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Slide 2 !!! Slide 3 Automatic Understanding of the Images Slide 4 Many techniques of medical computer vision, e.g. X-ray images processing, analysis and recognition are now very known Slide 5 All such techniques exist because every image can be represented as a two-dimensional function One can find some details of the image on the function structures Slide 6 understanding Automatic understanding of the images (and other signals) is a new scientific idea in the area of computer vision and signal processing. Traditional (and very known) approach in such technique includes: processing digital image processing analysis computer analysis of the image recognition automatic recognition of the image Slide 7 Relations and connections between the traditional sub-items of total computer vision task presents following figure: Real object Registration Digital image Image processing Image description Decision based on the image Analysis Recognition Sometimes image processing task is performed many times until the image of desired quality is obtained! In simple cases Computer graphics Not discussed at this works! Image processing Slide 8 ...automatic understanding understanding of the images ? contribution What new contribution is added to this schema by... Slide 9 Let me explain it step by step Slide 10 Stage 0: Stage 0: Registration of the images (or other signals obtaining information) Object under consideration Raw - probably noisy and not very useful signals Slide 11 Stage 1: Stage 1: Preprocessing (mainly filtration) of signals Object under consideration Signals cleaned and enhanced Slide 12 Stage 2: Stage 2: Signal analysis and feature extraction Object under consideration Features and parameters extracted from signals Slide 13 Stage 3: Stage 3: Recognition and classification of signals Object under consideration Object classification and pattern recognition Slide 14 Stage 4: Signal understanding and semantic interpretation Object under consideration Merit sense and semantic content of the object Slide 15 Recent book describing the details: Slide 16 Image processing Image processing helps us to answer the question: how to increase quality and visibility of the image? Image analysis Image analysis helps us to answer the question: which are the exact values of selected features of the image? Image recognition Image recognition helps us to answer the question: to which classes (patterns) do the selected objects on the image belong? Lets try to analyze exact meaning of such items: Slide 17 Automatic image understanding helps us to answer following questions: follows What follows the visualized details ? & meaning What is the meaning of the features extracted from the image? & belong to particular classes What are the results of the fact, that some objects belong to particular classes? Slide 18 automatic understanding new The idea of automatic understanding is very new, so is necessary whowhen we ought to explain, why this new idea is necessary and who and when can use automatic understanding instead of simple recognition. Slide 19 Let me use for explanation......although the matter under consideration is definitely serious Slide 20 Lets think together... intelligent in sense of semantic content...how to describe criteria for intelligent selection of next pictures similar (in sense of semantic content) to the ones shown on the next slides from a multimedial database. Slide 21 Slide 22 Hallo! Honey !!!Splash!!!!!!! Slide 23 this Do you see this body?! Where ??!............... Brrryyymmss! Slide 24 classical image analysis For solving of selected problem using classical image analysis one needs following operations: Segmentation of all pictures and selection of important objects on all images Extraction and calculation of the main features of the selected objects Objects classification and recognition Selection of features and classes of recognized objects which are the same on all images under consideration Searching in database for images having the same objects with the same features Slide 25 Performing pointed out steps for the first image under consideration we can find: Object number 1: features:... Recognition = Women Object number 2: features:... Recognition = vehicle Slide 26 Performing pointed out steps for next image under consideration we can find: Object number 1: features:... Recognition = Women Object number 2: features:... Recognition = Vehicle Object number 3: features:... Recognition = Man Slide 27 Performing pointed out steps for last image under consideration we can