US6549646B1
Expired / Lapsed
US
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Divide-and-conquer method and system for the detection of lung nodule in radiological images
Assignee: Riverain Medical Group LLC
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Filing: 2000-02-15
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Granted: 2003-04-15
•
Expires: 2020-02-15
Abstract
Abstract A divide-and-conquer (DAC) method and system improve the detection of abnormalities, like lung nodules, in radiological images via the use of zone-based digital image processing and artificial neural networks. The DAC method and system divide the lung zone into different zones in order to enhance the efficiency in detection. Different image enhancement techniques are used for each different zone to enhance nodule images, as are different zone-specific techniques for selecting suspected abnormalities, extracting image features corresponding to selected abnormalities, and classifying the abnormalities as either true or false abnormalities.
Inventors
- 👤 Hwa-Young Michael Yeh
- 👤 Jyh-Shyan Lin
- 👤 Yuan-Ming Fleming Lure
- 👤 Xin-Wei Xu
- 👤 Ruiping Li
- 👤 Rong Feng Zhuang
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-11-09 21:42
CPC Classifications
G; G06; G06T; G06T7/00; G06T7/0002; G06T7/0012; G06V; G06V30/00; G06V30/10; G06V30/24; G06V30/242; G06V30/244; G06T2207/00; G06T2207/30; G06T2207/30004; G06T2207/30061
Claims Analysis
Total Claims: 104
Claim 1
Independent Claim
101 words
A method of identifying abnormalities in radiological images of the chest, the method comprising the steps of: receiving a digital chest image; discarding pixels outside of the lung zone; dividing the lung zone into different overlapped zones based on the geometry and subtlety distribution and image characteristics of abnormalities and normal anatomic structure in each zone; enhancing abnormality signals by using enhancement techniques specifically developed for each zone; preliminarily selecting suspect abnormalities in each zone; extracting image features in each zone; classifying abnormalities and normal anatomic structures in each zone; integrating outputs from different zones; and providing an indication of abnormalities.
Claim 2
Independent Claim
101 words
A method of identifying abnormalities in radiological images of the chest, the method comprising the steps of: receiving a digital chest image; discarding pixels outside of the lung zone; dividing the lung zone into different overlapped zones based on the geometry and subtlety distribution and image characteristics of abnormalities and normal anatomic structure in each zone; enhancing abnormality signals by using enhancement techniques specifically developed for each zone; preliminarily selecting suspect abnormalities in each zone; extracting image features in each zone; classifying abnormalities and normal anatomic structures in each zone; integrating outputs from different zones; and providing an indication of abnormalities.
Claim 3
Dependent Claim
49 words
A method according to claim 1 , wherein the step of dividing further comprises the steps of: extracting a non-obscured lung zone using a neural network; and obtaining an obscured lung zone by excluding from the lung zone the pixels of the image corresponding to the non-obscured lung zone.
Claim 4
Dependent Claim
43 words
A method according to claim 2 , wherein said different overlapped zones comprise: a zone of obscured lung; a zone of clavicle; a zone of peripheral lung edge; a zone of peripheral lung central; a zone of hilum; and a zone of diaphragm.
Claim 5
Dependent Claim
18 words
A method according to claim 3 , wherein said zone of obscured lung includes spine, heart, and mediastinum.
Claim 6
Dependent Claim
55 words
A method according to claim 3 , wherein said zone of clavicle comprises an area extending from the tops of right and left portions of said non-obscured lung zone and covering a region comprising a predetermined portion of the vertical distance toward the bottoms of said right and left portions of said non-obscured lung zones.
Claim 7
Dependent Claim
37 words
A method according to claim 3 , wherein said zone of peripheral lung edge is obtained by the step of: performing image processing methods including morphological processing with a structure element along the contour of the ribcage.
Claim 8
Dependent Claim
13 words
A method according to claim 6 , wherein said morphological processing comprises erosion.
Claim 9
Dependent Claim
37 words
A method according to claim 3 , wherein said zone of peripheral lung central is obtained by the step of: performing image processing methods including morphological processing with two structure elements along the contour of the ribcage.
Claim 10
Dependent Claim
13 words
A method according to claim 8 , wherein said morphological processing comprises erosion.
