CN108280826A - A kind of solid nodules feature extraction of CT lungs Asia and detection method - Google Patents

A kind of solid nodules feature extraction of CT lungs Asia and detection method Download PDF

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Publication number
CN108280826A
CN108280826A CN201810063319.7A CN201810063319A CN108280826A CN 108280826 A CN108280826 A CN 108280826A CN 201810063319 A CN201810063319 A CN 201810063319A CN 108280826 A CN108280826 A CN 108280826A
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lung
candidate
asia
feature extraction
feature
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徐枫
陈建武
肖谋
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Beijing Yi Intelligent Technology Co Ltd
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Beijing Yi Intelligent Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/13—Edge detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/136—Segmentation; Edge detection involving thresholding
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10081—Computed x-ray tomography [CT]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30061—Lung
    • G06T2207/30064—Lung nodule

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  • Engineering & Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Radiology & Medical Imaging (AREA)
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Abstract

The invention discloses a kind of solid nodules feature extraction of CT lungs Asia and detection methods, include the following steps：Obtain lung's CT images；Lung's CT images of acquisition are pre-processed；Candidate nodule is carried out to pretreated lung's CT images to divide to obtain Candidate Set；Feature extraction is carried out to Candidate Set and obtains feature set；Lesion classification is carried out according to feature set；Lung neoplasm testing result is obtained according to lesion classification results.In the present invention, feature extraction is carried out in several ways, convenient for more comprehensively carrying out category filter, and application environment feature extraction, doubtful Lung neoplasm is accounted for the relationships such as other positions and the position of other doubtful Lung neoplasms in the picture, the sensitivity for improving classification solves existing lung Asia solid nodules computer detection method there are sensitivity low, the problems such as false sun rate is high.

