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Medical image classification method based on KAP digraph model

A medical image and classification method technology, applied in the field of medical information, can solve the problems of high time complexity and low classification accuracy, and achieve the effects of reducing extraction time, improving representativeness, and improving practical value

Inactive Publication Date: 2016-01-27
HARBIN ENG UNIV
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AI Technical Summary

Problems solved by technology

Existing classification algorithms cannot describe medical images well, resulting in low classification accuracy and high time complexity

Method used

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  • Medical image classification method based on KAP digraph model
  • Medical image classification method based on KAP digraph model
  • Medical image classification method based on KAP digraph model

Examples

Experimental program
Comparison scheme
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Embodiment 1

[0039] First preprocess the medical image:

[0040] 1. Extract the ROI region from each original brain CT image in the original image library;

[0041] 2. Intercept the ROI area and correct it;

[0042] 3. Calculate the trough distribution of the gray histogram in the ROI region of the image, and obtain the trough table of the gray histogram;

[0043] 4. According to the threshold value set in the valley table, the texture is extracted multiple times from the image to obtain a multi-level texture image;

[0044] 5. Finally, the multi-level texture image is normalized into an image whose size is COLUMN×ROW;

[0045] 6. Extract the corner points of the texture image;

[0046] 7. Store the coordinates of the extracted corner points in the corresponding coordinate queue;

[0047] Save the preprocessed image in the corresponding database. After the above process, each original image corresponds to a texture corner point storage queue. First, use the corner points in the storag...

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Abstract

The invention belongs to the technical field of medical information, and particularly relates to a medical image classification method based on a KAP digraph model. The method includes that classification requests are proposed by images to be classified; the images to be classified are original medical image data; and an image pre-processing process, including an interested ROI area is extracted from the original medical images, the gray-scale histogram of the image ROI area is calculated, the trough list of the gray-scale histogram of the image ROI area is obtained, images are graded based on the trough list to extract texture characteristics, the graded texture images are standardized to unified size based on the actual needs, and the angular points of the images are extracted at the texture parts, is carried out. The texture angular point extraction method effectively reduces the angular point extraction time, and meanwhile, the texture parts are the most drastic positions of grey-scale changes and are the important positions for reflecting the information content in the images, and the representativeness of the angular points is improved.

Description

technical field [0001] The invention belongs to the technical field of medical information, and in particular relates to a medical image classification method based on a KAP directed graph model. Background technique [0002] Because medical images contain rich images and medical information, in recent years, data mining technology for medical images has become a hot spot in the interdisciplinary research of medicine and computer science. With the rapid development of medical digital equipment, medical information databases are widely used. The structured text information of patients and a large amount of unstructured medical image information provide rich data resources for data mining of medical images. Medical images can effectively assist physicians in detecting and locating pathological change areas and judging their benign and malignant in the diagnosis process, so they are widely used in the clinical diagnosis process. However, doctors with different knowledge backg...

Claims

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Application Information

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IPC IPC(8): G06K9/32G06K9/62
CPCG06V10/25G06F18/24
Inventor 潘海为吴枰韩启龙谢晓芹高琳琳战宇翟霄李文博
Owner HARBIN ENG UNIV
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