Claim 11
Dependent Claim
55 words
A method according to claim 3 , wherein said zone of hilum is obtained by the step of: performing image processing methods including morphological processing with two structure elements along the contour of the ribcage; and performing image processing methods including morphological processing with a structure element along the contour of the heart and spine.
Claim 12
Dependent Claim
17 words
A method according to claim 10 , wherein at least one of said morphological processings comprises erosion.
Claim 13
Dependent Claim
33 words
A method according to claim 3 , wherein said zone of the diaphragm is obtained by the step of: performing image processing methods including morphological processing with a structure element along the diaphragm.
Claim 14
Dependent Claim
13 words
A method according to claim 12 , wherein said morphological processing comprises erosion.
Claim 15
Dependent Claim
39 words
A method according to claim 3 , wherein said step of enhancing abnormality signals further comprises the step of: applying specific image enhancement methods tailored to each zone independently to suppress background structures and to further enhance abnormality-to-background contrast.
Claim 16
Dependent Claim
43 words
A method according to claim 14 , wherein said image enhancement method tailored to said zone of obscured lung comprises the step of: using contrast enhancement methods, to obtain a more uniform histogram of the zone and enhance the contrast of obscured lung.
Claim 17
Dependent Claim
32 words
A method according to claim 14 , wherein said image enhancement method tailored to said zone of clavicle comprises the steps of: performing edge detection methods; and using an edge removal method.
Claim 18
Dependent Claim
16 words
A method according to claim 16 , wherein said edge detection methods include a Hough transform.
Claim 19
Dependent Claim
34 words
A method according to claim 14 , wherein said image enhancement method tailored to said zone of peripheral lung comprises the step of: using edge detection and removal methods to suppress the ribcage structure.
Claim 20
Dependent Claim
18 words
A method according to claim 18 , wherein said edge detection and removal methods include a Hough transform.
Claim 21
Dependent Claim
49 words
A method according to claim 14 , wherein said image enhancement method tailored to said zone of peripheral lung central comprises the step of: performing image subtraction using median and matched filtering by spatially convolving the pixels of the zone with multiple sphere profiles of different size nodule images.
Claim 22
Dependent Claim
46 words
A method according to claim 14 , wherein said image enhancement method tailored to said zone of hilum comprises the step of: performing image subtraction using median and matched filtering by spatially convolving the pixels of the zone with multiple synthetic nodule profiles of different sizes.
Claim 23
Dependent Claim
33 words
A method according to claim 14 , wherein said image enhancement method tailored to said zone of diaphragm comprises the step of: increasing the abnormality-to-background contrast using an inverse contrast ratio mapping technique.
Claim 24
Dependent Claim
42 words
A method according to claim 3 , wherein said step of preliminarily selecting suspect abnormality areas further comprises the steps of: clustering the suspect abnormality areas into different groups; and using different threshold and sensitivity and specificity values in each different zone.
Claim 25
Dependent Claim
27 words
A method according to claim 23 , wherein said step of preliminarily selecting suspect abnormality areas in said zone of obscured lung is performed with high specificity.
Claim 26
Dependent Claim
26 words
A method according to claim 23 , wherein said step of preliminarily selecting suspect abnormality areas in said zone of clavicle is performed with high sensitivity.
Claim 27
Dependent Claim
28 words
A method according to claim 23 , wherein said step of preliminarily selecting suspect abnormality areas in said zone of peripheral lung edge is performed with high sensitivity.
Claim 28
Dependent Claim
28 words
A method according to claim 23 , wherein said step of preliminarily selecting suspect abnormality areas in said zone of peripheral lung central is performed with high sensitivity.
Claim 29
Dependent Claim
26 words
A method according to claim 23 , wherein said step of preliminarily selecting suspect abnormality areas in said zone of hilum is performed with high specificity.
Claim 30
Dependent Claim
26 words
A method according to claim 23 , wherein said step of preliminarily selecting suspect abnormality areas in said zone of diaphragm is performed with high specificity.