find: Object number 1: features:... Recognition = Women Object number 2: features:... Recognition = Vehicle Object number 3: features:... Recognition = Man Object number 4: features:... Recognition = Man Slide 28 Summarizing our information we can do such induction: WomenVehicleOn all images we can find two objects: Women and Vehicle ManMenOn some images there are also object Man, but not on all - so Men can be automatically considered as not important in searching criteria Women VehicleResult: computer finds and presents as desired output all images with Women and Vehicle Slide 29 For example such an image can be obtained from the multimedial database : (For people form non-communistic countries: It is a very known allegory of the Soviet poster named all young girls ought to work on tractors) Slide 30 Let see some real examples... Slide 31 bellow Vehicle In fact the image presented bellow ought to be taken from the Web database although it does not contain Vehicle at all! Slide 32 proper meaning It is proper solution because in fact general meaning of all presented images is covered by the sentence: Now we can see, why (and because of who) the mens life can be so often shortened! Slide 33 Slide 34 Slide 35 Slide 36 Slide 37 I apologize... It was off course only joke (I apologize...) content... because very often images apparently very different in fact can hide the semantically identical the matter although is quite serious... Slide 38 images understanding? What is than the sense of images understanding? present visible Basically we do need a method of extraction of some kind of semantic content, which is present in the image, but is not simply visible on it. This job can be difficult, because the matter of things is often hidden and needs understanding instead of simple recognition! Slide 39 Technically speaking, the difference between image recognition and image understanding includes following assumptions: recognitionfixed number of a priori known classes differentiationin case of recognition we always have a fixed number of a priori known classes and the task demands only extraction of all these features of the image, which are necessary and sufficient for differentiation between classes under consideration. After processing we do obtain the number (or name) of proper class; understanding potential number of possible classes goes to infinity. automatic reasoning processin case of understanding we have not any a priori known classes or templates, because in fact the potential number of possible classes goes to infinity. So after processing we do obtain a description of the image content without using any a priori known classification, because even the criteria of classification are constructed and developed during the automatic reasoning process. Slide 40 The starting point of all our research was analysis of medical images Slide 41 Difficult medical images! Urography of kidney and ureter ERCP image of pancreas Coronarograhpy of heart arteries Slide 42 MRI image of cerebral hemispheres CT image of cerebral hemispheres Actual area of our research: Brain Images Slide 43 Images of M Images of M icroscopic Structure and Tissue Slide 44 Development of proper methods for automatic interpretation of such images is very difficult because of two types of troubles: n First, morphology of health organ is different for every human being, so we have not any kind of template of proper view of the analyzed object. n Second, deformations of the organ shape and size can be very different in the form, number and in localization also if the diseases are in fact identical! Slide 45 Lets compare two examples of ERCP images of pancreatic duct with pancreatits (permanent inflammation) The illness is the same, but the images are definitely different! Nobody can show any pattern or template on the image, which may be pointed as a signal of this kind of illness. Slide 46 The same situation we can find when analyzing of ERCP images presented pancreatic cancer. pattern Who can point out here the pattern for recognition ??! Slide 47 Another typical example: cardiological imaging (coronarography) with evident symptoms of serious cardiac illnesses. normal form localization Every patient has quite different shape of normal arteries and definitely another form and localization of pathological changes (stenosis). Slide 48 Similar problems one can find during analysis of X-ray images of urters and kidney pelvis imaging (with pathological narrowing and other possible deformations). Different images, the same illness! Slide 49 n Problem of proper interpretation