Multi-parameter MRI prostate cancer CAD method and system based on two kinds of classifiers

Abstract

The invention discloses to a multi-parameter MRI prostate cancer CAD method and system based on two kinds of classifiers, relating to the medical image processing field. The multi-parameter MRI prostate cancer CAD method comprises candidate focus automatic detection and candidate focus computer aided diagnosis. The candidate focus automatic detection comprises steps of respectively performing pre-processing on three MRI sequences of each case: T2WI, DWI, ADC to make resolution ratios and sizes of the T2WI, the DWI, the ADC identical, wherein pixels of a same position basically correspond to a same part of a human body, and respectively extracting point characteristics on three kinds of MRI sequences of each case, and inputting the point characteristics into a focus detection classifier to obtain a candidate focus. The candidate focus computer aided diagnosis comprises steps of calculating regional characteristics of the candidate focus in three kinds MRI sequences of each case and inputting the regional characteristics into a focus diagnosis classifier to obtain a corresponding diagnosis result. The multi-parameter MRI prostate cancer CAD method and system based on two kinds of classifiers can provide a series of quantized indexes and a malignant probability value.

Description

technical field [0001] The invention relates to the field of medical image processing, in particular to a multi-parameter MRI prostate cancer CAD method and system based on two classifiers. Background technique [0002] PCa (Prostate Cancer, prostate cancer) is the second most common cancer in men in the world. In the United States, about 1 / 6 men will get prostate cancer, and 1 / 36 men will die from this disease. At present, the diagnostic methods for prostate cancer include: TRUS (Trans-rectal Ultrasound, ultrasound-guided transrectal) prostate biopsy and PSA (Prostate-specific Antigen, prostate-specific antigen) serum examination. A prostate biopsy can be very uncomfortable for the patient and serious infections can occur. Prostate-specific antigen is specific to prostate tissue, but not to prostate cancer. There is considerable overlap in serum PSA results between prostate cancer and benign prostatic hyperplasia. [0003] Different from the above two methods, multi-param...

AI Technical Summary

Problems solved by technology

[0006] At present, there are some methods to extract pixel features. These methods generally use the statistical features in a local window centered on a certain pixel as the feature of the point. The disadvantage of these methods is that the pixel features used cannot describe the whole picture of the entire lesion area. Not as well understood by physicians as regional traits

[0007] Other methods extract features of lesion candidate areas, such as size, shape, texture, and asymmetry. The disadvantage is that cancer lesions in the prostate area need to be obtained by manual segmentation by experienced doctors. In addition to texture features, more effective features are needed to describe the lesion area of ​​prostate cancer

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