Object Identification in Ultrasound Scans

Transcription

1 Object Identification in Ultrasound Scans Wits University Dec 05, 2012

2 Roadmap Introduction to the problem Motivation Related Work Our approach Expected Results

3 Introduction Nowadays, imaging devices like X-Rays, MRIs and USs are commonly used to image internal human body parts.

4 Introduction Nowadays, imaging devices like X-Rays, MRIs and USs are commonly used to image internal human body parts. these imaging modalities provide exceptionally good views of these internal anatomies

5 Introduction Nowadays, imaging devices like X-Rays, MRIs and USs are commonly used to image internal human body parts. these imaging modalities provide exceptionally good views of these internal anatomies Examples of these internal human body parts include the heart, the liver, the brain and the kidneys.

6 Introduction Nowadays, imaging devices like X-Rays, MRIs and USs are commonly used to image internal human body parts. these imaging modalities provide exceptionally good views of these internal anatomies Examples of these internal human body parts include the heart, the liver, the brain and the kidneys. The use of these tools allow disease diagnosis, surgical planning, radiotherapy and tracking of the disease progress to be done better.

7 Introduction - continued Medical image analysis is aimed at: processing measuring quantifying embedded structures of medical images with improved accuracy, repeatability and efficiency.

8 Introduction - continued However, the study of medical images is heavily dependent on the radiologists visual interpretation. This process is not only subjective and time consuming it also depends on how experienced the radiologist is.

9 Introduction - continued Furthermore, anatomical structures in inter and intra human beings often differ in their appearance, in their shape as well as in their size Also, these anatomical structures tend to vary as the person grows old, tend to differ from one individual to the other, with a person s gender

10 Introduction - continued Although modern imaging techniques provide very good views of the human internal anatomy there is still limited use of computers for an automated quantification and analysis of the large amounts of medical images

11 Introduction - continued Although modern imaging techniques provide very good views of the human internal anatomy there is still limited use of computers for an automated quantification and analysis of the large amounts of medical images This limitation makes medical image analysis an important research field.

12 Introduction - continued Although modern imaging techniques provide very good views of the human internal anatomy there is still limited use of computers for an automated quantification and analysis of the large amounts of medical images This limitation makes medical image analysis an important research field. The use of a computer-aided system also helps alleviate some of the challenges faced by the heavy dependence on the radiologist

13 Deformable Models Deformable models are some of the algorithms that have been developed to aid doctors in diagnosing diseases. These algorithms search an image for a particular human organ and attempt to correctly delineate the exact boundary of the anatomical structure. The size, shape, location and appearance of the extracted boundary save as useful tools in aiding doctors reach meaningful conclusions concerning the patient s health ASMs are part of deformable models

14 Motivation US medical image segmentation has been due to a number of factors. These include the nature of these images attenuation diffraction presence of speckle.

15 Speckle Generally, US images have a grainy appearance which is the result of a spatial stochastic process known as speckle. Speckle also causes low contrast of boundaries.

16 Speckle in US B-mode images US B-mode is one of the current most frequently used imaging techniques for diagnostic purposes using an US device

17 Speckle in US B-mode images US B-mode is one of the current most frequently used imaging techniques for diagnostic purposes using an US device Should the speckle in the image of the functional tissue of an organ (as opposed to the supporting and connecting tissue) be seen as image signal or should it be seen as undesirable noise?

18 Speckle Figure: An US image example

19 Speckle in US B-mode images - continued Using stochastic analysis, many researchers of US B-scanning have described the character of the US speckle as random noise Other researchers view speckle in some clinical US B-scan images as associated with the microstructure of tissue parenchyma. Such view therefore qualifies speckle to be considered as image signal and thus can be used as a basis for diagnosis.

20 Nephron The nephron are the basic unit of the kidney and are responsible for filtering the blood (removing wastes) Many kidney diseases tend to attack the nephrons. This causes the nephrons to lose the filtering capacity. Damage to the nephron can happen very fast, usually, as a result of poisoning or injury. However, some kidney diseases destroy the nephrons in a slow and silent manner

21 Kidney Example Figure: Human kidney parts showing the nephron

22 Speckle in US B-mode images We use US B-mode scan images from different body parts within and across patients where the parenchymal body tissue plays a useful role in diagnosing a patient.

23 Speckle in US B-mode images We therefore, do not consider speckle as noise, but as image signal. Thus, we directly segment our images. For segmenting the image, we use a deformable model.