of such images can not be solved by means of traditional image processing, picture analysis and pattern recognition. n Only way is formulating of new paradigm of images interpretation and development of new method of its full automatisation based on application of advanced tools of artificial intelligence. n Automatic reasoning about images semantic content, performed on the base of picture analysis is called automatic understanding of the image. On the base of analysis of many examples of medical images we try formulate such three assumptions: Slide 50 Fundamental features of the automatic understanding: n We try to simulate natural method of thinking of the physician, who needs to understand the illness before formal diagnosis and selection of treatment. n First we make linguistic description of the of the merit content of the image using special kind of image description language. Thanks to that idea we can describe every image without pointing to any limited number of a priori described classes. n The linguistic description of the image content build such way is the base for understanding of the image for diagnosis or for indexing of multimedial database. Slide 51 New view for analysis and semantic classification of visual data including Understanding Systems and This new schema includes pattern analysis and semantic classification. It can be used for intelligent creation of multimedia representation in Visual Data Understanding Systems for Web applications. Slide 52 When we use traditional pattern recognition all process of the image analysis is based on feed- forward (one-direction flow of signals) scheme. Object Taking of the image (reception) Medical image Analysis X=3,75 y=2,54 z=-8,72........... Recognition Image features Decision Diagnosis: narrowing of left coronary artery! Slide 53 source of knowledge during image understanding we always have two-directional flow of information: input data stream (all features obtained by means of analysis of the image under consideration) must be compared with the stream of demands generated by dedicated source of knowledge. The demands are always connected with particular (selected) hypothesis of semantic interpretation of the image content. In contrast to this simple scheme... Slide 54 understanding two-directional So we assume understanding of the image as two-directional flow of information. Object Image acquisition Medical image Analysis X=3,75 y=2,54 z=-8,72........... Linguistic description Image features Knowledge about images Description of image merit content Demands about image merit content Cognitive resonance Under- standing of image content Demands are kind of postulates, describing desired values of some (selected) features of the image. The selected parameters of the image under consideration must have desired values when some assumption about semantic interpretation of the image content can be validated as true. Slide 55 image understanding Such structure of the system for image understanding corresponds with one of the very known models of natural visual perception by man, named knowledge based perception. Slide 56 Humans eye can not recognize object if the brain have not a template for such view. knows It remains true although the brain knows the object, but in another view, what means another signals. For example - lets try to recognize the face of a known man. Slide 57 If we have no experience in seeing some kind of images - we can not recognize even very known object! Lets try recognize, who is this man? Do you know them? Slide 58 The right answer is... President George W. Bush works on a Habitat For Humanity house in Tampa, Tuesday, June 5, 2001. oficial This is oficial WHITE HOUSE PHOTO by Eric Draper Slide 59 This situation is easier... becuse is typical and known Slide 60 who is Bush Perhaps somebody can not recollect, who is Bush ? without So, let me present the other experiment without recognition. recognize Perhaps you are thinking: It is not easy recognize somebody on first look? Slide 61 fast Lets explain fast, which is the main difference between such two variants of a very known image? Difficult to tell? Slide 62 And now? Slide 63 generates hypothesis. selected During observation process mans brain in every moment generates hypothesis. Natural perception in fact is not only processing of visual signals obtained by eyes. It is mainly mental cognitive process, based on hypothesis generation and its on-line verification. Verification is performed by permanent comparing of the selected features of the image with the expectations taken from