Claim 31
Dependent Claim
53 words
A method according to claim 3 , wherein said step of classifying includes the steps of: training specific classifiers using the selected suspects and abnormality in each zone to classify an abnormality and normal anatomic structures in each zone; and training at least one neural network by using cross-validation using SUB-A z techniques.
Claim 32
Dependent Claim
28 words
A method according to claim 30 , wherein said step of classifying further comprises the step of, for said zone of obscured lung, using a back-propagation neural network.
Claim 33
Dependent Claim
40 words
A method according to claim 30 , wherein said step of classifying further comprises the step of, for said zone of clavicle, comparing the density around the clavicle bone area of both the left and right lungs to detect abnormalities.
Claim 34
Dependent Claim
29 words
A method according to claim 30 , wherein said step of classifying further comprises the step of, for said zone of peripheral lung edge, training a back-propagation neural network.
Claim 35
Dependent Claim
42 words
A method according to claim 30 , wherein said step of classifying further comprises the step of, for said zone of peripheral lung central, training a back-propagation neural network using those of said selected suspects located in said zone of peripheral central.
Claim 36
Dependent Claim
27 words
A method according to claim 30 , wherein said step of classifying further comprises the step of, for said zone of hilum, using a convolution neural network.
Claim 37
Dependent Claim
26 words
A method according to claim 30 , wherein said step of classifying further comprises the step of, for said zone of diaphragm, thresholding a matching filter.
Claim 38
Dependent Claim
35 words
A method according to claim 30 , wherein a desired performance of each of said zone-based classifiers is obtained by training each zone classifier to have sensitivity and specificity performance according to the particular zone.
Claim 39
Dependent Claim
22 words
A method according to claim 30 , wherein said cross-validation using SUB-A z is used to cross-validate the classifier for each zone.
Claim 40
Dependent Claim
38 words
A method according to claim 1 , wherein said step of integrating outputs comprises the step of combining the outputs from different zones based on the sensitivity and specificity performance for each zone in said step of classifying.
Claim 41
Dependent Claim
32 words
A method according to claim 1 , further comprising the step of: utilizing a parallel processing engine for processing each zone, said parallel processing engine processing each zone independently and in parallel.
Claim 42
Dependent Claim
40 words
A method according to claim 3 , wherein said step of extracting image features in each zone comprises the step of, for the zone of obscured lung, extracting size, circularity and elongation for each suspected abnormality area in that zone.
Claim 43
Dependent Claim
28 words
A method according to claim 41 , where said step of extracting size, circularity and elongation comprises the step of employing a bit quads method to extract size.
Claim 44
Dependent Claim
37 words
A method according to claim 3 , wherein said step of extracting image features in each zone comprises the step of, for the zone of clavicle, extracting gradient features for each suspected abnormality area in that zone.
Claim 45
Dependent Claim
17 words
A method according to claim 43 , where said gradient features include amplitude and orientation edge maps.
Claim 46
Dependent Claim
42 words
A method according to claim 3 , wherein said step of extracting image features in each zone comprises the step of, for the zone of peripheral lung edge, extracting circularity, area and moment invariants for each suspected abnormality area in that zone.
Claim 47
Dependent Claim
45 words
A method according to claim 3 , wherein said step of extracting image features in each zone comprises the step of, for the zone of peripheral lung central, extracting circularity, size and amplitude and orientation edge maps for each suspected abnormality area in that zone.
Claim 48
Dependent Claim
37 words
A method according to claim 3 , wherein said step of extracting image features in each zone comprises the step of, for the zone of hilum, normalizing image pixels of each suspected abnormality area in that zone.
Claim 49
Dependent Claim
82 words
A method according to claim 47 , wherein said step of normalizing image pixels comprises the steps of: computing an average pixel value, μ 0 , of the 8-connected pixels surrounding each pixel of a suspected abnormality area; and for each pixel value x, mapping the pixel value to a new pixel value, x*, by using the equation, x * = 1 1 + - β ( x - μ 0 ) where β is a decaying control parameter.
Claim 50
Dependent Claim
35 words
A method according to claim 3 , wherein the step of extracting image features in each zone comprises, for the zone of diaphragm, extracting circularity and size for each suspected abnormality area in that zone.