24 Speckle in US B-mode images Since speckle is random, it causes quite a number of false positives in a deformable model algorithm that directly searches an US image for a particular anatomical structure within an image. As a result, the evolving model usually ends up trapped in locations that are far from the boundary thus not providing the doctor useful information for monitoring a patients s health or for diagnosing the patient

25 Research Problem We attempt to develop a generic system that will correctly delineate the regions of interest from US B-mode scan images taken from different human body parts within and across patients where the parenchymal body tissue plays a useful role in diagnosing a patient.

26 Research Problem We attempt to develop a generic system that will correctly delineate the regions of interest from US B-mode scan images taken from different human body parts within and across patients where the parenchymal body tissue plays a useful role in diagnosing a patient. The results of the segmentation will be for clinical applications.

27 Motivation dependence on radiologists brain drain of radiologists shortage of US machines graduate radiologists deployed in public sectors quickly move to the private sectors

28 Shape Segmentation In statistical terms, shape segmentation can be viewed as optimizing a conditional probability p(c I ). Here, p(c I ) is a representation of the model parameters that describe the targeted object in the image and C is a set of continuous model parameters defining the shape and position of the object

29 Statistical Model An initial approximation to the correct solution is usually first obtained from either provided prior knowledge or via some form of rigid object knowledge.

30 Statistical Model An initial approximation to the correct solution is usually first obtained from either provided prior knowledge or via some form of rigid object knowledge. To refine the segmentation results, optimization techniques ACMs, ASMs and AAMs can then be used.

31 Statistical Model An initial approximation to the correct solution is usually first obtained from either provided prior knowledge or via some form of rigid object knowledge. To refine the segmentation results, optimization techniques ACMs, ASMs and AAMs can then be used. Sometimes, general-purpose optimization techniques (like the gradient descent method or the simplex method) are used to find the optimal solution.

32 Statistical Model An initial approximation to the correct solution is usually first obtained from either provided prior knowledge or via some form of rigid object knowledge. To refine the segmentation results, optimization techniques ACMs, ASMs and AAMs can then be used. Sometimes, general-purpose optimization techniques (like the gradient descent method or the simplex method) are used to find the optimal solution. In order to guarantee the convergence of these algorithms to the optimal solution, p(c I ) should be smooth and have only one global maximum.

33 Statistical Model An initial approximation to the correct solution is usually first obtained from either provided prior knowledge or via some form of rigid object knowledge. To refine the segmentation results, optimization techniques ACMs, ASMs and AAMs can then be used. Sometimes, general-purpose optimization techniques (like the gradient descent method or the simplex method) are used to find the optimal solution. In order to guarantee the convergence of these algorithms to the optimal solution, p(c I ) should be smooth and have only one global maximum. This global maximum is the correct solution.

34 Statistical Model An initial approximation to the correct solution is usually first obtained from either provided prior knowledge or via some form of rigid object knowledge. To refine the segmentation results, optimization techniques ACMs, ASMs and AAMs can then be used. Sometimes, general-purpose optimization techniques (like the gradient descent method or the simplex method) are used to find the optimal solution. In order to guarantee the convergence of these algorithms to the optimal solution, p(c I ) should be smooth and have only one global maximum. This global maximum is the correct solution.

35 Statistical Model A generative model tries to learn a generation process specific to the data population that is of interest. Rather than directly modeling p(c I ), the generative model learns a conditional probability p(i C) and a prior probability p(c) The Bayes theorem is used to calculate the posterior probability p(c I ) during the testing stage.

36 Research Question Can we, using a statistic model and a modified ASM, design an US image segmentation system that is robust in terms of attenuation, diffraction and presence of speckle in these images and correctly delineates the boundary of interest from an US B-mode scan image (where speckle is considered as image signal )?

37 Research Question Can we, using a statistic model and a modified ASM, design an US image segmentation system that is robust in terms of attenuation, diffraction and presence of speckle in these images and correctly delineates the boundary of interest from an US B-mode scan image (where speckle is considered as image signal )? Can we analyze the False Acceptance Rate (FAR) and the false rejection Rate (FRR) of the system and investigate methods of reducing these errors?

38 Data visits to the ultrasonography department at the Charlotte Maxeke Johannesburg Academic Hospital as well as to the doctors who use the captured ultrasound images

39 Training Phase Landmarks - annotated by the expert Align the set of training shapes - using a generalized Procrustes Analysis generation of the Point Distribution Model Generate the targeted structure from the Point Distribution Model

40 Searching and Segmentation Phase we view shape segmentation as optimizing a conditional probability density function p(c I ), therefore, we first learn the model initialize the shape model in the image search for the boundary of the targeted structure align the models perform necessary adjustments we perform a sequential probability ratio test at each new model

41 Learning the model Using our set of training shape models, we first learn the statistical model from which the sample population (our region of interest) come from using the image gray scale levels of the selected region.