previous visual experience. Slide 64 physicians How and when physicians really can use this technology? T The answer can be formulated on the base of plot in form of T letter Slide 65 T T Fast selection for screening purposes Accurate analysis of the very- -complicated medical cases Indexing of multimedial medical databases More precise analysis for some cases Slide 66 A few more detail information... Slide 67 Screening (with automatic understanding of the images) T T Fast selection of the images for screening purposes Slide 68 Hand-made screening (in theory...) Group of people Physician doing selection Pointing of suspicion person for more detail investigation Standard medical investigation Slide 69 Hand-made screening (in practice...) Group of people Physician doing selection Standard medical investigation Everybody OK.! Slide 70 Semi-automatic screening (with use of the automatic understanding of the images) Slide 71 Group of people Physician doing selection Standard medical investigation Algorithm for the automatic understanding of the image contents Alert signal! Very detail analysis Analysis is fast and accurate! clinical Pointing of suspicion person for clinical investigation Slide 72 Analysis of difficult diagnostic problems Accurate analysis of the very- -complicated medical cases T T Slide 73 Patients data Surgery ? Pharmacology? ? Patients organ with not known disease Board of experts (physicians) Slide 74 Surgery ? Pharmacology? ! Patients data Algorithm for the automatic understanding of the image contents Suggestion: cancer Slide 75 Searching in mulitimedial medical databases T T Indexing of multimedial medical databases Slide 76 Multimedial database Index file Algorithm for the automatic understanding of the image contents Semantic description of the image contents Identification code Identification Name Illness description Images #123ABC Slide 77 Multimedial database Index file Algorithm for the automatic understanding of the image contents Semantic description of the image contents Identification code Identification Name Illness description Image User Example Description of the selected case Question Slide 78 Example Multimedial database Index file Semantic description of the image contents Identification code Identification Name Illness description Image User Description of the selected case Question Answer Optimal therapy Slide 79 Example: Semantic information representation in understanding of images with different lesions Images of: urinary tract (left), coronary vessels (upper right) and pancreatic duct (bottom right) Slide 80 cognitive resonance Verification of the hypothesis is based on the process called cognitive resonance parsing In future works we do identify cognitive resonance with parsing of linguistic description of the image content. terminal symbols non- terminal In this parsing process strings of terminal symbols (statements describing features of the image) are converted to single non- terminal symbols, which can be interpreted as semantic description of the merit sense (for example diagnosis). Slide 81 description The graph grammar as a tool for description of the images before its automatic understanding Slide 82 Elementary example of scene analysis using EDT graph grammar (I) Examples of analyzed scenes I II III Slide 83 Elementary example of scene analysis using EDT graph grammar (II) 1. Analysis of scene described by the following primitives: - building - car - tree b d a 2. Definition of connection primitives: p r s t u v x y Nouns Verbs Slide 84 Elementary example of scene analysis using EDT graph grammar (III) 3. Description of analyzed scenes I II III b ad tv b a d d tv v b a d dd u tv v Slide 85 Elementary example of scene analysis using EDT graph grammar (IV) 4. Graph based description of analyzed scenes I II III b b b a a a d d d d dd t u v tv tv v v b( v d t a( v d)) b( v d t a) b( v d t a( v d u d)) Slide 86 iterative The main idea of cognitive resonance is based on iterative performing of such steps: Lets assume semantic description of some image in usual form of the string of terminal symbols: meaning Working hypothesis nr. 1 about meaning of this image leads to the assumption, that image must include at least one pattern: We must search linguistic description of the image for localization Not found! meaning The working hypothesis nr 1 about meaning of the image must be failed! Slide 87 iterative The main idea of cognitive resonance is based on iterative performing of such steps: Lets assume semantic description of some image in usual form of the string of terminal symbols: meaning Working hypothesis nr. 2 about meaning of this image leads to the assumption, that image must include at least one pattern: We must search linguistic description of the image for localization Lets try again... exactly sure Hypothesis nr. 2 can be now find as a more probable, but we stillane not exactly sure, is the hypothesis true or not, because for its full validation it is necessary to test also another assumptions taken from this hypothesis. Slide 88 The description of the cognitive resonance showed on the previous slide is the most simplified one. In fact the set of methods and formulas used by real parser designed by us especially for this works is much, much more complicated! We show it now on base of some real (and interesting!) examples Slide 89 Computer-Aided Diagnosis of Neoplasm and Pancreatitis n To diagnose lesions of pancreatic ducts characterising neoplasm and chronic pancreatitis - Symptoms of pancreas neoplasm: local stenoses or dilatations of pancreatic duct or cysts on external borders of the pancreatic duct - Symptoms of pancreas neoplasm: local stenoses or dilatations of pancreatic duct or cysts on external borders of the pancreatic duct - Symptoms of chronic pancreatitis: incorrect lateral ramifications and local stenoses or dilatations - Symptoms of chronic pancreatitis: incorrect lateral ramifications and local stenoses or dilatations Slide 90 ERCP Images with Chronic Pancreatitis Slide 91 ERCP Images with Pancreas Neoplasm Slide 92 Computer-Aided Diagnosis of Urinary Tracts n To diagnose lesions pointing to the existence of renal calculi or deposits those obstacles cause artresia of urinary tracts leading to diseases such as extra-renal uraemia or hydronephrosis n To diagnose the correct morphology of renal pelvis and renal calyx with the use of graph grammars Slide 93 RTG Images of Left Renal Pelvises with Ureter Ducts Slide 94 Computer-Aided Diagnosis of Coronary Arteries n To detect lesions characteristic cardiac ischemic states those states are caused by atheromatosis lesions in coronary vessels which cause stenosis of artery lumen n Cardiac ischemic disease can take the form of stable or non-stable angina pectoris or infarct Slide 95 Coronographical Images of Coronary Arteries with Stenoses Slide 96 Parsing we try to describe now on the base of following structure of the whole processing procedure. Our method is based on four consecutive steps: Slide 97 Let me remind you schema of very known structural analysis of images, which is the base and tool for our method. Input Image Image Pre-processing Syntactic Analysis Classification & recognition Image Representation Segmentation Definition of image primitives and relations between them Slide 98 Classification of Pattern Recognition Methods Slide 99 Stages of Preliminary Image Processing n Segmentation and filtration n Skeletonisation n Analysis of real and verification of apparent skeleton ramifications n Smoothing of skeleton n Straightening transformation Slide 100 Examples of preprocessing stages, which must be performed before starting the automatic understanding process Only proper concentrate Only proper initial processing of the image let us concentrate during understanding of the image on its important semantic features, not on noises or artifacts. Slide 101 Method of Segmentation Mikrut Z.: A METHOD OF LINEAR STAR-SECTIONS APPLIED FOR OBJECT SEPARATION IN ERCP IMAGES (ICIP96) Slide 102 Method of Segmentation Slide 103 Mikrut Z.: A METHOD OF LINEAR STAR-SECTIONS APPLIED FOR OBJECT SEPARATION IN ERCP IMAGES (ICIP96) Averaging & median filtration Method of Segmentation Slide 104 Result of Segmentation & Filtration Original image Binary image Slide 105 Classical Pavlidis Algorithm of Skeletonisation Neighborhood templates for skeletal points P - actually considered skeletal point 0 - points from image background 2 previous skeletal points P P is a skeletal point if at least one from each of X and Y point sets is not a background point Slide 106 Skeletonisation & Smoothing of Skeleton Pavlidis skeletonisation Averaging of skeleton Slide 107 Analysis of Lateral Ramifications Determination of parts of pancreatic duct Detection of apparent ramifications 28% 45% 27% Slide 108 Example of goal-oriented processing: Differentiation between real and apparent lateral ramifications Duct with chronic pancreatitis Duct with pancreas neoplasm Slide 109 Results of artifacts elimination Slide 110 Algorithm of Straightening Transformation(I) Geometric transformation which creates width graphs of the studied structure ERCP image of pancreatic duct with