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Mammary gland lesion detection method and device

A detection method and breast technology, applied in the field of image processing, can solve problems such as limited accuracy in judging breast masses, poor user experience, poor detection structure accuracy of breast masses, etc.

Pending Publication Date: 2020-05-08
深圳蓝影医学科技股份有限公司
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Problems solved by technology

The disadvantage of this method is that the threshold for distinguishing tumors from non-tumors only comes from the gray information of artificial statistics, and the threshold value is closely related to the number of statistical data and the selected features, and the gray features based on statistical information are very effective in distinguishing tumors. and non-mass are not well characterized
Therefore, the accuracy of this method for judging breast lumps is limited, resulting in poor detection structure accuracy of breast lumps and poor user experience

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  • Mammary gland lesion detection method and device
  • Mammary gland lesion detection method and device
  • Mammary gland lesion detection method and device

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[0037] In order to make the above objects, features and advantages of the present invention more clearly understood, the present invention will be described in further detail below with reference to the accompanying drawings and specific embodiments.

[0038] refer to figure 1 , showing a flow chart of steps of an embodiment of a breast lesion detection method of the present invention, which may specifically include steps 101-103:

[0039] Step 101 , using the self-learning ability of the artificial neural network to establish the correspondence between the image features of the breast image and the breast lesion area.

[0040] In the embodiment of the present invention, the self-learning function of the artificial neural network can be used to master the correspondence function between the image features of the breast image and the breast lesion area by training and learning the acquired data. For example, the artificial neural network algorithm is used to analyze the image ...

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Abstract

The embodiment of the invention provides a mammary gland lesion detection method and device, and the method comprises the steps: building the corresponding relation between the image features of a mammary gland image and a mammary gland lesion region through the self-learning capability of an artificial neural network; obtaining current image features of the current mammary gland image; determining a current mammary gland lesion area corresponding to the current image feature according to the corresponding relation; and determining a current mammary gland lesion area corresponding to the current image feature, including determining the mammary gland lesion area corresponding to the image feature the same as the current image feature in the corresponding relationship as the current mammarygland lesion area. The method can improve the judgment accuracy of breast lesions, such as breast lumps, and improve the user experience effect.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a breast lesion detection method and a breast lesion detection device. Background technique [0002] Breast cancer is a common malignant tumor, and early diagnosis and treatment are the keys to reducing breast cancer mortality. The lesions in breast images include masses, calcifications, bilateral asymmetry, and structural distortion. Among them, masses and clusters of calcifications are the most common imaging signs of breast cancer. Therefore, the automatic detection of masses and calcifications has also become an important issue. Two main aspects of computer-aided diagnostic systems. Among them, lumps have always been a difficulty in computer-aided diagnosis systems due to their blurred edges, different shapes, and low contrast with surrounding tissues. [0003] Artificial neural network, such as deep learning, processes the input information layer by layer, so as t...

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Application Information

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IPC IPC(8): G06T7/00
CPCG06T7/0012G06T2207/20081G06T2207/30068
Inventor 鄢照龙孙瑞超王永贞李勇陈晶
Owner 深圳蓝影医学科技股份有限公司
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