42 Learning the model Using our set of training shape models, we first learn the statistical model from which the sample population (our region of interest) come from using the image gray scale levels of the selected region. Thus during the search process, each pixel encountered will either be coming from this statistical model or not.

43 Learning the model Using our set of training shape models, we first learn the statistical model from which the sample population (our region of interest) come from using the image gray scale levels of the selected region. Thus during the search process, each pixel encountered will either be coming from this statistical model or not. We next define the conditional probability p(s I )which describes the conditional distribution of the shape model S when given the image data I.

44 Learning the model - continued Rather than directly modeling p(s I ), we learn the conditional probability p(i S) and the prior probability p(s) instead.

45 Learning the model - continued Rather than directly modeling p(s I ), we learn the conditional probability p(i S) and the prior probability p(s) instead. We adopt the expectation maximization maximum likelihood algorithm proposed by Anca for estimating the posterior probability p(s I )

46 Learning the model - continued Rather than directly modeling p(s I ), we learn the conditional probability p(i S) and the prior probability p(s) instead. We adopt the expectation maximization maximum likelihood algorithm proposed by Anca for estimating the posterior probability p(s I ) This algorithm describes the probability of the hypothesized shape model S fitting to the image I

47 Searching for the boundary of the targeted structure In each landmark of the shape S, we search for the possible edge point of the target along a line. This line is perpendicular to the landmark and is made up of p points below and p points above the landmark. We search for the target point in the following way:

48 We first use a derivative operator to compute the gradient magnitude since our aim is to find the boundary

49 We first use a derivative operator to compute the gradient magnitude since our aim is to find the boundary Since the image has a very low signal-to-noise ratio, we use our new proposed exploration/selection (E/S) algorithm to determine the next best point to move from the current landmark.

50 We first use a derivative operator to compute the gradient magnitude since our aim is to find the boundary Since the image has a very low signal-to-noise ratio, we use our new proposed exploration/selection (E/S) algorithm to determine the next best point to move from the current landmark. The E/S algorithm determines whether the current point in the landmark profile is the right candidate to be the edge of the targeted structure. This algorithm is introduced so as to avoid spurious points being selected as the best points.

51 Displacement vector When the edge searching process is done for each point and the new possible landmarks have been detected for each point, we then define a displacement vector where each point is the differences in the x and y values between the shape S and the shape Y.Thedisplacementvectorisdefinedas S =(dx 1, dy 1,...,dx k, dy k,...,dx n, dy n ) T (1) where the difference vector between the edge point for the k th model point and the k th edge point is given by (dx k, dy k ).

52 Searching Currently, the required boundary of the feature of interest is represented by the n edge points whose coordinates are given by the vector Y =(S + S). With the shape model in its new position, we perform a SPRT test so as to be certain that the shape model is not trapped in alocalmaxima.

53 Parenchyma speckle The extracted region of interest contains information about the structure of the body tissue parenchyma and in an US B-mode scan image, this information is presented as speckle in the image. We, therefore extract this information in the following way:

54 We learn the distribution of the parenchyma speckle using the pixel brightness level within the region of interest.

55 We learn the distribution of the parenchyma speckle using the pixel brightness level within the region of interest. We use a mixture of three RiIG distributions to learn the model distribution

56 We learn the distribution of the parenchyma speckle using the pixel brightness level within the region of interest. We use a mixture of three RiIG distributions to learn the model distribution We then compute the maximum a posterior estimator of the learnt model using the expectation maximization maximum likelihood algorithm

57 We learn the distribution of the parenchyma speckle using the pixel brightness level within the region of interest. We use a mixture of three RiIG distributions to learn the model distribution We then compute the maximum a posterior estimator of the learnt model using the expectation maximization maximum likelihood algorithm Finally, we compute the optimal segmentation based on the estimated distributions as well as a statistical prior for disease-free parenchyma body tissue.

58 Parenchyma speckle We learn the local brightness distribution of the parenchyma speckle pattern of our US B-mode scan images using a mixture of the recently proposed statical model of speckle in US images. This model is the Rician Inverse Gaussian (RiIG) distributions proposed by Elto We use a mixture of RiIG distribution because parenchyma in body tissue is made up of different varying cells. The obtained information serves as a strong basis for diagnosis purposes

59 Expected Results We expect that our algorithm will be able to correctly delineate the objects of interests from various US B-mode scan images of different body parts. Our results should be the same as those identified by the radiologists.

60 THANK YOU!