pancreatitis Slide 111 Algorithm of Straightening Transformation(II) A - the skeletal point in which object width is measured B define neighborhood of point A, and determine position of the guiding line C D - result of translation of the line C so that it passes through the skeletal point E - width measuring line, perpendicular to line D F - points of intersection of width measuring line and outer object contour These points are rotated around point A by an angle which results in object straightening Slide 112 Algorithm of Straightening Transformation(III) Algorithm of straightening transformation () { Read object contour; /*creation of object boundaries transformed (i.e. straightened)*/ for any skeletal point { Draw the guiding line; Draw the line measuring widths of the object; Find points of intersection of the contour and width measuring line; Determine location of these points in relation to the guiding line; Rotate the contour points around the skeletal point by an angle opposite to an angle between width measuring line and y-axis; } /*linearization of obtained table with border coordinates*/ Sort border points; Remove redundant points in the straightened contour; Improve continuity of the obtained width graph; } Slide 113 Original image Results of Straightening Transformation Width graph Width graph obtained for the duct with chronic pancreatitis Lines determine width profiles Marked areas of real ramification Slide 114 Scheme of Encoding Width Graphs as Terminal Symbols I Approximation of width graph contour by polygon Approximation angles for the lower part of the graph (signs altered) -28, -23, 15, 11, 31, -31, 11, 3, 37, -29, 8, 6, 6, -29, -2, 8, 105, 43, -40, -81, -14, 7, -10, 11, 66, -56, -64, 5, -28 Approximation angles (in degrees) for the upper part of the graph 7, 22, 3, -4, 65, 70, 6, -78, -125, -99, 10, 11, -12, 0, 4, 19, -28, 41, 2, -109, -146, -40, 15, 40, 13, -80, -147, -66, 12, 23, 16, -18, -145, -151, -72, 8 Slide 115 Vocabulary of the grammar Scheme of Encoding Width Graphs as Terminal Symbols Image features: angles between segments of polygon line approximating of width graph of the organ Terminal symbols as elements of linguistic description taking into account merit sense of the image Please note unusually form of the vocabulary! Slide 116 Scheme of Encoding Width Graphs as Terminal Symbols Vocabulary of our grammar (conditions for terminal symbols) Approximation angles for the lower part of the graph -28, -23, 15, 11, 31, -31, 11, 3, 37, -29, 8, 6, 6, -29, -2, 8, 105, 43, -40, -81, -14, 7, -10, 11, 66, -56, -64, 5, -28 Approximation angles for the upper part of the graph 7, 22, 3, -4, 65, 70, 6, -78, -125, -99, 10, 11, -12, 0, 4, 19, -28, 41, 2, -109, -146, -40, 15, 40, 13, -80, -147, -66, 12, 23, 16, -18, -145, -151, -72, 8 Sequence of terminal symbols for the lower part of the graph ns, ns, s, s, s, ns, s, p, s, ns, p, p, p, ns, p, p, n, g, ng, ni, ns, p, ns, s, i, ni, ni, p, ns Sequence of terminal symbols for the upper part of the graph p, s, p, p, i, i, p, ni, nn, nn, s, s, ns, p, p, s, ns, g, p, nn, nn, ns, s, g, s, ni, nn, ni, s, s, s, ns, nn, nn, ni, p Slide 117 Scheme of Syntactic Analysis of Pancreatic Duct Syntax analysis diagram for width graphs obtained for pancreatic ducts Slide 118 Attributed Grammar Describing Morphological Lesions in Pancreatic Duct (I) G = (V N, V T, SP, STS) V N non-terminal symbols set V T terminal symbols set SP production set, STS starting symbol V N = {SYMPTOM, CYST, STENOSIS, DILATATION, BRANCH, HI, LO, P, S, G, I, N, NS, NG, NI, NN} V T = {p, s, ns, g, ng, i, ni, n, nn} Slide 119 Attributed Grammar Describing Morphological Lesions in Pancreatic Duct (II) SP: n SYMPTOM CYST Symptom=cyst n SYMPTOM STENOSIS Symptom=stenosis n SYMPTOM DILATATION Symptom=dilatation n SYMPTOM BRANCHSymptom=branch Description of recognized pathological lesions Slide 120 Attributed Grammar Describing Morphological Lesions in Pancreatic Duct (III) n CYST HI P LO | HI S LO | HI NS LO n STENOSIS NS S | NS G | NS P S | NS P I n STENOSIS NG S | NI NS I | NI S n DILATATION S P NG | S G NS | S NS | G NS n BRANCH I NI | I NS | S NG | G NI | G S NN | S NS NN n BRANCH N G NG NI | I P NI NN | G P NN | G S NI NN Description of various forms of cysts, stenoses, shapes of branches and dilatations Slide 121 n HI I | G n LO NI | NG n N n | n N w sym := w sym + w n ; h sym := h sym + h n n NN nn | nn NN w sym := w sym + w nn ; h sym := h sym + h nn n I i | i I w sym := w sym + w i ; h sym := h sym + h i n NI ni | ni NI w sym := w sym + w ni n G g | g G w sym := w sym + w g ; h sym := h sym + h