Description

A kind of solid nodules feature extraction of CT lungs Asia and detection method
Technical field
The present invention relates to technical field of medical image processing more particularly to a kind of solid nodules feature extractions of CT lungs Asia and inspection Survey method.
Background technology
In the latest 20 years, lung cancer has become the lethal most cancer in the whole world, and the U.S. dies of the case of cancer within 2012 In only lung cancer just account for sum 28%, so for the early detection of lung cancer --- Lung neoplasm detection just it is most important.
Since two thousand seven, it is more and more suggested for the computer-aided detection system of lung solid nodules, and by Gradual perfection.But for lung Asia solid nodules, be difficult to capture in CT, therefore certainly for the computer of lung Asia solid nodules The research of dynamic detection method is also less, and that there are sensitivity is low for existing lung Asia solid nodules computer detection method, and false sun rate is high The problems such as, prodigious difficulty is brought to the auxiliary detection of lung Asia solid nodules, there are larger harmfulness.
Invention content
It is an object of the invention to：A kind of solid nodules feature extraction of CT lungs Asia and detection method are provided, existing lung is solved Sub- solid nodules feature extraction is not comprehensive, and then leads to that computer detection method sensitivity is low, false positive rate is high.
The technical solution adopted by the present invention is as follows：
A kind of CT lungs Asia solid nodules feature extracting method, includes the following steps：
S1：Obtain lung's CT images；
S2：Lung's CT images of acquisition are pre-processed；
S3：Candidate nodule is carried out to pretreated lung's CT images to divide to obtain Candidate Set；
S4：Feature extraction is carried out to Candidate Set and obtains feature set.
Further, the step S2 is as follows：
S201：Lung's CT images are split using bivalve value dividing method；
S202：Excess pixel, which is removed, by edge detection method obtains area-of-interest.
Further, the step S3 is as follows：
S301：Area-of-interest is split using morphological method；
S302：It rejects the vascular system contacted with area-of-interest and obtains Candidate Set.
Further, the feature extraction includes：Brightness extraction, texture feature extraction, Shape Feature Extraction, environment Feature extraction.
Further, the environmental characteristic extraction includes 4 kinds of modes：
Mode one：Candidate Set all pixels are calculated to the distance on lung boundary and bronchial tree, and calculate average value, standard Difference, minimum value and maximum value are used as environmental characteristic；
Mode two：Minimum rectangle frame is set around lung, and constructs three-dimensional x, y, z coordinate, to obtain candidate Relative position, and calculate the distance of minimum rectangle lower left bezel corner and the distance to lung Asia solid nodules center；
Mode three：Customized bounding box around segmentation candidates region is divided, wherein bounding box is divided for tracheae Or a part for blood vessel segmentation,
Absolute overlapping=N5
Wherein N4For the quantity of candidate regions pixel, N5It is the pixel quantity in bounding box；
It will absolutely be overlapped and be used as environmental characteristic with relative superposition rate；
Mode four：Calculate Candidate Set in candidate nodule quantity, and calculate candidate nodule around distance 30mm and 50mm with The distance of the quantity of interior candidate nodule and nearest candidate nodule is used as environmental characteristic.
A kind of CT lungs Asia solid nodules detection method, includes the following steps：
S1：Lesion classification is carried out according to feature set；
S2：Lung neoplasm testing result is obtained according to lesion classification results.
Further, the step S1 is as follows：
S101：Linear discriminant ratio calculation is carried out to feature；
S102：Choose maximum 5 features of ratio；
S103：Classified using 5 features of linear classifier pair；
S104：Classified to residue character using 10 graders of GentleBoost.
In conclusion by adopting the above-described technical solution, the beneficial effects of the invention are as follows：
1, in the present invention, by being detected automatically to lung Asia solid nodules, existing lung Asia solid nodules computer is solved The problems such as that there are sensitivity is low for detection method, and false sun rate is high, improves detection accuracy.
2, in the present invention, feature extraction is carried out in several ways, convenient for more comprehensively carrying out category filter, improves inspection Look into sensitivity.
3, the present invention in, application environment feature extraction, to doubtful Lung neoplasm in the picture with other positions and it is other doubt The relationships such as the position like Lung neoplasm account for, and improve the sensitivity of classification.
Description of the drawings
Fig. 1 is flow chart of steps of the present invention；
Fig. 2 is that feature of present invention extracts classification chart；
Fig. 3 is four kinds of pixel region dividing modes of the invention；
Fig. 4 is lesion classification process figure of the present invention.
Specific implementation mode
In order to make the purpose , technical scheme and advantage of the present invention be clearer, with reference to the accompanying drawings and embodiments, right The present invention is further elaborated.It should be appreciated that described herein, specific examples are only used to explain the present invention, not For limiting the present invention.
Embodiment 1
As shown in Figure 1, a kind of CT lungs Asia solid nodules feature extracting method, includes the following steps：
S1：Obtain lung's CT images；
S2：Lung's CT images of acquisition are pre-processed；
S3：Candidate nodule is carried out to pretreated lung's CT images to divide to obtain Candidate Set；
S4：Feature extraction is carried out to Candidate Set and obtains feature set.
The present invention carries out feature extraction for lung Asia solid nodules, and then is convenient for detection and judges, solves existing lung The problems such as that there are sensitivity is low for sub- solid nodules computer detection method, and false sun rate is high, improves detection accuracy.
Embodiment 2
On the basis of embodiment 1, the step S2 is as follows：
S201：Lung's CT images are split using bivalve value dividing method；
S202：Excess pixel, which is removed, by edge detection method obtains area-of-interest.
Lung's CT images are split using the method for double threshold segmentations, and gas removing pipe, blood vessel are gone by edge detection Equal excess pixels, tentatively obtain the coarse segmentation about doubtful nodule candidate.