Claim 51
Independent Claim
205 words
A system for identifying abnormalities in radiological images of the chest, comprising: (a) an image input unit, said image input unit receiving a digital chest image; (b) a memory unit for storing at least one digital chest image; (c) an image processing unit that detects said abnormalities, the image processing unit comprising: an image zoning unit that divides said digital chest image into overlapped zones, thereby generating a zoned image; a zone-based image enhancement unit that enhances each zone of said zoned image using an enhancement technique tailored to that zone, thereby generating a zone-enhanced image; a zone-based suspect selection unit that processes each zone of said zone-enhanced image to extract zone-grouped suspect abnormality areas; a zone-based feature extraction unit that processes said zone-grouped abnormality areas of each zone using a technique tailored to that particular zone, thereby generating zone-grouped suspect features; a zone-based classification unit that uses different classifiers tailored to the different zones to process said zone-grouped suspect features and to thereby generate suspect classification scores corresponding to said zone-grouped suspect abnormality areas; and a zone-based data fusion unit that processes said suspect classification scores associated with said zone-grouped suspect abnormality areas to provide final abnormality areas; and (d) an image output unit.
Claim 52
Independent Claim
205 words
A system for identifying abnormalities in radiological images of the chest, comprising: (a) an image input unit, said image input unit receiving a digital chest image; (b) a memory unit for storing at least one digital chest image; (c) an image processing unit that detects said abnormalities, the image processing unit comprising: an image zoning unit that divides said digital chest image into overlapped zones, thereby generating a zoned image; a zone-based image enhancement unit that enhances each zone of said zoned image using an enhancement technique tailored to that zone, thereby generating a zone-enhanced image; a zone-based suspect selection unit that processes each zone of said zone-enhanced image to extract zone-grouped suspect abnormality areas; a zone-based feature extraction unit that processes said zone-grouped abnormality areas of each zone using a technique tailored to that particular zone, thereby generating zone-grouped suspect features; a zone-based classification unit that uses different classifiers tailored to the different zones to process said zone-grouped suspect features and to thereby generate suspect classification scores corresponding to said zone-grouped suspect abnormality areas; and a zone-based data fusion unit that processes said suspect classification scores associated with said zone-grouped suspect abnormality areas to provide final abnormality areas; and (d) an image output unit.
Claim 53
Dependent Claim
42 words
A system according to claim 50 , wherein at least one of said zone-based image enhancement unit, said zone-based suspect selection unit, said zone-based feature extraction unit, and said zone-based classification unit comprises components that implement parallel processing of the various zones.
Claim 54
Dependent Claim
61 words
A system according to claim 50 , wherein said image zoning unit discards pixels of said digital chest image located outside the lung region; further divides the lung region into obscured and non-obscured lung zones; and sub-divides the non-obscured lung zone into further zones based on the geometry and subtlety distribution and image characteristics of abnormalities in each of the zones.
Claim 55
Dependent Claim
26 words
A system according to claim 52 , wherein said further zones include the following: obscured lung, clavicle, peripheral lung edge, peripheral lung central, hilum, and diaphragm.
Claim 56
Dependent Claim
17 words
A system according to claim 53 , wherein said obscured lung zone includes spine, heart, and mediastinum.
Claim 57
Dependent Claim
54 words
A system according to claim 53 , wherein said clavicle zone comprises an area extending from the tops of right and left portions of said non-obscured lung zone and covering a region comprising a certain portion of the vertical distance toward the bottoms of said right and left portions of said non-obscured lung zones.
Claim 58
Dependent Claim
36 words
A system according to claim 53 , wherein said image zoning unit obtains said peripheral lung edge zone by performing image processing methods including morphological processing with a structure element along the contour of the ribcage.
Claim 59
Dependent Claim
13 words
A system according to claim 56 , wherein said morphological processing comprises erosion.
Claim 60
Dependent Claim
36 words
A system according to claim 53 , wherein said image zoning unit obtains said peripheral lung central zone by performing image processing methods including morphological processing with two structure elements along the contour of the ribcage.
Claim 61
Dependent Claim
13 words
A system according to claim 58 , wherein said morphological processing comprises erosion.
