2012Jiguang zazhiRequires access

Value of color Doppler ultrasonography in differentiating the benign and malignant breast nodules

Jian Liu

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Abstract

To extract the tumor blood vessels of the breast tumors in color Doppler ultrasonography,upon which study the relationship between blood flow signals and pathological nature of the tumors as well as the relationship between the tumor size and blood flow signals.The Hill-Climbing algorithm and K-means clustering algorithm together are applied to segment the images,precisely extracting the tumor blood vessels.According to the imaging principles and clinical knowledge,three rules are set out for counting tumor blood vessels,upon which differentiate the benign and malignant breast tumors.The experiment results have showed that the degree of vascularity in the malignant breast tumors is higher than that of the benign breast tumors.When setting 5 as the cutoff value,the differentiation accuracy is 70.16%.The degree of vascularity within a tumor has certain positive correlation with the tumor size.In other words,the larger the tumor in size,the higher the degree of vascularity within it.Therefore the degree of vascularity can reflect the pathological status of the benign and malignant breast tumors,and the color Doppler ultrasonography is effective in differentiating the benign and malignant breast tumors.

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What this paper is about

To extract the tumor blood vessels of the breast tumors in color Doppler ultrasonography,upon which study the relationship between blood flow signals and pathological nature of the tumors as well as the relationship between the tumor size and blood flow signals.The Hill-Climbing algorithm and K-means clustering algorithm together are applied to segment the images,precisely extracting the tumor blood vessels.According to the imaging principles and clinical knowledge,three rules are set out for counting tumor blood vessels,upon which differentiate the benign and malignant breast tumors.The experiment results have showed that the degree of vascularity in the malignant breast tumors is higher than that of the benign breast tumors.When setting 5 as the cutoff value,the differentiation accuracy is 70.16%.The degree of vascularity within a tumor has certain positive correlation with the tumor size.In other words,the larger the tumor in size,the higher the degree of vascularity within it.Therefore the degree of vascularity can reflect the pathological status of the benign and malignant breast tumors,and the color Doppler ultrasonography is effective in differentiating the benign and malignant breast tumors.

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Available abstract

To extract the tumor blood vessels of the breast tumors in color Doppler ultrasonography,upon which study the relationship between blood flow signals and pathological nature of the tumors as well as the relationship between the tumor size and blood flow signals.The Hill-Climbing algorithm and K-means clustering algorithm together are applied to segment the images,precisely extracting the tumor blood vessels.According to the imaging principles and clinical knowledge,three rules are set out for counting tumor blood vessels,upon which differentiate the benign and malignant breast tumors.The experiment results have showed that the degree of vascularity in the malignant breast tumors is higher than that of the benign breast tumors.When setting 5 as the cutoff value,the differentiation accuracy is 70.16%.The degree of vascularity within a tumor has certain positive correlation with the tumor size.In other words,the larger the tumor in size,the higher the degree of vascularity within it.Therefore the degree of vascularity can reflect the pathological status of the benign and malignant breast tumors,and the color Doppler ultrasonography is effective in differentiating the benign and malignant breast tumors.

Key concepts: Vascularity, Medicine, Blood flow, Color doppler, Breast tumor, Benign tumor, Pathological, Pathology

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Value of color Doppler ultrasonography in differentiating the benign and malignant breast nodules — Research Paper | ScholarLens