Embodiment 3
On the basis of embodiment 1 or 2, the step S3 is as follows：
S301：Area-of-interest is split using morphological method；
S302：It rejects the vascular system contacted with area-of-interest and obtains Candidate Set.
Doubtful nodule candidate is divided, area-of-interest is further divided using more accurate morphological method, The vascular system contacted is rejected simultaneously, to obtain final Candidate Set.
Embodiment 4
As shown in Fig. 2, on the basis of embodiment 1, the feature extraction includes：Brightness extraction, textural characteristics carry It takes, the extraction of Shape Feature Extraction, environmental characteristic.
Further, the environmental characteristic extraction includes 4 kinds of modes：
Mode one：Candidate Set all pixels are calculated to the distance on lung boundary and bronchial tree, and calculate average value, standard Difference, minimum value and maximum value are used as environmental characteristic；
Mode two：Minimum rectangle frame is set around lung, and constructs three-dimensional x, y, z coordinate, to obtain candidate Relative position, and calculate the distance of minimum rectangle lower left bezel corner and the distance to lung Asia solid nodules center；
Mode three：Customized bounding box around segmentation candidates region is divided, wherein bounding box is divided for tracheae An or part for blood vessel segmentation.
Absolute overlapping=N5
Wherein N4For the quantity of candidate regions pixel, N5It is the pixel quantity in bounding box；
It will absolutely be overlapped and be used as environmental characteristic with relative superposition rate；
Mode four：Calculate Candidate Set in candidate nodule quantity, and calculate candidate nodule around distance 30mm and 50mm with The distance of the quantity of interior candidate nodule and nearest candidate nodule is used as environmental characteristic.
Further, the brightness extracting mode is as follows：
As shown in figure 3, four kinds of different modes, which are respectively adopted, carries out set of pixels division, mode A1：Using segmentation candidates region Pixel quantity as set of pixels；Mode A2：Minimum rectangle frame is built around segmentation candidates region, set of pixels is minimum Pixel in rectangular shaped rim；Mode A3：It is extended around on the basis of segmentation candidates region, extended mode is with 3x3x3 Rectangular pixels extend, and set of pixels is the pixel extended in rear region；Mode A4：Expand around on the basis of segmentation candidates region Exhibition, extended mode are extended with the rectangular pixels of 5x5x5, and set of pixels is the pixel extended in rear region；
It uses size for the standard histogram of 50HU to divide region to pixel for statistical analysis, and calculates maximum value, most Small value, average value, standard deviation, entropy are to feature extraction.
The texture feature extraction mode is as follows：
Texture feature extraction A2 in such a way that pixel in brightness extraction divides, lays equal stress on and is sampled as 16x16x16 With two area-of-interests of 32x32x32；
The two area-of-interest carry out offices of 16x16x16 and 32x32x32 of 2D local binaries method to resampling are used first Portion's two-value method calculates, and carries out binaryzation to 8 fields around each pixel, uses size for the standard histogram of 1HU It is for statistical analysis, and maximum value, minimum value, average value, standard deviation, entropy are calculated to feature extraction.
Then haar wavelet transform method is used to be decomposed into 4 frequency bands to the 32x32x32 area-of-interests transformation of resampling, 4 frequency bands constitute 4 voxel regions, are counted wherein carrying out standard histogram to 3 regions from high frequency band Analysis, and maximum value, minimum value, average value, standard deviation, entropy are calculated to feature extraction.
The Shape Feature Extraction mode is as follows：
Four kinds of modes are respectively adopted and carry out shape feature calculating：
Mode B1:Volume spheric region S identical with candidate region is defined at candidate region center, globoid calculates Formula:
Wherein V1It is the pixel volume that spheric region S corresponds to candidate region, VSIt is spheric region S pixel volumes.
Mode B2：Bounding box is arranged in A2 by the way of pixel divides in brightness extracts, and marker x, y, z is sat Mark, calculation formula：
Wherein N1It is the quantity of candidate regions pixel, N2It is the quantity of pixel in bounding box.
Mode B3：Bounding box is arranged in A2 by the way of pixel divides in brightness extracts, and its x, y, z is marked to sit Mark, calculation formula：
Wherein N1It is the quantity of candidate regions pixel, N3It is out to out bounding box (max (dimx, dimy, dimz)) interior pixel Quantity.
Mode B4：Bounding box is arranged in A2 by the way of pixel divides in brightness extracts, and its x, y, z is marked to sit Mark, calculation formula：
Wherein V3 is the volume of bounding box.
Embodiment 5
As shown in figs. 1 and 4, a kind of CT lungs Asia solid nodules detection method, includes the following steps：
S1：Lesion classification is carried out according to feature set；
S2：Lung neoplasm testing result is obtained according to lesion classification results.
Further, the step S1 is as follows：
S101：Linear discriminant ratio calculation is carried out to feature；
S102：Choose maximum 5 features of ratio；
S103：Classified using 5 features of linear classifier pair；
S104：Classified to residue character using 10 graders of GentleBoost.
Lesion classification is carried out on the basis of feature extraction, classification schemes are classified using dual-stage, classified in the first stage When only utilize 5 features, the acquisition process of this 5 features is：To all feature calculation Fisher linear discriminants ratios got, 5 features of maximum 5 features of ratio as the first stage are taken, linear classification is then used on the basis of this 5 features Device classifies to candidate.First stage is substantially carried out the rejecting of preliminary classification and false positive candidate, then by remaining time Choosing carries out second stage classification.Second stage classification uses 10 graders of GentleBoost, all using what is got before Feature classifies to first stage remaining candidate, and obtains final lesion classification results.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all essences in the present invention All any modification, equivalent and improvement etc., should all be included in the protection scope of the present invention made by within refreshing and principle.