g n NG ng | ng NG w sym := w sym + w ng n S s | s S w sym := w sym + w s ; h sym := h sym + h s n NS ns | ns NS w sym := w sym + w ns n P p w sym := w sym + w p ; h sym := h sym + h p W sym, h sym denotes the width and the height of the symptom W sym, h sym denotes the width and the height of the symptom Definition of ascending and descending parts of the detected symptoms Attributed Grammar Describing Morphological Lesions in Pancreatic Duct (IV) Slide 122 Sequential transducer foe semantic analysis of the shapes q i denotes the i th state of transducer, i=1,2,3,4 number of sinquad Q i / - denotes that in the i th state appear terminal belonging to i th sinquad and no symbol is writing to output ( - an empty symbol) Q i j/ij - denotes that in the i th state appear terminal belonging to j th sinquad and sequence ij is written to output Slide 123 Results of Understanding of the shape of Pancreatic Duct with Chronic Pancreatitis ns, ns, s, s, s, ns, s, p, s, ns, p, p, p, ns, p, p, n, g, ng, ni, ns, p, ns, s, i, ni, ni, p, ns p, s, p, p, i, i, p, ni, nn, nn, s, s, ns, p, p, s, ns, g, p, nn, nn, ns, s, g, s, ni, nn, ni, s, s, s, ns, nn, nn, ni, p Legend BRANCH DILATATION STENOSIS Sequence of terminal symbols for recognized lesions in the upper part of the graph Sequence of terminal symbols for recognized lesions in the lower part of the graph Slide 124 Parser Control Table state 0 $accept : _SYMPTOM $end s shift 18 g shift 14 ii shift 13 n shift 19 ns shift 15 ng shift 16 ni shift 17. error SYMPTOM goto 1 CYST goto 2 STENOSIS goto 3 BRANCH goto 4 DILATATION goto 5 I goto 6 NI goto 10 G goto 7 NG goto 9 S goto 11 NS goto 8 N goto 12 state 1 $accept : SYMPTOM_$end $end accept. error... state 79 BRANCH : I NI S I P NN NS NN_ (35) NN : NN_nn nn shift 73. reduce 35 11/127 terminals, 14/200 nonterminals 57/400 grammar rules, 80/600 states 0 shift/reduce, 0 reduce/reduce conflicts reported 54/0 working sets used memory: states,etc. 969/0, parser 5200/61 57/450 distinct lookahead sets 15 extra closures 109 shift entries, 1 exceptions 62 goto entries 0 entries saved by goto default Optimizer space used: input 330/0, output 5200/196 196 table entries, 0 zero maximum spread: 265, maximum offset: 265 Slide 125 Distribution of Sinquads in LSFD procedure Cyst_or_Ramification(): if (there is a terminal which belongs to I st sinquad or p - i k >2) then Symptom = Cyst; else Symptom = Ramification; procedure Thickening_or_Cyst(): if (w sym >3/2*h sym ) then Symptom = Thickening ; else Symptom = Cyst; p- i k stands for the presence of i terminal symbols between p and k terminal element in the input sequence Slide 126 Representation of Lesions in LSFD Lesions are detected by an analysis of transitions between sinquads, e.g. transition indicates appearance of : II IV lateral ramifications II I IV or II IV cysts and dilatations (also II III IV in backward analysis) IV II and IV I II stenoses (also IV III II in backward analysis) Slide 127 Results of Recognition in Pancreatic Duct with Chronic Pancreatitis Area of a detected side ramification, thickenings & stenoses Original image Recognized symptoms Slide 128 Area of a detected side ramification, cyst & thickening with stenoses Results of Recognition in Pancreatic Duct with Pancreas Cancer Original image Recognized symptoms Slide 129 Next example Ureters... Slide 130 Results of Straightening Transformation Original image Width graph Slide 131 Scheme of Encoding Width Graphs of Ureters into Terminal Symbols Approximation of width graph Intervals for terminal symbols Slide 132 STS = SYMPTOM SP: SYMPTOM STENOSIS Symptom=Stenosis SYMPTOM DILATATION Symptom=Dilatation STENOSIS NV H V | NV V | NV H DILATATION V H NV | V NV | V H V v | v Vw sym := w sym + w v ; h sym := h sym + h v NV nv | nv NVw sym := w sym + w nv ; h sym := h sym + h nv H h | h Hw sym := w sym + w h ; h sym := h sym + h h Attributed Grammar Describing Morphological Lesions in Ureters G U = (V N, V T, SP, STS) V N = {SYMPTOM, STENOSIS, DILATATION, H, V, NV} V T = {h, v, nv} Slide 133 Result of Recognition in Upper Segment of Ureter Area of a detected stenosis Original image Recognized lesions Slide 134 Result of Recognition in Upper Segment of Ureter Area of a detected stenosis Original image Recognized lesions Slide 135 Approximation of Renal Pelvis Skeletons Slide 136 G edt =( , r, P, Z) , = {pelvis_renalis, calix_major, calix_minor, papilla_renalis} = {PELVIS_RENALIS, CALIX_MAJOR,CALIX_MINOR} = {x, y, z} for y (-30 , 30 ), x (30 , 180 ), z (-30 , -180 ), Z = {PELVIS_RENALIS}P: EDT-Graph Grammar Describing Morphology of Renal Pelvises