Claim 62
Dependent Claim
34 words
A system according to claim 53 , wherein said image zoning unit obtains said hilum zone by performing image processing methods including morphological processing with two structure elements along the contour of the ribcage.
Claim 63
Dependent Claim
13 words
A system according to claim 60 , wherein said morphological processing comprises erosion.
Claim 64
Dependent Claim
31 words
A system according to claim 53 , wherein said image zoning unit obtains said diaphragm zone by performing image processing methods including morphological processing with a structure element along the diaphragm.
Claim 65
Dependent Claim
13 words
A system according to claim 62 , wherein said morphological processing comprises erosion.
Claim 66
Dependent Claim
33 words
A system according to claim 53 , wherein said zone-based image enhancement unit implements specific image enhancement methods tailored to each zone independently to suppress background structures and to further enhance abnormality-to-background contrast.
Claim 67
Dependent Claim
28 words
A system according to claim 64 , wherein said zone-based image enhancement unit comprises zone-specific image enhancement units, each of which implements one of said image enhancement methods.
Claim 68
Dependent Claim
37 words
A system according to claim 64 , wherein the image enhancement method tailored to said obscured lung zone uses contrast enhancement methods to obtain a more uniform histogram and enhance the contrast of the obscured lung zone.
Claim 69
Dependent Claim
15 words
A system according to claim 66 , wherein said contrast enhancement methods include histogram equalization.
Claim 70
Dependent Claim
28 words
A system according to claim 64 , wherein the image enhancement method tailored to said clavicle zone includes performing edge detection methods and using an edge removal technique.
Claim 71
Dependent Claim
16 words
A system according to claim 68 , wherein said edge detection methods include a Hough transform.
Claim 72
Dependent Claim
29 words
A system according to claim 64 , wherein the image enhancement method tailored to said peripheral lung zone uses edge detection and removal methods to suppress the ribcage structure.
Claim 73
Dependent Claim
18 words
A system according to claim 70 , wherein said edge detection and removal methods include a Hough transform.
Claim 74
Dependent Claim
43 words
A system according to claim 64 , wherein the image enhancement method tailored to said peripheral lung central zone performs median and matched filtering by spatially convolving the pixels of the zone with multiple synthetic sphere profiles of different sizes of nodule images.
Claim 75
Dependent Claim
35 words
A system according to claim 64 , wherein the image enhancement method tailored to said hilum zone performs matched filtering by spatially convolving the pixels of the zone with multiple sizes of synthetic nodule profiles.
Claim 76
Dependent Claim
28 words
A system according to claim 64 , wherein the image enhancement method tailored to said diaphragm zone increases the abnormality-to-background contrast using an inverse contrast ratio mapping technique.
Claim 77
Dependent Claim
33 words
A system according to claim 53 , wherein said zone-based suspect selection unit clusters suspect abnormality areas into different groups and uses different threshold and sensitivity and specificity values in each different zone.
Claim 78
Dependent Claim
17 words
A system according to claim 75 , wherein in said obscured lung zone high specificity is used.
Claim 79
Dependent Claim
16 words
A system according to claim 75 , wherein in said clavicle zone high sensitivity is used.
Claim 80
Dependent Claim
18 words
A system according to claim 75 , wherein in said peripheral lung edge zone high sensitivity is used.
Claim 81
Dependent Claim
18 words
A system according to claim 75 , wherein in said peripheral lung central zone high sensitivity is used.
Claim 82
Dependent Claim
16 words
A system according to claim 75 , wherein in said hilum zone high specificity is used.
Claim 83
Dependent Claim
16 words
A system according to claim 75 , wherein in said diaphragm zone high specificity is used.
Claim 84
Dependent Claim
77 words
A system according to claim 53 , said zone-based classification unit comprising specific classifiers corresponding to each of said zones, each of said classifiers being trained using selected suspect and abnormality data of a particular zone, to enable that classifier to classify an abnormality and normal anatomic structures in the particular zone; and wherein at least one of said specific classifiers comprises a neural network, said neural network being trained by using cross-validation using SUB-A z techniques.
Claim 85
Dependent Claim
21 words
A system according to claim 82 , wherein the classifier corresponding to the obscured lung zone comprises a back-propagation neural network.