Claims (7)

1. a kind of CT lungs Asia solid nodules feature extracting method, which is characterized in that include the following steps：
S1：Obtain lung's CT images；
S2：Lung's CT images of acquisition are pre-processed；
S3：Candidate nodule is carried out to pretreated lung's CT images to divide to obtain Candidate Set；
S4：Feature extraction is carried out to Candidate Set and obtains feature set.
2. a kind of CT lungs Asia solid nodules feature extracting method according to claim 1, which is characterized in that the step S2 tools Steps are as follows for body：
S201：Lung's CT images are split using bivalve value dividing method；
S202：Excess pixel, which is removed, by edge detection method obtains area-of-interest.
3. a kind of CT lungs Asia according to claim 1 or claim 2 solid nodules feature extracting method, which is characterized in that the step S3 is as follows：
S301：Area-of-interest is split using morphological method；
S302：It rejects the vascular system contacted with area-of-interest and obtains Candidate Set.
4. a kind of CT lungs Asia solid nodules feature extracting method according to claim 1, which is characterized in that the feature extraction Including：Brightness extraction, texture feature extraction, Shape Feature Extraction, environmental characteristic extraction.
5. according to a kind of CT lungs Asia solid nodules feature extracting method of claim 1 or 4, which is characterized in that the environment Feature extraction includes 4 kinds of modes：
Mode one：Candidate Set all pixels are calculated to the distance on lung boundary and bronchial tree, and calculate average value, standard deviation, most Small value and maximum value are used as environmental characteristic；
Mode two：Minimum rectangle frame is set around lung, and constructs three-dimensional x, y, z coordinate, to obtain candidate phase To position, and calculate the distance of minimum rectangle lower left bezel corner and the distance to lung Asia solid nodules center；
Mode three：Customized bounding box around segmentation candidates region is divided, wherein bounding box be tracheae divide or A part for blood vessel segmentation,
Absolute overlapping=N5
Wherein N4For the quantity of candidate regions pixel, N5It is the pixel quantity in bounding box；
It will absolutely be overlapped and be used as environmental characteristic with relative superposition rate；
Mode four：The quantity of candidate nodule in Candidate Set is calculated, and is calculated around candidate nodule within distance 30mm and 50mm The distance of the quantity of candidate nodule and nearest candidate nodule is used as environmental characteristic.
6. a kind of CT lungs Asia solid nodules detection method, which is characterized in that include the following steps：
S1：Lesion classification is carried out according to feature set；
S2：Lung neoplasm testing result is obtained according to lesion classification results.
7. a kind of CT lungs Asia solid nodules detection method according to claim 6, which is characterized in that the step S1 is specifically walked It is rapid as follows：
S101：Linear discriminant ratio calculation is carried out to feature；
S102：Choose maximum 5 features of ratio；
S103：Classified using 5 features of linear classifier pair；
S104：Classified to residue character using 10 graders of GentleBoost.
CN201810063319.7A 2018-01-23 2018-01-23 A kind of solid nodules feature extraction of CT lungs Asia and detection method Pending CN108280826A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110415206A (en) * 2019-04-08 2019-11-05 上海墩庐生物医学科技有限公司 A method of identification adenocarcinoma of lung infiltrates parting

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102842132A (en) * 2012-07-12 2012-12-26 上海联影医疗科技有限公司 CT pulmonary nodule detection method
EP3095376A1 (en) * 2014-02-07 2016-11-23 Hiroshima University Endoscopic image diagnosis support system
CN106780460A (en) * 2016-12-13 2017-05-31 杭州健培科技有限公司 A kind of Lung neoplasm automatic checkout system for chest CT image

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102842132A (en) * 2012-07-12 2012-12-26 上海联影医疗科技有限公司 CT pulmonary nodule detection method
EP3095376A1 (en) * 2014-02-07 2016-11-23 Hiroshima University Endoscopic image diagnosis support system
CN106780460A (en) * 2016-12-13 2017-05-31 杭州健培科技有限公司 A kind of Lung neoplasm automatic checkout system for chest CT image

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110415206A (en) * 2019-04-08 2019-11-05 上海墩庐生物医学科技有限公司 A method of identification adenocarcinoma of lung infiltrates parting
CN110415206B (en) * 2019-04-08 2023-12-15 上海墩庐生物医学科技有限公司 Method for identifying lung adenocarcinoma infiltration typing

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Application publication date: 20180713