure on standard fuzzy c-means algorithm along with the spatial neighborhood information,entropy term,and tolerance vector into a fuzzy clustering structure for segmenting the DCE-BMRI.The significant feature of our proposed algorithms is its capability tofind the optimal membership grades and obtain effective cluster centers automatically by minimizing the proposed robust objective functions.Also,this article demonstrates the superiority of the proposed algorithms for segmenting DCE-BMRI in comparison with other recent kernel-based fuzzy c-means techniques.Finally the clustering accuracies of the proposed algorithms are validated by using silhouette method in comparison with existed fuzzy clustering algorithms.
文摘Finding subtypes of cancer in breast cancer database is an extremely dificult task because ofheavy noise by measurement error.Most of the recent clustering techniques for breast cancerdatabase to achieve cancerous and noncancerous often weigh down the interpretability of thestructure.Hence,this paper tries to find effective Fuzzy C-Means-based clustering techniques toidentify the proper subtypes of cancer in breast cancer database,This paper obtains the objectivefunction of ffective Fuzzy C-Means clustering techriques by incorporating the kermel induceddist ance function,Renyi's entropy function,weighted dist ance measure and neighborhood ternsbased spatial context.The efectiveness of the proposed methods are proved through the ex-perimental works on Lung cancer database,IRIS dataset,Wine dat aset,Checkerboard dataset,Time Series dataset and Yeast dataset.Finlly,the proposed methods are implemented suc-cesfully to cluster the breast cancer dat abase into cancerous and noncancerous.The clusteringaccuracy has been validat ed through error matrix and silhouette method.