Claim 86
Dependent Claim
36 words
A system according to claim 82 , wherein the classifier corresponding to the clavicle zone comprises means for comparing the density around the clavicle bone area of both the left and right lungs to detect abnormalities.
Claim 87
Dependent Claim
22 words
A system according to claim 82 , wherein the classifier corresponding to the peripheral lung edge zone comprises a back-propagation neural network.
Claim 88
Dependent Claim
22 words
A system according to claim 82 , wherein the classifier corresponding to the peripheral lung central zone comprises a back-propagation neural network.
Claim 89
Dependent Claim
20 words
A system according to claim 82 , wherein the classifier corresponding to the hilum zone comprises a convolution neural network.
Claim 90
Dependent Claim
19 words
A system according to claim 82 , wherein the classifier corresponding to the diaphragm zone thresholds a matching filter.
Claim 91
Dependent Claim
34 words
A system according to claim 82 , wherein a desired performance of each of said classifiers is obtained by training each classifier to have a sensitivity and specificity performance according to the particular zone.
Claim 92
Dependent Claim
21 words
A system according to claim 82 , wherein cross-validation using SUB-A z is used to cross-validate the classifier for each zone.
Claim 93
Dependent Claim
32 words
A system according to claim 82 , wherein said zone-based data fusion unit comprises means for combining the outputs from different zones based on sensitivity and specificity performance for each zone's classifier.
Claim 94
Dependent Claim
31 words
A system according to claim 50 , said image processing unit comprising a parallel processing engine for processing each zone, said parallel processing engine processing each zone independently and in parallel.
Claim 95
Dependent Claim
32 words
A system according to claim 92 , said parallel processing engine encompassing said zone-based image enhancement unit, said zone-based suspect selection unit, said zone-based feature extraction unit, and said zone-based classification unit.
Claim 96
Dependent Claim
34 words
A system according to claim 53 , wherein said zone-based feature extraction unit comprises means, for the obscured lung zone, for extracting size, circularity and elongation for each suspected abnormality area in that zone.
Claim 97
Dependent Claim
24 words
A system according to claim 94 , where said means for extracting size, circularity and elongation employs a bit quads method to extract size.
Claim 98
Dependent Claim
31 words
A system according to claim 53 , wherein said zone-based feature extraction unit comprises means, for the clavicle zone, for extracting gradient features for each suspected abnormality area in that zone.
Claim 99
Dependent Claim
17 words
A system according to claim 96 , where said gradient features include amplitude and orientation edge maps.
Claim 100
Dependent Claim
36 words
A system according to claim 53 , wherein said zone-based feature extraction unit comprises means, for the peripheral lung edge zone, for extracting circularity, area and moment invariants for each suspected abnormality area in that zone.
Claim 101
Dependent Claim
39 words
A system according to claim 53 , wherein said zone-based feature extraction unit comprises means, for the peripheral lung central zone, for extracting circularity, size and amplitude and orientation edge maps for each suspected abnormality area in that zone.
Claim 102
Dependent Claim
31 words
A system according to claim 53 , wherein said zone-based feature extraction unit comprises means, for the hilum zone, for normalizing image pixels of each suspected abnormality area in that zone.
Claim 103
Dependent Claim
82 words
A system according to claim 100 , wherein said means for normalizing image pixels comprises: means for computing an average pixel value, μ 0 , of the 8-connected pixels surrounding each pixel of a suspected abnormality area and, for each pixel value x, for mapping the pixel value to a new pixel value, x*, by using the equation, x * = 1 1 + - β ( x - μ 0 ) where β is a decaying control parameter.
Claim 104
Dependent Claim
32 words
A system according to claim 53 , wherein the zone-based feature extraction unit comprises, for the diaphragm zone, means for extracting circularity and size for each suspected abnormality area in that zone.
References Cited (Backward Citations)
USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
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| 3.5 Years (Year 4) | 2006-04-14 | 2006-10-14 | 2007-04-15 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2010-04-14 | 2010-10-14 | 2011-04-15 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2014-04-14 | 2014-10-14 | 2015-04-15 | $7700.00 | Window Closed / Lapsed |